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Building Radio frequency IDentification for the Global Environment
Report: Methodology for manufacturing process analysis for RFID implementation
Authors: Alexandra Brintrup (Cambridge Auto-ID Lab), Paul Roberts (NESTLÉ), Mark Astle (NESTLÉ)
March 2008 This work has been partly funded by the European Commission contract No: IST-2005-033546
About the BRIDGE Project: BRIDGE (Building Radio frequency IDentification for the Global Environment) is a 13 million Euro RFID project running over 3 years and partly funded (€7,5 million) by the European Union. The objective of the BRIDGE project is to research, develop and implement tools to enable the deployment of EPCglobal applications in Europe. Thirty interdisciplinary partners from 12 countries (Europe and Asia) are working together on : Hardware development, Serial Look-up Service, Serial-Level Supply Chain Control, Security; Anti-counterfeiting, Drug Pedigree, Supply Chain Management, Manufacturing Process, Reusable Asset Management, Products in Service, Item Level Tagging for non-food items as well as Dissemination tools, Education material and Policy recommendations. For more information on the BRIDGE project: www.bridge-project.eu This document results from work being done in the framework of the BRIDGE project. It does not represent an official deliverable formally approved by the European Commission. This document: In this document we aim to develop a set of process analysis tools to help organisations identify opportunities where RFID can bring value. The process analysis tools form the opportunity analysis phase of the roadmap followed in the manufacturing work package, where value addition through waste reduction is identified. The remainder of the roadmap consists of several other windows of analysis organisations need to consider, including an intermediary feasibility analysis phase where application requirements relating to information flow, feasibility, human factors and IT infrastructure are collected, a business case phase where the benefits derived from RFID implementation are compared against the costs of implementation before deployment.
Disclaimer: Copyright 2007 by (Cambridge Auto ID Lab, Nestlé) All rights reserved. The information in this document is proprietary to these BRIDGE consortium members This document contains preliminary information and is not subject to any license agreement or any other agreement as between with respect to the above referenced consortium members. This document contains only intended strategies, developments, and/or functionalities and is not intended to be binding on any of the above referenced consortium members (either jointly or severally) with respect to any particular course of business, product strategy, and/or development of the above referenced consortium members. To the maximum extent allowed under applicable law, the above referenced consortium members assume no responsibility for errors or omissions in this document. The above referenced consortium members do not warrant the accuracy or completeness of the information, text, graphics, links, or other items contained within this material. This document is provided without a warranty of any kind, either express or implied, including but not limited to the implied warranties of merchantability, satisfactory quality, fitness for a particular purpose, or non-infringement. No licence to any underlying IPR is granted or to be implied from any use or reliance on the information contained within or accessed through this document. The above referenced consortium members shall have no liability for damages of any kind including without limitation direct, special, indirect, or consequential damages that may result from the use of these materials. This limitation shall not apply in cases of intentional or gross negligence. Because some jurisdictions do not allow the exclusion or limitation of liability for consequential or incidental damages, the above limitation may not apply to you. The statutory liability for personal injury and defective products is not affected. The above referenced consortium members have no control over the information that you may access through the use of hot links contained in these materials and does not endorse your use of third-party Web pages nor provide any warranty whatsoever relating to third-party Web pages.
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TABLE OF CONTENTS
1. INTRODUCTION ......................................................................................................................................... 5
2. THE SEVEN WASTES VERSUS RFID ................................................................................................... 6
3. A PRACTICAL ROADMAP TO RFID VALUE IDENTIFICATION ..................................................... 10
3.1 DATA COLLECTION ............................................................................................................................... 10 3.1.1 Physical Process Mapping (PPM) ......................................................................................... 11 3.1.2 UML Use Case Diagrams (UCD) ........................................................................................... 13
3.2 DATA DEPENDENCY ............................................................................................................................. 15 3.3 DATA VISIBILITY ................................................................................................................................... 18 3.4 PRODUCTION RESPONSIVENESS APPROACH (PRA) .......................................................................... 21
4. USING THE TOOLKIT.............................................................................................................................. 26
5. CONCLUSION ........................................................................................................................................... 27
6. REFERENCES........................................................................................................................................... 28
List of Tables TABLE 1 TOYOTA PRODUCTION SYSTEM TYPES OF WASTAGE REDUCTION THROUGH RFID ............................... 9 TABLE 2 SUMMARY OF DISTURBANCES ................................................................................................................ 22 TABLE 3 DISTURBANCE RESPONSES .................................................................................................................... 23 TABLE 4 MAPPING TOOLS FOR RFID IMPLEMENTATION ...................................................................................... 26
