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Simulation as a Strategic Planning Tool
at Fteet Maintenance Facility Cape Scott
Wayne Rockwell
Submitted in partial fulfillment of the requirements for the Degree of
MASTER OF ENGINEERING
Major Subject: Industrial Engineering
DALHOUSIE UNIVERSITY
Halifax, Nova Scotia August, 200 1
@ Copyright by Wayne Rockwell
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Table of Contents
........................................................................... List of Figures
List of Tables ........................................................................... List of Abbreviations ................................................................
............................................................ .................... Abstract ..,
Chapter 1 : INTRODUCTION ......................................................... 1 . 1. Background ................................................................ 1.2. The Balanced Scorecard ................................................. 1.3. Problem Statement ........................................................ 1.4. Objectives ................................................................. 1 .5 . Methodology ...............................................................
Chapter 2: DELEGATED MAINTENANCE BUDGET CONCEPT ............ 7
2.1. Resource Allocation ....................................................... 7
2.2. Random Demand Arriva1 ................................................. 9
2.3. Dedicated Maintenance Period ........................................... 9
2.4. Planning, Estimating. and Prioritization ................................. 9
Chapter 3: DEMAND ON REPAIR FACILITY RESOURCES ................... 11
3.1. Canadian Patrol Frigate Maintenance Policy ........................... 11
3.2. Types of Maintenance ................................................... 12
3.2.1. Preventive Maintenance RF/SS Designation .................. 12
......................................... 3.2.2. Corrective Maintenance 12
........................................... 3.3. Validation of Expected Dernand 13
3.3.1. HMCS ST JOHNS Activity Based Costing Study ........... 14
.............................. 3.3.2. Introduction of the Victoria Class
.......................................... 3.4. Balancing Resources to Demand
................................................... Chapter 4: MODEL DESCRIPTION
.......................................................... 4.1. Model Description
................................................................ 4.2. Main Network
4.2.1. Creation of Preventive Maintenance Demand ................ ................ 4.2.2. Creation of Corrective Maintenance Demand
4.2.3. Customer Availability and FMFCS Capacity ................ ....................................................... 4.2.4. Data Output
........................ 4.2.5. Fitting the job within the Work Period
......................... 4.2.6. Calling Customer Availability VSNs
....................................... 4.2.7. Resource and Gate B Iocks
............................................... 4.2.8. Calling Alter VSNs
................................................................ 4.3. VSN PMS-75
............................................................... 4.4. VSN CHA-AV
.................................................................. 4.5. VSN Worker
........................................................................ 4.6. VSN Fit
.................................................................... 4.7. VSN Res-1
....................................................................... 4.8. VSN Size
........................................ 4.9. Model Validation and Verification
............. Chapter 5: DEMAND VISUALIZATION THROUGH MODELMG
.............................. 5.1. Demand with a Deterministic Anival Time
........................... 5.1.1 Time Based Preventive Maintenance
5.1.1.1. Actual Service Times matched
........................... to the Maintenance Profile
........................ 5.1.1.2. RF/SS at the Capability Level
................................ 5.1.2. Impact of Customer Availability
...................... 5.1 .3 . Dedicated Maintenance Period Duration
5.1.4. Impact of Resource Availability ................................ 44
..................................... . 5.1.4.1 Resource Strength 44
........ 5.1.4.2. Responsiveness versus Worker Utilization 45
5.1.4.3. Seasonal Worker Availability ........................ 48
5.2. Minimum Required Resource for Maximum Permitted
................................................................ Wait in Queue 50
Chapter 6: MODEL CAPABILITIES DEVELOPED FOR
FUTURIZ ANALYSIS ....................................................... Dernand with a Deterministic Arriva1 Time
.......................................... and a Probabilistic Service Time
Demand with a Stochastic Amval Tirne
............................................. and a Stochastic Service Time
6.2.1. Customer Availability ........................................... Steady State Analysis .......................................................
6.3.1. Wait-time in Queue ............................................... 6.3.2. Worker Availability ..............................................
Scheduling Rules ............................................................
.......................... Chapter 7: EFFICIENCY THROUGH VISUALIZATION 66
....................................... 7.1. Alignment of Resources to Demand 66
7.1.1. Load Leveling and Core Capability ............................ 72
............................................ 7.1.2. Customer Availability 73
......................... Chapter 8: IMPLEMENTATION AND CONCLUSIONS 74
................................................................... 8.1. Conclusions 74
8.1.1. Forecasting Demand .............................................. 74
8.1.2. Visualization of Strategic Measures ............................ 75
....................................................... 8.2. Model Implementation 76
............................................. 8.2.1 . Validation of Demand 76
8.2.2. Data Cleansing .................................................... 8.2.3. Mode1 Flexibility .................................................
...................................... 8.2.4. Human Resource Training
8.2.5. Strategic Direction ............................................... ............................................................... 8.3. Future Research
Chapter 9: REFERENCES
Appendix A: HALIFAX CLASS MAINTENANCE PROFILE .....................
Appendix B: HOURS TO CALENDAR DATE CONVERSION ..................
Appendix C: ATTRIBUTES AND GLOBAL VARiABLES .......................
Appendix D: THE "AWESIM" VISUAL NETWORK MODEL ................... D . 1 . The Control Statement ...................................................... D.2. The Main Network .......................................................... D.3. Visual Sub-Networks .......................................................
Appendix E: PM ROUTINES MATCHED TO ACTUAL SERVICE TIMES ....
Appendix F: PREVENTIVE MAINTENANCE
AT THE CAPABILITY LEVEL .........................................
................ Appendix G: PM ROUTINES USED AS DJPUT IN THE MODEL
Appendix H: ANNUAL PREVENTIVE MAINTENANCE PER WORKER .....
vii
LIST OF FIGURES
Figure 4.1.
Figure 4.2.
Figure 4.3.
Figure 4.4.
Figure 4.5.
Figure 4.6.
Figure 4.7.
Figure 4.8.
Figure 4.9.
Figure 4.10.
Figure 4.1 1 . Figure 4.12.
Figure 4.13.
Figure 4.14.
Figure 4.15.
Figure 4.16.
Figure 4.17.
Figure 5.1.
Figure 5.2.
Figure 5.3.
Figure 5.4.
Figure 5.5.
Figure 5.6.
Figure 5.7.
Figure 5.8.
Figure 5.9.
Creation of Preventive Maintenance Demand ............... Creation of Corrective Maintenance Demand ..............
................................................ Gates and Awaits
Collecting and Writing the Data ............................. .................................................... Entity Flush
.............................. Delaying to Next Work Period
.............................. Calling Customer Availability
................................................. "Findar" Nodes
................................................ Resource Blocks
..................................................... Gate Blocks
................................. Calling Resource Alter VSN
..................................... Grnerating PM activities
......................................... Customer Availability
.................................................... VSN Worker
.......................................................... VSN Fit
..................................................... VSN RES-f
........................................................ VSN Size
Demand Arrivai at the Marine Machinery Shop ........... ......................... Demand in the Maintenance Profile
RF/SS Maintenance Arriva1 at the Lagging Shop ......... RFISS Maintenance Amval at the AW Weapons Shop .. Impact of Customer Availability on Demand .............. Workforce Utilization - RefngeratiodAir
............................................. Conditioning Shop
........... . Wait-tirne in Queue Marine Machinery Shop
Wait-time in Queue . HMCS Ville De Quebec ........... Idleness with an initial capacity of thirty two workers ....
Page
19
19
20
22
23
24
25
25
26
26
26
28
29
30
31
31
32
37
38
39
40
41
viii
Figure 5.10.
Figure 6.1.
Figure 6.2.
Figure 6.3.
Figure 6.4.
Figure 6.5.
Figure 6.6.
Figure 6.7.
Figure Dl . Figure D2 .
Figure D3 .
Figure D4
Figure DS . Figure D6 . Figure D7 . Figure D8 .
Figure D9 .
Idleness with an initial capacity of seventy workers ...... Marine Machinery Shop Worker idleness .
...................................... Preventive Maintenance
Marine Machinery Shop idleness .
Preventive and Corrective Maintenance ................... Marine Machinery Shop .
........................... Steady State Wait-time in Queue
Above Water Weapons Shop .
........................... Steady State Wait-time in Queue
Marine Machinery Shop Worker Idleness .
Year Five of the Steady State Simulation .................. Marine Machinery Shop . Wait Time in Queue (FIFO) ... Above Water Weapons Shop .
.................................. Wait Time in Queue (FIFO)
.................................................. Main Network
VSN PMR-82 . RF Designated Preventive Maintenance
................................................... for Penod 82
VSN PMS-82 . SS Designated Preventive Maintenance
................................................... for Period 82
.......................................................... VSN Fit
.................................................... VSN Worker
VSN Alter . One example of fi@-three similar VSNs ... ........................................................ VSN Size
VSN STJ-AV . One example of seven similar VSNs ..... VSN STJCM . One example of seven similar VSNs .....
LIST OF TABLES
Table 2.1.
Table 2.2.
Table 5.1.
Table 5.2.
Table 5.3.
Table 5.4.
Table 5.5.
Table 5.6.
Table 5.7.
Table 5.8.
Table 6.1.
Table 6.2.
Table 6.3.
Table 6.4.
Table 6.5.
Table 7.1.
Table 7.2.
Table 7.3.
Table 7.4.
Table FI .
Table F2 . Table G 1 . Table G2 . Table H 1 .
Page
Total FY 01/02 Target Workload ............................ 8
Break down of Formation Work .............................. 8
Wait-time in Queue due to Customer Availability (hours) . 42
........... impact of DMP Duration on Wait-time in Queue 42
............. Assigned Resource based on Activity Duration 43
......... Assigned Resource impact on Wait-time in Queue 43
Wait-time in Queue with Limited Resources ................ 48
Holiday Period Resource Reduction .......................... 49
Impact of Seasonal Resource Availability
on Wait-time in Queue .......................................... Required initial Resource Capacity ...........................
. ................. Corrective Maintenance Fiscal Year 99/00
..................... Marine Machinery Shop Responsiveness
. .................................... Wait-time in Queue FIFO
. .................. Wait-time in Queue Customer Prioritized
.......................... Wait-time in Queue- Modified FIFO
Steady State Worker Availability
................ and Capability Response (Original Strength)
..................................... Balancing Worker S trength
S teady State Worker Availability & Capabili ty Response
(Balanced S trength) ............................................. ............... Customer Perspective of Balanced Resources
RF Preventive Maintenance .................................... ..................................... SS Preventive Maintenance
.................................... RF Designated PM Routines
..................................... SS Designated PM Routines
....................................... PM at the Capability Level
LIST OF ABBREVIATIONS
CPF - Canadian Patrol Frigate
CM - Corrective Maintenance
DMB - DeIegated Maintenance Budget
DMP - Dedicated Maintenance Period
E, M&R - Engineering Maintenance & Repair
FMFCS - Fleet Maintenance Facility Cape Scott
M&R - Maintenance and Repair
NaMMs - Naval Maintenance Management System
OPSCHED- Operational Schedule for the Fleet
PM - Preventive Maintenance
RF - Repair Facility
SS - Ship Staff
ABSTRACT
Fleet Maintenance Facility Cape Scott (FMFCS), the Naval repair facility for the east coast Canadian Navy, operates in an environment of continuous change. The common theme in this change is the requirement to do more with less. The repair facility has been down-sized in recent years due to a reduced overall budget. Efficiency gains were sought through re-engineering, and attempts were made to constrain the demand on FMFCS resources through the use of financial shadow budgets, devolved to the repair facility customers.
Concem is now growing that the actual maintenance requirements of the Fleet were underestimated, and are not adequately reflected by the devolved budgets. As the Navy wrestles with quantifjing the actuai maintenance requirements of the Fleet, the repair facility's demographics, through lengthy hiring *es, indicate that strategic capabilities are reduced, and in some instances, are in jeopardy of king lost.
FMFCS's corporate strategy has identified several goals. Responsiveness and eficiency are two of these strategic goals. Development of a long-term human resource plan is another. The fwst two goals are conflicting. The balance in their relationship requires a subjective decision, and this decision then affects the third goal, the development of a human resource plan.
The relationship between these strategic goals is complicated and affected by many factors. Future demand on the repair facility has to be identified at the capability level in order to quantifi and analyze the balance between strategic objectives. The availability of the customers and the repair facility workers influence the timing of satisfjmg demand. Additionally, demand has always been treated as amiving randomly. As the demand input into FMFCS has k e n constrained, histoncal output is not matched to the actual requirements, complicating forecasting of actual demand.
A simulation mode1 is presented that forecasts demand and visualizes the relationship between strategic goals. Inputs are the forecasted demand arrival, customer availabi lity and the availability of the repair facility workers. Mode1 outputs identiQ responsiveness in satisQing demand, and the corresponding work-force utilization. The visualization of the relationship between these two strategic goals provides a common discussion base for the development of a long-range human resource plan.
xii
CHAPTER 1
INTRODUCTION
1.1. Background
Located in Halifax, Nova Scotia, Fleet Maintenance Facility Cape Scott (FMFCS) is the
East Coast Canadian Naval Repair facility. FMFCS is directly responsible to the
Commander Maritime Forces Atlantic for the provision of Naval Engineering,
Maintenance and Repair services to the fleet.
The military units, groups, and agencies using FMFCS resources are referred to as
customers. Throughout this report, we will be using this terminology synonymously with
the Naval ships we study. FMFCS output is over one million billable labour hours. More
than fifty customers expect their demands to be satisfied by these labour hours. Meeting
customer demand requires a skilled workforce. At FMFCS, workers are grouped into
fifty-three different capability areas representing various skill-sets. These skill-sets range
from traditional areas such as welding to high-tech radar repair.
The FMFCS customer base has changed in recent years. Phased into the Fleet through the
early to mid 1990s, the Canadian Patrol Fngate (CPF) replaced the "Steamers" which had
been in service since the 1960s. The Oberon Class submarines have k e n
decornmissioned and their replacements, the Victona Class submarines, are now k i n g
introduced into the Navy. Both the Canadian Patrol Frigate and the Victoria Class
submarines are state of the art war ships comprised of both conventional and modem
high-tech systems. New maintenance philosophies such as "repair by replacement*' have
also been introduced with the vessels. These changes have altered the historical demand
at the capability level. Certain services are no longer required in the volume they once
were, while the introduction of new equipment requires training and development of new
skill-sets in the FMFCS workforce.
The maintenance facility itself has changed significantly as well. in 1994, an engineering
and maintenance functional review recomrnended to amalgamate the capabilities
provided by the Naval Engineering Units, Ship Repair Units, and Fleet Maintenance
Groups under a single management structure. The three organizations merged into
FMFCS in 1996. With this merger a signifiant reduction in resources was realized.
In summary, both the customers and the FMFCS workforce have and are continuing to
change. Throughout this change, it has been difficult to determine the required number of
workers to match to the demand at the capability level. A plan that identifies target
workloads for al1 customers is produced annually by FMFCS but the work is not broken
down to the FMFCS capability level. As a result, soliciteci work is not optimal1 y balanced
to available capacity at the capability level.
In Chapters 2 and 3 we will introduce an existing framework that constrains demand
input to FMFCS and ensures available labour-hours are shared arnongst customers. We
will discuss how an evolving shift in policy alters this framework, compounding the
imbalance of work at the capability level as FMFCS target workloads are altered over the
next three years. Managers understand that this shift in policy will affect the FMFCS
workforce over the next few years. A quote from Vice-Admiral Maddison [13, p. 21
highlights the requirement: "There is a rising need to detemine how much maintenance
is truly necessary to meet the readiness levels assigned to Our ships. During this period,
resources will be adjusted as necessary to maxirnize efficiency. You are to develop the
appropriate HR plans to meet these expectations."
1.2. The Balanced Scorecard
FMFCS has adopted the Balanced Scorecard Methodology as a t w l to be used in
developing and comrnunicating its corporate strategy (see Kaplan and Norton [91). The
aim is to utilize the scorecard approach to communicate strategy to al1 levels and assist
the organization to improve continuously products, services, and cornpetencies in an
ever-changing environment. Under this methodology, performance measures are tied to
corporate strategy. Many performance metrics remain under development at FMFCS and
their developrnent has been cornplicated by the varying demand on FMFCS resources.
Comparisons to industry standard may not be applicable as civilian shipyards generall y
bid on known packages of work with a known timetable for completion while the "niche"
work of FMFCS is the ability to respond to unpredicted demand. The requirement of the
Navy to respond quickly to events around the world is reflected in the work requirement
of the repair facility. As a result, the maintenance facility experiences work-loads that
Vary significantly frorn week to week. Scheduling a consistent and balmced workload is
desirable for the efficient use of FMFCS resources but this goal conflicts with the
requirements of the customer.
Use of the Balanced Scorecard comrnunicates strategic multiple goals. Some of the
proposed measures within the Balanced Scorecard are conflicting. For exarnple,
workforce utilization and responsiveness cannot be optimized simultaneously. The
balance between conflicting goals is a subjective preference of the decision maker but
before the decision maker can decide what balance to choose, it must be understood how
multiple goals trade-off against one another. While the Balanced Scorecard
communicates multiple goals, tools that illustrate the relationships among these goals are
required.
1.3. Problem Statement
Variance of future demand arriva1 at the capability level complicates strategic planning
and uncertain demand makes it difficult to predict cause and effect relationships between
multiple goals. Relying stnctly on the short term outlook may result in less than optimal
decisions. For example, the short term outlook may indicate massive hiring is required at
some time while six months later it may be apparent that layoffs are required. Private
industry often reacts to demand variability in this fashion, but FMFCS has lirnited
flexibility to hire and layoff at short notice. Lead time is required to train the workforce
and lost capabilities cannot be immediately replaced. Current demographics at FMFCS
suggest that additional workers will be required in the next few years. A longer t e m view
of the actuai demand on these resources will aid in identifying the required number of
workers and their desired specialties. A workforce that is balanced to the long term
demand must be established and subjective decisions need to be made in determining an
appropnate balance between multiple goals.
1.4. Objectives
This investigation has two pnmary objectives. First, we will indicate how a significant
portion of demand c m be forecasted with relative certainty. The second objective is to
use this forecasted demand to present in graphical and tabular format the cause and effect
relationship between certain strategic measures. We will refer to this presentation
throughout the remainder of the report, as visualization. A computer simulation will be
used to model how FMFCS processes demand. The model .may be used as a decision
analysis tool for FMFCS strategic planning. The tool, through visualization of the
problem, will provide a cornrnon base for debate amongst the decision rnakers.
Development of corporate strategy requires forecasting of future demand on resources at
the capability level. Adbitionally, strategic capabilities that are required regardless of
efficiency must be identified. This knowledge is required for long terrn hiring practices,
training, and capital outlay. FMFCS must be in a position to support its fbnding level and
the simulation model may be used to communicate and support long term planning.
Visualization through application of the model aids in arriving at an understanding of
how demand arrives, how it is satisfied, and the resulting tradeoffs between strategic
measures. Harrel [8. p. 751 states: "Sometimes a mode1 is built purely as a
communication aid to enable others to visualize the complex dynamics of a system." We
have developed the model as an analysis tool but it is also valuable as a tool for
expressing and supporting ideas and observations.
A secondary objective is to raise awareness of the magnitude of unsatisfied demand and
its potential impact on FMFCS resources at the capability level. Current initiatives are
underway which will direct this demand to FMFCS within the next few years. Decisions
should and can be made now as to how this anticipated demand will be satisfied.
We study seven primary customers. These seven customers make up approximately one
quarter of the total annual demand on FMFCS. We identify calendar-based preventive
maintenance as having a deterministic arriva1 time. Historical data from FMFCS is used
to match actual service times of the required capability area(s) for each maintenance
routine. The requirzd calendar-based maintenance routines are identified for each month
of the maintenance profile. The p e n d in the maintenance profile is assessed for each
ship for fiscal year 2001/2002 and their corresponding demand input into a simulation
model. Historical FMFCS data is used to build empirical probability distributions in order
to input customer demand having a stochastic arriva1 rate and service time. Other inputs
into the model are customer availability and worker strength at the capability level.
Customer availability is input from the operational schedule of the ships for fiscal year
2001/2002. The number of worken in each capability area is adjusted in the model to
match historical patterns during traditional holiday periods.
The model was built and is presented in stages. Building the model in this manner
facilitates model verification and validation as the model output at each stage c m be
evaluated against anticipated output. Presenting the model and its output in stages
provides visualization of different factors in demand arrival.
Output from the model is used to present forecasted annual demand at the capability
level. The relationship between workforce utilization, customer availability.
responsiveness, and worker availability is presented. This level of detail has not
previously been available to the decision maker(s) for strategic planning purposes. Using
the simulation capabilities of the model, potential strategic decisions may be evaluated
and compared before they are implemented. While software acquisition and model
development requires some capital outlay, the advantage of simulation is that risk is
reduced during the decision implementation process, thus increasing the potential of
greater savings.
CHAPTER 2
DELEGATED MAINTENANCE BUDGET CONCEPT
The Fleet Maintenance Facility capability plan, for a given fiscal year, is the overall plan
to match the target workload to avaiiable resources. Establishing a credible target
workload is a challenging task as the workload or customer demand is highly variable.
Many high level decisions are based on the capability plan, therefore it is important that
as realistic a plan as possible be provided. The Delegated Maintenance Budget (DMB) is
the concept used today as the foundation on which the capability plan is built.
The FMFs simulate a private-sector (business-like) environment, in which customer
involvement is critical. This concept is known as the Delegated Maintenance Budget
(DMB) concept. The DMB concept provides cost awareness and was intended to
motivate customers to ration their demands (see FMF Business Rules [6]).
Institutionalized in 1996, the DMB concept was envisioned as constraining customer
demand on FMF Cape Scott resources. The concept was initiated d u h g a p e n d of
severe downsizing and financial cutbacks. The sections of this chapter will outline the
framework of this concept and introduce how demand arrives at FMF Cape Scott.
2.1. Resource Allocation
The available labour-hours at FMF Cape Scott are considered the resource. Respective
customers are allocated a portion of the total available as an annual budget. Customers
are to monitor their budget and constrain their demand requests to fall within their
allocation of resources for the fiscal year. The quantity of resources allocated to
respective customers is determined through the Fleet Support Process. Table 2.1 is taken
from the draft capability plan and represents a breakdown of the target work-load to be
undertaken by FMFCS for fiscal year 200112002. Table 2.2 breaks formation work down
further, identifying targeted work for primary customers. In this study, we consider the
demand of the seven Canadian Patrol Frigates of the HALIFAX Class. We observe that
work allocated to the HALIFAX Class represents approximately one quarter of the total
annual billable work targeted for implementation by MFCS workers. The 30 1,560 hour
allocation represents the surn of the targeted labour-hours for the seven customers from
al1 capability areas.
Table 2.1. Total FY 0 1/02 Target Workload (Labour-hours)
Work Category E,M&R Other Billable Total Billable
Formation Work Strategic W ork Planned Efficiency
Totafs 1,050,256 80,870 1,131,126
Table 2.2. Break down of Formation Work (Labour-hours)
Customer Class €,MIR Other Biilable Total Billable
HALIFAX (7) lROQUOtS (2) AOR (1) VICTORIA (3) MWVIAuxiliary Non-Ship
Totals 620,000 47,740 667,740
2.2. Random Demand Arriva1
Customers subrnit work orders for each task requiring FMF resources. The work order
identifies the task and the custorner availability. The scope of these tasks varies
significantly from simple jobs consisting of less than an hour of service tirne to those
requiring thousands of hours from various capabilities. Work requests arrive randomly at
FMFCS, depending on the needs and desires of the respective customers. Associated
capability areas and service times may also be considered randomly distributed.
2.3. Dedicated Maintenance Period
During the operational schedule of the customer, periods are established where the
priority is the completion of maintenance. These periods are known as Dedicated
Maintenance Periods (DMPs), and it is during these periods that FMFCS has full access
to the customer. Typically, there are three to four Dedicated Maintenance Periods for
each customer in the fiscal year. The duration of the DMP generally ranges from three to
six weeks.
2.4. Planning, Estimating, and Prioritization
Work requests are not necessarily planned and estimated in the order that they arrive. As
demand exceeds capacity in some capability areas, work requests are prioritized as to
where planning efforts will be directed. FMFCS typically has more than one customer
concurrently in a Dedicated Maintenance Period. Pnoritization is therefore also required
in the scheduling of planned work when customers compte for FMFCS capabilities in
the same time period.
Most individual work requests are rolled up into projects corresponding to customer
Dedicated Maintenance Periods. The planning process for these projects starts six weeks
prior to the commencement of a Dedicated Maintenance Penod. Work requests not
implemented due to material delay or capacity constraints are delayed until the next work
period. In this study, we are concerned with the delay arising from unavailable capacity
and make the assumptions that al1 required materials are immediately available and al1
work is immediately pianned. First-in-first-out scheduling is used to visualize the
processing of demand. Scheduling rules used within the mode1 are discussed further in
Chapter 6.
CHAPTECR 3
DEMAND ON REPAIR FACILITY RESOURCES
Determining future maintenance demand is a difficult task. Historically the demand has
been constrained under the DMB concept. Even within this envelope, however, the
arrival time, service time and capability level requirements of individual tasks are
unknown until the work request is planned and estimated. This uncertainty makes long
term planning difficult. Before examining the demand within the frarnework of the DMB,
it is worthwhile to review how the current CPF labour-hour allocation was arrived at.
Subsequently, we will discuss an apparent shift in focus towards determining and
satisfying the actual demand.
3.1. Canadian Patrol Frigate Maintenance Policy
The maintenance profile for the HALIFAX Class is provided as Appendix A (see also
NaMMs, [15]). An area of note is the 3740 person-hour per month allocation. This
estimate of required work is for "running repair", the work required of the Fleet
Maintenance Facility. This figure has k e n used in subsequent policy levels since 1990 as
an approximation of the demand and is used in the DMB allocation where each customer
is allocated approximately forty thousand labour-hours per year.
A one hundred and forty four month cycle is used as the maintenance profile. Dedicated
Maintenance Periods and Docking periods are scheduled at intervals in the maintenance
cycle in order for the repair facilities to complete the required demand.
3.2. Types of Maintenance
Different types of maintenance make up the demand. Running repair can be considered to
consist primarily of two types; preventive maintenance, and corrective maintenance.
Preventive Maintenance comprises those tasks carried out to reduce the likelihood of
systern failure or to confirm that the systern is operating within specified performance
limits. Corrective Maintenance comprises those tasks which must be carried out to restore
a failed or detenorating item to a satisfactory level of operation (see NaMMs [ 151).
3.2.1. Preventive Maintenance RF/SS Designation
Preventive Maintenance may be calendar based or condition based. Calendar based
preventive maintenance is required at set intervals in the maintenance profile. Preventive
maintenance routines are also designated in accordance with who was originally intended
to complete the routine. Repair Facility (Ri?) designated routines were intended for
completion by FMFCS resources while Ship Staff (SS) designated routines were intended
for completion by onboard maintainers.
3.2.2. Corrective Maintenance
Corrective maintenance has a random mival time. One can use the analogy of a car.
Breakdowns occur whether the car is new or used aithough the frequency of breakdown
increases with age. The analogy also holds ûue for the relationship between preventive
and corrective maintenance. Regular servicing or preventive maintenance of your car
reduces the frequency of unexpected failure. Potential cost of corrective maintenance is
high therefore historical failure distributions are used to schedule preventive
maintenance, balancing the known cost of servicing to the expected cost of repair.
Another factor in the nsk anaiysis is the timing of system failure. Driving your car on an
express-way or deploying a warship into a combat zone increases the impact of
unexpected system failure. Not accomplishing valid preventive maintenance may lead to
costly corrective maintenance in the future. For exarnple, not properly maintaining the
coating in a ballast tank will lead to corrective maintenance that will cost an order of
magnitude more than the Preventive Maintenance (see Lisota and Hedderich, [12]).
3.3. Validation of Expected Demand
Estimates of CPF maintenance requirements provided as a contract deliverable in the
initial project are still k i n g used today. The intent had been that these original estimates
would be validated over the life of the vessels. Limited resources have prevented this
validation. Poor data collection over the years has hindered efforts to visualize even the
effort expended on maintenance, thus validation of the original schedules is difficult at
best. Corporate knowledge has been lost over the years and methodologies used by the
contractor in estimating the maintenance requirement are not readily available.
Since validation has not occurred, the allocation of maintenance hours for running repair
under the DMB concept should be viewed with healthy skepticism as a representation of
the actual demand. As the focus shifts from constraining demand within the DMB
concept to satisfying the actual demand of the customers, the problem of satisfying
demand at the capability level becomes more complicated. The original challenge had
been to implement a previously detennined arnount of work for each customer, albeit that
the work was not defined to the capability level. The additional challenge created by the
shift in focus towards satisfying the actual demand adds an additional unknown as now
the total of al1 FMFCS resources may not be large enough for the total of ai1 customer
demand. Th5 impact of this change in focus is magnified when demand is considered at
the capability level.
We will observe several complicating factors for consideration in forecasting demand
arriva1 at the capability level as we develop the model. The objective of this Chapter is to
farniliarize the reader with the initial macro view of customer demand and FMFCS
service. The DMB concept was institutionalized in an effort to constrain demand.
Resources were reduced to fall in line with this constraint. Customer budgets were
intended to ensure excess demand was not submitted to the resource provider, Fleet
Maintenance Facility Cape Scott. The point of note to be made here is the shift in focus
towards determining and satisfying the actual customer demand. The following two sub-
sections, support observation of this shift in focus. With this shift in focus the
requirement to understand customer demand and the ability to satisfy it becomes cntical.
33.1. HMCS ST JOHNS Activity Based Costing Study
For years anecdotal evidence suggested that there was too much maintenance for the
number of onboard maintainers. The author's own experience as a Combat Systems
Engineer serving on HMCS HALIFAX supports this observation. A study conducted on
HMCS ST JOHNS, and presented at the 2000 MARE Technical Seminar initiated a
forma1 review of the maintenance of HMC Ships. The study used Activity Based Costing
to illustrate the onboard maintainer's available time for the conduct of maintenance.
