tez raporu 2.dönemdgjdg.docx

8/11/2019 Tez Raporu 2.Dnemdgjdg.docx

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YAAR UNIVERSITY

FACULTY OF ENGINEERING

DEPARTMENT OF INDUSTRIAL ENGINEERING

GRADUATION PROJECT

PINAR SU PRODUCTION GROUP

PROJECT by

HALIL CANER KARAOGLU

DOGUKAN ANIL YILDIZ

CENGIZHAN AYDIN

PROJECT SUPERVISORS

OMER OZTURKOGLU

ADALET ONER

ZMR, 2014


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Table of Contents

1. INTRODUCTION .................................................................................................................................. 1

2. GENERAL INFORMATION ABOUT PINAR SU .................... ..................... ..................... ......... 1

3. MICRO SYSTEMS OF PINAR SU ................... ..................... ..................... ..................... ................ 4

3.1.1. Water of Display Parameters ..........................Error! Bookmark not defined.

3.1.2. Layout of Facility ............................................................................................. 6

3.1.3. Processes in Production Lines .......................................................................... 7

4. OBSERVATIONS AND SYMPTOMS .................. ..................... ..................... ..................... ................ 9

4.1. Analysis of Production Processes ............................................................................................ 9

4.2. SYMPTOMS ................................................................................................................................ 104.2.1. Analysis of Downtimes in Production Line 1 and Line 2 .............................. 12

5. PROBLEM DEFINITION, TARGET & CRITICAL SUCCESS FACTORS.................. 13

5.1. TARGET & CRITICAL SUCCESS FACTORS.................... ..................... ..................... 13

6. LITERATURE REVIEW AND MODELING PERSPECTIVE.......................... ................. 14

6.1. Literature Review........................................................................................................................ 14

6.2. Modeling Perspective ................... ..................... ..................... ..................... ..................... ....... 19

7. PLAN OF THE PROJECT................................................... Error! Bookmark not defined.

8. RISK AND RISK AVOIDANCE ........................................... Error! Bookmark not defined.

9. CONCLUSION ............................................................................. Error! Bookmark not defined.


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List of Tables

Table 1 : Monthly Distribution Graph of Water Coming From Mountain ................. 4

Table 2 : Monthly Distribution Graph of Water Coming From Mountain for PC ... 5

Table 3 : Monthly Distribution Graph of Water Coming from Mountain for Pet

Bottle .......................................................................... ........................................................ ................. 5

Table 4: Process Flow Diagram ..................................................... ............................................ 8

Table 5: Ratios of Downtimes ................................................................................................. 12


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List of Figures

Figure 1: Pinar Su Facilities in Turkey .................... Error! Bookmark not defined.

Figure 2: Market Share of Pinar Su ........................... Error! Bookmark not defined.

Figure 3: Pet and Polycarbonate sales..................... Error! Bookmark not defined.

Figure 4: Exporting Sales in Pnar Su....................... Error! Bookmark not defined.

Figure 5: Pet & Glass Bottle Segmentations and Polycarbonate.6

Figure 6: Layout of Pinar Su Aydn/Bozdoan Facility.7

Figure 7: Fishbone Diagram of Breakdown of Machines..11

Figure 8: Arena Simulation Model Draft..15


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1. INTRODUCTION

Pnar Su San ve Tic A.S. is located in Bozdoan / AYDIN and their field of

activity is spring water bottling and bottle production. Pnar Su is a member of the

Yaar group which one of Turkeys biggest and most highly respected corporate

groups.Pinar Su established and started first non-recyclable packaged water

production of Turkey in 1984 in Izmir, Menderes. Annual production reached

100.000 tones after the 12 year production in Menderes so facility has moved to

Bozdoan facility, one of the top-notch waters in the world in terms of taste in 1996.

Therefore, there are 155 employees and 2 engineers are working in Pnar Su

Bozdoan.

2.GENERAL INFORMATION ABOUT PINAR SU

Sales and distribution network of dealers located all over Turkey. Pinar Su

has 3 facilities that located in Adapazar, Isparta and Aydn as shown as Figure 1.

Water which Pnar Su obtains from its Bozdoan, Gkeaa, Akaaa springs is

supplied to customers in Turkey and in the nearly thirty countries to which the

company exports. Mineral water supplied to the customer under the most natural and

hygienic conditions.Natural mineral water sourced from the Bozdoan, Gkeaa

and Akaaasprings supplied to market in all packaging formats. Bozdoan facility

is occupying a area of 64.000 m2 and its the biggest facility of Pinar Su. Annual

production capacities of Bozdoan, Gkeaa, Akaaaare 620.000, 607.000 and

210.000 tones respectively.