List of Figures FIGURE 1 ROADMAP TO RFID DEPLOYMENT ......................................................................................................... 6 FIGURE 2 BARCODE SCAN SCENARIOS .................................................................................................................. 7 FIGURE 3 PHYSICAL PROCESS MAPPING .............................................................................................................. 11 FIGURE 4 PPM - CASE EXAMPLE ......................................................................................................................... 13 FIGURE 5 UML USE CASE DIAGRAM ................................................................................................................... 14 FIGURE 6 UCD- CASE EXAMPLE.......................................................................................................................... 15 FIGURE 7 DATA DEPENDENCY DIAGRAM ............................................................................................................. 16 FIGURE 8 DDD – CASE EXAMPLE ........................................................................................................................ 17 FIGURE 9 DATA VISIBILITY DIAGRAM.................................................................................................................... 19 FIGURE 10 DATAVIS-CASE EXAMPLE .................................................................................................................. 20 FIGURE 11 IMPACT VERSUS DISTURBANCE (REPRESENTATIVE NUMBERS BASED ON EXPERIENCE) .................. 23 FIGURE 12 DISTURBANCE RESPONSE CAPABILITY CHART ................................................................................... 24 FIGURE 13 IMPACT/RESPONSE CHART ................................................................................................................ 25
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GLOSSARY BIF Business Integration Framework
DataVis Data Visibility Diagram
DDD Data Dependency Diagram
ERP Enterprise Resource Planning
FIFO First-in-first-out
GRAB Ground & Roast Aroma Boost
IBC Intermediate Bulk Container
IT Information Technology
JIT Just-in-time
MRT Material Resource Tracking
PPM Physical Process Mapping
PRA Production Responsiveness Audit
RFID Radio Frequency Identification
UCD Use Case Diagram
UML Unified Modelling Language
WIP Work-in-progress
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1. Introduction
The BRIDGE project manufacturing work package aims to develop tools and
methodologies helping manufacturing organisations give effective decisions upon RFID
implementation. Implementing Radio frequency identification (RFID) within the four walls of a
manufacturing plant requires extensive analysis and experimentation. The sixth deliverable
“Manufacturing process mapping methodology” from the work package provides a set of
tools to European manufacturing organisations to analyse existing business processes and
target areas where RFID can bring value and reduce waste.
“There is a clear need to extend internal wastage removal to the complete supply
chain. However, there are difficulties in doing this. These include lack of visibility along the
value stream and lack of appropriate tools for creating this visibility.” (Hines P. and Rich N.,
1997) The statement of Hines and Rich holds true not only along the supply chain but also in
manufacturing. The lack of visibility and tools for creating visibility is a hinder to obtain
maximum value from many internal manufacturing operations, including inventory, and work
in process management.
RFID is seen by many as a revolutionary enabler in automatic data capture. RFID
tags coupled with readers and information systems architecture can increase visibility of
operations by associating unique product identification with its current location, and by
synchronising the physical flow of components/products and the related information flow
without human intervention. In addition to being an enabler of visibility, RFID technology has
found uses in a variety of other manufacturing related applications in production automation
and inventory management, a review of which has been given in the project deliverable 8.1
Problem Analysis.
Despite RFID’s success, confusion still remains as to where it can help in
manufacturing. Questions remain as to what aspects should be considered when selecting
applications, which manufacturing wastage RFID may specifically address, and how these
wastages can be identified.
Our previous industrial survey highlighted the need for a structured framework for
RFID value identification and deployment (Brintrup et al 2007). Being a relatively young
technology part of the reason for companies’ confusion is the lack of meaningful generic
case studies and exemplary work. Another part of the reason is the lack of structured tools
and methods to help pinpoint where RFID can create visibility, help in operations and to what
extent.
Apart from developing an understanding of how RFID can help create value, an
understanding of business processes is vital prior to implementation to (1) estimate costs
realistically and to (2) assess risks associated with changes that RFID brings. Saygin points
out that business cases need to be built on defined rules, and without reaching a lean
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perspective on operations and workflow in an organization, RFID cannot bring visibility out of
a chaotic environment (Saygin C. and Sarangapani J., 2006), suggesting the need for a
complete understanding of business processes affected from RFID implementation.
Although there exist various generic business process modelling tools and methods,
none of them seem readily to capture different aspects of a manufacturing system from an
RFID value perspective. This observation was further strengthened in the problem and
requirements analysis phases of our project, after which a decision to draft an RFID-generic
process analysis roadmap was made.
Following on this need, in this document we aim to develop a set of process analysis
tools to help organisations identify opportunities where RFID can bring value. The process
analysis tools form the opportunity analysis phase of the roadmap followed in the
manufacturing work package (shown on Figure 1), where value addition through waste
reduction is identified. The remainder of the roadmap consists of several other windows of
analysis organisations need to consider, including an intermediary feasibility analysis phase
where application requirements relating to information flow, feasibility, human factors and IT
infrastructure are collected, a business case phase where the benefits derived from RFID
implementation are compared against the costs of implementation before deployment.
Figure 1 Roadmap to RFID deployment
2. The seven wastes versus RFID
Lean manufacturing is ‘a philosophy of production that emphasises the minimisation
of the amount of resources (including time) used in the various activities of the enterprise’.