Backed by hard data, their study validated what many people in the technical community
already knew to be true - that our technicians don? have enough time for maintenance,
and that the time they do have is king spent primarily on corrective maintenance ( see
Carosielli and Parent, [4]). This and other ongoing evaluations resulted in direction k i n g
promulgated from the Chief of Maritime Staff. The following excerpt from this direction
indicates how demand on repair facility resources may be affected: "PM which is beyond
the capacitylcapability of ship's staff is to be forwaràed for action by RF, where it is to be
actioned with the appropriate priority"(Maddison, [14]). Of note is the fact that this
additional demand was not envisioned in the original allocation of resources, under the
DMB concept. The possible impact on the DMB allocation was obsewed and commented
on by Commodore Sylvester 1201, the Navy's senior engineer. "The intended ability of
ships to offload some planned maintenance to shore-based units was also compromised
by delegated maintenance budgets, which at tirnes forced a choice between corrective and
planned maintenance. Ship's staff should not have to face this dilernma - again, they
should pass the maintenance dong, if necessary."
3.3.2. Introduction of the Victoria Class
While not included in the scope of this study, it is important to realize that other
customers compte with the CPF for FMFCS labour-hours. An area of uncertainty is the
future demand on resources resulting from the delivery of the Victoria Class submarine.
This unknown highlights the benefit of understanding demand requirements at the
capability level. Impact of competition for resources can only be visualized when demand
is studied at this level of detail.
3.4. Balancing Resources to Demand
The balance of workers to dernand has not been studied at the capability level. The
desired balance will be a preference of the decision-maker. Conflicting strategic goals
affect this preference. Previous business planning has been limited to annual labour-hours
per customer and work has not been identified at the capability level other than in broad
terms. A long-term view of demand arrival at the capability level is required in order to
balance the number of workers to demand. The shift in focus towards satisfying the
actual demand changes historicai demand requirement at the capability level therefore
forecasting of the actual demand is complicated. In Chapter 5, we use forecasted work in
a simulation mode1 to illustrate the relationship between strategic goals.
CHAPTER 4
MODEL DESCRIPTION
Chapter 4 describes the "AweSim" simulation model. Chapter 4 may be read in isolation
by the reader whose interest is primarily in the rnodeling, or it may be skipped in entirety
by the reader not faniiliar with the simulation language used. The problem is quickly re-
introduced for the benefit of the former type of reader.
Fleet Maintenance Facility Cape Scott is the repair facility for the East Coast Canadian
Navy. The customers of interest in this study are the seven Canadian Patrol Frigates
based on this coast. Their availability to have work done by the repair facility is restricted
by their operational cycle. The period of study will be the fiscal year 200112002. Each
customer typically has three or four Dedicated Maintenance Periods (DMP) per year. The
duration of a DMP is fifteen to thirty working days. During a DMP the repair facility has
full access to the customer with the priority k i n g completion of maintenance. Future
duration and timing of the customer's DMPs are available from the Operational
Schedule.
Work completed by the repair facility on the custorners is comprised of both preventive
and corrective maintenance. Calendar based preventive maintenance is input into the
model using sub-networks containing the preventive maintenance requirements for each
month of the maintenance profile. Corrective maintenance is input into the model by
sampling empirical distributions of the monthly dernand for each capability. The repair
facility workers are the resource in the system. They are broken down into fifty-three
capability areas. The availability of each respective resource alters a s the workers take
their holidays or are sick. The resource availability will be altered in accordance with
traditional holiday periods.
4.1. Mode1 Description
A simulation model is used to forecast the monthly demand on resources from directed
preventive maintenance routines and expected corrective maintenance. The simulation
package used to model the demand, is A wesim. The unit of time used in the discrete event
simulation is an hour. Al1 time has been converted to this unit of measure. A month is
represented by the available hours of work from the resource within that particular
month. Holidays are excluded and a 7.5 hour day is used as the standard work day.
Therefore, for exarnple, a typical month might be a 150 hour period. Appendix B maps
the hours used in the mode1 to calendar dates for the period of observation.
AweSim allows a model to be built using sub-networks referred to as visual sub-networks
(VSNs). The use of VSNs allows for rnodularity and allows for the reuse of network
sections in different locations. The hierarchical aspect of using the VSNs allows an easier
visudization of the model. The overall flow of the model is first presented by describing
the main network and more detail is provided in the description of individual VSNs.
Severd attributes and global variables are used in the model. They are listed in Appendix
C. Pritsker [17] provides a detailed explanation of the simulation package.
The simulation model is included as a compact disc. The commercial version of
"AweSim" Version 3.0 is required to run the simulation model. The bulk of the visual
networks and sub-networks are included as Appendices for quick reference, however the
simulation software will be required for a detailed look at the model as it is not practical
to include in other than electronic format.
4.2. Main Network
The main network diagram is included as Appendix D.2. The main network will be
described in blocks with an associated Figure for ease of visualization. Inclusion of some
types of nodes within the main network, are due to restrictions of the software. For
example, "findar" nodes must be in the main network as this is the network containing
the files they are to manipulate. Additionally, in this model, al1 the VSNs are cdled from
the main network. The main network is therefore somewhat cumbersome to present. To
facilitate the presentation, the main network will be described by breaking it down to
areas of functionality. Figures will be used to illustrate the concept being described, but
as examples will not contain complete detail. The reader is directed to Appendix D for
the complete network diagrams.
4.2.l. Creation of Preventive Maintenance Demand
A sequence of subroutines is used in the main network to create the "demand". An
exarnple of this sequence is shown in Figure 4.1. Each subroutine (or VSN) will generate
the activities representing the PM routines for each month in the ship maintenance
profile. These subroutines are labeled to indicate both the type of work k i n g generated
and the p e n d of the maintenance cycle. For example, the VSN labeled PMR-85,
represents the monthly Ynd line" preventive maintenance for period 85. The " R
indicates that the work is designated repair facility work. Twelve VSNs can be called to
represent the monthly demand for a year of observation or one hundred and fony four
VSNs can be looped to represent the demand over the complete maintenance cycle.
After a "create" node initiates the calling of the VSNs, ATRIB[3] is assigned the
corresponding number of the desired customer. Al1 subsequent entities cloned in this
string of VSNs will have an identical value as ATRIB(31. In the particular example of
Figure 4.1, 107 indicates the customer is HMCS Charlottetown. Immediately prior to the
calling of each VSN in the senes ATRIB[l] is assigned the value of TNOW in order to
capture the time of generation of the monthly workload. This is not required for the first
VSN in the string as TNOW is assigned to ATRIB[l] in the create node. Each VSN used
in the main network will be described in detail later. For now their function will be
described only in general terms.
Figure 4.1. - Creation of Preventive Maintenance Demand
4.2.2. Creation of Corrective Maintenance Demand
Corrective Maintenance is input into the model by sarnpling from empirical distributions
of histoncal demand. Corrective Maintenance is generated monthly for each capability
level. The capability area is assigned in ATRIB[Z] and the corresponding monthly
demand is assigned to ATRiB[4]. Figure 4.2 shows an example of how this demand is
created. Within the model, corrective maintenance demand is created for the fifty-three
capability areas in a similar fashion. Once created, the demand proceeds to the VSN
"size" where it is partitioned into blocks of demand representing separate activities and
assigned to a customer.
Figure 4.2. Creation of Corrective Maintenance Demand
42.3. Customer Availability and FMFCS Capacity
The nodes illustrated in Figure 4.3 are the center of the model. The gate node, labeled
"Queue", represents the availability of each of the customers under consideration.
Opening and closing of these gates are done in separate VSNs. The assert function,
within the gate node, determines which gate should be evaluated for each arriving entity.
For example entities arriving with an ATRIB[3) value of 102 will be assigned to the gaie
for the HALIFAX. Once an associated gate is "open", the entities flow to the VSN
"worker". This VSN determines the size of the activity and assigns the number of
resources to work on it.
CHECKPE OPM 150 m Figure 4.3. Gates and Awaits
Entity flow from the VSN "worker9* is branched into two paths. One branch directs the
entity flow to the VSN "fit" where the service duration is compared to the available time
remaining in the current or upcorning work period. One of two possible exits from this
VSN is taken depending on whether the activity can be completed in the time available. If
there is sufficient time in the work period to complete the activity, the entity takes exit
one and flows to the "open" node opening the gate "checkfit**. after a delay of .O5 hours.
The entity then flows to a "tenninate" node where it is destroyed. If there is not sufficient
time remaining in the work period the entity will take exit two of the VSN and will be
routed to the queue node "toolarge".
The second branch from the VSN "worker" directs the flow of entities to a "close" node
where the gaie b'checkfit" is closed. The entities then flow to the gaie "checkfit" where
they are held until the gate is opened by the flow of entities taking the first branch from
the VSN worker. The entities are held in this gate for a .O5 time period in order to
facilitate the clearing of al1 associated activities with a job that is detennined to be too
large for the current or upcoming work period.
Entities whose service times fit within the time allocation of the next available work
period proceed from the holding file and move to their associated await node. The
"await" node uses the assert function to detennine which resource the aniving entity
requires. The entity is then either placed in the file of the respective resource type if no
resources are available or seizes the required resource and begins service. The required
number of resources seized is determined by the value of ATRIB[S] which was assigned
in the VSN "worker".
4.2.4. Data Output
Figure 4.4 illustrates the subsequent routing. The entity flows to its respective "collect"
node where the time duration between the beginning of service and time of creation is
captured. The entity is then served and the resources freed. A "write" node is utilized to
wnte specified attributes of each entity to an external file. The data can then be presented
using Microsoft Excel or another convenient application.
Figure 4.4. Collecting and Writing the Data
In the above Figure, "collect" nodes are used to output data corresponding to particular
customers. Although not shown, it is easy to visualize how a similar approach is used to
collect data corresponding to specific capability areas.
4.2.5. Fitting the job within the Work Period
Returning, for a moment, to the VSN "Fit" in the entity flow from the first branch, we
observe the requirement of holding entities for a smail period of time at the 'gate" node
"checkfit". The second exit from the VSN "Fit" directs the entity, deerned as too large, to
the "queue*' node b'ttolarge'*. A detailed description of the group of nodes linked to this
queue node follows, however of significance at this point is observing that a "findar9*
node requires entities to be in a particular file before those entities can be manipulated.
Holding the entities at the gate "checkfit" for a small period of time ensures they are in its
associated file.
Figure 4.5 illustrates the nodes performing the function of flushing al1 activities
associated with the activity deemed t w large for the current work period. Observe that a
job in the repair facility may be comprised of several activities representing the different
capability areas of the repair facility. An entity arriving at the "queue" node "toolarge",
waits for the server to become available. The entity is then delayed by a small amount in
the activity. The use of the queue node and associated activity with a single server
ensures single file entity flow. A global variable is then assigned the value of ATRIB[6]
and the entity flows to a "findar" node where al1 of the entities k i n g held at the gate
"checkfit" are evaluated. ATRIB[6] of al1 the held entities is compared against the global
variable which has assumed the ATRIB[6] value of the entity that was deemed too large.
Al1 entities with an ATRIB[6] value identical to the global variable are flushed from the
file and routed to the node labeled "large". The entity triggering this flush flows from the
"findar" node to a "terminate" node where it is destroyed.
Figüre 4.5. Entity Flush
Entities flushed from the file and routed to the node "large" are then routed via a
condition on ATRiB[3] from the node "large" to an activity which delays them for a
duration representing the time p e n d between the carrent work period and the next
available work period for each respective customer. After the entities are delayed, they
are routed back to the gate node labeled b'queue" which opens at the start of the next work
period. Figure 4.6 illustrates the nodes performing the described functions.
Figure 4.6. Delaying to Next Work Period
4.2.6. Calling Customer Availability VSNs
Figure 4.7 illustrates how the VSNs representing customer avaiiability are cailed from the
main network. Fourteen, customer availability VSNs are called in the main network, two
for each customer. One pend of availability represents the customer's availability for
corrective maintenance while the other represents the customer's availability for
preventive maintenance. The second exit of the availability VSNs is the node label
"Clear". The availability VSNs are wntten such that an entity exiting via this second exit
represents the end of a work period for that respective customer. "Clear" is the node label
of the first of a string of "findar" nodes. This sequence of "findar" nodes tnggered by the
exit of the entity from the availability VSNs, flush the activities of the respective
customer from ail of the shops representing the different capability areas. The entities are
routed back to the gate node labeled "queue" and then wait for the next available work
period represented by the next opening of the gate. An exarnple of the sequence of
"findar" nodes is shown in Figure 4.8. Note tha: the actual network includes fifty-three
"findar" nodes in sequence.
Figure 4.7. Calling Customer Availability
i. FORWARD. MP, 1 1. PORWARD.DR, 1 B C -
I- tl*gm '- t\rm
Figure 4.8. "Findar" Nodes
4.2.7. Resource and Gate Blocks
Both the gate and resource blocks are contained in the main network. The resource blocks
shown in Figure 4.9 represent the fifty-three different capability areas at Fleet
Maintenance Facility Cape Scott. For convenience on1 y nine of the f i ft y-three resource
blocks are shown. The gate nodes in Figure 4.10 illustrate the assigned files for the
customers studied and the initial status of the gate.
Figure 4.9. Resource BIocks
102 HAL CL- 102 105 MON CLO- 105 108 U CLOSED 108 niiiulnm 103 üDQ C L O W 103 106 IRE C L O S W 106 m u l n 10) TOR CLOSm 104 107 C H A CLOSED 107 UIn m
Figure 4.10. Gate Blocks
4.2.8. Calling Alter VSNs
Figure 4.1 1 illustrates examples of VSNs k i n g called which alter the level of a
particular resource. Within the VSN, the resource capacity will be altered in accordance
with seasonal holiday patterns. Fifty-three "alter" subroutines are called from the main
network, one for each resource capability.
Figure 4.1 1. Calling Resource Alter VSN
4.3. VSN PMS-75
PMS-75, shown as Figure 4.12, is one exarnple of one hundred and forty four similar
subroutines. For convenience the exarnple subroutine shows only two PM routines, one
consisting of two activities and the other of one. This type of subroutine creates
deterrninistically scheduled preventive maintenance routines. The calling of the
subroutine generates the preventive maintenance routines scheduled for each monthly
period in the maintenance cycle of the customer. The number of branches specified in the
"enter VSN" node represents the number of individual activities in the preventive
maintenance routines plus one. The additional entity exits the VSN via exit one and
proceeds to cal1 the next VSN representing the preventive maintenance work for the next
period of the maintenance cycle. The entities representing the activities in the
maintenance routines are exited via exit two to the gate node "queue" in the main
network.
Within the VSN, each preventive maintenance routine is assigned an unique value as
ATRiB[6]. This identification allows al1 activities in a similar routine to be cleared back
to the respective gate node if one o f the activities is too large to fit in the work period.
This is done, as usually a maintenance routine is not started unless the whole thing can be
completed within the sarne work period. Note that there may be several activities in each
maintenance routine representing different capability areas. The assigning of this value as
ATRIB[6] aiso provides the capability to identify each routine in subsequent data
analysis. A text box identifies each routine in the VSN for ease of reference in future
editing. Values are assigned to ATRIB[2] and ATRIB[4] for each arriving entity. These
values represent the resource and service time respectively. Note that the service tirne
may be assigned from a probability distribution at this time.
Figure 4.12. Generating PM activities
4.4. VSN CHA-AV
CHA-AV is one example of fourteen subroutines modeling the availability of the
respective customer. A portion of the example VSN is shown in Figure 4.13. Two unique
global variables are used in each availability subroutine. One of the global variables,
XX[16] in the case of this exarnple, represents the duration of the next work period. The
other global variable, XX[17], represents the duration between work period or in other
words the p e r d when the customer is unavailable. Upon the first calling of the VSN, the
global variables are assigned values. An "open" node opens the gate signifying customer
availability. The global variable representing the remaining available time of the
customer is decreased by one hour each time the entity is looped. Once the global
variable reaches zero, the entity is routed by condition to a "close" node, which closes the
gate of the customer availability. Subsequently, two entities are branched from this node.
One carries on within the availability VSN, while the second leaves the VSN to trigger
the flushing of jobs belonging to this particular customer from the resource queues. The
entity remaining in the VSN is delayed for the duration of the time between work periods
where upon the process begins again for the next work period. Note this particular VSN
identifies four work periods. The duration of the work periods and the time until the start
of the next are input directly into the VSN using global variables.
Figure 4.13. Customer Availability
4.5. VSN Worker
This Visual Subnetwork assigns the amount of resource (number of workers) to be
assigned to an activity. VSN "Worker" is shown in Figure 4.14. The number of workers
depends on the service activity duration. As safety regulations dictate that a minimum of
two individuais must work on any particular job, this is the minimum number assigned.
The number of workers are assigned as ATRIB[5]. An activity condition on ATRIB[Z],
the activity duration, specifies which assign node the entity will be routed to.
Figure 4.14. - VSN Worker
4.6. VSN Fit
The Visual Subnetwork "Fit*' determines if the required activity service duration will fit
in the available work penod. The required service duration is a hinction of both the
service time, ATRIB[4], and the number of resources assigned in the VSN "workei',
A T R B [SI. An activity condition based on the customer, ATRIB[3], routes the entity
from the "enter" node to a "goon" n d e where the entity flows to one of two possible
exits from the VSN. If the required service duration is less than the respective global
variable, representing available time in each customers next or current work period, the
entity is routed to exit one of the VSN. If the required service duration is greater than the
time remaining in the customers work penod, the entity is routed to exit two of the VSN.
Visual Subnetwork "Fit" is shown as Figure 4.15.
Figure 4.15. VSN Fit
4.7. VSN RES-1
An example of a VSN altenng worker strength levels over a year penod is shown as
Figure 4.16. There is a similar VSN for each of the fifty-three FMFCS worker
capabilities. An entity is routed from the "enter VSN" node to a series of "alter" nodes.
The duration between the decreasing and augmenting of worker strength corresponds to
the available work hours during the holiday periods. Capabilities are at full worker
strength during the periods between pairs of "alter" nodes.
Figure 4.16. VSN RES-1
4.8. VSN Size
The single entity calling this VSN represents the monthly corrective maintenance demand
on a particular capability level. Within the VSN, entities are cloned as the work is divided
into work activities of 32 hours or less. Also within this VSN, the work is assigned to
customers, ATRIB[3], through the use of probability activities. Each CPF is assigned
corrective maintenance with an equal probability of occurrence. VSN "Size" is
represented in Figure 4.17.
Figure 4.17. VSN Size
4.9. Model Validation and Verifkation
Banks [ l p. 3991 States: "One of the most important and difficult tasks facing a model
developer is the verification and validation of the simulation model." Model validity
refers to representational validity and means that the model corresponds to the real
system, or at least accurately represents the data gathered and assumptions made
regarding the way in which the real system operates (see Harrel [8, p.871). One of the
challenges in ensuring the accuracy of the model was the questionable validity of the
input data. It is well recognized that the data base used by FMFCS contains data of poor
quality. Significant effort was expended in "scrubbing" the data used as input to the
simulation model. Questionable data was discarded. Thus the data used is considered to
be of relatively good quality. The required accuracy of the FMFCS rnodel is subjective. It
is important to remember that the model was built to aid decision making through
visualization. This visualization may be achieved by approximating the operation of the
"real" system. "Mode1 validity can be defined as the process of substantiating that the
model, within its domain of applicability, is sufficiently accurate for the intended
applications" (Schlesinger [18]). In many respects we will be using the model to visualize
the "what ifs?" and therefore it is difficult to compare to the "real" system. One of the
goals of validation is to ensure that the model is credible enough to be used by managers
and other decision makers. This particular goal is facilitated by the fact that the developer
will be the end user of the model. Therefore, the manager will be intimate with both its
capabilities and its limitations.
Banks [ l ] lists many cornmon sense suggestions for use in the verification process. One
of the simplest and most easily implemented is a close and thorough examination of
model output for reasonableness. This verification process was used as an iterative
process in the building of each step of the model. The process of monitoring output also
aided in observing the sensitivity of assumptions used in the building of the model.
CHAPTER 5
DEMAND VISUALIZATION THROUGH MODELLING
Visualization of a problem is perhaps the first and most important aspect of the
development of solutions. In order to attempt optimization of the resource utilization at
the Naval repair facility, it is first necessary to have a detailed understanding of the
demand on these resources. The random nature of customer requests for resources
complicates this visualization. In this chapter, we will investigate an approach to
eliminating a significant portion of the randomness of demand. Through this elimination,
we will be able to accurately forecast a significant portion of the actual CPF demand at
the capability level.
A terminating simulation will be used to visualize aspects of satisfying this forecasted
demand. "A terminating simulation is one that nins for some duration TE, where E is a
specified event (or set of events) which stops the simulation" [I, p. 4341. Initially, the
output is simplified, as we have not considered the random nature of the service times,
therefore statistical analysis of the output is not required. Visualization of many of the
factors in demand arriva1 is achievable by using the model as a prograrnming language.
This visualization will provide a valid foundation for future resource optimization.
The use of the model as an analysis tool will be discussed in Chapter 6 where random
variables will be used to model input.
5.1. Demand with a Deterministic Arriva1 Time
One method of reducing a portion of the randomness of demand arriva1 is to identify the
demand having a deterministic arrival time. A review of historical customer requests
indicates that a significant portion of demand could be considered as k i n g of this type.
While the occurrence of corrective maintenance is tmly random, most other demand
arrivai may be considered as having a deterministic arrival. An approach to dealing with
the random nature of corrective maintenance will be discussed in Chapter 6.
True optimization of the scheduling of work may be elusive. Pinedo [16, p.761 States:
"Resource-constrained project scheduling problems with random processing times have
not received muc h attention in the literature". The technical complications w hich arise in
scheduling problems when the resources are scarce or there are constraints on the
execution of the tasks become even more difficult when there is some uncertainty in
availability of resources, task duration or in the constraints associated with allowable
SC hedules (see Dempster [5, p.61). An integer-programming approach to optimizing
preventive maintenance scheduling with deterministic service times is investigated by
Lee [9] and may be of interest for future research although the problems are quite
different both in scope and flexibility of constraints. Clarification of the problem is the
first step and may lead to heuristic solutions.
5.1.1. Time Based Preventive Maintenance
Time based preventive maintenance is perforrned at some set interval regardless of the
condition of the equipment. This interval may be cdendar-based or based on hours run.
Calendar-based preventive maintenance is used extensively for the numerous systems
compnsing the Canadian Patrol Frigate. Identifying the arrival of this type of
maintenance is possible without interaction from the customers. Each one of the
customers is on a 144 month maintenance cycle. A central database exists (outside of
FMFCS) which identifies the monthly preventive maintenance requirement for the
complete cycle. Therefore, the arrival of this type of maintenance at FMFCS can be
predicted simply by identifying the p e n d of the maintenance profile for each customer.
Within the database, preventive maintenance is designated as either k i n g RF or SS. The
RF designation indicates the maintenance initially intended for completion by the repair
facility. SS designated maintenance was intended for completion by Ship Staff
technicians. The original manufacturer estimate of service time for each maintenance
routine is also available in the database.
5.1.1.1. Actual Service Times matched to the Maintenance Profile
Output data from the FMFCS for fiscal year 199912000 was matched to the preventive
maintenance routines in the central database. A database table was built so that actual
maintenance service times could be compared to the original manufacturer estimates.
Appendix E contains a query of this database. The "sum" column of the Appendix
represents the sum of the service times for al1 of the required activities in the maintenance
routine. Several obsewations are made from examination of this data. They are:
a. The RWSS designation is not consistent with the work k i n g completed by
the repair facility. For the period of observation, more SS designated preventive
maintenance was completed by the repair facility than RF designated preventive
maintenance;
b. The manufacturer estimate of service time varies significantly from the
actual service times. For the period of observation the mean service time was 4.9
times greater than the manufacturer estimate; and
c. Consistent completion of the maintenance routines is not evident in the
output data. Routines are completed for some customers and not others
notwithstanding the fact that the routine was scheduled for al1 in the maintenance
profile.
These observations support a previous discussion in Chapter 3, conceming the validity of
the labour-hours assigned to CPF maintenance. It is quite evident that if al1 of the directed
preventive maintenance is completed, the resulting required labour-hours would be
significantly greater than originally envisioned.
Using actual service times, the preventive maintenance demand was projected for fiscal
year 2001/2002. The monthly maintenance routines based on the annual period in the
maintenance profile of each customer were input into the model. Maintenance routines
were only used in the model if actual service times were available from the year of
available historical data. Figure 5.1 illustrates how, by using this approach, demand
arrivai can be visualized at the capability level. The Marine Machinery Shop is used as an
exarnple. A break down of demand arrival for al1 capability levels is included as
Appendix F.
Marine Machinery Shop
Figure 5.1. Demand Arrival at the Marine Machinery Shop
-We note that we have shown future demand at the capability level. This has not
previously been done at FMFCS. Also of note is that this demand is accurate for the
fiscal year k ing observed. This is important, as demand will change from year to year
based on the p e n d in the maintenance profile of respective customen and this
fluctuation should be understood. For exarnple, al1 seven customers may al1 be in an
annual period of heavy demand in a given year, and they may al1 be in an annual pend
of light demand the next. Periods of heavy monthly demand result from the occurrence of
twenty-four and fony-eight monthly maintenance routines and are therefore not included
in every arioual period. Figure 5.2 illustrates this point, where the total RF demand of
p e n d ninety-six is due to the second occurrence of forty-eight monthly routines in the
maintenance profile.
1 RF Pleuontive Maintenance
Figure 5.2. Demand in the Maintenance Profile
5.1.1.2. RF/SS at the Capability Level
Both RF and SS designated preventive maintenance routines were input into the model.
The RF designated PM routines included in the model are identified in Appendix G. 1. A
number of RF routines were not input into the model as their service times were not
available in the historical FMFCS data for fiscal year199912000. The SS designated PM
routines used in the model are identified in Appendix G.2. It is intended that the
significant impact of SS designated preventive maintenance routines on resource
availability be visualized. Data indicates that many SS routines are consistently
forwarded to FMFCS for completion. Other SS designated PM routines are submitted
sporadically. As it was not practical to input al1 of the SS designated routines k i n g
routed to the repair facility, the more consistent routines with the largest service times
were chosen for demonstration. For the purpose of modeling it is assumed that the arriva1
of the chosen SS routines are consistent from ship to ship. A goal of this project is to
highlight the benefit of formally reviewing the RFBS designation. Once formalized, al1
of the hiture preventive maintenance demands on the repair facility resources can be
accurately forecasted at the capability level.
Of note is the demand on resources at the capability level for the SS designated
maintenance. It can not be assumed that taking on additionai SS designated maintenance
will load capabilities proportionally to the loading provided by the RF designated
maintenance. This potential difference is visudized in Fi y r e 5.3 and Figure 5.4, showing
RF/SS demand arrivai for the Lagging and Above-Water-Weapons shops. We observe
that demand on the lagging shop resources is primarily from RF designated preventive
maintenance while the demand on the AWW shop resources is primarily from SS
designated preventive maintenance.
Lagging Shop
Figure 5.3. RF/SS Maintenance Arrivai at the Lagging Shop
AW Weapons Shop
Figure 5.4. RF/SS Maintenance Arriva1 at the AW Weapons Shop
5.1.2. Impact of Customer Availability
In the previous sections, we have discussed how a significant portion of future demand
can be forecasted, by recognizing deterministic arriva1 times. This portrayal is not
complete however, as there are other factors affecting the arrival of demand. One of these
factors is the availability of the customer. The bulk of the customer's work is completed
when the repair facility has access to the customer. This availability is subject to the
operational SC hedule of each respective ship.
Prior to the start of the fiscal year the Navy promulgates an overall schedule of ship
availability. The periods of availability to the repair facility are referred to as Dedicated
Maintenance Periods (DMPs). For simplicity, it has been assumed that al1 of the
preventive maintenance completed by the repair facility occurs during these pends of
availability. Periods of customer availability for fiscal year 01/02 were input into the
model. The periods of availability were taken from OPSKED 01 Version 1. The
flexibility of the model in accounting for work requiring different periods of availability
will be discussed in Chapter 6.
In order to provide a more complete picture of demand arrival, we must consider the case
where the demand has arrived and the customer is available. Figure 5.5 illustrates how
the demand on the Marine Machinery Shop is affected by the availability of the
customea. "Gate" represents demand present when a customer is available. We observe
that this portraya1 of demand alters the loading of the shop resources. In this particular
exarnple a large amount of work is shifted from the first four months of the year into
August and September. Visualizing the demand in this manner indicates the potential of
optirnization through scheduling of customer availability. For exarnple. two customers
requiring a large arnount of labour-hours from a sirnilar work centre should not have
coincidental periods of availability. Conversely. the effect of the operational schedule on
responsiveness is visualized if optimal "customer availability" scheduling is constrained.
Competition for finite resources will increase the wait-time in queue.
Marine Machinery Shop
2 l
2 6000 I 1 Amve ; 4000 1 rn Gate 2 0 2000 A
O
Figure 5.5. Impact of Customer Availability on Demand at the Marine Machinery Shop
Wait-time in queue may also be viewed from the perspective of each customer. Table 5.1
shows some mode1 output statistics representing the wait-time in queue. Initially, we
assume infinite resource availability thus wait-time is only a function of the limited
access to the customer. The significant impact of customer availability on responsiveness
is quite evident. We note the large standard deviation and maximum wait time.
Customers with longer periods between DMPs experience a longer wait-time in having
their demand satisfied.
Output data is a representation of customer availability for fiscal year 2001/2002 only.
This availaoility is looped in the model in order to provide a period of customer
availability for al1 work originating in the fiscal year. Customer availability will be
different for fiscal year 20022003 and this will change the wait-times shown in the table.
The mode1 provides the flexibility to input further customer availability when it is known.
Table 5.1. Wait-time in Queue due to Customer Availability (hours)
Customer VILLE DE QUEBEC CHARLOTTETOWN FREDERICTON MONTREAL TORONTO ST JOHNS HALIFAX
Mean Std Dev # Obs Min Max 176.473 220.178 486 O 750 309.1 99 259.144 415 O 675 146.744 210.096 479 O 751 f 54.768 1 1 8.373 487 O 330 1 39.299 1 49.758 328 O 510 224.497 21 4.01 8 477 O 487 41 4.373 402.973 433 O 930
5.1.3. Dedicated Maintenance Period Duration
Dedicated Maintenance Periods Vary in length. This variation impacts demand arrival, as
some jobs are too large in scope to conduct in a given DMP, and therefore must wait in
queue for a subsequent maintenance period of sufficient duration. The model has been
built to accommodate for this scenario. Table 5.2 shows the effect on wait-time in queue.