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Figure 1 : Pinar Su Facilities in Turkey

Pinar Su is producing 3 main products as pet bottles, glass bottles and

polycarbonate in Turkey. Their pet segmentations are 0.33, 0.5, 1, 1.5, 5, 10 lt. Glass

bottle products are 0.33 and 0.75 lt. Polycarbonates are producing as only 19lt.

Increasing investments of multinational firms like Nestle, Danone, Coca Cola

is a sign for the growth of the market. Trend in the market to increase the number of

spring sources in order to optimize logistic cost. Pnar Suscompetitive advantages

are superior quality standards, logistical strengths, a talent for keeping a close watch

on national and international customer trends and preferences and transforming them

into marketable products. In this market conditions, Pinar Su market share is %7 and

this market share is success against global companies. Small scale local producers

which occupying %48 as shown as in figure 2 are causing a fragmented market

structures.


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Figure 2 : Market Share of Pinar Su

Pet bottle sales and Polycarbonate sales are biggest parts of market as shown

as figure 3. According to sales of pet and glass bottles, pet bottles occupying

approximately %33 of whole sales. Polycarbonate sales percent is approximately

%67.Pinar Su sales are increasing every year within market share in routine

conditions. Sales can possibly change according to lack of spring water amount. It

could be reason to ups and downs in sales.

Figure 3 : Pet and Polycarbonate Sales

Pnar Su was exported to Germany for the first time in 1985. As shown as

Figure 4, Germany has the biggest pie as % 46. England and other countries are other

important customers of Pnar Su.


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Figure 4 : Exporting sales in Pnar Su

3.MICRO SYSTEMS OF PINAR SU

In Aydn-Bozdoan, there are totally 8 production lines, 6 of them producing

pet bottle and 2 of them producing glass bottle. Line 1,2,3 are very critical because of

0.5 and 0.33 pet bottle production. These two segmentations are occupying most of

all production. In order to reach customer demands, line 1&3 must be working in

every shift. Line 2 is back-up production line for line 1&3. Other lines are producing

other segments of pet bottle, glass bottles and 19L polycarbonates.

Table 1 : Monthly Distribution Graph of Water Coming From Mountain

0

5000

10000

15000

20000

25000

30000

Tonnes

Tonnes


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Table 2 : Monthly Distribution Graph of Water Coming From Mountain for PC

Table 3 : Monthly Distribution Graph of Water Coming from Mountain for Pet Bottle

In these graphs we analyzed the amount of spring water. Therefore we

analyzed pet bottles and PC respectively. The amount of spring water is increasing

between April-October months due to weather conditions. In other times, the amount

of water is not makes a big difference. The interval of amount of water is 5000

tonnes and 14000 tonnes per year. The most important thing is in these graphs, the

0

2000

4000

6000

8000

10000

12000

14000

16000

PC Tonnes

PC Tonnes

0

2000

4000

6000

8000

10000

12000

14000

16000PET Tonnes

PET Tonnes


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amount of water make a big increase according to months. In these months every day

is so precious for company.

In Aydin Bozdogan facility, there are 6 pet bottle lines, 2 glass bottle lines

and 2 polycarbonate lines. These production lines are producing 9 different

segmentation products. These segmentations are 0.33lt, 0.5lt, 0.75lt, 1lt, 1.5lt, 5lt,

10lt and 19lt polycarbonate. First 5 lines are producing only pet bottles. Line 6 and 8

are producing glass bottles. Line 7 and 9 are producing 19L polycarbonates.

Segmentations of pet bottles are 0.33lt, 0.5lt, 1lt, 1.5lt, 5lt and 10lt. Glass bottle

segmentations are 0.33lt and 0.75lt. Best-seller segmentations of Pinar Su are

0.33lt&0.5lt pet bottles and 19L polycarbonates.

Figure 5 : Pet & Glass Bottle Segmentations and Polycarbonate

Line 1 has 50 tones production capacity per hour. Line 2 has 34 tones

production capacity for 0.5 lt segmentation and 16 tones production capacity for

0.33lt segmentation per hour. Line 3 has 89 tones capacity for 1.5lt production

capacity per hour and line 4 has 30 tones production capacity for 10lt per hour.

3.1.1. Layout of Facility

Total production and warehouse area is 64,000m2 in Bozdogan facility and

enclosed area is 17.000m2. 8 production lines are located side by side. The ninth line

is located behind of these lines and next to shipping dock. Production lines and

warehouse are nested. Line 1 and Line 2 are the longest lines of production lines.


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Other lines lengths are equal each other. All lines are starting after inflating process

and finish with palletizing, stretching processes. Warehouse area starts from

palletizing and palletizing process is final process of production. Transportation

between lines is easy because all lines are parallel. This situation provides early

intervention ability to workers in production cases.