Lean manufacturing involves identifying and eliminating non value adding activities and
focuses on the start-to-end value streams rather than the idea of optimising individual
departments in isolation. Waste is a term frequently associated with lean manufacturing. In
this section we look into the seven wastes of manufacturing systems (Ohno T, 1988) and
Feasibility
Analysis
Business
Case
Opportunity
Analysis
• Identify areas of value addition
through waste reduction
• Select applications for further
consideration
• Assess feasibility of applications
from organisational compatibility, operational reliability, technical
feasibility points of view
• Gather requirements for IT
infrastructure and human factors
• Quantify waste removal and map
onto value drivers
• Draw soft benefits Compare
benefits against costs of implementation
Deployment
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consider how they can be reduced using RFID to move towards a lean organisation. The
following wastes are given:
1. Overproduction, which discourages a smooth flow and leads to excessive lead and
storage times.
2. Waiting, which occurs when time is being used ineffectively.
3. Transport, a non-value adding operation which involves goods being moved around.
4. Inappropriate processing, which occurs when systems or procedures more complex than
necessary are used, leading to excessive transport and poor quality.
5. Unnecessary inventory is unused capital, leading to storage costs, or possible quality
deterioration of goods if the time of storage is critical to its health.
6. Unnecessary motion refers to the ergonomics of production when workers need to move
in unnatural positions repetitively, possibly leading to tired workers and compromises on
quality.
7. Defects are costs directly attributed to wastage of produced material that could potentially
bring revenue.
Figure 2 Barcode scan scenarios Let us consider an occasion when a barcode scan during a goods issue operation to
a physically transforming process step is not carried out at step B (Figure 2 (a)). We know
from our previous industrial survey that this occurs frequently in the normal operations of a
factory, especially during peak seasons where temporary operators are employed. The
information system shows a certain amount of material under a process step C while the
material is actually on its way to undergo its next process step D.
The above scenario has various implications in the above waste categories where
RFID technology offers a number of direct and indirect benefits.
• In the case that the machines allocated to the subsequent process need reconfiguration
the information system may ask for the changes to be made in advance. Looking at the
Process A Process B Process C Process D
Actual flow of batch
Information
system
Process A Process B Process C Process D Actual flow of
batch
Information
system
(a) Scenario 1: Barcode scan missed
(b) Scenario 2: Wrong barcode scan
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alerts from the information system, Process B awaits the arrival of the next batch
although the process has already been carried out, missing out on the valuable time that
can be used to prepare machinery for the actual next batch. Although Process D is the
process that has to be getting ready for the arrival of the batch, it assumes there still is
time. This mismatch leads to a waste of time, i.e. waiting waste.
• The batch may be transported back to Process B for it to be repeated since we have lost
traceability on whether it has actually been carried out, leading to possibly inappropriate
processing, transportation waste and possibly defects.
• The shift manager may decide to scrap the batch if traceability for that process was
critical; for instance in the case of a batch testing process leading to defect waste.
Let us consider another scenario (Figure 2 (b)) where the worker scans the wrong
barcode and associates another batch type with the subsequent process. Since the Process
A for this batch is not completed the batch might be sent for re-processing leading to
wastage in transport, waiting, and possible defects.
• With the scanning of the wrong barcode, two different batches from one are created,
leading to an inaccurate picture of inventory and overproduction of batches for which the
information system displays to have little stock.
• The set of machine resources carrying out Process A seem to be occupied with the batch
assigned to it, while in reality it is not. This causes other batches to wait in the queue
until the error is found out and corrected.
• If the initial batch record is associated with a quality restriction and the newly aggregated
batch is not, the scan error may lead to the production of substandard quality goods,
leading to severe defect wastage. In both of the scenarios if the error is noticed and
correction attempted, time spent to management of information is increased, leading to
waiting wastage.
Although the above scenarios are typical of work in progress management (WIP), if
WIP products are taken as an analogy to assembly operations in automated production
control, the above mistakes can easily be replicated.
In inventory management, reliance on barcode scanning may result in overproduction
wastage, as wrong scans are performed for in and out of the warehouse. The search for the
correct products lead to transport and waiting wastage, and the deterioration of
overproduced or untraceable products in the warehouse lead to defect wastage.
In terms of JIT inventory control, loss of visibility occurs if a barcode is damaged,
leading to overproduction, unnecessary inventory and undermine of the JIT operation,
whereas RFID being more durable can offer higher guarantees for a successful JIT
environment from this perspective.
For the asset tracking and maintenance cluster of RFID in manufacturing, if assets
are used to carry WIP products and thus used to control the production, the same principles
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of WIP management apply. On the other hand, if tagged assets are machinery and
equipment, RFID can help reduce waiting and defect wastage by providing real time visibility
of their condition.
Finally, under all RFID application scenarios, workers are saved from handheld
barcode scanning operations if appropriate readers are used, leading to the elimination of
unnecessary motion wastage. In addition manual record taking, counting, or manual checks
can be reduced or eliminated using RFID enabled systems. These are all clustered under
unnecessary motion wastage.
Table 1 summarises the types of wastage can be reduced by RFID under the cluster
of RFID applications reviewed in the previous section.