Table 5.2. Impact of DMP Duration on Wait-tirne in Queue (hours)
Du ration Neg lected Duration Considered
Customer Mean Std Dev Max Mean Std Dev Max
VilledeQuebec 176 220 750 234 251 915 Charlottetown 309 259 675 311 256 675 Fredericton 146 210 751 164 210 751 Montreal 154 118 330 212 198 1140 Toronto 139 149 510 144 162 1245 St Johns 224 214 487 254 300 1537 Halifax 414 402 930 438 441 1380
The number of workers assigned to each activity in a job varies and is difficult to predict
accurately. For this reason, a heuristic approach was used in assigning the number of
resources to each activity within the model. Of note is the sensitivity of the maximum
wait-time in queue to this assignment of resources. Some activities with large service
times may require few resources while others may require many. The former requires
Maintenance Periods of longer duration than the latter. The method used treats ail
activities consistently as outlined in Table 5.3. Table 5.4 illustrates the effect on wait-
time of assigning five resources to activities of duration greater than 32 hours. Lirniting
the number of workers assigned to large jobs to four, significantly increases the
maximum observed wait-time. This occurs, as decreasing the number of workers on a job
increases the required service duration. As a result, fewer DMPs of sufficient duration are
available to complete the large jobs and these Iarge jobs are required to wait longer for an
appropriate window of opportunit y. Within our moâel, workers are assigned to activities
in accordance with Table 5.3.
Table 5.3. Assigned Resource based on Activity Duration
Service Time(hours) # of Resource
Service Time<=7.5 7 . 5 ~ Service Time <=16 1 GeService Time
Table 5.4. Assigned Resource impact on Wait-time in Queue
Max Five Workers Max Four Workers
Customer Mean Std Dev Max Mean Std Dev Max
Ville de Quebec 228 246 915 234 251 915 Charlottetown 309 259 675 311 256 675 Fredericton 154 207 751 1 64 210 751 Montreal 193 168 795 21 2 198 1140 Toronto 141 154 802 1 44 162 1245 St Johns 239 245 960 254 300 1537 Halifax 414 402 930 438 441 1380
5.1.4. Impact of Resource Availability
The remaining factor to consider in demand arriva1 is worker availability. Consider
Figure 5.4 and imagine the impact on fixed resources during the August p e n d . It is
possible to have the required resources available to handle the peak demands but annual
workforce utilization would be poor. Restricting the number of workers prevents demand
from k i n g satisfied imrnediately. Through use of the model we are able to visualize the
tradeoff between work-force utilization and responsiveness.
5.1.4.1. Resource Strength
The capacity, of each of the capability levels of F M F Cape Scott, is contained in
Appendix H. FMF Cape Scott does have some flexibility in hinng temporary and casual
workers. "Overtime" and "contracting out" are used as off-ramps in periods of peak
demand. This flexibility is limited by a number of factors. Competition for skilled
workers limits their availability. Additionally, certain ski11 sets at FMFCS are unique to
Naval requirements and surplus demand in these areas c m not be offset to the local
economy. Funds are limited for the types of work which can be contracted out. For these
reasons and model simplicity it will be assumed that the workforce is constant, and that
the avenues of overtime use and the contracting out of jobs is not available.
The break down of work to the capability level, shown in Appendix H, illustrates how
preventive maintenance is loaded on different resources. Potential problem areas become
apparent through this simple visualization. For example, the percentage of preventive
maintenance demand of available worker labour hours is over one hundred percent in the
Above-Water-Weapons Shop. Visualization in this manner allows attention to be directed
to potential problem areas. The heavy work load in the AWW Shop is a previously
identified area of concem at FMFCS and provides sorne model validation. The model
provides visualization of the impact of completing al1 preventive maintenance routines. In
this particular area the demand is caused by the large service time of "fire control radar"
maintenance routines and the relatively few trained workers avaiiable to satisfy the
demand.
5.1.4.2. Responsiveness versus Worker Utilization
An advantage gained through moàeling is the visualization of relationships between
conflicting goals. The relationship of interest in our exarnple is the one conceming "wait-
time in queue" and "resource utilization." The terms used at the Fleet Maintenance
Facility are responsiveness and efficiency, responsiveness k i n g a measure of the time-
required to react to customers' requirements and efficiency k i n g a measure of billable
output. The visualization of this tradeoff is important because in some instances we may
find ourselves trying to achieve conflicting objectives (see Harrel [8], p. 23). For
example, maximizing resource utilization may conflict with rninimizing waiting times. If
the goal is to maximize resource utilization, or billed hours in Our case, then one need
only keep the work queue in front of the workers continually full. Unfortunately. this
builds up "work-in-process". Large queues result in long waiting times, hence dissatisfied
customers o r lost opportunity. When job prioritization is used in such an environment it is
possible that low priority jobs are never commenced.
Another parameter that must be considered is customer availability. We have already
discussed the effect of customer availability on "wait-time" in queue. This effect is much
greater when we consider lirnited workers. An analogy that may be used is a restaurant
(see Harrell [8, p. 1281). During meal hours the restaurant is extremely busy,
corresponding to periods of ship availability. In the case of the restaurant, we understand
that averages alone should not be used to make decisions conceming number of servers
required. It would be absurd, for example to conclude that because two servers (waiters
or waitresses) are only busy an average of 40 percent dunng the day that only one server
is needed. This average measure reveals nothing about the utilization of the servers
during peak periods of the day.
In the case of the repair facility, we must consider the worker utilization during the peak
monthly periods. Figure 5.5 is an exarnple o f model output showing workforce utilization
over time. The figure portrays the utilization of workers in the Refrig/AC Shop, where
time is rneasured in hours. We note that the maximum utilization of workers is nine,
corresponding to the maximum number of workers available. We note periods of
"idleness", such a s between hour 444 and 667, corresponding to p e n d s when either
demand is satisfied or customers are not available. Demand arriving in periods when al1
workers are busy will wait in queue until the sufficient number of workers for the
activity, are free. Several periods are obsewed in Figure 5.6 when al1 workers are busy.
Workers vs. TIME: RUN 1
Figure 5.6. Workforce Utilization - RefrigeratiodAir Conditioning Shop
Figure 5.7 is an illustration of the model output identifying "wait-time" in queue for
Marine Machinery Shop Jobs. The rneasure may also be output for a particular customer,
including the activities from al1 capability areas as shown in Figure 5.8. The Y axis of the
histogram is divided into periods of seventy-five hours or ten working days. The bar on
the far right hand side, for example, represents the number of activities with a wait-tirne
in queue of 1 10 working days or greater.
Marine Machinery: RU N 1 ! i
N ;%] I
! I
Figure 5.7. Wait-time in Queue-Marine Machinery S hop
lm4 VILLE DE QUEBEC: RUN 1
N O
160. 110, 1w.
O
f 1 ai, 80.
O 60, b 40. s
20, O - i I
Figure 5.8. Wait-time in Queue - HMCS Ville de Quebec
In surnmary, the three proceeding figures have presented workforce utilization and
responsiveness. These mesures can be shown for the individual capability areas or can
be combined to illustrate responsiveness to a specific customer.
The effect of limited resources on wait-time in queue, using FMFCS worker strength, is
seen in Table 5.5. We observe that some customers are affected more than others, due to
different periods of availability. Within the model service is first-in-first-out, but once a
customer becomes unavailable its work must reenter the queue once the customer is
available again. Mode1 flexibility in visualizing other scheduling rules will be discussed
in Chapter 6. It should be noted that the figures in Table 5.5 represent only twelve
months of input. Customer availability is extended beyond twelve months in order to
capture service completion, however cornpetition for resources dwindles as activities are
completed thus the data must be interpreted carefully. The mode1 has the flexibility to
extend the period of observation.
Table 5.5. Wait-time in Queue with Limited Resources
Customer
Ville de Quebec Charlottetown Fredericton Montreal Toronto St Johns Halifax
Unlimited Resource
Mean Std Dev Max 234 251 915 31 1 2 s 675 164 210 751 21 2 198 1140 1 44 162 1245 254 300 1537 438 441 1380
Limited Resource
Mean Std Dev Max 260 273 1595 327 251 735 218 239 878 261 238 1140 223 274 1751 384 440 2341 501 500 2734
5.1.4.3. Seasonal Worker Availability
The repair facility workforce is comprised of unionized trade sets and military workers.
Management influence on when the workers take their holidays is lirnited. It is well
recognized that during traditional holiday periods the available workforce will be
significantly lower. Worker capacity within the model is altered to reflect seasonal
availability. Alterations made to worker capacity are as stated in Table 5.6. Initial
resource capacity at the shop level, as previously stated, is included as Appendix H.
Percentage reductions in the number of workers are rounded to the nearest integer.
As time duration is measured in hours within the model, the periods of reduction are
shown both in calendar dates and the co~esponding period in hours. Weekends and
Statutory hoiidays have previously been removed from consideration. Mode1 flexibility
ailows worker availability to be altered to the level of detail required by the user. These
particular figures have been chosen to visualize the impact of further limiting resources
over traditional holiday periods. Sick time and other causes of resource unavailability
have not k e n considered, therefore the reader is to note that the overall level of resource
availability is optirnistic. The use of sick time and other benefirs affecting worker
availability varies frorn shop to shop. Studying worker availability at the shop level
requires a detailed analysis. With the optimistic approach applied consistently to al1
shops, we expect a shorter wait-time in queue and higher worker idleness in the model
output. It is interesting to note that decreasing worker availability in the model to
represent holiday or sick time increases workforce utilization. An available workforce
should not be perceived as k i n g less efficient than an absent workforce, therefore it is
important that this aspect be understood.
The impact of seasonal worker availability on responsiveness is shown in Table 5.7. The
impact is observable, but the increase in the wait-time in queue is relatively small. This is
expected as only a fraction of FMFCS demand has been input into the model. Most work
centers have available capacity even when reduced dunng holiday periods.
Table 5.6. Holiday P e n d Resource Reduction
Period % Reduction Calendar Date Hour Period of Reduction
Spring Break 50 11-17 Mar 1762 - 1800 Summer 50 15 Jul-15 Aug 540 - 690 Hunting Season 25 1Nov-15Nov 1095-1170 X-mas 50 20 Dec - 3 Jan 1200 - 1390
Table 5.7. Impact of Seasonal Resource Availability on Wait-time in Queue
Constant Availability Seasonal Availability
Customer
Ville de Quebec Charlottetown Fredericton Montreal Toronto St Johns Halifax
Mean Std Oev Max 260 273 1595 327 251 735 21 8 239 878 261 238 1140 223 274 1751 384 440 2341 501 500 2734
Mean Std Dev Max 260 274 1610 333 251 750 220 242 1063 266 244 1355 240 310 1996 391 448 2332 526 518 3044
5.2. Minimum Required Resource for Maximum Permitted Wait in Queue
Expenments may be done with the mode1 to determine the minimum resource level
required, such that a maximum allowable wait in queue is not exceeded. Many
simplifications have been made to facilitate the visualization therefore the results of this
process should not be interpreted as FMFCS having a resource capacity surplus in certain
capability areas. To prevent possible misinterpretation, we will focus on1 y on those shops
where either maximum wait time was increased, due to limited resources, or a significant
increase in the mean was observed.
For our exarnple, we determine the required number of additional workers in each work
centre, such that the wait-time in queue in the case of unconstrained resources is not
affected. Table 5.7 shows the resulting wait-time change at the capability level due to
limited resources. Under the heading of required resources, we observe the number of
workers necessary to approximately maintain the mean and max wait-time in queue.
Table 5.8. - Required Initial Resource Capacity
shop Unlirnited Resource Umited Reswrce Required Resource
Mean Std Dev M a x 235 298 1537 294 35û 1537 310 376 1387 278 326 1387 253 281 1537 269 315 1537 228 234 930 217 272 930
Mean Std Dev Max Workers 239 299 1553 11 297 351 1549 6 378 392 1387 7 283 329 1424 12 27 1 281 1553 32 276 315 1549 23 418 335 1626 9
1007 676 2734 4
Mean Std Dev Max Worlcers 236 298 1537 18 295 350 1540 1 O 310 376 1387 16 278 326 1387 20 255 280 1537 70 271 314 1537 34 231 233 930 54 244 291 952 20
Augmenting the number of workers in the mode1 satisfies the responsiveness
requirement, but the tradeoff to increased responsiveness is a decrease in workforce
utilization. The change in worker utilization is visualized in the following two figures
which ponray the available or idle workers over the course of the year. The Marine
Machinery Shop is used for visualization where we have increased the workforce from
thirty-two to seventy in order to satisfy Our hypothetical responsiveness requirement.
Figure 5.9 represents idleness when thirty-two workers are used while Figure 5.10
represents idleness when seventy workers are used. We observe a large increase in
worker availability for the gain in responsiveness. A balance between these two measures
requires a subjective decision.
-A Availablevs.TIME: RUN 1
Figure 5.9. Idleness with an initial capacity of thirty two workers
Figure 5.10. Idleness with an initial capacity of seventy worken
Visualization in the manner presented in this Chapter illustrates that al1 factors should be
considered when attempting to optimize responsiveness or efficiency. Before this
visualization can occur, we note that the work first has to be identified at the capability
level. Utilizing the detemiinistic arriva1 of calendar-based preventive maintenance has
aliowed this identification and facilitated the visualization.
CHAPTER 6
MODEL CAPABILITIES DEVELOPED FOR FUTURE ANALYSIS
The model was built using the simulation software package "AweSim". We have not
discussed the use of the software package in generating random events up to this point.
Using "AweSim" as a programrning language we have presented a tool for visualization
and understanding of a complex problem. The capability of the software to model random
events extends the usefulness of the tool. Previously we have visualized some of the
aspects of demand arrivai and the tradeoff between efficiency and responsiveness. Initial
decisions can be made through this visualization aione, however the model was built with
the flexibility of further analysis. As model input data is limited to a fraction of the total
customers, a detailed simulation anaiysis will not be conducted at this point. Instead, we
use the CPF demand to introduce the capabilities of the model and discuss techniques for
dealing with the random demand arrival at FMFCS and the probabilistic nature of service
times.
6-1. Demand with a Deterministic Arriva1 and a Probabilistic Service Time
Preventive Maintenance has been identified as a type of maintenance with a deterministic
arrival time. Actual histoncal service times have k e n used in the model to project
service times of further demand. Each service time used is an actual service time taken
from histoncai data. Limiting to one observation provides a poor representation of the
possible range of service tirnes for any given activity. The use of the mean of several
observations would provide a clearer picture although the impact of variance still would
not be observable.
The capability exists in the model to assign parameters from a given probability hinction
for each activities' service time. In order to input this information into the model, it is
first necessary to fit a distribution to the service time of each activity of each preventive
maintenance routine. While commercial software packages are available to facilitate the
fitting of a probability distribution, the required historical data is not.
6.2. Demand with a Stochastic Amval Time and a Stochastic Sewice Time
We have discussed the random nature of corrective maintenance. There are many
possible approaches in trying to forecast the arriva1 of this type of demand. For example.
historical data could be used at the system level and required service could be projected
based on the failure frequency. This demand could then be input into the model and a
probabilistic service time assigned to a corresponding capability level for each required
activity. Analysis at this level requires extensive effort therefore a heunstic approach is
applied.
One possible heuristic approach is the use of empirical probability distributions (see Law
and Kelton [IO. p. 3521) to represent the monthly corrective maintenance demand from
the resources of each shop or capability level at the repair facility. Table 6.1 contains a
monthly breakdown of the corrective maintenance completed by FMF Cape Scott for
fiscal year 99/00. This historical data should not be considered as the actual demand for
the period, as historical output was constrained by the Delegated Maintenance Budget.
The mode1 has the capability for sampling from empirical distributions and this data has
been used to feature this use. Evaluation and selection of an appropriate heuristic
approach is left to future research, and therefore model output in this illustration should
not be considered as more than an illustration of the potential of the model.
63.1. Customer Availability
We re-visit the effect of customer availability in this section in order to present the
capability of the model in dealing with appropriate periods of customer availability. for
varying types of work. The data in Table 6.1 may be used to illustrate an approach to
forecasting corrective maintenance. Customer availability is different, however, for
corrective mainteriance than preventive maintenance. While preventive maintenance is
Table 6.1. Corrective Maintenance - Fiscal Year 99/00
SHOPS 10011 10012 10013 10019 10020 10021 1 0023 10030 10040 10050 10055 10090 10091 10092 11060 11061 11062 1 1 O70 11071 11080 11081 11100 11101 11112 11122 121 16 12142 12143 12145 12146 12147 12148 12149 12154 1 21 55 12156 12158 13124 13130 13132 131 33 131 35 1 31 36 131 37 131 51
Apr May Jun Jul Aug Sep Oct Nw Dec Jan Feb Mar
O O O 16
470 888 O O O O
16 300 98
49 2 Total
primarily limited to the Dedicated Maintenance Periods, corrective maintenance occurs
during these periods and when the customer is available alongside. Although beyond the
scope of this study, the capability to consider the period of customer availability
depending on the type of demand is particularly valuable when considering work
scheduled to occur in a fixed window of opportunity such as system trials.
It is worthwhile observing the impact of additional work on responsiveness and
efficiency. While the empirical distributions input into the model to represent corrective
maintenance were built using a heuristic approach, it is pointed out that the figures used
may be conservative as they were taken from FMFCS historical output as opposed to
historical demand. One mn will be considered in this terminating simulation, as at this
point we are only interested in visualizing possible behavior of the system at intermediate
points. As a result of limitation to one run, the input of corrective maintenance will be
from limited sarnples of the empirical distributions and may not represent typical values.
The Machine Shop will be used as an example of how corrective maintenance affects
responsiveness and efficiency. This effect will Vary significantly from shop to shop as the
percentage of corrective maintenance to total varies from shop to shop. We will also take
the opportunity to observe the impact of increased periods of availability for corrective
maintenance. Table 6.2 shows previous responsiveness to preventive maintenance and the
resulting change when corrective maintenance is input into the model. We consider the
case where the sarne periods of customer availability are applied to both types of
maintenance and the case where there is increased customer availability for corrective
maintenance. Figure 6.1 and Figure 6.2 show the resulting idleness of machine shop
resources when considering preventive maintenance alone and when cornbined with
corrective maintenance.
Table 6.2. Marine Machinery Shop Responsiveness (hours)
Demand Input Mean Std Dev Max
Wait-time in queue is increased, as expected, when we consider the additional corrective
maintenance demand. Increasing the period of customer availability for corrective
maintenance lowers the wait-time in queue. Increasing the opportunity to complete CM
reduces cornpetition with preventive maintenance for limited labour-hours.
' -4 Available vs. TIME: RUN 1
Figure 6.1. Marine Machinery Shop Worker idleness - Preventive Maintenance
-4 Availabls vs. TIME: RUN 1
Figure 6.2. Marine Machinery Shop Worker idleness - Preventive and Corrective
Maintenance
W e note that the idleness of the workers has changed somewhat, although the influence
of the custorner availability has kept the pattern of idleness similar. The addition of the
corrective maintenance has reduced the availability of the workers. In other words,
workforce utilization has increased.
6.3. Steady State Analysis
We have used a terminating simulation to observe the behavior of the system and to
visualize the impact of different factors on demand arrival at the shop ievel. Through use
of the terminating simulation, we observe responsiveness and efficiency for a given fiscal
year. What limits this observation is the assumption that the initial queue is empty and
that no further work arrives afier the beginning of the twelve month of observation.
A steady-state simulation may be conducted over a longer duration and remove these
limitations, however it does introduce some new assumptions. "It should be mentioned
that stochastic processes for most real systems do not have steady-state distributions,
since the characteristics of the model change over time. For example, in a manufacturing
system the production scheduling rules and the facility layout (e.g., number and location
of machines) may change from time to time. On the other hand, a simulation model
(which is an abstraction of reality) may have steady-state distributions, since
characteristics of the model are often assumed not to change over time" [17, p. 5301.
In Our particular case, we will be conducting a steady-state analysis of one fiscal year.
The model has the capability to specify the actual periods of the maintenance cycle for
each customer, however would require more data input into the model. Corrective
maintenance is input through the use of empirical probability distributions. One sample
from the distribution represents one month of corrective maintenance for the
corresponding shop. The assumption here is that the empirical distribution adequately
represents this type of work over the duration of the simulation. The correlation between
capabilities is not captured with this method.
A period of ten years is chosen for the steady-state simulation of fiscal year 2001/2002.
As we have discussed, the model has been built to visualize demand and the tradeoff
between efficiency and responsiveness, and a statistical analysis of the output will not be
conducted. Nevertheless, a limited view of the steady-state simulation output is included
to highlight the capability of the model in this area for further study. See Law and Kelton
[IO] for a discussion of output anaiysis.
6.3.1. Wait-time in Queue
Figure 6.3 plots the wait-time in queue over a ten year simulation for the Marine
Machinery Shop. This output shows the fluctuation in the wait-time but also indicates
that the trend is not rising. The peak wait-times are high. We have seen through
observation with the terminating solution that the availability of the customer is a
significant factor in delay. Augmenting the resource capacity reduces the overall average
delay but does not eliminate the peak delays which are a function of the customer
availability .
MArine Machinery - Wait Time
Figure 6.3. Marine Machinery Shop- Steady State Wait-time in Queue
The steady-state output for the Above Water Weapons Shop in Figure 6.4 shows an
increasing trend for wait-time. This suggests that there are not enough workers to handle
the demand. As more work is carried over, the mean wait-time increases.
AWW Weapons - WaitTime
Figure 6.4. Above Water Weapons Shop - Steady State Wait-time in Queue
6.3.2. Workei Availability
We are able to record the worker availability for any given period of time within the
steady-state simulation. Figure 6.5 shows the available workers in year five of the
simulation for the Marine Machinery Shop. W e note the sirnilarities with Figure 6.2 of
the terminating simulation. The similarities indicate the significance of the periods of
customer availability. In both figures we observe the periods of idleness corresponding to
periods where al1 of the customers are not available to FMFCS. We note, in Figure 6.5,
the carry-over of work from subsequent years to the beginning of the fifth year where
resource is not idle. In the steady-state observation, a warm-up period is required in order
to account for the queues being initially empty. This wam-up period is identified and
discarded in a statistical analysis.
3~ Availablevs. TIME: RUN 1
Figure 6.5. Marine Machinery Shop Worker Idleness -Year Five of the Steady State
Simulation
6.4. Scheduling Rules
"As the industnalized world develops, more and more resources are becoming critical.
Machines, manpower, and facilities are now commonly thought of as critical resources in
production and service activities. Scheduling these resources leads to increased
efficiency, utilization, and ultimately, profitability" [16. p. 11. The resource of focus in
this study is the FMFCS labour force. Scheduling rules are introduced both as an
interesting area of future research and to further aid in the visualization and
understanding of this complex problem.
The first in first out (FIFO) scheduling mle is often used as a baseline against other
heuristic rules. Within Our mode1 the scheduling mle which we have been using is a
modified FLFO rule. As customers become unavailable the shop queues are emptied and
the work re-inserted when the customer becomes available again. New work arriving
between p e n d s of customer availability may arrive at the queue before the old work is
re-inserted, thus we do not have a tmly FIFO scheduling rule. By using a true FIFO rule,
we observe a different wait-time.
Customer demand is often prioritized due to the importance of upcoming deployments.
This priority may change several times throughout the course of a given fiscal year. An
exarnple is utilized to show the possible impact of this type of prioritization. Table 6.3
contains customer wait-time data for a steady-state simulation of eighteen thousand five
hundred hours using a FIFO rule. Table 6.4 contains the same information when utilizing
a customer prioritization rule, and Table 6.5 contains data when our original modified
FIFO rule is observed.
Table 6.3. Wait-time in Queue - Tme FWO
Customer Priority Mean Std Dev
HALIFAX Nil VILLE DE QUEBEC Nil TORONTO Nil MONTREAL Nil FREDERICTON Nil CHARLOTTETOWN Nil ST JOHNS Nil
Table 6.4. Wait-time in Queue - Customer Prioritized
Customer Priority Mean
HALIFAX 7 449 VILLE DE QUEBEC 6 256 TORONTO 5 393 MONTREAL 4 228 FREDERICTON 3 193 CHARLOTTETOWN 2 21 1 ST JOHNS 1 207
Table 6.5. Wait-time in Queue - Modified FIFO
Customer Priority Mean
HALIFAX Nil VILLE DE QUEBEC Nil TORONTO Nil MONTREAL Nil f REDERICTON Nil CHARLOTTETOWN Nil ST JOHNS Nil
Std Dev
1386 893
1153 410 354 231 387
Std Dev
1427 450
1 324 503 418 323 670
# Obs
1 1770 1241 6 10601 12421 12288 1 1750 1231 9
# Obs
1 1496 12206 10220 12590 12508 12039 12606
# Obs
10932 12590 10093 12556 12438 1 1976 12332
Max Value
6573 651 2 6450 6608 5798 5940 6574
Max Value
13292 881 7
1 1472 14600 7070 2068 91 56
Max Value
12961 3388
1 1447 51 33 61 68 261 6 6872
The data presented in the tables must be interpreted carefully. We observe some expected
trends. For example, pnoritizing customen lowers the mean wait-time for the high
prionty customer, St Johns, and a true FIFO pnoritization levels the max wait-time
amongst customers. The changing number of observations clouds the picture. Data is
collected only on those entities passing through the simulation model, or in other terms
completed work at FMFCS. The FIFO scheduling nile ensures that al1 activities are
processed in order of arriva1 once the respective customer is available and provides
further insight.
An increasing queue size for ail customers indicates an unstable system. We observe that
extending the duration of the steady-state FiFO simulation increases the wait-time in
queue for al1 customers, therefore we suspect an unstable system. It is necessary to
investigate the individual capability areas of FMFCS in order to determine where this is
happening. The Marine Machinery Shop and Above Water Weapon shops are used as
examples.
Figure 6.6 and Figure 6.7 illustrate how the wait-time in queue is affected for the Marine
Machinery and Above Water Weapons shop respectively. The trend of increasing wait-
time for the AWW Shop has been previously identified but is made more visible by using
the RF0 rule. Al1 new jobs must wait in the FlFO queue therefore variability is reduced
and the increasing wait-tirne is very apparent. We observe that additional resources are
required in this particular shop for the demand and customer availability of fiscal year
2001/2002, in order for d l jobs to be completed. The cyclical nature of the steady-state
analysis is noted when observing the Marine-Machinery Shop output in Figure 6.6. Ten
peaks in wait-time represent the ten-year customer availability cycle. We also note that
the trend in wait-time is not rising and conclude that in the steady-state observation al1
jobs wil1 be completed in this particular shop.
Marine Machinery - Wait Time I I E! 1200, 1
Figure 6.6. Marine Machinery Shop - Wait-time in Queue (FIFO)
AWW -Weapons - Wait Tirne
Time (Hours) I
Figure 6.7. Above Water Weapons Shop - Wait-time in Queue (FIFO)
CHAPTER 7
EFFICIENCY THROUGH VISUALIZATION
increasing efficiency has been the primary goal of Fleet Maintenance Facility Cape Scott
in recent years. Use of the mode1 visualizes a tradeoff between efficiency and
responsiveness. A strategic measure in the balanced scorecard, optimizing responsiveness
has not been well understood. indeed, the goal of optimizing responsiveness has been
neglected in the building of previous FMFCS capability plans. The break down of work
to activities at the capability level is critical in this visualization as attempts to increase
efficiency in one area by taking on additional jobs may decrease responsiveness in
associated capabilities to an unacceptable level. Conversely, by not understanding the
role of customer availability on responsiveness it is possible to unnecessarily augment
resource capacity in attempting to decrease the wait-time in queue, and thus negatively
affect overail efficiency.
There is obviously a balance to be found between the objectives of efficiency and
responsiveness. This balance must be determined by the decision-maker, but before this
decision can be made the tradeoff must be fully understood.
7.1. Aiignment of Resources to Dernand
The last ten years has k e n a period of tremendous change for FMFCS. Downsizing has
reduced its resources significantly. Concurrently the customer base has changed and with
this change the demands on FMFCS resources have also changed. Certain capability
levels are now in greater demand while others are no longer required in the volume they
once were. We have discussed in Chapter 3 how a shift in focus towards satisfying al1
demand of the primary customers may require further balancing of resources.
Breaking the demand down to the capability level identifies if balancing of resources is
required. We have described how we can project preventive maintenance demand at the
capability level. Additionally a heuristic approach has been introduced to forecast the
corrective maintenance requirements. Table 7.1 shows the average availability of workers
as compared to the resource strength. As well the wait-time in queue is displayed at the
capability level. This data is the output of a steady-state simulation for fiscal year 01/02.
Statistical analysis was not done, as we are only interested in presenting the capabilities
of modeling. The transient of the simulation output has not been removed and duration of
the steady-state simulation was lirnited to ten years. The data in Table 7.1 is presented
only to introduce the capabilities of the model and should not be interpreted as indicating
surplus capacity. Data input in this project was limited to approximately one quarter of
the total FMFCS annual work and seven primary customers therefore we expect the
model to indicate surplus capacity. Additionally we have not considered types of work
such as trials, which is the bulk of demand on certain capability areas. What can be seen
from the data is what capability areas are subjected to the demand input into the model. It
is important to remember that the corrective and preventive maintenance demand of the
Canadian Patrol Frigates has not been completely represented.