Lengths of production lines can change by workers. Current layout is optimal

layout according to facilitys workers. Length of line 1 & 2 is 11.526 cm. Length of

lines can decrease or increase by workers according to production needs.

Figure 6 : Layout of Pinar Su Aydn/Bozdoan Facility

3.1.2. Processes in Production Lines

Totally there are 13 stages to produce water in Bozdogan facility. Production

can be classified as 3 main processes in facility. First main process is transfer of

spring water, second main process is processing pet preforms (raw material) and

other main process is basic processes of water bottles. These 3 main processes are

common in every segment production.


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Table 4: Process Flow Diagram

Spring water is transferring from mountain to facilitys total 600 tones water

tanks with pipelines every single day. Total water amount can change every day

according to spring water source. Weather conditions are only reason to affects to

spring water amount. Distance from mountain to facility is 8 km and it provides big

advantage to facility in order to making production. Quality of spring water is

another advantage of Pinar Su. After the transferring, spring water waits in water

tank for a while. Duration of waiting time is up to demands and production plans.

The water which waiting in water tank goes to ozonation and final process is

distribution of water to production lines.


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Meanwhile in raw material area, pet preform are coming with boxes and

transferring to inflating machine by workers. Inflating machine never starves. Pet

preform raw materials are always being on hand. Pet preforms goes to two type of

oven. Temperatures of these ovens are 40 Cand 60 C. After the inflating processes

for pet preforms, pet preforms transforms to pet bottles and they are preparing for

washing process. In washing process, bottles turns 360 Cand goes to detailed

washing via heliozoan machine. Detailed washing is doing by well water.

At the beginning of third main processes, spring water meets with pet bottles

and goes to filling process. Remaining steps of production is common for all lines.

After the filling process, bottle goes to capping process via conveyor. Then following

processes labeling, shrinking, palletizing and stretching are finishing production and

products are transferring to warehouse with forklifts.

4.OBSERVATIONS AND SYMPTOMS

4.1. Analysis of Production Processes

Filling-Inflating-Capping: These operations are processing in common

machine and average 15.000 pet bottles are producing in this machine per hour.

Another important detail is defective pet bottle units do not pass 25 units during the

shift. Rotation speed of filling machine is 4000 cycle greater than inflating machine

and filling amount directly proportional with inflating amount. This system avoids

collapse in production line. Capping is last process of machine.

Labeling: Rotation speed of labeling machine is maximum 18.000 cycle per

hour. Minimum value of rotation is 5000 cycle. In this process, bottles are coming

one by one then all bottles are going to shrinking process according to pallet size.

Shrinking: %95 of shrinking processes are processing as 24 bottle pallet size.

Machine is shrinking 560 packages per hour and 4200 packages in one shift.

Rotation speed of machine is maximum 24.000 cycle. Duration of bottles shrinking

is 39 seconds average.


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Palletizing & Stretching: Palletizing and stretching is last process of all

production lines. There are two types of stretching. One of them are product

returned, the other of them is stretch machine returned. Duration of any palletizing

process is average 7 minutes 21 seconds and duration of any stretching process is

average 1 minute 54 seconds in normal conditions for 24 bottle pallets. 1 pallet is

including 7 level and 2016 pet bottles. Segmentation change takes two shift and it

happens one day in month.

4.2. SYMPTOMS

We analyzed firstly breakdown of machines and stated as important

observation. Fishbone diagram can easily show causes of breakdown of machines as

seen in Figure 7.

Figure 7 : Fishbone Diagram of Breakdown of Machines.


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Fishbone Diagram shows 6 main reason to breakdowns and alternative reasons to

corresponding main reasons. Main reasons are possibly classified as method,

management, human, environmental factors, machine and others.

Method failures are:

Machine setup

Cleaning.

Machine setup times are very critical for production. It can affect to next or previous

steps of production. Increasing machine setup times absolutely would decrease

production total speed and amount of outputs. Cleaning is dividing into two type.

First type is routine cleaning. Every machine is cleaning in every 2 weeks.

Management failures are:

Shift Change

Training

Shift Change: There are 3 shifts in Pinar Su Bozdogan. Shift are categorized as A, B,

C. Shift duration is 8 hour. Working production lines can possibly change according

to shifts. A and B shifts are more intense than night shift C. In A and B shifts are

producing more products.

Training: Pinar Su company is giving training sessions to workers at least 2 in a

month. Training sessions are very important for productivity of facility.

Human failures are vacations of workers, day-off and breaks in shift.

Machine failures are most important failure of production system. Problems

are categorized as maintenance of facility, breakdowns of machines and producing

defective parts. Most effective failure is breakdown of machines. As well as,

defected products is very important problem for facilities. On the other hand, Filling


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machine able to miss bottles to filling. Consequently, this situation causes to lose

customer satisfaction.