Table 1 Toyota Production System types of wastage reduction through RFID
Work-in-progress
management
Inventory management
Manufacturing asset tracking and
maintenance
Manufacturing control
Overproduction Know how much of which
goods/materials are WIP
Know how much of which goods/materials
are in stock
- Enable automated JIT
strategies
Waiting Know where finished
goods/materials are
Know where finished goods/ raw materials
are
Know where assets are
Know condition of assets
Increase product autonomy in distributed
control systems Transport Know where WIP
goods/materials should be brought
to
Know where nearest finished goods /raw
materials are
Know location of nearest available
assets
Where applicable implement automated routing on
production lines Inappropriate processing
Know which goods/materials are suitable for which
processing
Know which raw materials suitable for
which processing
Eliminate production errors due to incorrect manufacturing asset
maintenance
Know which goods/materials are suitable for
which processing Unnecessary inventory
Eliminate mistaken WIP
goods/inventory association
Improve visibility level
Improve inventory visibility
Eliminate unnecessary buffers
waiting for asset maintenance
-
Unnecessary motion
Eliminate manual data collection
Eliminate manual counts
Eliminate manual checks for
maintenance
-
Defects Reduced scraps due to improved
traceability
Know finished goods /raw materials expiry dates and implement
suitable protocols
- -
In addition to the above considerations where errors in barcode scanning can occur,
other benefits of RFID in manufacturing arise from moving onto innovative applications such
as distributed control systems where RFID acts as an enabler to the “intelligent product”
(McFarlane D. et al., 2003). Using RFID, the product may possess a unique identity, can
communicate with its environment through sensory information, and can retain data about
itself. The unique identity may point to the appropriate agent software residing on the
network, enabling the product to make decisions relevant to its own destiny. Advantages of
the system include robustness to disturbance, and flexibility to changes or extensions. On
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the other hand it points to a dramatic change in a company’s manufacturing philosophy.
Henceforth, the discussion encompasses only centrally controlled non-holonic environments
in this document.
Having mapped RFID use to manufacturing wastage elimination, the second step is
to provide organisations with the set of tools to analyse which waste can be targeted, and
where implementation can bring value.
3. A practical roadmap to RFID value identification
The previous section looked into manufacturing system waste reduction using RFID.
In this section four process analysis tools are suggested to allow practitioners to assess
manufacturing processes from an RFID value addition point of view. Value addition has been
collected under three topics: value addition during process data collection, through
conforming to data dependencies and through process visibility increase.
3.1 Data collection
RFID may automate data collection throughout manufacturing processes. Two types
of manufacturing waste are created in situations where data collection is performed through
barcode scanning or manual data entry: unnecessary motion performed by operators and
transport waste, created by bringing items to scan locations. To identify where these types of
waste are created and whether RFID can address them, two tools, offering different angles of
view are suggested: Physical Process Mapping (PPM), and UML Use-Case Diagram (UCD).
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3.1.1 Physical Process Mapping (PPM)
Figure 3 Physical process mapping
Physical process mapping is designed to identify where data collection operations lie
along the manufacturing plant. The resulting map depicts data entry and pull locations and
the method of data collection or entry (such as manual barcode scans, paper or computer
entries), projected among a representation of the manufacturing locations (Figure 3). Current
data pull and push points are numbered. Where there is more than one of the same type of
data point (such as one hundred moulding machines, each consisting of the same data step)
only one data point is depicted and the number of different units are given next to it. In
addition to providing information on data collection operations, the physical representation
also illustrates the complexity of production routes from a geographical point of view. The
diagram acts as an intuitive start point in thinking where RFID can be potentially replace
manual data collection and the extent of data operations. It provides a snapshot of data
projected upon operations, and can bring to light which operations are not associated with
data and not traceable.
Case Example:
For the reader to gain a better understanding of the approach, brief case examples are
presented in this document. The application work package partner Nestlé is a multi-national
confectionary and food producer following a centrally controlled batch manufacturing
philosophy. The company looked into deploying RFID for the Intermediate Bulk Container
(IBC) management process using the PPM tool.
2 3x5
Raw and finished goods stock
Machining Finishing
Shipping
1
5
4
Preparation
Buffer
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Figure 4 shows the IBC management PPM. It is revealed that the increasingly complex
current production routes result in high numbers of WIP buffers and high probability of errors
in storing and locating items. A total of 12 process steps use barcode scans, with many
variations in the types of products and process step locations. When discussed with the
Nestlé team, the following action points were summoned as a result of analysis with PPM:
• The high number of barcode scanning operations coupled with complexity of routes and
processes result in unnecessary scanning motion which could be automated with RFID.
• The high number of wrapping locations dictates a more cost effective solution. Installing
RFID on forklift trucks and tagging wrapping point locations can be a option.
• Washing and tipping processes are not fully tracked, giving rise to possible in
inappropriate processing, overproduction, waiting and unnecessary inventory wastage.
• The high number of IBCs and locations make inventory counts error-prone (i.e.
overproduction, and unnecessary inventory). An RFID based inventory management
system can be beneficial.