Table 7.1. - Steady State Worker Availability and Capability Response (Original
S trength)
Capability
Coating Tiling MPNA Hull lnsp MPNA Eng Foundry Plate Welding MPNA Sheet Metal Shipwright Sail Liferaft Rigging Pipe Fabrication Lagging Chernical Clean Diesels Gas Turbines Diesel Insp Machine Tool Die Marine Machinery MTWC Mar Mach Control Systems Refrig AC H ydraulic Mech Eng TOM Comm Nav Eng Comm SEWE EW Radar CCS CCS Eng MULTOTS ATS NESTRA ATS Nav Aids Antenna
Sttength & Eff ickncy Resource Capacity
25 6
11 14 4
54 24 2 1 23 13
81 45 10 6
35 7
12
27 7
32 O
23 9
20 20 5
12 3 1 13 7
16 8 7 6
11 12 17
Average Availability
17.53 3.85 8.00
12.70 3.60
38.62 20.1 6 15.08 16.49 1 1.83
64.42 23.70 6.36 4.66
14.77 4.38 7.19
23.75 6.21
10.12 8.25
14.19 2.34
13.72 16.28 4.47
10.83 25.55 1 1.82 5.33
12.61 7.32 6.29 5.21
fO.14 t 0.00 13.1 1
Wait Time in Queue (Hours) Average Wait-time
140 334 178 160 1 O9 1 53 120 138 1 34 1 39
1 54 1 72 203 199 1 46 377 198
1 55 117 257 1 93 204
1090 147 207 100 114 1 44 110 1 54 141 113 1 O7 1 O9 1 O3 140 155
Standard Deviation
183 287 21 8 234 1 52 198 173 177 167 224
203 21 6 21 9 240 184 376 241
206 1 54 248 205 242
1742 200 239 165 169 199 1 50 1 75 1 79 1 63 155 160 149 191 1 82
Marine Elec Eng Elec Test Heavy Elec Cabling Mar Elec Syst UW Ranges UWW Eng Syst Sonar Surf Elec CANTASS Sonar VDS Nixie AW Weapons Periscope Opt UWW Mech Eng AWW Mech Eng AW Sys Eng Under Water Weap
We are able to use the mode1 output (presented in Table 7.1) to illustrate the potential
gains of balancing worker strength to demand at the shop level. Examining the output we
notice that five of the capability areas have a significantly longer wait-time than the other
capabilities. As expected. it is also evident from the output that most shops are not k i n g
utilized to their potential. An exarnple is used to illustrate the potential of balancing the
worker strength. Maintaining the total FMFCS worker strength. we decrease worker
strength in underutilized capabilities and increase worker strength in the capabilities with
longer wait-times. Table 7.2 shows the worken exchanged in the example.
Table 7.2. Balancing Worker Strength
# Workers 2 2 2
10 7 3 3
Shop ûecreased
Welding Welding Tool & Oie Riggirig Plate Cabling SailMeraft
Shop Increased
Tiling Gas Turbine Marine Machinery Refrig AC AWW AWW AWW
Table 7.3 shows the resulting changes in the mode1 output at the capability level.
Capabilities with a change in worker strength are in bold. We observe a lower wait-tirne
in queue at the shops where we have augmented worker strength. Average worker
availability is decreased in those shops where we have decreased worker strength.
Table 7.3. - Steady State Worker Availability and Capability Response (Balanced
Strength)
Capability
Coating Tiling MPNA Hull lnsp MPNA Eng Foundry Plate Welding MPNA Sheet Metal Shipwright Sail Liferatt Rb9 in9 Pipe Fabrication Lagging Chemical Clean Diesels Ga8 Turbines Diesel lnsp Machine Tool Die Marine Machinery MTWC Mar Mach Control Systems Refrig AC Hydraulic Mech Eng TDM Comm Nav Eng Comm SEWE EW
Strength & Effiiiency Resource Capacity
25 8
11 14 4
47 20 2 1 23 8
71 45 10 6
35 9
12 27 5
36 O
23 19 20 20 5
12 3 1 13 7
Average Availability
17.1 3 5.32 7.98 12-73
3.6 32.25 16.49 15.08 16.49 6.83
55.1 2 23.7 6.44 4.66
14.77 6.33 7.19
23.68 4.59
13.68 8.25
14.34 10.65
1 -63 16.4 4.47
10.88 25.67 11.82 5.33
Wait Time in Queue (Hours) Average Standard Wait-time Deviat ion
Radar CCS CCS Eng MULTOTS ATS NESTRA ATS Nav Aids Antenna Marine Elec Eng Elec Test Heavy Elec Cabling Mar Elec Syst UW Ranges UWW Eng Syst Sonar Surf Elec CANTASS Sonar VDS Nixie AW Weapons Periscope Opt UWW Mech Eng AWW Mech Eng AW Sys Eng Under Water Weap
The change in wait-time may also be viewed from the perspective of the customer. Table
7.4 shows mode1 outputs using the original worker assignment and the balanced worker
assignment.
Table 7.4. - Customer Perspective of Balanced Resources
Customer
Halifax Ville de Quebec Toronto Montreal f redericton Charlottetown St Johns
Mean Wait-time (Hours) Original Balanced
Max Wait-time (Hours) Original Balanced
# Obsenrations Original Balanced
No attempt has been made to balance the resources optimally within the example. The
example is utilized only to portray the potential value of the model. Obviously a
statisticd comparison will be required once al1 the input data is gathered (see Law and
Kelton [IO]). Balancing resources in this particular exarnple (when most shops are under
capacity) increases both overall responsiveness and workforce utiiization. Subjective
preferences will become an issue when it is required to balance the workforce when the
majority of shops are over capacity. Issues such as strategic requirernent and feasibility of
"contracting out" will complicate the decision.
7.1.1. Load Leveling and Core Capability
Due to the nature of the work carried out by FMFCS, some of the capability areas are of a
strategic value. An exarnple of a strategic capability would be the Under Water Sound
Range which is maintained and rnanned by FMFCS resources. This type of a resource is
difficult to load level through the course of the year. Other resources rnay be load-leveled
by bringing in additional work during periods of traditional customer unavailability.
Another approach would be to contract out work where there is a local resource base in
periods of peak demand or hire temporary and casual workers during these peak periods.
Yet another approach is to identify those areas that can be contracted out in totality.
Al1 of these approaches to load leveling are either being utiIized or are under
consideration by FMFCS. Al1 forms of load leveling are difficult to implement due to
demand uncertainty and poor visualization at the capability level. Additionally, FMFCS
has severe constraints in their ability to adjust their workforce capacity. Funding is
limited for the hiring of additional workers in any fonn.
Using a mode1 to visualize possible cause and effect relationships rnay aid the decision-
maker. Highlighting areas of probable over- and under-capacity may emphasize the
requirement to define what is the core capability and which capabilities are required as a
strategic asset. Responsiveness and the ability to meet peak demand may be more critical
in certain capability areas than others.
7.1.2. Customer Availability
Although the impact of customer availability on demand has been recognized historically,
it has been difficult to quantify. Responsiveness and worker utilization is a function of
this availability. Limited flexibility in the scheduling of these periods of customer
availability exists. At one extreme, the customers would be available to have work
completed one hundred percent of the time. As this is not possible, it is important to
understand the impact of limited availability on responsiveness and worker idleness. The
mode1 provides this support and the ability to evaluate scenarios within the customer-
availability flexibility.
CfiAPTER 8
IMPLEMENTATION AND CONCLUSIONS
8.1. Conclusions
We have developed a simulation mode1 that c m be used as a strategic planning tool for
FMFCS. We have shown how the simulation model may be used to predict the cause and
effect relationships between two primary strategic measures, workforce utilization and
responsiveness. In Chapter 7, we presented an example of how the model uses these
measures to compare different manning levels at the FMFCS capability level and
discussed how different periods of customer availability may be evaluated in the same
manner.
8.1.1. Forecasting Demand
By taking advantage of the deterrninistic nature of calendar-based preventive
maintenance we have accurately forecasted a significant portion of demand at the
capability level. We have also gained insight as to the potential volume of this type of
demand as the constraints of the DMB concept are relaxed. Observations from historical
data used in this project support other studies indicating that al1 directed preventive
maintenance is not k ing completed.
During the process of analyzing histoncal FMFCS data we observed possible reasons for
historicaily low predictions of preventive maintenance demand. Comparing actual service
times to the original manufacturer estimate revealed that manufacturer estimates are
conservative. Additionally, we observed that the RF/SS designation is not consistent with
the preventive maintenance routines k ing completed by FMFCS. The large number of
SS-designated PM routines being directed to FMFCS for completion was not envisioned
in the original DMB allocation.
The designation will have to be reviewed in order to forrnalize which maintenance
routines will be consistently directed to FMFCS for completion. We have shown how the
total demand of calendar-based preventive routines can be accurately forecasted at the
capability level, if it is deemed desirable to complete this review. Indications are that this
demand combined with other existing types will increase the required allocation of
FMFCS labour-hours to a level significantly higher than the current DMB allocation.
A heuristic approach to forecasting corrective maintenance has been shown. The
accuracy of this approach needs to be validated. We observe that the approach is
currently Iirnited due to the constraints of the DMB concept. Historical data does not
exist on demand that was not satisfied due to a customer's allocation being exceeded.
Therefore, data will have to be collected on al1 corrective maintenance requirements in
order to forecast future corrective maintenance demand.
8.1.2. Visualization of Strategic Measures
The simulation model was used to visualize the tradeoff between conflicting goals at
FMFCS. Attention was focused on two measures, workforce utilization and
responsiveness. The model was built in stages to illustrate the impact of different factors
of demand arrival on these measures. We observed that customer availability has a very
significant impact on both of these key measures. Augmenting resource strength in
certain areas may not have the level of effect desired in increasing responsiveness due to
these other factors. Visualization will aid the decision maker(s) in defining strategic
objectives. For example, using only a fraction of the total annual demand, we have gained
insight with respect to capability areas where demand exceeds FMFCS's available
capaci ty.
8.2. Model Implementation
Many challenges exist that must be addressed prior to the successful implementation of
the simulation model. These challenges range from data cleansing to internai culture
change. The following paragraphs are not al1 encompassing, but note several
requirements which should oçcur prior to model implementation.
8.2.1. Validation of Demand
The bulk of the demand put into our simulation model was calendar-based preventive
maintenance. We have discussed the requirement for validating the RF/SS designation in
determining which maintenance routines are to be done by Fleet Maintenance Facility
Cape Scott. An assumption that has been made is that the designated routines are firm
requirements. This assumption requires validation. Through development of the model,
we have shown both that the demand is not currently k i n g satisfied and that the demand,
if it was al1 to be done, exceeds capacity in certain FMFCS capabilities. This initial
visualization, by itself, may be cause for validating the original maintenance
requirements recommended in the original manufacturer estimates. Lisota and Hedderich
[12], in ii review of the United States Navy preventive maintenance requirement, suggest
that a thirty-five percent labour-hour reduction, from the ship total, is obtainable.
8.2.2. Data Cleansing
Service-time estimates used for FMFCS planning purposes are generally higher than the
actual service-times charged in time recording. Within the simulation model, we have
used actual service-times. Scheduling inefficiencies may be built into the planning
estimates. Or the fact that histoncal layoffs were done based on the volume of "reserve"
time in particulas work centers may influence the input of data. Regardless, service-time
data will have to be validated. Initiatives are underway at FMFCS to "Blue Book"
common jobs such as preventive maintenance routines. Observations made through the
development of the simulation mode1 support the requirement for this initiative. By
cataloguing common jobs, historical service times will be captured. The model has been
built to input service-times from a probability distribution. It is envisioned that
continuous improvement techniques will trend service-times downward. Simply through
advanced identification of demand, it will be possible to increase efficiency. For
example, many preventive maintenance routines require Crane services. Through project
management, it will be possible to optimally schedule this service,
The model has confinned that lirnited customer availability has a significant impact on
both responsiveness and efficiency. Identifying preventive maintenance routines which
could be conducted outside of a customer's DMP and within the Halifax available periods
would mitigate the effect of customer availability. Identifying the impact of conducting
the preventive maintenance routine on the ship's "notice for power" would determine
whether the routine could be done outside of the DMP. This could be documented within
the "Blue Book".
8.2.3. Moàel Flexibility
As discussed, it is anticipated that both arriva1 and service-times of maintenance routines
will change. Therefore, the model requires flexibility. The rnethod that we have used to
input preventive maintenance routines into the model can be improved. Updating
maintenance routines and their associated service times should be done outside of the
model in a spread sheet or data base program, and read into the simulation model. In this
manner, data could be updated clerically.
8.2.4. Human Resource Training
Within the FMFCS business management department, analysts establish the annual work
delivery plan and measure performance against it. In order to incorporate the simulation
model within this process, the analysts will require training in probability theory and
simulation. Training in statistics has previously been identified as a requirement.
8.2 S. Strategic Direction
Within the model, we have introduced the tradeoff between responsiveness and
efficiency. Policies within the Department of National Defense such as Altemate Service
Delivery should dictate a review of the strategic capabilities to be retained by FMFCS, in
the long term. Once deemed essential for retention, the balance between responsiveness
and efficiency will have to be addressed for each FMFCS capability in a human resource
plan. Training duration for certain capabilities and current FMFCS demographics suggest
decisions are required soon or some capabilities will be lost unintentionally.
8.3. Future Research
We have studied the preventive and corrective maintenance demand requirements of
seven customers. A detailed steady-state analysis will require that al1 types of demand
frorn al1 customers be input into the model. The pnnciples demonstrated in this study
may be applied to al1 customers, but it will be necessary to validate the approach used to
forecast the component of demand with a stochastic amival rate. Future research is
required to either validate the heuristic approach presented, using empirical distributions,
or to determine an alternative forecasting method.
An interesting area for future research is the decision process. The simulation model
provides an anticipated output for strategic measures but once these measures are
obtained, for possible decision alternatives, a subjective decision is required. Software
has been developed (see Barzilai and Gmndke [2]) that incorporates new developments in
this field. Future research is required to determine if simulation c m be combined with the
decision analysis software.
CHAPTER 9
REFERENCES
Banks, J., and Carson, J.S., and Nelson, B.L., Discrete-Evenr System Simulation, Prentice-Hall, hc., 1996.
Bardai, J., and Grundke, E., Scientific Decisions inc. Preference Function Modelling. Retrieved from the World Wide Web
Brown, MG., Keeping Score, Quality Resources, 1996.
Carosielli, L., Lieutenant-Commander, Canadian Navy, and Parent, J., Lieutenant Xommander, Canadian Navy, "HMCS St, John's Maintenance Capability Study," Maritime Engineering Journal, pp. 17-20, Summer 2000.
Dempster, M.A.H., Lenstra, J.K., and Rinnooy Kan, A.H.G., Deteministic and Stochastic Scheduling, D.Reide1, 1982.
Fleet Maintenance Facility Cape Scott, Business Rules Version 3.2, March 2000.
Fleet Maintenance Facility Cape Scott, FMF Cape Scott Level3 Drufi Capabilify Plan F Y 200112002, August 2000.
Harrel, C., and Tumay, K., Sitnulation Made Easy, Institute of Industrial Engineers, 1995.
Kaplan, R.S., and Norton, D.P., The Balanced Scorecard, Harvard Business Schwl Press, 1996.
Law, A.M., and Kelton, W .D., Simulation Modeling and Analysis, McGraw-Hill, 1991.
Lee, B.P., Integer Programming Approaches to Vehicle Fleet Maintenance Scheduling, Master of Applied Science Thesis, Technical University of Nova Scotia, 199 1.
Lisota, G. and Hedderich, C., 'Total Productive Maintenance (TPM): A Powerful Tool for the U.S. Navy," Technical Papers from AMSEC LLC, 1999-2000.
Maddison, G.R., Vice-Admiral, Canadian Navy, "Fleet Maintenance Facilities- Core Funding & Most Efficient Organization Way Ahead," Memorandum MS: 1000-23 (CMS), March 2000.
Maddison, GR., Vice-Admirai, Canadian Navy, "Maintenance of HMC Ships," Memorandum MS: 11900-0 (DMMPP), October 2000.
Naval Maintenance Management System Manual Volume 1, NaMMs Policy and Procedures, Issued on the Authority of the Chief of Defense Staff, 1993.
Pinedo, M., and Chao, X., Operations Scheduling with Applications in Manufacturing and Services, hin/McGraw-Hill, 1999.
Pritsker, A.A.B., and O'Reilly, J.J., Simulation with Visual SLAM and AweSim, Wiley, 1999.
Schlesinger,S., Terminology for Mode1 Credibility, Simulation. 32(3): 103- 4,1979.
Sule, D.R., Industrial Scheduling, PWS, 1997.
Sylvester, J.R., Commodore, Canadian Navy, "HMCS St. John's Maintenance Study a good "reality check"," Maritime Engineering Journal, p. 3, Fa11 2000tWinter 200 1.
Wheelwright, S.C., and Makridakis, S., Forecasting Methods for Management, Wiley, 1980.
APPENDIX A HALIFAX CLASS MAINTENANCE PROFILE
NO. 1 2 3
5
Trials & Work-ups
A
ITEM 1 REMARKS
6
7
O Tirne in Months 48
Maintenance Profile Time Base Docking Interval Programmed Work Periods: a. Dedicated Maintenance Period (DMP) b. Docking Work Period (DWP) c. Refit and Dockinp
Activity Rate
144 months 48 months 3 consecutive weeks ( 19 days j in each operational quarter. 3 weeks (approximately mid cycle) for docking. if required (during DMP). Not Applicable An average of 1520 person-hour work per ship, per operating month for the first 36 months. Thereafter. increase FMF person-hour work per operational month until 3740 person-hour work per month are scheduled,
4
after 60 months. 160 operational days per year at sea: 95 operational days per year in harbour. at 8 hours
Technical OP1 and OC1
ldeal Maintenance Profile
Repair Facility person-hour Allocation for Running Repair
notice NDHQ OPI: M;MEM/DSE MARLANTNARPAC OPI: N42 EM
OCI: FMF
APPElYDIX B
HOURS TO CALENDAR DATE CONVERSION
Fiscal Year 0 1/02
Calendar Date
1 April
1 May
1 June
1 Jufy
1 August
1 September
1 October
1 November
1 December
1 January
1 February
1 March
Hour Period
APPENDIX C
ATTRIBUTES AND GLOBAL VARIABLES
Attributes
ATRIB[l] - time of creation of work request
ATRIB[2] - resource identification (capability)
ATRU3131 - customer identification
ATRIB[4] - service time in hours
ATRB[S] - level of resource assigned to an activity
ATRIB[6] - assumes unique value for each PM routine
ATRIB[7] - assumes value of TNOW when entity leaves the customer gate node.
ATRIB[B] - assumes the value of one or two representing RF and SS preventive
maintenance reflectively.
ATRIB[9] - assumes the value of zero or one representing preventive and corrective
maintenance reflectively.
Attribute Values
ATRIB [2]
File # Resource/S hop
1 Coatings
2 Tiling
3 MP/NA Hull Inspections
4 MP/NA Engineering
5 Foundry
6 Plate
7 Welding
MPNA Sheet Metal
Shipwright
SailLi feraft
Rigging
Pipe Fabrication
Lagging
Chernical Cleaning
Diesels
Gas Turbines
Diesel Inspection
Machine
Tool & Die
Marine Mac hinery
MTWC-Marine Machinery
Control Systems
Re frig/AC
Hydraulic
Mechanical Engineering
TDM
Cornm/Nav Engineering
Communications
Surveillance/Elec Warfare Eng
Electronic Warfare
RadarKCS
CCS Engineering
MULTOTS Engineenng
ATS NESTRA Engineenng
ATS
Nav Aids
ATRIB[3]
File #
100
101
102
103
104
105
106
107
108
Antenna
Marine Electrical Engineering
Electrical Test
Heav y Electrical
Cabling
Marine Electrical S ystems
Under Water Ranges
UWW Eng Systems
AWW Mechanical Engineering
CANTASS/Sonar Sab
VDS/Nixie Handling
AW Weapons
Under Water Weapons
UWW Mechanical Engineering
AWW Mechanical Engineering
AW Systems Eng
Under Water Weapons
CustomerIS hip
HMCS ATHABASKAN
HMCS lRUQUOIS
HMCS HALFAX
HMCS VLLE DE QUEBEC
HMCS TORONTO
HMCS MONTREAL
HMCS FREDERICTON
HMCS CHARLOTTETOWN
HMCS ST JOHNS
109 HMCS PRESERVER
Global Variables
XX[l] -used to pass the value of ATRIB[3] to the sequence of FINDAR nodes which
clear the shops of a particular customer's activities once the customer is no longer
available.
XX[2] -used to globally identify the length of the current o r upcoming work p e n d for
HMCS ATHABASKAN.
XX[3] -used to globally identify the time period between the current o r upcoming work
period and the subsequent work period for HMCS ATHABASKAN.
XX[4] -used to globally identify the length of the current or upcoming work period for
HMCS IROQUOIS.
XX[5] -used to globally identify the time p e h d between the current o r upcoming work
period and the subsequent work period for HMCS IROQUOIS
XX[6] -used to globally identify the length of the current or upcoming work pend for
HMCS HALIFAX.
XX[7] -used to globally identify the time period between the current o r upcoming work
period and the subsequent work pend for HMCS HALIFAX.
XX[8] -used to globally identify the length of the current o r upcoming work period for
HMCS VILLE DE QUEBEC.
XX[9] -used to globally identify the time period between the current o r upcoming work
period and the subsequent work period for HMCS VILLE DE QUEBEC.
XX[ 101-used to globally identify the length of the current o r upcorning work period for
HMCS TORONTO.
XX[l 11-used to globally identify the time period between the current o r upcoming work
period and the subsequent work period for HMCS TORONTO.
XX[l2] -used to globally identify the length of the current o r upcoming work period for
HMCS MONTREAL.
XX[13] -used to globaily identify the time period between the current or upcoming work
period and the subsequent work period for HMCS MONTREAL.
XX[14] -used to globally identify the length of the current or upcoming work period for
HMCS FERDERICTON.
XX[15] -used to globally identify the time period between the current or upcorning work
p e n d and the subsequent work period for HMCS FREDERICTON.
XX[16] -used to globally identify the length of the current or upcoming work p e n d for
HMCS CHARLOTTETOWN.
XX[17] -used to globally identify the time period between the current or upcoming work
pend and the subsequent work period for HMCS CHARLOTTETOWN.
XX[18] -used to globally identify the length of the current or upcoming work period for
HMCS ST JOHNS.
XX[19] -used to globaily identify the iime period between the current or upcoming work
period and the subsequent work period for HMCS ST JOHNS.
XX[20] -used to globally identify the length of the current or upcorning work period for
HMCS PRESERVER.
XX[21] -used to globaily identify the time period between the current or upcoming work
p e n d and the subsequent work period for HMCS PRESERVER.
XX[22] - used to pass the value of ATRiB[6] to the FINDAR node which flushes the
gate node (File 15 1) of al1 the activities of a job if one of the activities is too large
for the current customer work period.
XX[23] -used to globaily identify the length of the current or upcoming period of
customer availability for corrective maintenance for HMCS HALIFAX.
XX[24] - used to globally identify the time p e n d between the current or upcoming
period of customer availability for corrective maintenance and the subsequent
period of availability for HMCS HALIFAX.
XX[25] -used to globally identify the length of the current or upcorning period of
customer availability for corrective maintenance for HMCS VILLE DE
QUEBEC.
XX[26] - used to globally identify the time period between the current or upcoming
period of customer availability for corrective maintenance and the subsequent
period of availability for HMCS VILLE DE QUEBEC
XX[27] -used to globally identify the length of the current or upcoming period of
customer availability for corrective maintenance for HMCS TORONTO.
XX[28] - used to globally identify the time period between the current or upcoming
pend of customer availability for corrective maintenance and the subsequent
period of availability for HMCS TORONTO.
XX[29J -used to globally identify the length of the current or upcoming p e n d of
customer availability for corrective maintenance for HMCS MONTREAL.
XX[30] - used to globally identify the time pend between the current or upcoming
period of customer availability for corrective maintenance and the subsequent
period of availability for HMCS MONTREAL.
XX[3 11 -used to globally identify the length of the current or upcoming period of
customer availability for corrective maintenance for HMCS FREDERICTON.
XX[32] - used to globally identify the time period between the current or upcoming
period of customer availability for corrective maintenance and the subsequent
period of availability for HMCS FREDERICTON.
XX[33] -used to globally identify the length of the current or upcoming period of
customer availability for corrective maintenance for HMCS
CHARLOTTETOWN.
XX[34] - used to globally identify the time pend between the current or upcoming
p e n d of customer availability for corrective maintenance and the subsequent
p e n d of availability for HMCS CHARLO'ITETOWN.
XX[35] -used to globally identify the length of the current or upcoming period of
customer availability for corrective maintenance for HMCS ST JOHNS.
XX[36] - used to globally identify the time period between the current or upcoming
period of customer availability for corrective maintenance and the subsequent
period of availability for HMCS ST JOHNS.
APPENDlX D THE 44AWESIM" VISUAL NETWORK MODEL
D.1. The Controi Statement
GEN,,,, 1 ,YES,YES; ARRAY,1,12,{ 1/12,~12,3/12,4/12,5/12,6/12,7/12,8/12,9/12,10/12,11/12,1); ARRAY,3,12,{0,0,1,2,111,112,301,322,358,385,451,1170}; ARRAY,4,12,{5 l,78,78,lOl,l27,l32,l44,l47,l7l,l93,2oO,227); ARRAY,5,12,{0,0,0,0,0,1,3,12,13,15,225,32}; ARRAY,6,12, {0,0,0,0,0,0,0,0,0,0,8,104); ARRAY,7,12, (226,294,384,424,538,569,597,687,695,761,899,1535 } ; ARRAY,8,12,{32,173,193,216,225,247,264,297,321,342,357,499}; ARRAY,9,12,{48,161,189,228,247,266,415,420,536,559,626,814); ARRAY,10,12,{ 143,366,389,458,475,479,489,545,762,986,1268,1761}; ARRAY,l 1,12,{0,0,0,0,0,20,30,32,33,38,48,76}; ARRAY,l2,12,{213,235,317,484,534,544,553,622,677,758,841,972); ARRAY,13,12,{284,384,541,600,604,712,744,875,877,908,926,1122}; ARRAY,14,12,{ 16,17,48,49,119,157,168,254,258,310,498,5 18); ARRAY,15,12,{3,22,23,26,42,44,57,61,66,72,133,196); ARRAY,16,12,{0,60,66,153,192,224,280,284,393,418,445,1096); ARRAY,17,12,{0,0,0,0,0,0,0,0,0,24,248,296); ARRAY, 18,12, {0,0,0,0,0,0,0,0,0,4,8,120) ; ARRAY,19,12,{41,42,63,73,87,101,110,124,125,140,180,788); ARRAY,20,12,{0,1,1,1,2,8,8~9,16,19,54,72); ARRAY,2 1,12,{0,217,297,380,4 17,549,585,664,758,795,1290,1476) ; ARRAY,22,12,{0,0,0,0,1,8,9,25,43,80,98,1034}; ARRAY ,23,12, { 32,96,224,263,277,350,372,427,433,489,576,979 } ; ARRAY,24,12,{0,0,6,48,96,1 Il, 152,160,176,240,416,552); ARRAY ,2S, 12, { 1,16,290,297,446,450,470,490,5 13,690,708,847 ) ; ARRAY,26,12,{8,17,22,54,59,77,93,96,117,149,345,389}; ARRAY,27,12,{0,0,0,0,0,0,0,0,0,1,3,3}; ARRAY,28,12,{0,0,0,0,0,0,0,0,16,20,86,126); ARRAY,29,12,{0,0,52,64,72,13 1,209,209,221,320,535,949}; ARRAY,30,12,{0,0,0,0,0,0,1,4,6,12,15,56); ARRAY,3 1,12,{0,0,0,67,103,104,172,208,208,23 1,272,275); ARRAY,32,12,{0,0,39,40,87,128,19 1,219,283,352,489,888); ARRAY,33,12,{0,0,0,0,0,0,0,0,0,1,5,16); ARRAY,34,12,{0,0,0,0,0,0,0,0,0,0,0,56);
ARRAY,35,12,{0,0,0,0,0,0,0,0,0,0,0,0); ARRAY,36,12,{0,0,0,0,0,0,0,0,0,0,0,0); ARRAY,37,12,(0,0,0,0,32,40,50,64,74,95,247,554); ARRAY,38,12,(0,0,0,48,54,148,177,224,252,520,527,2089); ARRAY,39,12,(0,0,1,2,4,8,54,62,153,162,200,261); ARRAY,40,12,{0,0,0,0,0,0,8,16,16,46,88,112); ARRAY,4 1,12,{0,0,0,0,0,0,3,32,49,58,67,325) ; ARRAY,42,12,{0,0,6,12,16,23,72,89,127,140,142,452); ARRAY,43,12,{ 1,2,58,73,152,253,261,33 1,492,501,507,944); ARRAY,44,12,{0,0,0,0,0,0,0,0,0,0,0,0); ARRAY,45, ~2,{0,0,0,0,0,0,0,0,0,0,0,0); ARRAY,46,12, {0,0,0,0,0,0,0,0,0,0,24,48); ARRAY,47,12, {0,0,0,0,0,0,0,0,0,64,68,96); ARRAY,48,12,(0,0,0,0~0,0,1,2,4,7,16,64); ARRAY,49,12,(25,115,210,470,527,565,771,876,888,1040,1041,1926); ARRAY,SO, 12, (0,0,0,0,0,0,0,0,0,0,0,0); ARRAY,S 1,12, (0,0,0,0,0,0,0,0,0,0,8,105); ARRAY,52,12,(0,0,0,4,8,10,16,16,19,38,112,300); ARRAY,53,12,(0,0,0,1,1,4,20,48,53,81,88,98); ARRAY,54,12,(0,0,0,0,0,1,1,2,40,41,49,128); ARRAY ,2,12, ( 62,205,297,3 15,460,462,468,492,65 l,729,898,17 19 ) ; LIMITS,36,,,9; NET; INITIALIZE,O.O, 18500,YES,,NO; RECORD, 1 ,,TNOW, "TIME", { AWESIM ) ,,9250,11100,, { { NNRSC(2O),"Available", ) ) ; RECORD, 1 ,,TNOW,"TiME1'* { AWESIM ) ,,TTBEG,TTFIN,, { (NRUSE(20),"Workers",) 1; FIN;
D.2. The Main Network
Figure Dl . Main Network
Figure D 1. Main Network (continued).
110 WDQCn CLOS= 110 114 CHACM CLOSW 114 L E E K n n RCM CLOSCD 111 ils Sncn nos= lis E l r u l m
Figure D 1. Main Network (continued).
Figure Dl. Main Network (continued).
Figure Dl. Main Network (continued).
D.3. Visual Sub-Networks
0
Fi y r e D2. VSN PMR-82 - RF Designated Preventive Maintenance for Period 82
Figure D3. VSN PMS-82 - SS Designated Preventive Maintenance for Period 82
D3. Visual Sub-Networks (continued).
Figure M. VSN Fit.
Figure D5. VSN Worker.
D3. Visual Sub-Networks (continued).
Figure D6. VSN Alter - One example of fifty-three similar VSNs
Figure D7. VSN Size.
D3. Visual Sub-Networks (continued).
Figure D8. VSN STJAv - One example of seven similar VSNs.
Figure D9. VSN STJCM-AV - One example of seven similar VSNs.
APPENDIX E
PM ROUTINES MATCHED TO ACTUAL SERVICE TIMES
) RF=[ NDlD 1 JOB-DESCRIPTION JSwn IM-ES- 1 BILL-TO 1 RF C-22-010-023MY- LIFERAW INFLATABLE 20 32 39 HMCS
PM C-22-010-023/NY-002 24M PM C-22-010-023MY- LIFERAFT INFLATABLE 20 LIFERAFT INFLATABLE 20M 24M PORT CLUTCH 24M STBD CLUTCH C-24-304-000MY-001 24M C-24-304-000/NY-001 48M MAlN GEARlNG STBD PM ROUTINE C-24-306- 24M PM MAlN AND CROSS CONNECT GEARlNG PM 24M C-24-309-000/NY- 6M ROUTINE C-24-311- 6M ROUTINE C-24-311- 6M ROUTINE C-24-311- 48M PM C-24-312-000MY- 6M PM ROUTINE C-24-312- 6M ROUTINE C-24-312- PM 48M C-24-312-OOO/NY- 12M PM C-24-545-BO1 M Y - 24M ROUTINE C-24-545- COUPLING ASSEMBLY MAlN MAlN SHAFT FLEX MAlN SHAFT FLEX CPLG PM C-24-545-AOO/NY-001 PM C-24-545-AOOMY-001 12M PM C-24-545-BOOMY- 1 2M ROUTINE C-24-548- 6M ROUTINE C-24-548- 12M C-24-548-000MY-002 72M ON PDE 72M PM C-24-548-000/NY- DIESEL 20 PAG V280 MPC ENGINE,DIESEL,PILESTICK PM C-24-548-00MY-003 48M CONTROLLABLE PlTCH PM C-24-559-000MY-004
39 HMCS HALIFAX 39 HMCS MONTREAL 9 HMCS MONTREAL
42 HMCS 5 HMCS VlLLE DE 5 HMCS VlLLE DE 5 HMCS ST JOHNS
95 HMCS 12.5 HMCS TORONTO 12.5 HMCS ST JOHNS
13 HMCS 13 HMCS TORONTO 13 HMCS ST JOHNS 5 HMCS HALIFAX 5 HMCS HAllFAX 5 HMCS 30 HMCS HALIFAX 10 HMCS HALIFAX 10 HMCS 30 HMCS ST JOHNS 6 HMCS MONTREAL
40 HMCS 20 HMCS MONTREAL 40 HMCS VlLLE DE 40 HMCS VlLLE DE 20 HMCS MONTREAL 4û HMCS MONTREAL 6 HMCS MONTREAL
14 HMCS 14 HMCS 14 HMCS TORONTO 14 HMCS VlLLE DE 14 HMCS MONTREAL 14 HMCS 14 HMCS MONTREAL 20 HMCS HALIFAX 7.5 HMCS MONTREAL 7.5 HMCS MONTREAL
C-26411- 48 M PM C-26-411 -EOOMY- C-26-411 -FOONY- 48M PM C-26-411 -FW/NY- C-26-428-000INY- FWD & AFT SWBD (48 C-27-778-000INY- PM 24M C-27-778-000MY- C-27-783- PM 48M C-27-783-AOOMY- C-27-783- pm routine c-27-783-a00lny- C-27-784-000/NY- 6M PM C-27-784-0001NY-001 C-27-784-0001NY- PM C-27-784-OOOMY-001 LP C-27-784-000lNY- PM C-27-784-OOONY-001 LP C-27-784-000INY- TANK PRESSURE STEEL C-27-784- PM C-27-784-AOOMY-001 12 C-27-784- 12M C-27-784-AOO/NY-001 C-27-784- 12m c-27-784-a00/ny-O01 C-27-784- PM C-27-784-AOOMY-001 C-27-784- PM C-27-784-AOOMY-O01 C-27-885-000INY- 1 2M C-27-885-000MY-00 1 #2 C-27-885-0001NY- C-27-085-000/NY-001 1 2M #1 C-27-885-000MY- C-27-885-000tNY-001 1 2M C-27-886-0001NY- pm 48M C-27-886-0ûOMY-001 C-27-893-000lNY- 1 2M ROUTINE C-27-893- C-27-A99- ENGINE DIESEL SEAWATER C-27-A99- PM C-27-A99-000MY-001 C-27-B08- PM 24M C-27-B08-000MY- C-28-010-008lNY- 24 M PM C-28-010-008MY- C-28-402-0001NY- HULL STRUCTURE C-28-402-000/NY- PM C-28-402-000MY-002 6M C-28-419-000INY- 24M C-28-419-000/NY-00 t C-28-419-0001NY- PM C-28-419-OOOMY-OOl C-28-420-0001NY- 48 MO. PM-SHELL HULL- C-28-420-0001NY- PM ROUTINE C-28-420- C-28-422- DOOR SPECIAL PURPOSE C-28-454- 24M C-28-454-AOOMY-001 C-28-454- PM C-28-454-AOOMY -00 1 C-28-455- INTERNAL DECKS- C-28-460- 24M C-28-460-DOOMY-001 C-28-460- PM C-28-460-DOOMY-001 C-28-460- PM C-28-460-DOOMY-00 1 C-28-463-000INY- 24M PM C-28-463-000MY- C-28464-000INY- PM C28 464 000MY001 C-28-509-000INY- 48M PM C-28-509-000MY- C-28-514-000INY- 24M PM C-28-514-00MY-001 C-28-514-OW/NY- 24M PM C-28-514-000MY- C-28-514-O/NY- 24M PM C-28-514-000MY- C-28-514-0001NY- 24M PM C-28-514-000MY-
10 HMCS HALIFAX 8 HMCS HALIFAX
ô0 HMCS ST JOHNS 16 HMCS ST JOHNS 10 HMCS ST JOHNS 10 HMCS TORONTO 4 HMCS HALIFAX 5 HMCS MONTRfEAL 5 HMCS MONTREAL 4 HMCS TORONTO 5 HMCS ST JOHNS 5 HMCS TORONTO 5 HMCS TORONTO 5 HMCS HALIFAX 5 HMCS VILLE DE 6 HMCS 6 HMCS TORONTO 6 HMCS ST JOHNS
ôû HMCS ST JOHNS 36 HMCS TORONTO 3 HMCS TORONTO 3 HMCS 3 HMCS ST JOHNS
16 HMCS MONTREAL 20 HMCS TORONTO 20 HMCS HALIFAX 4 HMCS MONTREAL 4 HMCS 30 HMCS HALIFAX 30 HMCS HALIFAX f 5 HMCS TORONTO 14 HMCS MONTREAL 14 HMCS HALIFAX 2 HMCS
12 HMCS VILLE DE 12 HMCS MONTREAL 22 HMCS HALIFAX 5 HMCS HALIFAX
20 HMCS 15 HMCS HALIFAX 7 HMCS MONTREAL 7 HMCS MONTREAL 7 HMCS MONTREAL 7 HMCS HALIFAX
C-28-514-000MY- PM C-28-514-000MY-001 C-28-514-000MY- PM C-28-514-Oûû/NY-001 C-28-514-000lNY- PM C-28-514-0OûMY-O01 C-28-588-000lNY- PM C28 588 000MY001 C-29-010-012/NY- PM C-29-010-012AUY-O14 C-29-010-012lNY- PM C-28-396-000MY-001 C-29-OlO-O12/NY- PM C-29-010-012/-O14 24M C-29-010-01 M Y - PM C-29-010-012lNY-014 C-29-010-01 M Y - PM C-29-010-012MY-014 C-29-010-012lNY- PM C-29-010-01SMY-O14 C-29-OlO-O12/NY- PM C-29-010-012/NY-014 C-29-OlO-O12/NY- PM C-29-010-01 MY-O1 4 C-29-010-01 ZNY- PM C-29-010-012/NY-014 C-29-010-012/NY- PM C-29-010-012/NY-014 C-29-010-012/NY- PM C-29-010-012MY-014 C-29-010-01 ZNY- PM C-29-010-012lNY-014 C-29-OlO-012lNY- PM C-29-010-012lNY-014 C-29-010-01 2/NY- PM C-29-010-012/NY-014 C-29-OlO-O1Z/NY- PM C-29-010-012/NY-014 C-29-010-01 ZNY- PM C-29-010-012MY-014 C-29-OlO-O12/NY- PM C-29-010-012lNY-014 6M C-29-OlO-O12/NY- PM C-29-010-012MY-014 6M C-29-354-000MY- 24M MAIN REF SYSTEM C- C-29-354-000MY- C-29-354-000/NY-001 12M #1 C-29-354-000MY- C-29-354-000/NY-001 24M C-29-354-000MY- PM C-29-354-000MY-001 C-29-356-000MY- AIR DISTRIBUTION SYSTEM C-29-390-000lNY- C-29-390-000INY-001 24M # 9 C-29-390-000/NY- C-29-390-000MY-001 24M #7 C-29-390-000MY- C-29-390-000MY-001 24M #8 C-29-390-000/NY- PM C-29-390-000MY-001 C-29-390-000/NY- PM C-29-390-000MY-001 C-29-390-000INY- PM C-29-390-000MY-001 C-29-390-000MY- PM C-29-390-000/NY-O01 C-29-390-000lNY- PM C-29-390-000/NY-001 C-29-390-0001NY- PM C-29-390-000/NY-001 C-29-390-WO/NY- PM C-29-390-OOOMY-001 C-29-390-000/NY- PM C-29-390-000MY-001 C-29-390-000/NY- PM C-29-390600MY-001 C-29-390-000lNY- PM C-29-390-000/NY-001 C-29-390-0001NY- PM C-29-390-000/NY-001 C-29-390-000INY- PM C-29-390-000/NY-001 C-29-390-000/NY- PM C-29-390-000/NY-002
7 HMCS HALIFAX 7 HMCS HALIFAX 4 HMCS 7 HMCS 4 HMCS HALIFAX 1 HMCS HALIFAX
27 HMCS HALIFAX 1 HMCS HALIFAX 1 HMCS HALIFAX 4 HMCS HALIFAX 4 HMCS HALIFAX 4 HMCS HALIFAX 4 HMCS HALIFAX 4 HMCS HALIFAX 4 HMCS HALIFAX 4 HMCS HALIFAX
27 HMCS HALIFAX 27 HMCS HALIFAX 27 HMCS HALIFAX 4 HMCS HALIFAX
2.5 HMCS HALIFAX 2.5 HMCS HALIFAX 18 HMCS 7 HMCS TORONTO
18 HMCS VILLE DE 32 HMCS HALIFAX 20 HMCS MONTREAL 27 HMCS 27 HMCS 27 HMCS 13 HMCS HALIFAX 13 HMCS HALIFAX 13 HMCS HALIFAX 13 HMCS HALIFAX 13 HMCS HALIFAX 13 HMCS HALIFAX 27 HMCS HALIFAX 27 HMCS HALIFAX 27 HMCS HALIFAX 27 HMCS HALIFAX 27 HMCS HALIFAX 27 HMCS HALIFAX 13 HMCS HALIFAX 13 HMCS HALIFAX
C-29-390-000/NY- PM C-29-390-WMY-002 C-29-390-000MY- PM C-29-390-WMY-002 C-29-390-000/NY- PM C-29-390-000MY-002 C-29-390-000/NY- PM C-29-390-000/NY-002 C-29-390-000MY- PM C-29-390-000MY-002 C-29-390-000/NY- PM C-29-390-OOOMY-002 C-29-390-000/NY- PM C-29-390-000MY-002 C-29-390-000/NY- PM C-29-390-000MY-002 C-29-390-000/NY- PM C-29-390-000MY-002 C-29-390-000MY- PM C-29-390-WMY-002 C-29-390-000MY - PM C-29-390-WMY-002 C-29-390-000MY- PM C-29-390-000NY-002 C-29-390-000MY- PM C-29-390-WMY-002 C-29-390-000MY- PM C-29-390-WMY-002 C-29-390-000/NY- PM C-29-390-000MY-002 C-29-390-000/NY- PM C-29-390-WNY-002 C-29-390-OOOMY- PM C-29-390-OOOAUY-002 C-29-390-000MY- PM C-29-390-WMY-002 C-29-390-000/NY- PM C-29-390-000/NY-002 C-29-390-000MY- PM C-29-390-000MY-002 C-29-390-000MY- PM C-29-390-OOOAUY-002 C-29-390-000/NY- PM C-29-390-000MY-002 C-29417-000MY- 24M NO. 1 85 TON CHILLER C-29-417-000MY- 24M NO. 2 85 TON CHILLER C-29-417-000MY- 24M NO. 3 85 TON CHILLER C-294l7-WMY- 48M C-29-417-000/NY-001 #2 C-29-417-OOO/NY- 48M PM C-29-417-000NY- C-29-417-OOO/NY- C-29-417-000MY-001 24 M C-29-4 1 7-000/NY- C-29-4 1 7-000/NY-001 24 M C-29417-000/NY- C-29-417-000/NY-001 24M C-29-417-000/NY- C-29-417-000NY -001 24M C-29-417-W/NY- C-29-417-000MY-001 48M - C-29-417-000/NY- C-29-417-000MY -001 48M - C-29-417-000/NY- C-29-417-000MY-001 48M - C-29-417-000/NY- CHILLER SET 85 TON C-29-417-000/NY- CHILLER SET 85 TON C-29-417-OOO/NY- CHILLER SET 85 TON C-29417-OOOMY- CHILLER SET, 85 TON C-29417-OOOMY- PM C-29-417-000MY-001 C-29-417-000/NY- PM C-29-417-000AUY-001 C-29417-000/NY- PM C-29-4f7-OOO/NY-00I C-29-417-000MY- PM C-29-417-000MY-001 C-39-158-000MY- 12M C-39-158-OOOMY-Wl C-39-158-000/NY- 24M RECOVERY ASSIST
27 HMCS HALIFAX 27 HMCS HALIFAX 27 HMCS HALIFAX 27 HMCS HALIFAX 27 HMCS HALIFAX 27 HMCS HALIFAX 27 HMCS HALIFAX 27 HMCS HALIFAX 27 HMCS HALIFAX 27 HMCS HALIFAX 27 HMCS HALIFAX 27 HMCS HALIFAX 27 HMCS HALIFAX 27 HMCS HALIFAX 27 HMCS HALIFAX 27 HMCS HALIFAX 27 HMCS HALIFAX 27 HMCS HALIFAX 27 HMCS HALIFAX 27 HMCS HALIFAX 27 HMCS HALIFAX 27 HMCS HALIFAX 38 HMCS 38 HMCS 38 HMCS 54 HMCS HALIFAX 54 HMCS HALIFAX 38 HMCS TORONTO 38 HMCS 38 HMCS TORONTO 38 HMCS TORONTO 54 HMCS ST JOHNS 54 HMCS ST JOHNS 54 HMCS ST JOHNS 38 HMCS TORONTO 38 HMCS VlLLE DE 38 HMCS VlLLE DE 38 HMCS VILLE DE 54 HMCS HALIFAX 38 HMCS MONTREAL 38 HMCS MONTREAL 38 HMCS MONTREAL 7 HMCS TORONTO
15 HMCS VILLE DE
RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF ROD SS
C-39-158-000MY- PM 24M C-39-158-000MY- C-39-158-000INY- PM C-39-158-000MY-001 C-39-161- PM 48M C-39-161 -AOO/NY- C-51-010-002lNY- HF ANTENNA WHIPS C-51- C-51-010-002INY- 12M PM C-51-010-002lNY- C-51-010-002INY- 24M PM C-51-010-002MY- C-51-010-002MY- 24M PM ANTENNA WHlP 18 C-51-010-002/NY- 24M PM C-Sl-OlO-002(NY- C-5 1-01 0-002INY- 6M PM C-5l-OlO-OO2/NY-O26 C-51 -01 O - 0 0 2 ~ ~ - PM C-51 -01 O - G O ~ Y - O ~ ~ 24 C-5l-OlO-002/NY- PM C-5 1 -01 0-002NY-026 C-5 1-01 0*002/NY- PM C-51-010-002INY-026 C-51-010-002INY- PM C-51010-002MY-026 24 C-51-010-002INY- PM ROUTINE C-51-010- C-57-010-01 OINY- 24M ROUTINE C-57-010- C-58-251-002INY- PM C-58-251-002MY-001 C-59-723-000lNY- RADAR SET AN/SPS-49 C-66-315-000lNY- BINOCULAR BIG EYE SYS C-66-315-000MY- BINOCULAR SYSTEM MK3 C-66-315-000lNY- C-66-315-0001NY-001,6M, C-66-315-000lNY - C-66-315-0001NY- C-66-3 1 5-000lNY- C-66-315-0001NY- C-66-315-000lNY- PM C-66-315-000MY-001, C-66-315-OOOINY- PM C-66-315-000MY-001, C-69-703-000lNY- PM C-69-703-000MY-001 C-69-703-000lNY- PM C-69-703-000MY- C-69-703-000MY- SONAR HULL OUTFIT CS C-69-771-000lNY- PM C-69-771-000MY- C-69-799- PM C-69-799-AOOMY-001 C-69-799- 48M ROUTINE C-69-799- C-69-799- PM 48M ROUTINE C-69-799- C-69-802-000lNY- 24M C-69-802-000/NY-001 C-69-802-000lNY- 24M C-69-802-000MY -001 C-69-802-000/NY- C-69-802-0001NY-001, 24M, C-69-802-000lNY- C-69-802-000NY-001,24M, C-69-802-WOMY- PM 48 MONTHLY ROUTINE C-70-270-0001NY- MAIN GUN SYSTEM 57 MM C-77-304-0001NY- 24M PLANNED C-87-274-0001NY- 48M ROUTINE C-87-274- C-97-OlO-OO6lNY- HALON FlRE SUPPRESSION C-97-010-006MY- HI FLEX HOSES 60 PM C-27-948-WOMY- PM C-27-948-000MY-001 C-03-010-106lNY- 24M C-03-010-106MY-082
15 HMCS MONTREAL 15 HMCS HALIFAX 15 HMCS MONTREAL 12 HMCS MONTREAL 12 HMCS VlLLE DE 12 HMCS HALIFAX 12 HMCS VlLLE DE 24 HMCS HALIFAX 12 HMCS VlLLE DE 12 HMCS MONTREAL 12 HMCS MONTREAL 12 HMCS MONTREAL 12 HMCS MONTREAL 12 HMCS ST JOHNS 14 HMCS ST JOHNS 20 HMCS 30 HMCS MONTREAL 18 HMCS 36 HMCS 16 HMCS 18 HMCS VlLLE DE 36 HMCS ST JOHNS 18 HMCS TORONTO 36 HMCS 6 HMCS 6 HMCS VlLLE DE 6 HMCS TORONTO
42 HMCS VlLLE DE 7 HMCS ST JOHNS 7 HMCS ST JOHNS 7 HMCS ST JOHNS
25 HMCS MONTREAL 25 HMCS MONTREAL 25 HMCS VlLLE DE 25 HMCS VlLLE DE 32 HMCS ST JOHNS 24 HMCS 6 HMCS MONTREAL 9 HMCS ST JOHNS
18 HMCS MONTREAL 18 HMCS VlLLE DE
O HMCS 3.5 HMCS VlLLE DE 3.5 HMCS VlLLE DE
C-03-010-1 WNY- 24M C-03-010- 1 OWNY-082 C-03-010-106/NY- 24M PM C-03-010-1 W N Y - C-03-010-1 WNY- 24M PM C-03-010-1 O6MV- C-03-010-10WNY- 24M ROUTINE ON HANGER C-03-010-1 O6MY- 6M PM C-03-010- 1 WNY-082 C-03-010-106lNY- 6M PM C-03-010-106MY-082 C-03-010-106MY- 6M PM C-O3-OlO-lO6MY-O82 C-03-010-106lNY- 6M PM C-03-010-106/NY-082 C-03-010-106NY- 6M PM C-03-010-1 WNY-082 C-03-010-106MY- 6M PM C-03-010-106/NY-082 C-03-010-106NY- 6M PM C-03-010-1061NY-082 C-03-010-106MY- 6M PM C-O3-OlO-l06/NY-O82 C-03-010-106MY- 6M PM C-03-010-106MV-082, C-03-010-106lNY- 6M PM C-03-010-106/NY-082, C-03-010-106/NY- 6M PM C-O3-OlO-lO6MY-O82, C-O3-OlO-lO6lNY- 6M PM C-03-010-1 06/NY- C-03-010-106/NY - 6M PM C-03-010-1067MY- C-03-010-106/NY- COMPRESSOR UNIT C-03-010-106MY- PLANNED 24 M C-03-010-106NY- PLANNED 24M C-03-010-1WNY- PLANNED 24M C-03-010-106/NY- PLANNED 24M C-03-010-106NY- PLANNED MAINTENANCE C-03-010-106lNY- PLANNED MAINTENANCE C- C-03-01 0-1 06/NY- PLANNED MAINTENANCE C- C-03-010-106lNY- PLANNED MAINTENANCE C- C-03-010-106lNY- PLANNED MAINTENANCE C- C-03-01 0-1 06lNY- PM C-03-01 O- t MNY-O82 C-03-010-106MY- PM C-03-010-106INY-082 6M C-03-010-106lNY- PM C-03-010-1 WNY-082 6M C-03-010-1 O6MY- PM C-03-0 t 0-1 06/NY-082 6M C-03-010-1061NY- PM C-03-010-1 06MY-082 6M C-O3-OlO-lO6lNY- PM C-O3-OlO-l06/NY-O82 6M C-03-010-1061NY- PM C-03-010-106/NY-O82 6M C-03-010-106lNY- PM C-03-010-106MY-082 6M C-03-010-106lNY- PM C-03-010-106MY-082 6M C-03-010-106/NY- PM C-03-010- 1 06/NY-082 6M C-03-010-1 MNY- PM C-03-010-106INY-082 6M C-03-010-1061NY- PM C-03-010-106INY-082 6M C-03-010-106JNY- PM C-03-010-106/NY-082 6M C-O3-OlO-l06/NY- PM C-03-010-1 06MY-082 6M C-03-010-106/NY- PM C-03-010-106MY-082 6M C-O3-0lO-l06/NY- PM C-03-010-106MY-082 6M C-03-010-106MY- PM C-03-010-106MY-082 6M
3.5 HMCS VILLE DE 3.5 HMCS HALIFAX 3.5 HMCS HALIFAX 3.5 HMCS VlLLE DE 1.5 HMCS HALIFAX 1.5 HMCS HALIFAX 1.5 HMCS HALIFAX 1.5 HMCS HALIFAX 1.5 HMCS HALIFAX 1.5 HMCS HALIFAX 1.5 HMCS HALIFAX 1.5 HMCS HALIFAX 1.5 HMCS HALIFAX 1.5 HMCS HALIFAX 1.5 HMCS HALIFAX 1.5 HMCS HALIFAX 1.5 HMCS HALIFAX 3.5 HMCS TORONTO 3.5 HMCS VlLLE DE 3.5 HMCS VlLLE DE 3.5 HMCS VILLE DE 3.5 HMCS VlLLE DE 3.5 HMCS VILLE DE 3.5 HMCS VlLLE DE 3.5 HMCS VlLLE DE 3.5 HMCS VlLLE DE 3.5 HMCS VlLLE DE 3.5 HMCS HALIFAX 1.5 HMCS HALIFAX 1.5 HMCS HALIFAX 1.5 HMCS HALIFAX 1.5 HMCS HALIFAX 1.5 HMCS HALIFAX 1.5 HMCS HALIFAX 1.5 HMCS HALIFAX 1.5 HMCS HALIFAX 1.5 HMCS HALIFAX 1.5 HMCS HALIFAX 1.5 HMCS HALIFAX 1.5 HMCS HALIFAX 1.5 HMCS HALIFAX 1.5 HMCS HALIFAX 1.5 HMCS HALIFAX 1.5 HMCS HALIFAX
C-03-010- 1 06MY- PM C-03-010-1 WNY-082 6M, C-03-010-1 O6INY- PM C-O3QlO-lO6/NY-O82 6M, C-03-010-106tNY- PM C-03-010-106MY-082 6M, C-03-010-1 WNY- PM C-O3-OlO-l06/NY-O82 6M, C-03-010-1 WNY- PM C-03-010-1 WNYO82 6M C-03-010-1 WNY- PUMP UNIT CENTRIFUGAL C-03-010-106MY- 12M PM ROUTINE C-03-010- C-03-OlO-lO6MY- 6M PM ROUTINE C-03-010- C-03-OlO-106MY- 6M PM ROUTINE C-03-010- C-21-010-001MY- 12M PM ROUTINE, C-21-010- C-21-010-0011NY- 12M ROUTINE C-21-010- C-21-010-0011NY- METEROLOG ICAL STATION, C-21-153-000lNY- DETECTOR WlND DIR AND C-21-153-000/NY- DETECTOR WlND DIR AND C-21-153-0001NY- DETECTOR WlND C-21-153-000MY- DETECTOR WlND C-21-154-000MY- DETECTOR WlND DIR & SP C-21-171-000MY- 24M PM ROUTINE,C-21-171- C-21-171-0001NY- 24M ROUTINE C-21-171- C-21-171-000MY- 24M ROUTINE C-21-171- C-24-304-000lNY- 12M C-24-304-000MY-001 (P) C-24-304-000MY- C-24-304-000/NY-001 1 2M - C-24-304-000MY- PM 24M C-24-304-000MY- C-24-305-000lNY- 24M PM C-24-305-000MY- C-24-305-0001NY- MAIN GEARING PORT C-24-305-0001NY- PM 24M C-24-305-000MY- C-24-306-000lNY- 24M PM C-24-306-000MY- C-24-309-000MY- 24M PM C-24-309-000MY- C-24-53-000MY- C-24-553-0001NY-001 24M C-24-535-000/NY- 1 2M PM ROUTINE C-24-535- C-24-541-000MY- 12M C-24-541-000MY-001 C-24-541-0001NY- 12M C-24-541-000MY-001 C-24-541-000lNY- 6M PM C-24-541-000INY-001 C-24-541-000lNY- C-24-251-0001NY-001 LM2500 C-24-541-000/NY- C-24-541-0001NY-001 12M C-24-541-000/NY- C-24-541-OWMY-001 12M C-24-541-000lNY- C-24-541-000INY-001 12M C-24-541-000MY- C-24-541-000MY-001 12M C-24-541-000MY- C-24-541-000MY-001 LM2500 C-24-541-000MY- C-24-541-000/NY-001 LM2500 C-24-541-000MY- C-24-541-000/NY-001 LM2500 C-24-54 1 -000/NY- C-24-587-000/NY-00 1 6M # 1 C-24-541-000MY- C-24-587-OWMY-001 6M #2 C-24--1-000lNY- GAS TU RBlNE LM 2500-30
3.5 HMCS HALIFAX 1.5 HMCS HALIFAX
2 HMCS HALIFAX 1.5 HMCS HALIFAX 1.5 HMCS HALIFAX 3.5 HMCS HALIFAX 4.5 HMCS HALIFAX 3.5 HMCS HALIFAX 2.5 HMCS HALlFAX
6 HMCS HALIFAX 6 HMCS ST JOHNS 6 HMCS MONTREAL 3 HMCS 3 HMCS 3 HMCS 3 HMCS
11 HMCS TORONTO 1 1 HMCS HALIFAX 11 HMCS ST JOHNS 11 HMCS ST JOHNS
1 HMCS TORONTO 1 HMCS TORONTO 2 HMCS ST JOHNS 5 HMCS HALIFAX 5 HMCS MONTREAL 5 HMCS ST JOHNS
3.5 HMCS HALIFAX 2 HMCS HALIFAX
38 HMCS VILLE DE 2.5 HMCS HALIFAX 16 HMCS MONTREAL 16 HMCS MONTREAL 13 HMCS HALIFAX 13 HMCS ST JOHNS 16 HMCS 16 HMCS VILLE DE 16 HMCS 16 HMCS VILLE DE 16 HMCS TORONTO 16 HMCS TORONTO 13 HMCS ST JOHNS 13 HMCS HALlFAX 13 HMCS HALIFAX 16 HMCS TORONTO