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Figure 4 PPM - Case Example
3.1.2 UML Use Case Diagrams (UCD) The Unified Modelling Language (UML) has been used to analyse the processes
found within complex real life systems including manufacturing systems. Use case diagrams
can be used to depict the functionality of the overall manufacturing system from an actor-
case point of view. The actors of the system interact with the system itself, the use cases, or
services, that the system knows how to perform, and the lines that represent relationships
between these elements. Detailing the system this way enables one to identify the level of
automation during data collection. The data collection action points in the PPM diagram are
connected to relevant actors (Figure 5).
Buffer
Filling
Packing
Washing
Cold Store
Moulding Line
1
3
6
12
11
13
11
11
Tipping
8
Wrapping
Filling
Buffer
J-Factory
E-Factory
Unwrapped Sweet
Management
4
7
2
5
Rework
9
10Rework
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Figure 5 UML Use Case Diagram The UCD gives a complementary view of how data collection is performed throughout
the manufacturing process. While the PPM shows physical data collection points and
complexity of production routes, the UCD shows by which actor the data is collected. The
next step is to find those actors that may cause errors and inaccuracies and analyse if RFID
based automated data collection is possible to replace the actor.
Case Example: Applied on a part of the Nestlé IBC management process, quality checking, the UCD
shows the existence of non-automated data pulling operations (Figure 6). Before an item is
tipped on the wrapping line, its quality status needs to be checked by the operator using a
barcode scanner and information displayer. Then several information system layers
(Middleware, MRT, BIF, and SAP) are parsed through to arrive at the quality status
information which is sent to the display.
Requirements raised from this exercise were the automation of barcode scan and
manual recording processes to result in a leaner manufacturing environment. The automated
RFID would take on the role of the operator to query quality status when an IBC is brought to
the wrapping location. The barcode actor is eliminated, and the operator takes on the new
role of “terminate process” if the display shows wrong quality status.
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Figure 6 UCD- Case Example
3.2 Data dependency
Through automation RFID helps make sure data dependency is respected throughout
manufacturing processes. Four types of manufacturing waste are created in situations where
data dependency is defaulted: waiting (if wrong information association results in delays
when error is noticed and correction attempted), defects (if wrong information association
results in wrongly processed products), overproduction (if wrong information association
results in producing more WIP products than necessary), unnecessary inventory (if wrong
information association results in producing more finished products than necessary). To
identify where these types of waste are created and whether RFID can address them, a Data
Dependency Diagram (DDD) is suggested (Figure 7).
Here each product value adding step is depicted as a product transformation step.
Each transformation step is dependant on a number of data, shown as input boxes to the
step. Data can be gathered using a number of ways, including manual data entry, manual
records on paper, or barcode scans. In addition, data itself can be transformed in terms of
format, for example from a paper recording to a mainframe computer. The frequency of data
collection is associated with the input.
Operator
Middleware
MRT
Operate barcode scanner
Route scanned data to Middleware
Display where new stock should be
brought and if item is of correct quality
status
Perform goods issue
and stock removal
operations
ERP
BIF
Process and send
information to MRT
Pass messages to MRT
Hold control recipes, transfer orders, purchase
orders and material master data
Barcode scanner
Display
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Resulting from this activity is a map of data dependencies existing across the process
flow. The next step is to understand (1) what would cause a data error for each process data
input, creating waiting, defect, overproduction or unnecessary inventory wastes, and (2) if
and how data collection frequency could be increased though RFID.
Figure 7 Data Dependency Diagram Case Example: Figure 8 shows an example process from the Nestlé GRAB oil process reviewed in
D8.1 and D8.2. Process steps were found to be highly data dependant and reliant on the
manual pull/push of information by the process operators, causing severe delays and errors.
Some process steps, although dependant on quality inspection data, do not come to a halt if
this data is not present, which ultimately leads to quality errors at later stages of production
with increasing cost of recalls. For instance, before goods are actually used or reworked,
three data are necessary: call for a tub, location of the tub (in line with FIFO), and quality
inspection. None of these data are automated and the collection of all relies on operator,
making it error-prone in terms of data completeness. RFID based FIFO inventory
management, and automation of the alarm raising when items fall below a predefined quality
status would make sure data dependency is respected in this process.
Product transformation step
Data
dependency
Data transformation step
Data
dependency�
Data
dependency�
Data
dependency�
Data
dependency�
*
*
batch
shift
shift
*
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Figure 8 DDD – Case Example
Scan freezer
Fill tub with GRAB oil from Silo
SSCC barcode
on tubCreate tub ID
Transport tub to freezer
Tub needed
Transport tub to chiller
Apply FIFO
Tub FIFO
locations
SSCC barcode Chiller barcodeGoods issue
barcode
Tub in freezer
Tub neededTub FIFO
locations
tub in chiller
Use and discard tub
Tub did not
expire
Send to rework
SSCC barcode
Goods issue to
the roaster
process order
Empty tub to processor
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3.3 Data Visibility
Visibility is significant contributor to giving effective stock order or goods issue
decisions throughout the manufacturing plant. Yet current methods for performing an
inventory count or for tracking asset movement do not provide real-time visibility leading to
decisions based on outdated, inaccurate information (Lu B.H. et al., 2006). Lack of visibility
on WIP and finished inventory is the root cause of the Bullwhip Effect in the forecast-driven
supply chain, where safety stocks for each supply chain participant are increased due
greater observed variation. Two types of manufacturing waste are created in situations
where visibility of operations is compromised: overproduction (when low visibility leads to the
belief that the work-in-progress stock of levels a given item is lower than it really is), and
unnecessary inventory (when low visibility leads to the belief that the finished stock level of a
given item is lower than it really is). Within a single manufacturing plant, RFID may enable
increases in data visibility at two levels: from batch level to item level throughout
manufacturing processes, and tracking stock at individual manufacturing processes. The
combination of the two gives the decision makers a more accurate, real-time sense of on-
going operations in terms of the time it takes to complete a process, associated batch or
item, the outcome of the process.