C-24-541-000MY- GAS TURBINE LM 2500-30 C-24-541-000/NY- LM2500 GAS TURBINE C-24-541-OOOMY- LM2500 GAS TURBINE C-24-54l-OOO/NY- LM2500 GAS TURBINE C-24-541-OWNY- LM2500 GAS TURBINE C-24-541-000/NY- LM2500 GAS TURBINE ASSY C-24-541-000MY- LM2500 GAS TURBINE ASSY C-24-541-000/NY- TURBINE GAS C-24-54l-W/NY- TURBINE GAS C-24-541-000/NY- TURBINE GAS C-24-545-001/NY- PLUMMER BEARING, FWD C-24-545-001/NY- PLUMMER BEARING, FWD
12M C-24-545-800MY-001 12M C-24-545-800/NY-001 6M ROUTINE C-24-545- PM C-24-545-BOOMY-001 THRUST BLOCK PORT THRUST BLOCK STBD 1 2M C-24-545-60 1 MY-001 1 2M PM C-24- 545-801 MY- 6M ROUTINE C-24-545- 24 M PM C-24-545-COOMY- 24M BULKHEAD SEAL LOC 24M C-24-545-COOMY-001 #4 24M C-24-545-COO/NY-001 #6 24M C-24-545-COOMY-001 24M C-24-545-COO/NY-00 1 24M C-24-545-COOMY - 24M PM C-24-545-COO/NY- 24M PM ROUTINE C-24-545- BULKHEAD SEAL NO 1 BULKHEAD SEAL NO 2 BULKHEAD SEAL NO 3 BULKHEAD SEAL NO 4 BULKHEAD SEAL NO 5 BULKHEAD SEAL NO 6 PM C-24-545-COO/NY-O01 PM C-24445-COOMY-001 PM C-24-588-000MY -001 12M C-24-545-DOOMY-001 36M C-24-545-DOOMY-00 1 36M C-24-545-000MY- 00 1 36M PM C-24-545-000MY- PM C-24-535-000MY-001
16 HMCS TORONTO 16 HMCS 16 HMCS 13 HMCS VlLLE DE 13 HMCS VlLLE DE 16 HMCS 16 HMCS 13 HMCS HALIFAX 13 HMCS HALIFAX 26 HMCS HALIFAX 22 HMCS MONTREAL 22 HMCS MONTREAL 9.5 HMCS TORONTO 9.5 HMCS VlLLE DE 2.5 HMCS 18 HMCS HALIFAX
9.5 HMCS TORONTO 9.5 HMCS TORONTO 9.5 HMCS VlLLE DE 10 HMCS HALIFAX
2.5 HMCS 21 HMCS MONTREAL 21 HMCS VlLLE DE 21 HMCS VILLE DE 21 HMCS VlLLE DE 21 HMCS 21 HMCS 21 HMCS VlLLE DE 21 HMCS MONTREAL 21 HMCS HALIFAX 21 HMCS ST JOHNS 21 HMCS ST JOHNS 21 HMCS ST JOHNS 21 HMCS ST JOHNS 21 HMCS ST JOHNS 21 HMCS ST JOHNS 21 HMCS HALIFAX 21 HMCS MONTREAL 3.5 HMCS
8 HMCS 1 ORONTO 22 HMCS VlLLE DE 22 HMCS VlLLE DE 22 HMCS MONTREAL 4 HMCS MONTREAL
C-24-545- PM C-24-545-000MY-O01 C-24-545- 12M C-24-545-DO1 MY-001 C-24-545- 12M C-24-545-001 MY-001 C-24-545- 36M C-24-545-DO1 MY-001 C-24-545- 36M C-24-95-DO1 MY-001 C-24-545- PM 36M C-24-545-D01MY- C-24-545- PM C-24-545-001 INY-001 C-24-545- PM C-24-545-001 MY-001 C-24-545- PM C-24-545-001 /NY-001 C-24-545- PM C-24-545-001 INY-001 C-24-545- PM C-24-545-001 MY-001 C-24-545- 12M C-24-545-EOOMY-001 C-24-545- 12M C-24-545-EOOMY-001 C-24-545- PM C-24-545-EOOMY-001 C-24-545- PM C-24-545-EOOMY-001 C-24-545- SEAL STERN SHAFT C-24-545- SEAL STERN SHAFT C-24-545- SEAL STERN SHAFT C-24-545- MAIN SHAFTING SYSTEM C-24-548-000/NY - PM C-24-548-000/MS-00 1 C-24-548-000/NY- 12M PM ROUTlN E C-24-548- C-24-548-000/NY- 6M PM C-24-548-000INY-004 C-24-552-000/NY- 12M PM C-24-552-000MY- C-24-552-WMY - C-24-552-000lNY-00 1 1 2M C-24-553-000/NY- 24M PM C-24-553400MY- C-24-553-W/NY- 24M ROUTINE C-24-553- C-24-553-000/NY- C-24-553-000lNY-001 24M C-24-553-W/NY- COUPLING SHAFT FLEXIBLE C-24-553-WMY- COUPLING SHAFT FLEXIBLE C-24-5S-W/NY - PM 24M C-24-553-000MY - C-24-553-W/NY - PM C-24-553-000MY-001 C-24-554-000/NY - 6M PM C-24-554-OOOMY-001 C-24-554-000/NY - 6M PM C-24-554-000/NY-001 C-24-559-000/NY- 6M ROUTINE C-24-559- C-24-559-000/NY- 6M PM C-24-559-000/NY-003 C-24-559-000/NY- 6M PM ROUTINE C-24-559- C-24-559-000/NY- 6M PM ROUTINE C-24-559- C-24-559-000/NY- 6M ROUTINE C-24-559- C-24-559-000/NY- PROPELLER PlTCH CONT. C-24-559- 6M PM ROUTINE C-24-559- C-24-559- 6M ROUTINE C-24-559- C-24-560-000MY- 6M PM ROUTINE C-24-560- C-24-560-0001NY - 6M PM ROUTl N E C-24-560- C-24-560-000MY- PM 6M C-24-560-000MY-00 1
8 HMCS HALIFAX 4 HMCS TORONTO
35 HMCS TORONTO 35 HMCS VlLLE DE 35 HMCS VILLE DE 35 HMCS ST JOHNS 4 HMCS HALIFAX 4 HMCS HALIFAX 4 HMCS HALIFAX 4 HMCS MONTREAL 4 HMCS MONTREAL
18 HMCS VILLE DE 18 HMCS VlLLE DE 18 HMCS HALIFAX 18 HMCS HALIFAX 18 HMCS MONTREAL 18 HMCS MONTREAL 18 HMCS TORONTO 8 HMCS MONTREAL
1.5 HMCS MONTREAL 2 HMCS HALIFAX 2 HMCS HALIFAX
4.5 HMCS HALIFAX 9 HMCS MONTREAL
76 HMCS MONTREAL 38 HMCS ST JOHNS 38 HMCS VILLE DE 38 HMCS TORONTO 38 HMCS TORONTO 38 HMCS ST JOHNS 38 HMCS 5 HMCS HALIFAX 5 HMCS HALIFAX 5 HMCS 3 HMCS 6 HMCS HALIFAX 3 HMCS HALIFAX 3 HMCS
4.5 HMCS TORONTO 3.5 HMCS HALIFAX 3.5 HMCS
3 HMCS HALIFAX 3 HMCS HALIFAX 3 HMCS
C-24-561-000MY- 6M PM ROUTINE C-24-561- C-24-561-000MY- 6M PM ROUTINE C-24-SI- C-24-561-000MY- PM C-24-561-000MY-001 C-24-56l-OOO/NY- PM C-24-56 1 -000MY-001 C-24-581-000MY- PM C-24-581-000/NY-001 C-24-581-000INY- RAFf AND SHOCK MOUNTS C-24-588-000/NY- 24M PM C-24-588-000MY- C-24-588-000INY- C-24-588-000lNY-001 24M C-24-588-000INY- C-24-588-000lNY-001 24M C-24-588-000MY- C-24-588-000lNY-001 LOSCA C-24-588-000INY- C-24-588-000MY-001 LOSCA C-24-588-000/NY- LUBE STORAGE & C-24-588-000/NY- LUBE STORAGE & C-24-588- PM C-24-588-000INY-001 C-Z4-614-OOO/NY- C-24-614-OOOMY-OOl 6M - #1 C-Z4-614-OW/NY- C-24-614-000lNV-001 6M C-24-614-000/NY- C-24-614-OOOMY-OOl 6M C-24-614-000MY- C-24-614-000/NV-OOl 6M C-24-614-0001NY- LM2500 GAS TURBINE C-24-614-000/NY- LM2500 GAS TURBINE C-24-637-000/NY- C-24-637-000/NY-001 1 2M C-24-637-000/NY - C24-637-000MY 001 12M PM C-24-637-000MY- MOISTER SEPERATOR C-24-637-000/NY- MOISTURE SEPERATOR C-24-637-000/NY - MOISTURE SEPERATOR C-24-637-000/NY- PM C-24-637-000INY-001 6M C-24-640-000/NY- 6M PM ROUTINE C-24-640- C-24-640-000/NY- INFRARED SUPPRESSION C-24-640-000MY- INFRARED SUPPRESSION C-24-665- PM C-24-665-DOOMY-001 6M C-24-681-WMY- 6M PM C-24-681-000/NY-00 1 C-24-68l-UOO/NY- 6M PM C-24-681-000MY-00 1 C-24-685-000MY- 6M PM ROUTINE C-24-685- C-25-509-000/NY- 6M PM ROUTINE C-25-509- C-25-51 O-000/NY- 6M PM ROUTINE C-25-51 O- C-25-51 1 -000/NY- 12M C-25-51 1 -000MY-001 C-25-511-000/NY- PM C-25-511-000MY-001 C-25-511-000/NY- 6M C-25-511 -OoO/NY-002 C-25-512-OWMY- 6M PM ROUTINE C-25-512- C-25-534-000MY- 6M PM ROUTINE C-25-534- C-25-540-000MY- PM C-25-540-00/NY-001 6M C-25-540-000/NY- PM C-25-540-000MY-001 6M C-25-546-OOOMY- 12M ROUTINE C-25-546- C-25-546-000MY- 12M ROUTINE C-25-546-
3 HMCS HALIFAX 3 HMCS HALIFAX
3.5 HMCS HALIFAX 3.5 HMCS HALIFAX 14 HMCS HALIFAX 14 HMCS
3.5 HMCS HALIFAX 3.5 HMCS ST JOHNS 3.5 HMCS ST JOHNS 3.5 HMCS MONTREAL 3.5 HMCS MONTREAL 3.5 HMCS TORONTO 3.5 HMCS TORONTO 3.5 HMCS 15 HMCS HALIFAX 15 HMCS HALIFAX 15 HMCS VILLE DE 15 HMCS VILLE DE 15 HMCS MONTREAL 15 HMCS MONTREAL
18.5 HMCS TORONTO 18.5 HMCS TORONTO 18.5 HMCS ST JOHNS 18.5 HMCS 18.5 HMCS
14 HMCS HALIFAX 2 HMCS HALIFAX 2 HMCS MONTREAL 2 HMCS MONTREAL 2 HMCS HALIFAX 2 HMCS HALIFAX 2 HMCS HALIFAX
2.5 HMCS HALIFAX 6 HMCS HALIFAX 5 HMCS HALIFAX 8 HMCS TORONTO 8 HMCS HALIFAX 3 HMCS HALIFAX
5.5 HMCS HALIFAX 2 HMCS HALIFAX 1 HMCS HALIFAX 1 HMCS HALIFAX
15 HMCS 15 HMCS
C-25-546-000INY- COOLER,MAIN LUBE OIL C-25-547-000INY - 1 2M C-25-547-OOOMY -001 C-25-547-0001NY- 1 2M C-25-547-000/NY-001 C-25-547-000MY- 24M PM C-25-547-000MY-00 C-25-547-0001NY- 24M PM C-25-547-0001NY- C-25-547-0001NY- 48M ROUTINE C-25-547- C-25-547-0001NY- LUBE OIL PURIFIER C-25-547-000INY- LUBE OIL PURTFIER C-25-548-000INY - 6M C-25-548-0001NY -00 1 C-25-548-000MY- 6M PM ROUTINE C-25-548- C-25-548-000MY- PM C-25-548-000MY-001 6M C-25-549-0001NY- PM C-25-549-000.NY-001 C-25-549-000MY- PM C-25-549-000MY -001 C-25-549-000/NY- PM C-25-549-000INY-001 C-25-549-000MY- PM C-25-549-000MY-001 C-25-551-000MY- 1 2M C-25-551-000MY-001 C-25-551-OOWNY- 1 2M C-25-55 1 -000/NY-001 C-25-551-OOOINY- 6M PM ROUTINE C-25-551- C-25-551-0001NY- 6M PM ROUTINE C-25-551- C-25-551-000/NY- FUEL OIL CENTRIFUGE C-25-551-000MY- PM C-25-551-000MY-001 C-25-552-0001NY - PM C-25-552-0001NY -001 C-26-321- 12M PM C-26-321 -AOOMY- C-26-321- t 2M PM C-26-321 -AOOMY- C-26-321- 12M PM C-26-321 -AOOMY- C-26-32 1 - f 2M PM C-26-321 -AOOMY- C-26-32 1 - 6M PM C-26-321-AOOMY-002 C-26-324- 3 2M PM C-26-324-BOOMY- C-26-324- PM C-26-324-BOOMY-001 C-26-324- PM C-26-324-BOOMY-001 C-26-383-000/NY- 24M PM C-26-383-000MY- C-26-383-000INY- 24M PM C-26-383-000MY- C-26-383-0001NY - 24M PM C-26-383-000MY- C-26-383-000/NY- 24M PM C-26-383-000/NY- C-26-383-0001NY- PM C-26-383-000INY -002 C-26-383-000INY- PM C-26-383-000MY-002 C-26-411-000INY- 12M PM C-26-411-WMY- C-26-411-000/NY- 1 2M PM C-26-411-000MY- C-26-411-000/NY- ANNUAL INSPECTION NO. 2 C-26-411-0001NY- ENGINE DIESEL C-26411-0001NY- ENGINE DIESEL C-26-411-000INY- ENGINE DIESEL C-26-411-000MY- ENGINE DIESEL, C-26411-000MY- PM C-26-411-000MY-001
15 HMCS ST JOHNS 9.5 HMCS TORONTO 9.5 HMCS TORONTO
13.5 HMCS MONTREAL 13.5 HMCS MONTREAL 13.5 HMCS ST JOHNS 13.5 HMCS ST JOHNS 13.5 HMCS 2.5 HMCS HALIFAX 2.5 HMCS HALIFAX 2.5 HMCS HALIFAX
2 HMCS HALIFAX 2 HMCS HALIFAX 2 HMCS HALIFAX 2 HMCS HALIFAX
2.5 HMCS TORONTO 2.5 HMCS TORONTO
7 HMCS HALIFAX 7 HMCS HALIFAX
25 HMCS 25 HMCS HALIFAX 14 HMCS HALIFAX 8 HMCS HALIFAX 8 HMCS HALIFAX 6 HMCS HALIFAX 6 HMCS HALIFAX 3 HMCS HALIFAX 4 HMCS MONTREAL 4 HMCS HALIFAX 4 HMCS HALIFAX
50 HMCS HALIFAX 50 HMCS HALIFAX 50 HMCS HALIFAX 50 HMCS HALIFAX 50 HMCS HALIFAX 50 HMCS HALIFAX 35 HMCS MONTREAL 35 HMCS MONTREAL 35 HMCS ST JOHNS 35 HMCS HALIFAX 35 HMCS MONTREAL 37 HMCS HALIFAX 35 HMCS ST JOHNS 35 HMCS MONTREAL
C-26-411-000MY- PM C-26-411-OOOMY-001 C-26-411-0001NY- PM C-26-411-000MY-001 C-26-411-000MY- PM C-26-411-000MY-001 C-26411-000/NY- 6M PM C-26411-000NY-002 C-26-411-000MY- 6M PM C-26-411-0001NY-002 C-26411-000/NY- 6M PM C-26-411-000/NY-002 C-26-411-000MY- 6M PM C-26-411-000lNY-002 C-26-411 -ÇOOMY- PC-26-411 -FOO/NY-001 6M C-26-411 -FOOMY- PM C-26-411 -F00MY-0016M C-26-412-000INY- G ENER\ATOR,850 KW PM C-26-412-000INY- GENERATOR, 850 KW PM C-26-412-000INY- GENERATOR.850 KW PM C-26-412-000MY- GENERATOR.850 KW PM C-26-428-W/NY- PM C-26-428-000/NY-001 6M C-27-778-000/NY- 12M C-27-778-000MY-001 C-27-778-000INY- 24M PM C-27-778- WOINY- C-27-778-0001NY- 6M PM ROUTINE C-27-778- C-27-778-OWNY- STEERING CNTL SYSTEM C-27-782-000MY- PM C-27-782-000MY-001 C-27-783-0001NY- 12M PM C-27-783-000INY- C-27-783-000MY- PM C-27-783-000MY-O01 C-27-786-000/NY- PM C-27-786-000MY-001 C-27-791-00MY- 24M C-27-79 1 -000MY-002 C-27-791-000/NY- 6M PM C-27-791-000NY-002 C-27-791-000INY- PM ROUTlNE C-27-791- C-27-868-000MY- PM C-27-868-000MY-001 6M C-27-879-000INY- 6M PM C-27-879-000NY-001 C-27-879-000/NY- 6M PM C-27-879-OOOINY-001 C-27-880-000/NY - PM C-27-880-000/NY-001 6M C-27-880-000/NY- PM C-27-880-000MY-001 6M C-27-880-000INY- PM C-27-880-000MY-O01 C-27-885-000INY- 1 2M PM C-27-885-000NY- C-27-885-0001NY- 6M PM ROUTINE C-27-885- C-27-885-0001NY- C-27-885-000/NY-001 1 2M #1 C-27-885-000/NY- C-27-885-000INY-001 1 2M #2 C-27-885-000INY- DRYER,AIR-GAS C-27-885-0001NY- PM C-27-885-OOOMY-002 6M C-27-885-000INY- PM C-27-885-000MY-002 6M C-27-886-000INY- 6M PM ROUTINE C-27-896- C-27-886-000/NY- LP COMPRESSED AIR C-27-886-0001NY- PM C-27-886-000MY- 00 1 C-27-890-000/NY- PM C-27-890-000MY-00 1 6M C-27-893-WINY- 801LER AUXlLlARY STEAM C-27-893-000/NY- PM C-27-892-000MY- 001 -
35 HMCS MONTREAL 35 HMCS 37 HMCS 2 HMCS HALlFAX 2 HMCS HALIFAX 2 HMCS HALIFAX 2 HMCS HALIFAX 4 HMCS HALIFAX 4 HMCS HALIFAX 5 HMCS ST JOHNS 5 HMCS ST JOHNS 5 HMCS S i JOHNS 5 HMCS ST JOHNS
40 HMCS HALIFAX 4 HMCS TORONTO 4 HMCS HALIFAX 3 HMCS HALIFAX 4 HMCS
2.5 HMCS HALIFAX 3.5 HMCS HALIFAX 3.5 HMCS MONTREAL
1 HMCS HALIFAX 16 HMCS VILLE DE 12 HMCS HALIFAX 16 HMCS VILLE DE 6 HMCS HALIFAX
3.5 HMCS HALIFAX 3.5 HMCS HALlFAX 2.5 HMCS HALlFAX 2.5 HMCS HALIFAX 2.5 HMCS HALIFAX 1.5 HMCS HALIFAX 1.5 HMCS HALIFAX 1.5 HMCS MONTREAL 1.5 HMCS MONTREAL 1.5 HMCS ST JOHNS
2 HMCS HALIFAX 2 HMCS HALIFAX
2.5 HMCS HALIFAX 5 HMCS TORONTO 5 HMCS HALIFAX
5.5 HMCS HALIFAX 3 HMCS VILLE DE 4 HMCS VILLE DE
C-29-528-000INY- PM C-29-528--Y-001 C-29-528-000/NY- PM C-29-528-000MY-001 C-29-528-000MY- PM C-290528-000MY-001 C-38-196-0001NY- PM C-38-196-0OOMY-OO2 C-38-196-000/NY- PM C-38-196-000MY-OO2 C-38-229- PM C-38-229-COOMY-001 6M C-38-231-000lNY- 1 2M ROUTINE C-38-231- C-38-231-000INY- PM C-38-231-000MY-001 C-38-232-000/NY- 24M C-38-232-000INY-001 C-38-232-000/NY- 24M C-38-232-000MY-001 C-38-232-000INY- 24M C-38-232-000INY-001 C-38-232-000INY- 24M C-38-232-000/NY-001 C-38-232-000/NY- PLANNED 24 M C-38-288-000INY- 12M PM C-38-288-00MY- C-38-288-0001NY- 12M PM C-38-288-000INY- C-38-288-000/NY- 12M PM C-38-288-000/NY- C-38-288-000/NY- 12M PM C-38-288-000MY- C-38-288-000/NY- 1 2M PM C-38-288-000MY- C-38-288-000/NY- 1 2M ROUTINE C-38-288- C-39-143-OOO/NY- 24M PM C-39-143-000/NY- C-39-156-000/NY- PM C-39-156-000MY-001 60 C-39-157-0001NY- PM C-39-157-000MY-001 6M C-39-158-0001NY- 24M C-39-lS8-OOOINY-Wl C-39-158-000/NY- 6M PM ROUTINE C-39-158- C-39-158-WMY- R.A.S.T. C-39-159-000INY - PM 24M C-39-159-000MY- C-39-159-000/NY- PM C-39-159-000MY-001 C-39-161- 48M PM C-39-161 -AOO/NY- C-39-161- PM C-39-161 -AOOMY-002 C-39-165-000INY- C-39-165-000MY-001 12M - C-39-165-000INY- C-39-165-OOOMY-001 12M C-39-165-000/NY- C-39-165-0001NY-001 12M C-39-165-OOO/NY- NITROGEN DELIVERY C-39-168-000/NY- PM C-39-168-QOO/NY-OOl C-39-168-0001NY- PM C-39-168-000/NY-OOl C-39-169-000lNY- 6M PM ROUTINE C-39-169- C-39- 1 69-000/NY- C-39-l69-OOO/NY-OOl 6M JP- C-39-170-0001NY- 48M PM C-39- t 7O-OOOINY- C-39-173-000/NY- C-39-173-OWNY-001 6M JP5 C-46-010-031MY- BUS INTERFACE SET C-46-010-031/NY- BUS INTERFACE SET C-46-254-002hJY- 6M PM ROUTINE,C-46-254- C-46-254-002MY- RECORDER REPRODUCER C-46-254-002/NY- RECORDER REPRODUCER
2 HMCS HALIFAX 2 HMCS HALIFAX 2 HMCS HALIFAX 8 HMCS HALIFAX 8 HMCS HALIFAX
20 HMCS HALIFAX Il HMCS MONTREAL 11 HMCS ST JOHNS
6.5 HMCS VILLE DE 6.5 HMCS VILLE DE 6.5 HMCS VILLE DE 6.5 HMCS VILLE DE 6.5 HMCS VILLE DE
2 HMCS HALIFAX 2 HMCS MONTREAL 2 HMCS HALIFAX 2 HMCS HALIFAX 2 HMCS HALIFAX 2 HMCS MONTREAL 2 HMCS HALIFAX
15 HMCS 6 HMCS HALIFAX 7 HMCS 7 HMCS HALIFAX 7 HMCS ST JOHNS
16 HMCS ST JOHNS 16 HMCS HALIFAX
3.5 HMCS HALIFAX 3.5 HMCS HALIFAX
2 HMCS ST JOHNS 2 HMCS HALIFAX 2 HMCS TORONTO
3.5 HMCS TORONTO 3 HMCS HALIFAX 3 HMCS HALIFAX
1.5 HMCS HALIFAX 1.5 HMCS HALIFAX
1 HMCS HALIFAX 3 HMCS HALIFAX 2 HMCS ST JOHNS 2 HMCS ST JOHNS
4.5 HMCS HALIFAX 10 HMCS ST JOHNS 10 HMCS ST JOHNS
C-59-724-000MY- PM C-59-724-000MY-001 6M C-59-724-000MY- PREFORM 12M IN C-59-724-000MY- RADAR SET AN/SPS 505 12 C-59-753-000MY- 12M C-59-753-000MY-001 C-59-753-000/NY- PM C-59-753-000MY-001 6M C-60-172-000MY- PM C-60-172-000MY-001 C-69-700-000MY- ANMIQC-501 (V) 1 U N TEL C-69-700-001INY- 48M PM ROUTINE C-69-700- C-69-700-001 INY- COMMUN CATION SET C-69-713-0001NY- 6M PM C-69-713-000/NY-003 C-69-713-OOO/NY- TRANS SET AN/SLQ 25 C-69-713- DOOR ASSY MX-5242/SLQ C-69-713- DOOR ASSY MX-5242lSLQ C-69-713- 12M PM ROUTINE C-69-713- C-69-725-0001NY- PM C-69-725-000MY-001 6M C-69-725-000MY- PM C-69-725-000/NY-001 6M C-69-745-000INY- 6M PM C-69-745-0001NY -002 C-69-745-000/NY- TORPEDO HANDLING C-69-745-0001NY- TORPEOO HANDLING C-69-745-000MY- TOWED ARRAY HAND- C-69-745- 12M PM ROUTINE.C-69-745- C-69-745- 6M PM C-69-745-COO/NY-001 C-69-745- LEVEL WlND ASSY C-69-745- 6M PM C-69-745-HOOINY-001 C-69-745- HANDLING DRUM ASSY C-69-746- PM C-69-746-000NY-001 6M, C-69-746- PM C-69-746-BOOMY- C-69-746- TOWED BODY UNIT 29 C-69-771-000/NY- C-69-771-000/NY-001,12 M, C-69-771-000lNY- C-69-771-000lNY-001, MK 32 C-69-771-000/NY- C-69-771-000/NY-001 ,TORP C-69-771-000/NY- PM C-69-771-000fNY-001 6M C-69-77l-OOOINY- TORPEDO TUBE MK 32 C-69-771-000/NY- TORPEDO TUBE MK 32 C-69-802-000/NY- RECORDER-REPRODUCER C-70-266- DIRECTOR RADAR FlRE C-70-266- DIRECTOR RADAR FIRE C-70-266- DIRECTOR RADAR FlRE C-70-266- PM C-70-266-AOO/NY-001, C-70-267-000/NY - PM C-70-267-000MY-001, C-70-268-000MY- ANISWG 501 V HARPOON C-70-268-000INY- PM C-70-268-000/NY-001, C-70-312-000INY- 1 2M ROUTINE C-70-312- C-70-312-000INY- PM C-70-312-000MY-
8 HMCS HALIFAX 12 HMCS MONTREAL 12 HMCS MONTREAL 3 HMCS TORONTO 3 HMCS HALIFAX 7 HMCS HALIFAX
6.5 HMCS 6.5 HMCS ST JOHNS 6.5 HMCS MONTREAL 15 HMCS HALIFAX 15 HMCS TORONTO 4 HMCS TORONTO 4 HMCS TOROWO 2 HMCS HALiFAX
8.5 HMCS HALIFAX 8.5 HMCS HALIFAX 11 HMCS HALIFAX
8.5 HMCS TORONTO 8.5 HMCS TORONTO 11 HMCS TORONTO 18 HMCS HALIFAX 9 HMCS HALIFAX 9 HMCS TOROWO
18 HMCS HALIFAX 18 HMCS TORONTO 8 HMCS HALIFAX 8 HMCS 8 HMCS TORONTO
64 HMCS HALIFAX 32 HMCS TORONTO 32 HMCS TORONTO 64 HMCS HALIFAX 32 HMCS TORONTO 32 HMCS TORONTO 3 HMCS ST JOHNS
10 HMCS TORONTO 10 HMCS TORONTO 20 HMCS MONTREAL 10 HMCS TORONTO 20 HMCS VILLE DE 35 HMCS TORONTO 35 HMCS HALIFAX 50 HMCS ST JOHNS 50 HMCS ST JOHNS
SS C-97-010-006MY- 60 MONTHLY ROUTINE C- 237 7 HMCS ST JOHNS SS C-97-221-000INY- 12M PM ROUTINE C-97-221- 8 10 HMCS HALIFAX SS C-97-322-000lNY- 18M PM C-97-322-000MY- 12 21 HMCS MONTREAL SS C-97-325-000lNY- 12M PM C-97-325-000/NY- 8 6 HMCS TORONTO SS C-97-325-0001NY- 12M PM C-97-325-000MY- 8 6 HMCS ST JOHNS SS C-97-327-000MY - 1 2M PM C-97-327-000MY- 16 3 HMCS ST JOHNS
APPENDIX F
PREVENTIVE MAINTENANCE AT THE CAPABILITY LEVEL
Table FI. RF Preventive Maintenance.
Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Shop
1 2 3 4 5 6 7 8 9
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
Table F2. SS Preventive Maintenance.
Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Shop
1 2 3 4 5 6 7 8 9
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39
APPENDIX G PM ROUTINES USED AS INPUT IN THE SIMULATION
Table G1. RF Designated P M Routines
August 1997 - Pend 72 NDlD
C-57-010-0101NY-008
I
57-635-000 PELORUS STAND 24M ASSEMBLY
I
YES
PERlODlClTY ERN
57-633-CO0
1 69-799-AW COMPUTER SONAR DATA 12M
ITEM-NAME
24M 24M
57-633-EW 1 INDICATOR,COURSE
l
69-802-000 RECORDER- 24M YES REPROOUCER SET, SIGNAL DATA
INDICATOR.SHIP'S COURSE
57-634-000
51 -564-000 ANTENNA 24M
24M
GY RO COMPASS SYSTEM.INERTIAL NAVIGATOR/STABIUZED
I I , I I 151-675-000 IANTENNA. WHlP 18 FT. 124M
1
1
51 -727-000 ANTENNA. AS-51 71/SRC 24M
51 -795-000 ANTENNA 24M 51-584-000 RADIO SET, AWRC-507 ( 12M
HFX 8 NES CLASS SHlPS )
1
24-305-000 MAIN GEARING 24M STARBOARD
24-309-000 CROSS CONNECT 24M GEARING
24-31 1-000 CROSS CONNECT 6M FRICTION DlSC CCUTCH ASSEMBLY PORT/STBD
24-312-000 CRUIÇE ENGINE FRICTION 6M DlSC CLUTCH ASSEMBLY
24-545-A00 COUPLING 24M ASSEMBLY .MAIN SHAFT
YES
YES
YES
YES
1
C-27-784-AOOMY- 00 1 27-784-A00 TANK,PRESSURE STEEL 1 1 2M I
IVES C-27-886-000MY-001 27466QûO COMPRESSOR 124M [YES
24-548400
24-560-000
27-7'78-000 STEERING GEAR AND 24M CONTROL SYSTEMS (GENERIC)
27-783-A00 ACCUMULATOR.PNEUMAT 72M IC,AIR FLASK
72M
12M
ENGINE.DIESEL.PIELSnC K 20 PAG V280 MPC 20 CYL
PUMP UNIT.HEAD TANK HYDRAUUC OIL,PORT/SlBO
YES
YES
YES
24-640-000 INFRAREO SUPPRESSION 24M DEVICE
UNIT,ROTARY.LP AIR
27-893-000 BOlLER.AUXILIARY .STEAM 12M ,HIGH PRESSURE
27-904-000 HOT WATER CALORIFER 12M 27-A99-000 ENGINE. DIESEL 24M
(SEAWATER SERVICE SYSTEM)
27-808-000 IMS DRAINS SYSTEM 24M
YES
YES
YES
YES
29-357-000 AIR CONDlTlONlNG 24M EQUIPMENT GROUP
391 43-000 PARTS KIT,QUICK 24M DISCONNECT COUPLING ASSEMBLY (HIÇR)
YES
1
39-158-000 RECOVERY ASSIST 24M SECURE TRAVERSE SYSTEM
24-669-000 INSULATED ENCLOSURE 12M IANO SUPPORT SYSTEM
1
YES
C-28-514-000MY-002 2&514-000 WINCH, LlNE HANDLING 12M SYSTEM
C-22-010-0231NY-002 22-427-000 RElEASE,HYDROSTATIC, 24M
C-2&153-000MY -001 28- 1 53-000 LlGHT JACKSTAY 24M RUNNING RlGGlNG
C-28-454-AOOMY- 00 1 28-454-A00 LONGITUDINAL 24M STRUCTURAL BULKHEADS
J-2&460-DOO/NY-OOf 28-588-000 VENT OVERFLOW AND 24M SOUNDING SYSTEM
YES
YES
YES
YES
20) (HFX CLASS SHIPS)
29-526-000 COOLER UNIT, AIR (KU 300) (HM CLASS SHIPS)
29-527-000 COOLER UNIT,AIR (KU- 4000) (HFX CLASS SHIPS)
C-29-390-000/NY-001 29-390-000 FAN CO1L UN ITS ,FCU 500/1000/18?5 SERIES
29-390-001 CWLER UNIT.AIR FCU- 500-500-RH
29-390-002 COOLER UNIT, AIR FCU- 500-500-w
29-390-003 C O U R UNIT,AIR FCU- 1000-1 000-RH
29-390-004 COOLER UNIT, AIR FCU- 1000-1000-LH
29-390-005 COOLER UNIT,AIR FCU- 18751 875RH
24390-006 COOCER UNIT,AIR FCU- 18751 87!SLH
C-29-4664OOMY-001 29-466-000 COOLER UNIT,AIR.CENTRAL STATION
C-77-304-000/NY-001 77-304-000 NEC FILTRATION UNITS.111 Am2.3,4/4A
C-46-497-000INY-001 46-497-000 /COMPUTEA.DIGITAL
C-46498-000n\iY-OOi 46498-000 COMPUTER,DIGITAL ( HFX TRL 8 NES CLASS SHIPS )
MEASURES SET ANISLQ-
MOD 5 (BIG EYE)
C-70-332-000MY-001 70-332-000 WAVEGUIDE DRlER UNIT (GENERIC) (TRL, Hm. FSE. FSH)
C-69-703-OOOlNY -00 1 69-703-000 SONAR HULL OUTFIT CS SYSTEM (GENERIC)
C-70-345-000/NY -00 1 70-345-000 HEAW MACHINE GUN.50 CALIBRE (HFX, TRL, IRE, ANS, PTR, WR, MSA, SSO, ASL CLASS SHIPS)
24M YES
24M YES
24M YES
24M YES
24M YES
24M YES
24M YES
24M YES
24M YES
24M YES
24M YES
24M YES
36M
36M
12M YES
1 sen 1
NDlD ERN
BREATHING APPARATUSSELF CONTAINED,OTTO FUEL,SERIES (PTFI. MCD, HfX . VIC AND TRL CLASS SHIPS)
ITEM-NAME
INDICATOR.COURSE
C-69-799-AW./NY-001
C-51-010-002MY-026
69-799-A00
C-24-304-000MY-001
51 -564-000
C-24-545-BOO/NY-001 C-26411-FWMY-001 C-29-354-OOû/NY-001
COMPUTER SONAR DATA
51-675-000 51-681 -000 24-304-000
G22-010-023/NY-002
1 M
AMENNA
24-545-800 26-41 1 -FOO 24354-000
C-29-010-012MY-014
ANTENNA, WHlP 18 fT. ANTENNA MAIN GEARING CLUTCH CONTROLS
22-490-000
I
29-526-000 'COOLER UNIT,AIR (KU 13000) (HFX CLASS SHIPS)
24M
THRUST BLOCK,PORT EXHAUST SYSTEM [MAIN REFRIGERATION
29-525-000
AIR DISTRIBUTION SYSTEM COOCER UNIT.AIR FCU- 500-500-RH
COOLER LJNIT,AIR FCU- 500-500-LH
COOLER UNIT,AIR FCU- 1 000- 1 000-RH
COOCER UNIT.AIR FCU- 1000-1000-Lli
COOCER UNIT,AIR FCU- 1875-1 875-RH
COMPUTER,DIGITAL
YES
24M 24M 24M
SYSTEM
LIFERAFT.INFLATABLE.20 MAN,TUL-61 WASN
24M
Y ES
12M 1 2M 24M
24M
COOLER UNIT.AIR (KU- 2000) (HFX CLASS SHIPS)
Y ES
12M
24M
24M
24M
24M
24M
36M
I
Y ES
Y ES
Y ES
YES
YES
YES
YES
YES
COMPOTER,DIGITAL ( HFX TRL 8 NES CLASS SHIPS )
C-46-498-000/NY-001
24M
36M 46-498-000
Y ES
C-70-332-000/NY-001
C-69-703-00QMY-001
October 1997 - Period 74 1 I
WAVEGUIDE DRlER UNlT (GENERIC) (TRL HFX, FSE, FSH)
SONAR HULL OUTFIT CS SYSTEM (GENERIC)
'70-332-000
69-703-000
C-57-010-01 OMY-008
C-69-799-AOOINY-001
C-51-010-002IT4Y-026
1 M
1 M
PERlODlCllY
1
ITEM-NAME NDlD
57-633-€00
69-799-A00
51 -564-000
C-27-784-000/NY-001
C-29417-000/NY-001 C-22-0 1 0-023rNY-002
ERN
C-24-545-801 /NY -001
I
C-ZB-010-008/NY-044 128418-600 I
INDICATOR,COURSE
COMPUTER SONAR DATA
ANTENNA
27-784-000
29-41 7-000 22-490-000
C-Z9-OlO-O12/NY-O14
24M
1 M
24M
DECK ONE
29-526-000
24M 24M 24M
1
BLOCK,STARBOARD
LP COMPRESSE0 AIR SYSTEM
CHILLER SET.85 TON UFERAFT, INFLATABLE,20 MAN,TUL-61 WASN
24M
29-525-000
C-29-390-000/NY-001
51 -!565-000 51 -674-000 51 -681 -000
6M
24M 24M
COOLER UN1T.AlR (KU 3000) (HFX CLASS SHIPS)
C-46-497-000/NY-00 1
ANTENNA COUPLER,ANTENNA ANTENNA
24-545-BO1
COOLER UNITVAIR (KU- 2000) (HFX CLASS SHIPS)
24M
29-390-002
C-46-498-000MY -001
YES
YES
Y ES
THRUST I12M
24M
29-390-003
29-390-004
29-390-005
46-497-000
C-70-332-OWNY-00 1
C-69-703-000NY-001
YES
COOLER UNIT,AIR FCU- 500-500-LH
46-498-000
24M
COOLER UN1T.AIR FCU- 1000-1 000-RH
COOLER UNITVAIR FCU- 1 000-1 000-LH
COOLER UN1T.AIR FCU- 1 875-1 875-RH
COMPUTER.DIGITAL
70-332-000
69-703-000
24M
24M
24M
36M
COMPUTER,DIGlTAL ( HFX TRL 8 NES CLASS SHIPS )
36M
WAVEGUIM DRlER UNlT (GENERIC) (TRL HFX, FSE, FSH)
SONAR HULL OUTFIT CS SYSTEM (GENERIC)
1 M
1 M
1 1 1
NDlD ERN ITEM-NAME
COMPUTER SONAR DATA
ANTENNA 24M
COUPLER,ANTENNA 24M AMENNA, WHlP 35 FT 24M ANTENNA 124M DIRECTION FINDER SET 12M YES
K 20 PAG V280 MPC 20
YES ,HIGH PRESSURE
CHIUER SET.85 TON 24M YES LIFERAFT.INFLATABLE.20 24M MAN,WL-61 WASN l INTERNAL DECKS 24M YES
RUDDER
COOER UNIT,AIR (KU 24M YES 3000) (HFX CLASS SHIPS)
COOLER UNIT,AIR FCU- 500-500-RH
COOCER UNIT,AtR FCU- 500-500-LH
COaER UNIT,AIR FCU- 1 000-1 000-RH
COOLER UNIT,AIR FCU- 1 000- 1 000-LH
COOLER UNIT,AIR FCU- 1 875-1 875-RH
-1 YES
I I 1 1 1
I IC-70-270-000/NY-001 170-27O-OOO I MAIN GUN SYSTEM. 57 16M
C-70-332-000/NY-001
C-69-703900/NY-001
C-69-771-000MY-001
MM. MK 2 (HFX 8 NES ClASS SHIPS)
1
NDlD €RN ITEM-NAME
70-332-000
69-703-000
69-771 -000
COMPUTER SONAR DATA 1M
WAVEGUID€ DRlER UNIT (GENERIC) (TRL HM, FSE, FSH)
SONAR HULL OUTFK CS SYSTEM (GENERIC)
TORPEDO TUBE ASSEMBLY,MK 32 MOD 9
PUMP UNIT,HEAD TANK 6M HYDFIAULIC 0IL.PORTISTBD
LIFERAFT.INFLATABtE.20 24M MAN,T UL-61 WASN
28402-000 HULL STRUCTURE
29-390-001 COOLER UNIT,AIR FCU- 500-500-RH
29-390-002 COOLER UNIT,AIR FCU- 500-500-LH
29-390404 COOLER UNIT.AIR FCU- 1000-1000-LH
YES
I C-69-7034OOMY-001 69-703-000 SONAR HUU WTFIT CS
SYSTEM (GENEAIC)
C-70-332-000/NY-001
1
€RN ITEM-NAME PERIODICITY INCLUDED
70-332-000
C-57-010-010MY-008
WAVEGUIM ORlER UNlT (GENERIC) (TRL, HFX. FSE. FSH)
57-634-000
1 M
57-633-CO0
GY RO COMPASS SYSTEM.INERTIAL
C-69-799-AOOMY-001
C-26411-EOOMY-001 C-27-867-000MY-001
INDICATOR.SHIPS COURSE
C-29-390-000MY-001 12M YES
6M
1
57-a-000
57-667-000
69-799400
26-41 1-€00 27-867-000
29-390-002
29-390-003
29-390-004
24M YES
6M
6M
1 M
1
REPEAfER,MINIATLJRE TAPE,TYPE.C96070
REPEATER,GY RO COMPASS,SERVO DRIVEN,CARD TYPE,C96240
COMPUTER SONAR DATA
AIR IMAKE SYSTEM SERVICE STEAM 8 DRAIN
29-390-001
COOLER UNIT,AIR FCU- 500-500-LH COOLER UNIT,AIR FCU- 1 000- 1 000-RH
COOLER UNIT,AIR FCU- 1000-1000-LH
24M YES
t
SYSTEM COOLER UNIT,AIR FCU- 500-5WRH
C-59-753-000MY-001 59-753-000
t
C-70-332-000MY-001 '70-332-000
IMERROGATOR- TRANSPONDER SET
WAVEGUIDE DRlER UNlT (GENERIC) (TRL, HFX. FSE. FSH)
SONAR HULL OUTFIT CS SYSTEM (GENERIC)
C-69-703-000MY-001
i 1 - -
February 1998 - Penod 78
1 M
1 M 69-703-000
ITEM-NAME 1 I NDlO ERN PERlODlClTY INCLUOED
r
57-635900
57-667-000
-
C-57-010-01 O/NY-008
69799-A00
PELORUS STAND ASSEMBLY
REPEATER,GY RO COMPASS.SERV0 DRIVEN,CARD
57-633-CO0
57-633-€00 57-634900
6M
6M
COMPUTER SONAR DATA
y - 3 1 IQaMY-Oûl /24-311-Oûû
I 'c-24-312-000/~~-001 24-312-000
TYPE.C%240 1 6M
CROSS YJ(NECT FRICTION DISC CLUTCH ASSEMBLY PORTISTBD
CRUISE ENGIN€ FRICTION DISC CLUTCH ASSEMBLY
ENGINE.DIESEL,PELSTIC K 20 PAG V280 MPC 20 CYL
PUMP UNIT,HEAD TANK HYDRAULIC 0IL.PORTISTBD
DRYER,AIR-GAS DESICCANl,LP AIR SYSTEM
BOILER.AUXILI ARY, STEAM ,HIGH PRESSURE
C-24-548-000iNY-002
C-24-~-000/NY-001
1
i28-514-000 I I
28-588-000
I '28-588-000 i
29-390M1
29-390-004
29-390-005
58-251 -002
INDICATOR,SHIPOS COUFISE
INDICATOR.COURSE GYRO COMPASS SYSTEMJNERTIAL NAVIGATORISTABIUZED
24-548-000
24-560-000
HOT WATER CALORIFIER C-27-904-000hlY-001
6M
6M 6M
6M
6M
6M
6M
1 SM
6M
12M 27-904-000
WINCH, LlNE HANDLING SYSTEM
VENT OVERFLOW AND SOUNDlNG SYSTEM
VENT OVERFLOW AND SOUNDING SYSTEM
COOLER UNIT,AIR FCU- 500-500-RH
COOLER UNITVAIR FCU- 1000-1000-LH
COaER UNIT .AIR FCU- 1875-1 875-RH
ELECTRONIC SUPPORT MEASURES SET AWSLQ- 501 (HFX CLASS SHIPS)
YES
YES
YES
YES
YES
C-27-885-000lNY-001 27-885-000
12M
6M
6M
12M
24M 24M
12M
YES
YES
YES YES
YES
C-27-893-000MY-00 1
6M
27-893-000
66-31 5-000 BINOCULAR SYSTEM,MK : MOD 5 (BIG EYE)
t 1
NDlD ERN ITEM-NAME
WAVEGUIDE DRlER UNlT (GENERIC) (TRL HFX. FSE, FSH)
1
C-69-703-000/NY-001
64799-A00 COMPUTER SONAR DATA D
C-70-332-000MY-00 1
I
MAN,TUL61 WASN
70-332-000
69-703-000
2000) (HFX CUSS SHIPS)
SONAR HULL OUTFlT CS SYSTEM (GENERIC)
29-526-000 COOLER UNIT,AIR (KU 3000) (HFX CLASS SHlPS)
1 1
129-527-000 l COOLER UNIT.AIR IKU- I4ûûû) (HFX CUSS SHIPS)
C-29-356-OOOMY-001 24356400 AIR DISTRIBUTION SYSTEM
C-29-390-000MY-001 29-390-001 COOLER UNIT,AIR FCU- 500-500-RH
29-390-003 COOLER UNIT,AIR FCU- 1000-1000-RH
29-390-005 COOLER UNIT.AIR FCU- 1875- 1875-RH
C-70-332-000MY-001 70-332-000 WAVEGUIDE DRlER UNIT (GENERIC) (TRL, HFX. FSE. FSH)
I
C-69-703-000/NY-001 64703-000 SONAR HULL OUTFIT CS SYSTEM (GENERIC)
I prii 1998 - Period 80
NDlD ERN ITEM-NAME
I
PERIODICITY INCLUDED
PERlODlClTY =%= INCLUDEO
1
C-69-799-AW/NY-001 69-759-A00 COMPUTER SONAR DATA 1 M
EXHAUST SYSTEM 12M LP CAMPRESSED AIR 6M SYSTEM
LIFERAFT.lNFLATABLE.20 12M MAN.TUL-61 WASN
C-29-010-Ol2NY-014 29-525-000 COOLER UNIT,AtR (KU- 12M 2000) (HFX CLASS SHIPS)
COOLER UNIT,AtR (KU t2M 3000) (HFX CLASS SHIPS)
ë E m m q 7 4000) (HFX CLASS SHIPS)
COOLER UNIT,AIR FCU- 12M 500-500-RH COOLER UNIT,AIR FCU- 12M 500-500-LH
COOLER UNIT.AIR FCU- 12M 1 000-1 000-RH
COOLER UNIT,AIR FCU- 12M 1 875-1 875-RH
WAVEGUIM DRlER UNIT 1M (GENERIC) (TRL HFX, FSE, FSH)
SONAR HULL OUTFIT CS 1 M SYSTEM (GENERIC)
1 M a y 1 998 - Period 81
NDlD ERN ITEM-NAME PERlODlClTY
C-69799-AOOMY -001 69-79SAW COMPUTER SONAR DATA 3M
YES
YES
K 20 PAG V280 MPC 20
26-41 1-F00 EXHAUST SYSTEM 27-886-000 COMPRESSOR
UNIT.ROTARY,LP AIR
28-514900 'WINCH. LINE HANOLING SYSTEM
22-490-000 LIFERAFT. INFLATABLE.20 MAN.TUL-61 WASN
12M YES
29-524-000 COOLER UNIT.AIR (KU 12M IVES 1000) (HFX CUSS SHIPS)
29-525-000 COOLER UNIT.AIR (KU- 12M YES 2000) (HFX CLASS SHIPS)
29-527-000 COOLER UNIT,AIR (KU- 12M YES 4000) (HFX CLASS SHIPS)
29-390-001 COOLER UNIT,AIR FCU- 1500-MO-Rli i 29-390-002 COOLER UNIT,AIR FCU-
500-500-LH
C-70-332-000MY-001
C-69-703-000MY-001
C-70-270-OWMY-001
29-390-003
29-390-005
70-332-000
69-703-000
NOlD
COOLER UNITVAIR FCU- 1000-1 000-RH
COOLER UNIT,AIR FCU- 1 875- 1 875RH
WAVEGUIDE DRIER UNIT (GENERIC) (TRL, HFX, FSE. FSH)
SONAR HULL OUTFIT CS SYSTEM (GENERIC)
12M
12M
1M
I
YES
70-270-000
ERN
57-633-C00
MAIN GUN SYSTEM, 57 MM, MK 2 (HFX 8 NES ClASS SHIPS)
ITEM-NAME
INDICATOR,SHIP'S COURSE
12M
l 1 C-69-799-AOOMY -001 69-799-A00 COMPUTER SONAR DATA 1 M
1 1 t 1
C-24-560-000MY-001 124-560-000 1 PUMP UNIT,HEAD TANK 1 12M
127-885-000 DRY ERAIR W S 12M DESICCANT.LP AIR SYSTEM
27-A99-000 ENGINE. DIESEL 12M (SEAWATER SERVICE SYSTEM)
29-41 7-000 CHILLER SET,85 TON 12M 24-304-000 MAIN GEARING CLUTCH 12M
CONTROLS
22490900 LIFERAFT. INFLATABLE.20 1 2M MAN.TUL-61 WASN
1
YES
YES
Y ES
C-2-2-000~y-002
C-28-464-000MY-001
28-402-000
C-29-010-012MY.014
YES 2 M - 0 0 0
1
YES
HUU STRUCTURE
29-524-000
YES
1
29-525-000
6M
PADEYE INSTALLATION
I
YES
12M
COOLER UNIT,AIR (KU 1000) (HFX CLASS SHIPS)
1
1
12M
COOLER UNIT,AIR (KU- 2000) (HFX CUSS SHIPS)
L
12M
29-526-000
C-29-390-000MY-001
Y ES
YES
YES
29-527-000
COOLER UNIT,AIR FCU- 500-500-LH COOLER UNIT,AIR FCU- ,1000-1 000-RH
~COOLER UNIT,AIR FCU- 1875-1 875-RH
WAVEGUIOE DRIER UNIT (GENERIC) (TRL. HFX. FSE, FSH)
COOLER UNIT,AIR (KU 3000) (HFX CUSS SHIPS)
29-390-001
12M
12M
12M
1 M
COOLER UNIT,AIR (KU- 4000) (HFX CLASS SHIPS)
12M
12M
COOLER UNIT,AIR FCU- 500-500-RH
YES
12M YES
I I JUIY 1998 - Penod 83
NDlD €RN ITEM-NAME PERlOOlClTY INCLUDED
COURSE
1 M
SYSTEM.INERTIAL NAVIGATOWSTABIUZED
SONAR HULL OUFIT CS SYSTEM (GENERIC)
C-69-703-000MY-001
C-69-799-AWNY-001 69-799-A00 COMPUTER SONAR DATA 1 M
69-703-000
C-24-304-000MY -001 24-304-000 MAIN GEARING CLUTCH 1 2M COMROLS
C-24-559-000MY 904 24-559-000 CONTROLLABLE PlTCH 1 2M PROPELLERS & PR3PEUER SHAFTING
YES
YES
AIR lNTAKE SYSTEM 12M SERVICE STEAM 8 DRAIN 6M SYSTEM
C-29-417-000NY-001 29-41 7-000 CHILLER SET,85 TON 12M C-24-304-000NY-002 24-304-000 MAIN GEARlNG CLUTCH 12M
CONTROLS
C-22-010-023MY-002 22-490-000 LIFERAFT.INFLATABLE.20 12M MAN,TUL-61 WASN
C-28464-000/NY-001
COOLER UNIT,AIR (KU 1000) (HFX CUSS SHIPS)
z m q m - 3000) (HFX CUSS SHIPS)
28464-000
12M
1
29-525-000
YES PADEYE INSTALLATION
COOER UNIT.AIR (KU- 2000) (HFX CLASS SHIPS)
29-527-000
YES
12M
12M YES
COOLER UNIT.AIR (KU- 4000) (HFX CLASS SHIPS)
YES
12M YES
29-390-002
500-500-R H COOCER UNIT .AIR FCU- 500-500-LH
! 4
1
W T
COOCER UNIT,AIR FCU- 1000-1000-RH
COOLER UNIT,AIR FCU- 1875-1 875-RH
RADAR SET IMERROGATOR- TRANSWNDER SET
C-59-723-OOO/NY901 C-59-753-000/NY-001
I I I 1 I 1
12M
29-390-003
29-390-005
59-723-000 59-753-000
C-70-332-ûûûMY -001
G69-703-oOO/NY-001
C-69-771-OOOMY -001
August 1998 - Period 84 NDlD
- - - -
C-57-010-01 O/NY -008
YES
12M
12M
12M 12M
C-64799-AOO/NY -001
YES
Y ES
YES
70-332-000
64703-000
57-633-CO0
57-633-EO0 57-634-000
C-69g02-OOO/NY -001
C-51-584-ûûû/NY-OOI
l
1 69-771
INCLUDE D I I
C-24-311-0ûOMY-001
C-Z4-312-OOO/NY-OOl
WAVEGUIDE DRlER UNlT (GENERIC) (TRL, HM. FSE. FSH)
SONAR HULL OUTFIT CS
ERN -
1
1
' 1 2 ~
12M
12M
i
69-802-000
51 -584-000
1 M
1 M
TORPE DO TUBE ASSEMBLY.MK 32 MO0 9
ITEM-NAME 1 PERlODlClTY
1NDtCATOR.SHIP'S COURSE
INDICATOR,COURSE
57-635-000
57-667-000
24-31 1-000
24-31 2-000
SYSTEM (GENERIC)
12M
12M
PELORUS STAND ASSEMBLY
REPEATER,GY RO COMPASSSERVO DRIVEN,CARD TYPE,C96240
RECORDER- REPRODUCER SET, SIGNAL DATA
RADIO SET, ANAJRC-507 ( HFX 8 NES CLASS SHIPS )
12M
12M
12M
CROSS CONNECT FRICTION DISC CLUTCH ASSEMBLY PORT/ST%D
CRUISE ENGINE FRICTION DISC CLUTCH ASSEMBLY
YES
GYRO COMPASS SYSTEMJNERTIAL NAVIGATOWSTABIUZEO
69-799-A00
6M
ûM
12M
COMPLJTER SONAR DATA
I I I
C-24-54&000/NY -002 24-548-000
'27-893-000
'27-904-000 27-A99-000
39-1 SB-000
24-669-000
UNIT.ROTARY,LP AIR
BOILER.AUXILIARY,STEAll ,HIGH PRESSURE
HOT WATER CALORIFIER ENGINE, DIESEL (SEAWATER SERVICE SYSTEM)
RECOVERY ASSIST SECURE TRAVERSE SYSTEM
INSULATE0 ENCLOSURE AND SUPPORT SYSTEM
28-514-000
22-427-000
C-28-460-DOOMY-001 28-5-000 VENT OVERFLOW AND SOUNDING SYSTEM
C-28-588-000INY-001 28-568-000 VENT OVERFLOW AND SOUNDING SYSTEM
C-29-010-0124UY-014 29-525-000 COOLER UNIT,AIR (KU- 2000) (HFX CLASS SHIPS)
ENGINE.DIESEL,PIELSTIC K 20 PAG V280 MPC 20 CYL
C-24-560-000/NY-001
C-27-784-AOOMY-001 C-27-886-000/NY-001
22-490-000
3000) (HFX CLASS SHIPS)
1 2M
PUMP UNIT,HEAD TANK HYDRAULIC OL,PORT/STBD
TANK.PRESSURE STEEL COMPRESSOR
24-560-000
27-784-A00 27-886-000
WINCH, LINE HANOLING SYSTEM
RELEASE,HYDROSTATIC,
29-527-000 COOLER UNITVAIR (KU- 40) (HFX CLASS SHIPS)
1 2M
12M 12M
12M
12M
LIFERAFT.INFLATABLE.20 MAN.TUL-61 WASN
12M
I 129-390-003 I COOLER UNIT.AIR FCU- I l 2M I 1000-1 000-RH
COOLER UNIT,AIR FCU- 12M 1 000-1 000-LH
COOLER UNIT,AIR FCU- 12M 1875-1 875-RH 'COOLER UNIT,AIR FCU- 1 2 ~ 1 1 875-1 875-LH COOCER 12M UNIT.AIR.CENTRAL STATION
ELECTRONIC SUPPORT 12M MEASURES SET ANtSLO- 501 (HFX CîASS SHIPS)
BINOCULAR SYSTEM.MK 3 12M MO0 5 (BIG EYE)
1 C-70-332-OOONY-001 70-3û2-000 WAVEGUIDE ORlER UNIT 6M
(GENERIC) (TRL. Hf%. FSE. FSH)
C-69-703-000MY-001 64703-000 SONAR HULL OUlFlY CS 1 2M SYSTEM (GENERIC)
C-70-345-000MY-001 70-345-000 HEA W MACHINE GUN.50 f2M CALIBRE (Hm, TRL. IRE, ANS, PTR, PVR, MSA, SSO. ASL CLASS SHIPS)
C-97-305-OOO/NY-001 97-305-000 BREATHING 12M APPARATUS,SELF CONTAINED,OlTO FUEL.SERIES (PTR, MCD, HFX , VIC AND TRL CîASS SHIPS)
I
NDlD ERN ITEM-FIAME
C-69-799-AOO/NY-001 69-799-A00 COMPUTER SONAR DATA
C-24-304-000MY-001 24-304-000 MAIN GEARING CLUTCH CONTROLS
, C-24-545-BOO/NY-001 24-545-800 THRUST BLOCK,PORT IC-26-41 1 -FOOMY-001 26-41 1 -FM EXHAUST SYSTEM
C-29-354-000/NY-001 29-354-000 MAIN REFRIGERATION SYSTEM
1 2M YES
29-525900 COOCER UNIT.AIR (KU- 12M
I
29-526-000 COOLER UNIT,AIR (KU 12M 3ûOO) (HFX CLASS SHIPS)
YES
YES
29-356-000 AIR DISTRIBUTION 12M YES SYSTEM
29-390-001 COOLER UNITVAIR FCU- 12M YES 500-500-RH
29-390-002 COOCER UNIT.AIR FCU- f2M YES 500-500-LH
29-390-003 COOLER UNIT,AIR FCU- t2M YES 1000-1000-RH
1 , 1 I
129-390-004 ICoolER UNIT.AIR FCU- I12M 1 YES 1000-1000-LH
1 875- 1875-RH
(GENERIC) (TRL, HFX, FSE, FSH)
SONAR HULL OUTFIT C5 1M SYSTEM (GENERIC)
C-57-010-Of OMY -008
ERN ITEM-NAME PERlODlClTY -
57-633430 INDICATOR.COURSE t2M
69-799-A00 COMPUTER SONAR DATA 1M
1 1
24-545-801 I THRUST I12M -
6LOCK.STARBOARD
27-784-000 LP COMPRESSED AIR '6M SYSTEM
29-41 7-000 CHILLER SET,85 TON 12M 22490-000 LIFERAFT,INFLATABLE,20 12M
MAN.TUL-61 W A S N
28-41 8-800 DECK ONE 12M
29-525-000 CoolER UNITVAIR (KU- 12M 2000) (HFX CLASS SHIPS)
YES
YES
YES
YES
1 I
November 1998 - Period 87 NDlD
29-526-000 COOLER UNIT,AIR (KU 12M YES 3000) (HFX CLASS SHIPS)
29-390-002 COOLER UNITVAIR FCU- 12M YES 500-500-LH
29-390-003 COOLER UNIT,AIR FCU- 12M YES 1 000- 1 000-RH
29-390-004 COOCER UNIT,AIR FCU- 12M YES 1000-1000-LH
29-390-005 CO(XER UNIT,AIR FCU- 12M YES 1875-1 875-RH