To identify where visibility can be increased and its effects on inventory levels, a Data
Visibility Diagram (DataVis) is suggested (Figure 9). There are four simple steps involved in
this approach given as below.
For each process step:
1. Outline
• the visibility level i.e. batch or item level information
• to whom or what the process is visible to
• what is visible (e.g. time it takes process to be completed, location process takes
place, process success for associated items etc.)
2. Discuss how the level of visibility affects the next process step in terms of buffer or
work-in-progress stock
3. Modify the outlined visibility parameters
4. Discuss whether modified parameter increases the level of visibility and creates a
Decision impact Process step 1
Decision impact Process step 2
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positive impact on stock decision making
Figure 9 Data Visibility Diagram Case Example: A DataVis analysis was applied to a fragrance manufacturing process at a Cosmetics
firm. The DataVis shown on Figure 10 reveals that some parts of the process were not
captured, giving raise to inaccurate WIP inventory levels. Containers that carry WIP materials
were at times not visible as they always moved or their barcodes were damaged, and could
not be counted, leading once more to inaccurate inventory information.
When raw material is received, items were booked into the IT system only after
certain quality tests are done. This could result in delays finding raw material and waiting in
the production line for items from suppliers that were already in stock, and at times, re-
ordering of items. The final stages of the process, packaging and palleting, collected batch
level information which was only visible to the operator until dispatch. The line fill process
was not captured and items could be lost in the storage location associated with finished
items. It was found that not all data collected during process steps were visible at the ERP
level, where forecasters gather information from, which resulted in a requirement that
synchronised, timely and accurate information is visible at all levels of information hierarchy.
Furthermore, a transition from batch level information to item level information was required
in the dispatching process to provide accurate record of dispatched items.
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Figure 10 DataVis-Case Example
Weigh materials
Receive process order
Collect materials
Assemble
fragrance
Test quality
Test quality
Line fill for customer
Store
Pallet packing
Move to
warehouse
Dispatch
Material in/out of
inventory
Inventory moves to
WIP inventory
WIP inventory level
adjustment
New inventory type
created
New inventory
finished type
Inventory reduced
operator
operator
batch
batch
item
item
item
batch
item
batch
batch
batch
batch
batch
ERP
operator
ERP
operator
ERP
operator
operator
operator
operator
ERP
Receive raw material
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3.4 Production responsiveness approach (PRA)
Another route for organisations to identify value of RFID implementations is through
disturbance analysis. Production responsiveness is ‘the ability of a production system to
achieve its goals in the presence of disturbances’.
A disturbance is ‘a change occurring internally or externally to a production system,
which can affect its operational performance, and is either outside its control or has not been
planned for by the system’.
(Matson and McFarlane 1999) suggest that a sensible assessment of the impact of
disturbances can only be made with direct reference to an organisation’s production goals,
and that the overall affect of a disturbance covers the immediate effects of the disturbance
and the effects of any response. To achieve its goals in the presence of disturbances, a
production system must respond after the disturbance has occurred and/or have responded
in advance of the known possibility of the occurrence of the disturbance.
A Production Responsiveness auditing tool can be used to help a company evaluate
its current ability to handle disturbances affecting its production performance, and decide
appropriate actions for improving its responsiveness. In our case we look at actions possible
through the use of RFID to improve responsiveness.
The 5 steps of this audit are outlined as follows:
Step 1 Understand the operation: Tools like process mapping can be used to clarify
processes.
Step 2 Goal identification - Understand how operational performance is measured.
Step 3 Disturbance responsiveness assessment: For each type or each class of
disturbances a Disturbance Responsiveness Chart is plotted to capture the nature of the
disturbance and its impact on the process.
Step 4 Disturbance Response Capability Assessment: For each type of disturbance a
Capability Chart is also produced. This chart provides an assessment of how well the
capability can respond to the disturbance.
Step 5 Impact/Response Capability Summary Chart: The final step involves producing a
chart that can assist in comparing disturbances in terms of the current impact on production
goals, and the extent to which capabilities exist for overcoming them. This chart can be used
to help make decisions on improvement actions for adding or improving capabilities.