70-332-000 WAVEGUIDE ORlER UNIT 1 M (GENERIC) (TRL. HFX. FSE, FSH)
69-70x100 SONAR HULL O U T F ~ ~5 1 M SYSTEM (GENERIC)
1
ERN ITEM-NAME
57-633-E00 INDICATOR,COU RSE s 1
69-799-A00 COMPUTER SONAR DATA
1
58-2ûû-OOû DIRECTION FINDER SET 12M YES
24-548-000 ENGINE,DIESEL.PIELSTIC K 20 PAG V280 MPC 20 CYL
27-893900 BOILER.AUXILIARY,STEAM .HIGH PRESSURE
2941 7-000 CHILLER SET,85 TON 22490-000 LIFERAFT,INFLATABLE,20
MAN.TUL-61 WASN
12M YES
28-455-A00 INTERNAL DECKS 12M
28-420-DA0 RUDDER 12M
29-526-000 COOLER UNIT.AIR (KU 12M YES 3000) (HFX CLASS SHIPS)
29-390-001 COOLER UNIT,AIR FCU- 12M 500-500-RH
COOLER UNIT,AIR FCU- 500-500-Ui COOLER UNIT,AIR FCU- 1 000-1 000-RH
COOLER UNIT,AIR FCU- 1000-1000-LH
CWLER UNIT.AIR FCU- 1875-1 875-RH
WAVEGUIM DRIER UNIT (GENERIC) (TRL. HFX, FSE, FSH)
12M
12M
1 SM
12M
1M
YES
YES
YES
YES
C-69-703-000MY-001
C-69-771-000MY-001
1 I
Decernber 1998 - Period 88
1 NDlD ERN ITEM-NAME PERlODlClTY INCLUDED
C-70-270-000/NY-00 1
l
INDICATOR,SHIPS COURSE
69-703-000
69-771 -000
COMPUTER SONAR DATA
70-270-000
C-24-560-000MY-001 24-560-000 PUMP UNIT,HEAD TANK HYDRAULIC OIL,PORT/STBD
SONAR HUU OUTFIT C5 SYSTEM (GENERIC)
TORPEOO TUBE ASSEMBLY,MK 32 MOD 9
MAIN GUN SYSTEM, 57 MM, MK 2 (HFX 8 NES CLASS SHIPS)
I 1 1
C-28-402-000MY-002 28-402-000 HULL STRUCTURE 6M YES
I
3M
12M YES
C-29-390-000MY-001 29-390-001 C O U R UNIT ,AIR FCU- 1 SM YES 500-500-RH
29-390-002
29-390-003
29-390-004
COOLER UN1T.AIR FCU- 500-500-LH COOLER UNIT,AIR FCU- 1 000-1 000-RH
COOCER UNIT,AIR FCU- 1000- 1 000-LH
12M
t2M
12M
YES
YES
YES
I I I January 1999 - Period 89 1
NDlD ERN
1 ruarv 1999 - Period 90 1
1
NDlD ERN
(GENERIC) (TRL HFX,
SYSTEM (GENERIC)
INDICATOR,SHIPbS COURSE I_ GYRO COMPASS SYSTEM,INERTIAL . NAVIGATO WSTABILIZED
COMPUTER SONAR DATA
AIR INTAKE SYSTEM SERVICE STEAM 8 DRAIN SYSTEM
COOLER UNIT,AIR FCU- 500-500-RH
COOLER UNIT,AIR FCU- 500-500-LH
C O U R UNIT,AIR FCU- 1000-1 Wû-RH COOLER UNIT.AIR FCU- 1000-1000-LH
TRANSPONDER SET
WAVEGUIDE ORlER UNIT [GENËRIC) (TRL, HFX. FSE, FSH)
SONAR HULL CKITFIT CS SYSTEM (GENERIC)
i ITEM-NAME 1 PERIODICITY
COURSE
57-633-€00 57-634-000
INDICATOR.COURSE GYRO COMPASS SYSTEMJNERTIAL NAVIGATOWSTABIUZED
57-635-000
57-667-000
PELORUS STAND ASSEMBLY
REPEATER.GYR0 COMPASS,SERVO DRIVEN,CARD TY PE.C9624û
I
I I 1 1 1 1 C-27-9û4-000AUY-001 127-904-000 1 HOT WATER CALORIFIER
69-799-A00
24-31 1-000
24-312-000
24-548-000
24-548-000
24-560-000
27-885-000
27-893-000
COMPUTER SONAR DATA
CROSS CONNECT FRICTION OlSC CLUTCH ASSEMBLY PORTISTBD
CRUISE ENGINE FRICTIOPI DISC CLUTCH ASSEMBLY
ENGINE,DlESEL,PIELSTIC K 20 PAG V280 MPC 20 CYL
ENGINE,DIESEL.PIELSTIC K 20 PAG V280 MPC 20 CYL
PUMP UNIT,HEAD TANK HYDRAULIC OIL.PORT/STBD
DRYER.AIR-GAS 0ESICCANT.LP AIR SYSTEM
BOILER,AUXILIARY ,STEAN .HIGH PRESSURE
C-28-5 14-000MY-002
C-28460-DOWNY-001
C-28-588-000MY-001
C-29-390-000iNY-00 1
12M YES
6M
6M
12M YES
29-390904
29-390-005 24M YES
28-514-000
28-588-000
28-588-000
29-390-00 1
C O U R UNIT,AIR FCU- 1 000- 1 000-LH
COOLER UNIT,AIR FCU- 18751 875-RH
WINCH. LlNE HANDLING SYSTEM
VENT OVERFLOW AND SOUNOING SYSTEM
VENT OVERFLOW AND SOUNDING SYSTEM
COOLER UNIT, AIR FCU- 500-500-RH
C-5&251-000/NY-001
I
1
NDlD ERN ITEM-NAME PERiODICIlY
C-57-010-01 OMY-008 57-633-€00 IND1CATOR.COURSE 6M
ELECTRONIC SUPPORT MEASURES SET ANISLQ- 501 (HFX CLASS SHIPS)
'5&251-OO2
I
6M
C-66-315-000lNY-001 I
I , 1 I 1
66-315-000
I
WAVEGUIDE DRlER UNIT (GENERIC) (1% Hm, FSE. FSH)
SONAR HUU OUTFIT CS SYSTEM (GENERIC)
C-70-332-000INY-00 1
C-69-703-000jNY-001
C-69-799-AOOAUY -001
24M 1
1 1
I
6M
6M
70-332-000
69-703-000
COMPUTER SONAR DATA 69-799-A00
29-526-000
t
1 77-304-000 NBC FILTRATION 24M
UNITS,l/lA,2,3,4/4A
70-332-000 WAVEGUIM DRlER UNIT 1 M (GENERIC) (TRL, Hm. F SE, FSH)
69-703-000 SONAR HULL OUTFIT C5 1 M SYSTEM (GENERIC)
BINOCULAR SYSTEM,MK 3 MOD 5 (BIG EYE)
1 M
COOLER UNIT. AIR (KU- 2000) (HFX CLASS SHIPS)
C-29-010-01 M Y - 0 1 4
C-29-356-OOOAUY-001
C-29-390-000lNY-001
YES 6M
29-525-000
COOLER UNIT,AIR (KU 3000) (HFX CLASS SHIPS)
24M 29-527-000
E S
E S
IES
24M
COOLER UNIT,AIR (KU- 4000) (HFX CLASS SHIPS)
29-35û-000
29-390-001
29-390-003
29-390-005
IES
IES
f ES
AIR DISTRIBUTION SYSTEM
COOLER UNIT, AIR FCU- 500-500-R H
-ER UNIT-AIR FCU- 1000-1 000-RH
COOLER UNIT, AIR FCU- 1875-1 875-RH
6M
12M
24M 24M
24M
INCLUDED NOlD I ERN
C-69-799-AOOMY-001
ITEM-NAME
1
C-22-010-023/NY-002
PERlWlClTY
1 M 1
EXHAUST SYSTEM LPCOMPRESSEDAIR SYSTEM
C-26-411 -FOOMY-001 C-27-784-000MY-001
C-29-010-012/NY-014
69-799-A00
26-41 1 -F00 27-784-000
29-526-000
COMPUTER SONAR DATA
I
24M 22490-000
24525-000
9
I I 1
Mav 1999 - Period 93 1
48M 6M
LIFERAFT,INFLATABLE,20 MAN,TUL-61 WASN
COOLER UNIT,AIR (KU 3000) (HFX CLASS SHIPS)
COOLER UNIT,AIR FCU- 500-500-RH
I
COOLER UNIT,AIR (KU- 2000) (HFX CLASS SHIPS)
29-527-000
24M
24M
COOLER UNIT.AIR FCU- 500-500-LH
COOLER UNIT.AIR FCU- 1000-1 000-RH
COOLER UNIT,AIR FCU- 1875-1 875-RH
NBC FILTRATION UNITS, 111 A,2,3.4/4A
24M
YES
COOLER UNIT,AIR {KU- 4ûOO) (HFX CLASS SHIPS)
YES
WAVEGUIOE DRlER UNIT (GENERIC) (TRL HFX, FSE. FSH)
I 1 I
NDID 1 ERN ITEM-NAME 1 PERIOOICIN IINCLUDED
YES
24M
24M
24M
24M
1 M
SONAR HUlL OUTFIT CS SYSTEM (GENERIC)
24M
YES
YES
YES
1 M
YES
COMPUTER SONAR DATA 3M
ANTENNA 24M
K 20 PAG V280 MPC 20
EXHAUST SYSTEM 48M YES WMPRESSOR M M YES UNIT,ROTARY,LP AIR
SWITCHBOARDS,FORWAR 48M YES D & AFTER WINCH. LINE HANDLING 12M YES SYSTEM
DISTRIBUTION PANELS & 48M BREAKERS,6û HZ LIFERAFf.lNFLATABLE.20 24M MAN.fUL-61 WASN
28-514-000 WINCH, LlNE HANDLING 24M YES SYSTEM
29-524-000 COOLER UNIT,AIR (KU 24M YES 1000) (HFX CLASS SHIPS)
I
1 1 129-525-000 1 COOLER UNIT,AIR (KU- 124M ~YES 2000) (HFX CLASS SHIPS)
29-526000 COOLER UNIT,AIR (KU 24M YES 3000) (HFX CLASS SHtPS)
29-527-000
I i t t 129-390-002 I COOlER UNIT.AIR FCU- I24M 1 YES
C-29-390-000/NY-001
COOLER UNIT,AIR (KU- 4000) (HFX CLASS SHIPS)
29-390-001
C-77-304-OWNY-00 1
24M YES
COOLER UNIT.AIR FCU- 500-500-RH
29-390-003
29-390-005
77-304-000
i
24M
500-500-LH
COOLER UNIT,AIR FCU- 1000-1000-RH COOLER UNIT,AIR FCU- 1875-1 87 5-RH NBC FILTRATION UNITS, 1/1A,2,3,4/4A
YES
24M
24M
24M
YES
YES
YES
1 1
June 1 999 - Period 94 NDlD
COURSE
WAVEGUIM DRlER UNIT (GENERIC) (IRL Hm, FSE, FSH)
BOAT DAVIT SYSTEM OECK CRANE ASSEMBLY SONAR HULL OUfFIT C5 SYSTEM (GENERIC)
MAIN GUN SYSTEM, 57 MM, MK 2 (HFX 8 NES CLASS SHIPS)
ITEM-NAME ERN
COMPIJTER SONAR DATA 1 M
I
ANTENNA 24M PUMP UNIT .HEAD TANK 12M HYDRAULIC OIL.PORT/STBD
DRY ERAIR-GAS 12M YES DESICCANT.LP AIR SYSTEM
1 M
24M
ENGINE. DIESEL 24M YES (SEAWATER SERVICE SYSTEM)
24M 3M
12M
PERlOOlClTY
CHILLER SET,85 TON
CONTROLS
MAN,TUL-61WASN
INCLUDED
C-28-402-000MY-002
C-2&419-OOONY -00 1 C-28-464-W/NY -001
WINCH. LlNE HANOLING SYSTEM
28-402-000
2841 9-000 284û4-000
24M
H U U STRUCTURE
MASTS PADEYE INSTALLATION
YES
6M
48M 48M
YES
YES
C-29-010-012/NY-014 29-524-000 CWLER UNIT,AIR (KU 24M YES 1000) (HFX CLASS SHIPS)
29-525-000 CWLER UNIT,AIR (KU- 24M YES 2000) (HFX CLASS SHIPS)
I 29-526-000 COOLER UNIT.AIR (KU 24M YES
3000) (HFX CLASS SHlPS)
I I 1 1 I I
I I 129-527-000 ICoolER UNIT.AIR (KU- I24M IYES 14000) (HFX CLASS SHIPS)
I
C-29-390-000iNY -001 29-390-001 COOLER UNIT,AIR FCU- 500-500-RH
29-390-002 COOCER UNIT,AIR FCU- 500-500-LH
29-390-003 COOLER UNIT,AIR FCU- 1000-1 000-RH
29-390-005 COOCER UNIT,AIR FCU- 1875-1 875-RH
C-77-304-000MY-001 77-304-000 NEC FILTRATION UNITS,1/1A.2.3,4/4A
C-70-332-000/NY-001 70-332-000 WAVEGUIDE DRlER UN lT (GENERIC) (TRL, HFX, FSE. FSH)
C-69-703-000MY-001 69-703-000 SONAR HULL WTFIT CS SYSTEM (GENERIC)
1 i
July 1999 - Period 95 NDlD ERN ITEM-NAME
24M YES
24M YES
PERlODlClTY INCLUDED + 57-633-C00 INDICATOR,SHIP'S 24M
COURSE
57-634-000 GYRO WMPASS 24M YES , SYSTEM.INERTIAL NAVIGATOWSTABIUZED
69-799-A00 COMPUTER SONAR DATA
51-675-000 ANTENNA, WHlP 18 FT.
51 -727-000 AMENNA. AS-51 71/SRC 24M YES
24-304-000 MAINGEARINGCLUTCH 48M YES CûNTROLS
MAlN GEARING PORT CONTROLLABLE PITCH PROPELLERS & PROPELLER SHAFTING
AIR INTAKE SYSTEM SERVICE STEAM 8 DRAIN SYSTEM
CHILLER SET.85 TON MAIN GEARING CLUTCH CONTROLS
ELECTRONIC STEERING W M R O L SYSlEM
G24--/NY-001 C-24-559-OWNY-004
C-26-411 -EOO/NY-001 G27-8û7-000/NY-001
C-29-417-OOWNY -001 C-S4-3o4-oOo/NY -002
C-27-935-000/NY-001 1
24-306-000 24-559-000
26-41 1 -EOO 27-867-000
2941 7-000 24-304-000
27-935-000 24M
28420-000 28464-000
C-22-010-02WNY-002
YES
f
YES
YES
SHELL,HULL PADEYE INSTALLATION
1
YES
22-490-000
C-28-5 1 4-0001NY-001
C-29-OtO-Ol2/NY-O14
I I I I r I i 129-526-000 1 C W E R UNIT.AIR (KU I24M IVES
LIFERAFT.INFLATABE.20 MAN.T UC-6lWASN
48M 48M
29-525-000
I I
I 29-527-000 'COOLER UNIT,AIR (KU- 4000) (HFX CUSS SHIPS)
I I
YES YES
28-51 4-000
29-524-000
1
129-390-001 COOLER UNIT,AIR FCU- 24M 500-500-RH
29-390-002 COOLER UNIT,AIR FCU- 24M 500-500-LH
29-390-003 COOLER UNITVAIR FCU- 24M 1000-1000-RH
29-390-005 COOLER UNIT,AIR FCU- 24M 1875-1 075-RH
77-304-000 NBC FILTRATION 24M UNITS, 1/1A,2.3.4/4A
59-723-000 RADAR SET 12M 59-753-000 INTER ROGATO R- 12M
TRANSPONDER SET
1
YES
YES
YES
WINCH. LlNE HANDLING SY STEM COOLER UNITVAIR (KU 1000) (HFX CLASS SHIPS)
COOLER UNIT,AIR (KU- 2000) (HFX CLASS SHIPS)
YES
24M
24M
24M
70-332-000 WAVEGUIM ORlER UNIT 1M (GENERIC) (TRL, HFX, FSE. FSH)
69-703-000 SONAR H U U OUTFIT CS 1 M SYSTEM (GENERIC)
I I C-69-771-000MY-001 169-771 -000 ITORPEDO TUBE 112M
- 1 I I
NDlD ERN ITEM-NAME PERlODlClTY I
COURSE
57-633-€00 INDICATOR,COCIRSE 24M 57-634-000 GY RO COMPASS 24M
SYSTEM.lNERnAL NAVlGATO WSTABlLlZED
57-635-000 PELORUS STAND ASSEMBLY
57-667-000 REPEATER,GYRO WMPASS.SERV0 DRIVEN,CARD TY PE.C96240
C-69-700-001 MY-001 69-700-001 COMMUNICATION SET,SONAR.UNDERWATE R TELEPHONE
C-69-799-AOO/NY-001 69-799-A00 COMPUTER SONAR DATA
I
C-69-799-BOO/NY-001 64799-800 WNTROL. MONITOR GROUP
C-69-799-COOMY-001 69-799-CW TRANSMITER GROUP SONAR (GENERC)
REPRODUCER SET, SIGNAL DATA
C-51-010-002/NY-026 51-564-000 ANTENNA
I 1
1 151-675-000 IANTENNA, WHlP 18 FT. 124M I
51-727-000 ANTENNA. AS-51 71lSRC 24M
I 1 I
I 51 -795-000 I ANTENNA I24M C-5 1 -584-0001NY-001 51 -5&4-000 RADIO SET, ANNRC-507 (
HFX 8 NES ClASS SHIPS )
C-24-305900/NY-001 24-305-000 MAIN GEARING STARBOARD
C-24-309-0001NY-00 1 24-309-000 CROSS CONNECT GEARING
C-24-311-000iNY-001 24-31 1900 CROSS CONNECT FRICTION DISC CLUTCH ASSEMBLY PORT/STBD
C-24-312-000iNY-001 24-312-000 CRUISE ENGINE FRICTtON DISC CLUfCH ASSEMBLY
YES
YES
YES
YES
YES
YES
YES
YES
C-24-545-AOOMY-001
C-24-548400MY-002
C-24-560-000/NY-001
C-24-MO-OOOlNY-001
C-24-669900/NY-001
C-27-778-0001NY 901
C-27-783-AOOMY-001
C-27-784-AOOMY-001
YES
YES
C-27-886-000MY-001
C-27-893-000MY-001
C-27-904-000MY -001 C-27-A99-000MY-00 1
C-27-BO&000/NY-001
YES
YES
24-545-A00
24-548-000
24-5ûû-000
24-640-000
24-û69-000
27-77&000
27-783-A00
27-784-A00
27-8864W
27-893-000
27-904-000 27-A99400
27-B08-000
l 1 RECOVERY ASSIST 124M IVES
COUPLING ASSEMBLY.MAIN WATT
ENGlNE,DIESEL.PIELSnC K 20 PAG V280 MPC 20 CYL
PUMP UNIT,HEAD TANK HYDRAULIC OIL,PûRT/STBO
INFRARED SUPPRESSION DEVICE
INSULATED ENCLOSURE AND SUPPORT SYSTEM
STEERING GEAR AND COMROL SYSTEMS (GENERIC)
ACCUMULATOR.PNEUMAT IC,AIR FLASK
TANK,PRESSURE STEEL
C-29-357-000MY -005
C-39-143-ûûûmiY-002
SECURE TRAVERSE
COMPRESSOR UNIT,RECIPROCATING,DIV ING,KA14-5-5E
COMPRESSOR UNIT,ROTARY,LP AIR
6OILER.AUXILIARY ,STEAM ,HIGH PRESSURE
HOT WATER CALORIFIER ENGINE, DIESEL (SEAWATER SERVICE SYSTEM)
IMS DRAINS SYSTEM
I INsumTEo ENCLOSURE 112M i l
24M
12M
12M
24M
48M
24M
48M
12M
48M
12M
1 2M 24M
24M
29-357-
39-1 43-000
AND SUPPORT SYSTEM l
YES
YES
YES
YES
YES
YES
OlSTRlBüTlON 48M YES SYSTEM.AC.450 VOLT,3 PH
SWITCHBOARDS,FORWAR 48M YES D 8 AFTER
WINCH, LINE HANDLING 12M YES SYSTEM
OlSTRlBUTlON PANELS & 48M BREAKERS.400 HZ
AIR CONDlTlONlNG EQtJlPMENT GROUP
PARTS KIT,QUICK DISCONNECT WUPLfNG ASSEMBLY(H1FR)
48M
24M YES
C-22-010-023iNY -002
C-28-153-000/NY-001
C-28-397-DOO/NY -001
C-28422-COO/NY-001
C-28454-AOOINY-00 1
C-2&46O-WO/NY -001
C-28463-000MY-001
C-28-509-000MY-001
C-28-588-000/NY-001
C-29-010-012MY-014
I I I I 1 1- I I
22-427-000
22-490-000
28-1 53900
28-397-000
48M
24M
48M
48M
24M
28-422-CW
28-454-AW
28-588-000
28463-000
28-509-000
28-588-000
29-525-000
L
YES
YES
YES
YES
YES
29-526400
I
RELEASE.HY DROSTATIC, MK5F
LIFERAFT.INFLATABLE.20 MAN.TUL-61 WASN
LlGHT JACKSTAY RUNNING RIGGING
CHAIN CABLE
OOOR SPECIAL PURPOSE (HANGER)
LONGITUDINAL STRUCTURAL BULKHEADS
VENT OVERFLOW AND SOUNDING SYSTEM
RETRACTABLE POST,SR- 1 2C VERTICAL STORES CONVEYOR
VENT OVERFLOW AND SOUNDING SYSTEM
COOCER UNIT.AIR (KU- 2000) (HFX CLASS SHIPS)
COOLER UNlT,AIR (KU 3000) (HFX CLASS SHIPS)
1
24M
24M
24M
C-29-390-000MY-001
29-527-000
1 1 1 1 1 I
1 1 129-390-004 ICooLER UNIT.AIR FCU- 124M IVES 1
COOLER UNITVAIR (KU- 4000) (HFX CLASS SHIPS)
I
I
1
29-390-000
29-390-001
COOLER UNIT,AIR FCU- 500-500-LH
I
FAN COlL U NITS ,FCU 500/1000/1875 SERIES COOLER UNIT,AIR FCU- 500-500-RH
29-390-002
29-390-003
29-390-005
24M
-
YES
COOLER UNIT.AIR FCU- 1000-1 000-RH
1000-1000-IJi
COOLER UNIT.AIR FCU- 1 875- 1 875-RH
C-29-466-000MY -00 1
24M
24M
YES
YES
- .
29-390-006
29-466-000
COOLER UNIT,AIR FCU- 1875-1 875-LH
COOLER UNIT,AIR,CENTRAL STATION
24M
24M
YES
39-1 70-000 2 TON CHAIN HOIST AND T ROUEY ASSEM8LY ELECTRICAL OPERATED (HANGAR)
MEASURES S€T AWSLQ- 501 (HF- CLASS SHIPS)
66-315-000 BINOCULAR SYSTEM,MK 3 MOD 5 (81G EYE)
70-332-000 WAVEGUIDE ORlER UNIT (GENERIC) (TRL, HFX. FSE, FSH)
69-703-000 SONAR HULL OUTFIT C5 SYSTEM (GENERIC)
70-268-000 CONTROL SET TARGET TRACK ANSWG-501 (V)
CALIBRE (HFX, TRL. IRE, ANS, PTR, PVR, MSA. SSO, ASL CLASS SHIPS)
APPARATUS.SElF CONTAINED,OITO FUEL,SERIES (PTR, MCD, HFX , VIC AND TRL CLASS ISHIPS)
ERN ITEM-NAME
69-799-A00 COMPUTER SONAR DATA
51 -564-000 ANTENNA
26-41 1 -FW EXHAUST SYSTEM 29-354-000 MAIN REFRlGERATlON
SYSTEM
24M YES
24M
12M YES
5M
12M YES
E q = YES 7 YES
L
'29-525-000 COdlER UNIT.AIR (KU- 24M 2000) (HFX CiASS SHIPS)
~C-22-010-023/~~-002 '22490-000
I 1 1
/ I I NOlD 1 €RN ITEM-NAME 1 PERlOûICITY
24M 29-526400
29-356-000
29-390-001
29-390-002
29-390-003
29-390-004
24390-005
70-332-000
69-703-000
t I
C-69-799-AOOMY-001 69-799-A00 COMPUTER SONAR DATA 1 M
LIFERAFT.INFLATABLE.20 MAN .TUL-61 WASN
COOLER UNIT,AIR (KU 3000) (HFX CiASS SHIPS)
51 -564-000 ANTENNA 24M
24M
AIR DlSTRlBUTlON SYSTEM
COOCER UNIT,AIR FCU- 500-500-RH COOLER UNIT,AIR FCU- 500-500-LH COOLER UNIT,AIR FCU- 1000-1 000-RH
COOCER UNIT.AIR FCU- 1000-1000-LH
COOLER UNIT, AIR FCU- 1 875-1 875-RH WAVEGUIDE DRlER UNIT (GENERIC) (TRL. HFX, FSE. FSH)
SONAR HULL OUTFIT CS SYSTEM (GENERIC)
51 -565-000 ANTENNA 24M 51-674-000 COUPLER,ANTENNA 24M 51-681 -000 AN1 ENNA 24M
12M
24M
24M
24M
24M
24M
1M
1 M
I l I I I
I I C - ~ ~ - ~ ~ ~ - O O W N Y - O O I 127-784-000 I LP COMPRESSED AIR 16M
1 1 I ! 1
I I C-29-417-000MY-001 129-41 7-000 I CHILLER SET.85 TON I48M I
22-490-000 LIFERAFT.INFLATABLE.20 24M MAN,TUL-61 WASN
28418-800 DECK ONE 24M l
YES
NCLUOED =A
CooCER UNITVAIR (KU- 2000) (HFX CLASS SHIPS)
CoolER UNIT.AIR (KU 3000) (HFX CLASS SHIPS)
COOLER UNIT,AIR FCU- 500-500-LH
COOLER UNIT,AIR FCU- 1000-1 000-RH
COOLER UNlT.AIR FCU- 1000-1000-LH
COOLER UNITVAIR FCU- 1 875-1 875-RH
WAVEGUIDE DRIER UNIT (GENERIC) (TRL HFX FSE. FSH)
SONAR HUU OUFIT C5 SYSTEM (GENERIC)
24M
24M
24M
24M
YES
YES
YES
1
COMPASSSERVO DRIVEN,CARD TYPE .Cg6240
- -
ITEM-NAME NDlD
COMPUTER SONAR DATA 3M
ERN
1
ANTENNA 24M
INDICATOR,COURSE 24M
PER1001CITY
51 -672-000 COUPLE R,ANTENNA 24M 51-673-000 ANTENNA, WHlP 35 FT 24M 51 -681 -000 ANTENNA 24M 58-260-000 DIRECTION FINDER SET 12M YES
INCLUDED I
K 20 PAG V280 MPC 20 CYL
BOILER,AUXILIARY.STEAM ,HIGH PRESSURE
CHILLER SET.85 TON 48M YES LIFERAFT.INFLATABLE.20 24M
C-28910908rmY-044 28455-A00 INTERNAL DECKS
C-28420-DAOMY-001 28420-DA0 RUDDER
L 1 L
C-29410-012iNY-014 29-526-000 COOCER UNIT,AIR (KU 3000) (HFX CLASS SHIPS)
29-390-001 COOLER UNIT,AIR FCU- 500-500-RH
29-390-002 CWLER UNIT,AIR FCU- 500-500-LH
29-390-003 COOLER UNIT,AIR FCU- 1000-1000-RH
29-390-004 COOLER UNIT.AIR FCU- 1000-1000-CH
29-390-005 COOLER UNIT,AIR FCU- 1875-1 875-RH
39-1 61 -A00 MAINTENANCE STAND ASSEMBLY
70-332-000 WAVEGUIDE DRlER UNIT (GENERIC) (TRL. HFX, FSE, FSH)
69-703-000 SONAR HULL OUTFiT C5 SYSTEM (GENERIC)
69-771 -000 TORPEW TUBE ASSEMBLY,MK 32 MO0 9
C-70-270-000NY-001 70-270-000 MAIN GUN SYSEM, 57 MM, MK 2 ( H m 8 NES ClASS SHIPS)
1 I
DeMeber 1 999 - Period 1 00 NDlD ERN ITEM-NAME
24M YES
24M YES
24M YES
24M YES
24M YES
COURSE
WMPUTER SONAR DATA
ANTENNA, WHlP 35 FT
ANTENNA
24M 22-490-000
28-402-000
Table 62. SS Designated PM Routines
LIFERAFT.INFLATABLE.20 MAN,TUL-61 WASN
NDlD Name Frequency Reaource
HULL STRUCTURE
28390-001
29-390-002
29-390-003
29-390-004
70-332-000
69-703-000
C-58-258-000lNY-001 ELECTRONIC 12M 1 390 COUNTERMEASURES 1001 1
13154 10090 1 0092 10055
24M
24M
24M
24M
1 M
1 M
1
COOLER UNIT,AIR FCU- =-=-RH
COOCER UNITVAIR FCU- 500-500-LH GOOLER UNIT,AIR FCU- i000-1000-RH COOLER UNIT,AIR FCU- 1000-1 000-LH WAVEGUIDE DRIER UNIT (GENERIC) (TRL HFX, FSE, FSH)
SONAR HULL OUTFFT C5 SYSTEM (GENERIC)
RADAR SET ANISPS-505
6M
YES
YES
YES
CLOSE IN WEAPONS SYSTEM
YES
Service Time
C-70-312-0001NY-001 TRANSMITTER GROUP
CONTROL SET HARPOON MISSILE
TOWED BODY
TRAMSMllTER SET ANISRT-504 INTEGRATED MACHINERY CONTROL
CATHODIC PROTECTION
85 TON CHILLER
BOILER AUXlLlARY STEAM
C-27-783-000/NY -001 HP AIR
SYSTEM
C-26411 -OOO/NY-O01 DIESEL ENGINE (MWM)
C-25-551-000/NY-001 FUEL OIL CENTRIGUGE
NIL DATA
C-25-546-000/NY-001 COOLER MAIN LUBE OIL
C-25-511-W/NY -001 MAIN OIL LUBE SYSTEM
C-24-553-000/NY-00 1 GT HlGH SPEED SHAFf COUPLING
C-24-545-BOOMY-001 THRUST BLOCK PORT
C-24-541-000/NY-001 LM2500 12M GAS TURBINE
6M
C-24-545-COOtNY-001 MAIN SHAFT 24M BULKHEAD SEALS
C-24-545-DOOMY-O01 CENTRELINE 36M PLUMMER BEARING
C-25-547-OOO/NY-O01 LUBE OIL PURIFIER
C-27-791-002/NY-002 SEAWATER SERVICE 24M FlREMAlN
C-27-885-000/NY -001 DRYER, AIR-GAS LP AIR SYSTEM
C-39-158-OOOMY-O01 R.A.S.T.
C-28-402-000/NY-001 HULL STRUCTURE
C~28~395~OOO/NY401 R A S SYSTEM
C-28-422-W/NY -001 DOORS,HATCHES 8 SCUTTLES
C-70-266-AOO/NY -001 DIRECTOR FlRE CONTROL
C-51-584-000/NY -001 RADIO SET ANAJRC-507
C-26-428-W/NY -00 1 SWITCHBOARDS
APPENDIX H ANNUAL PREVENTIVE MAINTENANCE PER WORKER
Note: FMFCS Strength effective 22 March 01
Table Hl. PM at the Capability Level
Shop Desig Strength RF PM SS PM TOTAL PM P W r k (# Worken) (Hours) (Hours) (Houn) (HoursMlorker)