The responsiveness auditing tool can be applied to examine how well processes and
systems handle the different disturbances that occur during a manufacturing process, and
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highlight the level of their impact. These processes and systems can be examined more
closely to see if improvements can be made through the use of RFID technology.
An example of the approach is given with the Nestlé IBC management process in the
following paragraphs.
Step 1 Understand the operation
The processes involved in the IBC management activity have been heavily examined
using Value Stream Maps, Physical Process Mapping and UML diagrams in Deliverable 8.1.
Readers are referred to the aforementioned deliverable for an illustration of how the WP8
team developed an understanding of the process.
Step 2 Goal identification
Discussions held with the staff at the Halifax plant confirm the main goals of
operations as: Timely and cost effective response to production demand with seamless
operations and as little error handling as possible. The factors mentioned in this goal
statement are: timeliness, reduction of errors, and reduction of costs. Other factors
mentioned include customer satisfaction and loyalty through quality of goods which are
directly relevant to traceability of operations.
Step 3 Disturbance responsiveness assessment
This assessment is concerned with determining the nature of disturbances and their
impact on the goals of an organisation. Listed on Table 2 are the disturbances found in the
Halifax factory after consultation with Nestlé.
Table 2 Summary of disturbances
Code Disturbance (packing lines)
1
Machine breakdowns
2 Product history untraceable
3 Bottlenecks (FLT, hoist, storage space)
4
Materials do not arrive on time
5
Materials in incorrect quality state
Because the measures of frequency of occurrence, and average duration of a delay cover
both the nature and impact of a disturbance, it is felt that one disturbance impact measure
would enable the comparison of disturbances.
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The disturbance impact measure (per unit time) that will be used to compare disturbances is
given in the following equation.
Average Disturbance Impact = Average delay (min) x Frequency of disturbance (min)
The average delays times and frequencies are collected using semi-structured interviews
with the Nestlé managers, and represent average weekly figures.
Figure 11 Impact versus delay code (given on Table 3) (representative numbers based on experience)
Step 4: Disturbance Response Capability Assessment For disturbances identified in Step 3 a corresponding Disturbance Response Capability Chart
is produced as shown on Table 3. This step we determine the existing response capabilities,
their potential to solve disturbances and their current utilisation, once more through semi-
structured interviews with Nestlé managers.
• Each capability is assigned a value of 0, 1, 2 or 3 depending on the potential of that
capability to solve the disturbance. (3-High, 0-Low capability)
• Each capability is assigned a value of 0, 1, 2 or 3 depending on the utilisation of that
capability. (3-High, 0-No utilisation)
Table 3 Disturbance responses
Disturbance Code
Response code Response
1
1.1 Maintenance staff available
1.2 Stoppage analysis module (SAM) – improve and better plan 1.3 Scheduled maintenance
1.4 Spare parts
2 2.1 Barcoding 3 3.1 Spares 4
4.1 Planning and scheduling (partly due to better picture of material movement)
0
200
400
600
800
1000
1200
Avera
ge i
mp
act
1 2 3 4 5
Delay code
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4.2 Labour number increase 4.3 Increase local and buffer stock 4.4 Increase flexibility (substitute sweet type) 4.5 Prevent machine breakdowns (largest contribution)
5 5.1
Barcode based manual quality status check
5.2 More timely data validation checks (check materials in all locations validate on every movement)
5.3 More timely quality checks
5.4 Improved planning (more stock being available at the right time)
Figure 12 Disturbance response capability chart
Figure 12 shows the capability and utility of responses identified in Table 3.
Step 5: Impact/Response Summary Chart If impact of a disturbance is high and the potential of current capabilities to solve the
disturbance is low, additional improvement to existing capabilities should be strongly
considered.
Similarly, if the impact of a disturbance is high, the potential of current capabilities to
solve the disturbance is high, but the current utilisation of these capabilities is low, staff may
need further training or systems may need to be adapted to make better use of information
available. In our case we observe mostly the low utilisation of existing capabilities.
0
1
2
3
41.1
1.2
1.3
1.4
2.1
3.1
4.1
4.2
4.3
4.4
5.1
5.2
5.3
5.4
Capability rating
Utilisation rating
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Figure 13 Impact/Response Chart
Although untimely material arrival has a high disturbance impact there exist many
capabilities, mostly under-utilised. The same can be said for mid-impact machine
breakdowns.
Step 6: Areas for the use of ID technologies
By examining the Impact / Response table and or the Impact/Response chart, it is
possible to highlight processes that may benefit from the use of RFID technology. Although
untraceable history and materials arriving at the incorrect quality state have relatively low
disturbance when compared with machine breakdowns, and the potential of current
capabilities to solve the disturbance is high, the current utilisation of these capabilities is low.
The low utilised capabilities include respecting barcode scan processes. Automated data
capture quickly resents itself as one possible solution that can help utilise this capability.
Machine health diagnosis and prognosis using automated data capture and processing may
point to another area of potential improvement on the existing capability as only periodic
health checks are performed rather than prognosis and condition based maintenance. The
use of RFID technologies in this area are well researched and documented (PROMISE 2004,
DYNAMITE 2006). Improved planning and scheduling capability is another area of
improvement which can significantly affect materials not arriving on time or incorrect quality
state of materials. This is due to the significantly improved visibility on materials moving
through processes. Once visibility is increased, movement can be better planned with
appropriate business logic in place, resulting in a smoother flow. Additionally, materials with
0
200
400
600
800
1000
1200
1400
0 0.5 1 1.5 2 2.5 3 3.5
Capability
Dis
turb
an
ce im
pact
Machine breakdow n
History untraceable
Bottlenecks
Materials do not arrive on time
Material in incorrect quality state
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the incorrect quality state can be tracked earlier in the process and be dealt with without
causing stoppages in the line.
4. Using the toolkit
Table 4 shows a summary of the tools and their use in identifying where RFID can be
used to reduce relevant manufacturing waste. The opportunity analysis phase should consist
of identifying waste estimates in the organisation through a series of interviews with
managers, such that the results of the initial discussion can provide a basis for validating the
mapping process once it is completed. The mapping process can commence with the tool set
offering the estimated wastage. Descriptions of the wastes can be made to managers by
giving them relevant examples without introducing bias. Once mapping is complete a set of
requirements will emerge for the practitioner which can be used to devise a technical and IT
feasibility analysis in the next stage.
Table 4 Mapping tools for RFID implementation
Mapping tool
Waste Origin of tool
Particular strengths
PPM
Unnecessary motion Transport
New Identifies manual data collection points, geographical distribution of data locations leading to unnecessary movement of operators and products
UCD Object management group
Shows the use cases, that the current system knows how to perform, and actors taking part in system functionality. Can be used to differentiate what parts of the process are done by error prone actors, what parameters are modified by the information system.
DDD
Waiting Defects Overproduction Unnecessary inventory Inappropriate processing
New
Identifies process decision points to conclude on the importance of data capture, and what processes are affected from what errors Identifies what level of concurrency is involved in the operations and if process speed will improve if data dependency conformance is automated
DataVis Overproduction Unnecessary inventory
New Identifies how visibility levels and parameters affect batch sizes, and work in progress and finished inventory.
PRA All
(Thorne et al 2007) (Matson and McFarlane 1999)
Examines the impact of disturbances in a manufacturing process, helps understand current capabilities and utilisation of those capabilities to address disturbances. In doing so, examines whether RFID technologies can help improve existing capabilities or their utilisation.
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5. Conclusion
Following our survey and observations from existing literature regarding RFID
adoption plans and barriers to adoption, it has been found that one of the main obstacles to
implementation is the lack of analysis tools to show where and how RFID can bring value.
Building on this observation, we identified how RFID can serve as a vehicle to reduce the
seven wastes of manufacturing and outlined an analysis toolkit for RFID implementation in
manufacturing organisations.
The analysis is comprised of identifying where RFID can bring value through
automated data collection, conformance to data dependencies and improvements in visibility.
PPM and UML Use case diagrams show overall process information and target motion and
transport wastage. DDD diagram shows wastage that may occur due to disrespecting data
dependencies. DataVis diagrams show how visibility improvements can help make better
inventory decisions. Finally use of a production responsiveness audit is proposed to identify
current disturbances in operation, capabilities and utilisation of those capabilities where RFID
technologies are considered to improve existing capabilities or their utilisation. The toolkit
has been validated using industrial case studies throughout the document.
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6. References
Booch G., Rumbaugh, J., and Jacobson I. The Unified Modelling Language User Guide. Addison Wesley, 2000.
Brintrup A. RFID in Manufacturing: Initial Experiences in the BRIDGE Project. RFID Outlook: Towards a European Policy on RFID, vol. Lisbon, Portugal 2007.
DYNAMITE, 2006, "Dynamic Decisions in Maintenance (DYNAMITE)" home page: http://osiris.sunderland.ac.uk/%7Ecs0aad/DYNAMITE/Index.htm, accessed on 02/2008.
Hines P. and Rich N. The seven value stream mapping tools. Int. Journal of Production and Operations Management 1997; 17 (1): 46-64.
Lee H. and Ozer O. Unlocking the value of RFID. Production and Operations Management 2007; 16 (1): 40-64.
Lu B.H., Bateman R.J., and Cheng K. RFID enabled manufacturing: fundamentals, methodology and applications. Int. Journal of Agile Systems and Management 2006; 73-92.
Matson, J. B. and McFarlane, D. C. Assessing the Responsiveness of Existing Production Operations, International Journal of Operations and Production Management, 19 (8):765-784, July. 1999
PROMISE, 2004, "PROduct lifecycle Management and Information tracking using Smart Embedded systems (PROMISE)" home page: www.promise.no, accessed on 02/2008.
Ohno T. Toyota Production System: Beyond Large-Scale Production. Productivity Press, 1988.
Thorne A., Barret D., McFarlane D., Examining the impact of Auto ID technologies on Aircraft Turnaround Process, Industry Engineering and Management Systems (IEMS), Florida, 12-14 March 2007.
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