4.2.1. Analysis of Downtimes in Production Line 1 and Line 2

Primarily, we collected shift reports from facilitys workers. According to this

shift reports, we found out total amount of breakdowns of line 1 and line 2 between 1

February 2014 and 31 March 2014.

According to our analysis, there were two important downtimes category

which we classified as method and machine. Method breakdowns are occurring

mostly in line 2. Machine breakdowns are occurring mostly in line 1. We showed

this result in our chart as seen in below.

Table 5: Ratios of Downtimes

Machine breakdowns are occurring mostly in category which we named as

machine in fishbone diagram. These machine breakdowns are happening mostly in

inflating machine of line 1. Biggest reason of this inflating machine breakdown is

lack of air pressure and insufficient torque.

Method breakdowns are causing stops in production. Routine cleaning

operation is constant but machine setups can change according to demands. They

0

10

20

30

40

50

60

70

Machine Method Other

Causes of Downtimes -%

Line 1 Line 2


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need to change product segment and it takes two shift long. Machine setups are

taking long time. Machine setup is biggest problem of method category and it mostly

happens in line 2.

5.PROBLEM DEFINITION, TARGET & CRITICAL SUCCESS

FACTORS

Water amount in production lines depends on the arrival of water from

the spring water source. Our most important goal is determine failures period time.

Under the lights of symptoms, our problem is to determine the amount of

produced products from each line when a set of input such as flow rate of water from

source, mean time of failure given. Arena software simulation program will help us

to solve this problem. Productivity of production systems would be measured

through our arena simulation model. Our next critical step is putting values, data and

results of analyzes into the simulation model respectively. According to simulation

results, we can forecast our next interference.

In this project, we examine efficiencies of production lines, utilization of line

processes and determine optimal amount of produced goods. Determining amount of

production in lines can help to increase profits of factory and decrease loss of raw

materials. At the same time, we examine machine breakdowns, machine setups and

other stoppings of production lines. Within this information, we set up a Arena

Simulation model to reach our goals.

5.1. TARGET & CRITICAL SUCCESS FACTORS

Within this project, in line with the observations and symptoms, we determined

these issues,

Inefficient use of production lines

Low profitability from production lines

Long setup times and machine breakdowns

The problem is we look to increasing the efficiency of production lines and the

profitability from the lines.


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6. LITERATURE REVIEW AND MODELING PERSPECTIVE

6.1.Literature Review

SIMULATION OF MANUFACTURING SYSTEMS

ABSTRACT

This paper discusses how simulation is used to design new manufacturing systems and to

improve the performance ofexisting ones. Topics to be discussed include:manufacturing issues

addressed by simulation, simulation software for manufacturing applications, techniques for

building valid and credible models, and statistical considerations.

MANUFACTURING ISSUES ADDRESSED BY SIMULATION

The following are some of the specific issues that simulation is used to address in manufacturing:

The need for and the quantity of equipment and personnel

Number, type, and layout of machines for a particular objective

Requirements for transporters, conveyors, and other support equipment (e.g.,

pallets and

fixtures)

Location and size of inventory buffers

Evaluation of a change in product volume or mix

Evaluation of the effect of a new piece of equipment on an existing

manufacturing system

Evaluation of capital investments

Labor-requirements planning

Number of shifts

Performance evaluation

Throughput analysis

Time-in-system analysis

Bottleneck analysis


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Evaluation of operational procedures

Production scheduling

Inventory policies

Control strategies [e.g., for an automated guided vehicle system

(AGVS)]

Reliability analysis (e.g., effect of preventive maintenance)

Quality-control policies

The following are some of performance measures commonly estimated by simulation:

Throughput

Time in system for parts

Times parts spend in queues

Queue sizes

Timeliness of deliveries

Utilization of equipment or personnel

SIMULATION SOFTWARE FOR MANUFACTURING APPLICATIONS

Historically, simulation packages were classified to be of two major types, namely,

simulation languages and applications-oriented simulators. Simulation languages were general in

nature and model development was done by writing code. Simulation languages provided, in

general, a great deal of modeling flexibility, but were often difficult to use. On the other hand,

applications-oriented simulators were oriented specifically toward a particular class of

applications and a model was developed by using graphics, dialog boxes, and pull-down menus.

Simulators

were sometimes easier to learn and use, but might not have been flexible enough for some

problems.

However, in recent years vendors of simulation languages have attempted to make their

software easier to use by employing a graphical model-building approach. A typical scenario

might be to have a palette of model building icons located on one side of the computer screen.


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The icons are selected from the palette with a mouse and placed on the work area. The icons are

then connected to indicate the flow of entities through the system of interest.

Finally, one double-clicks on an icon to bring up a dialog box where detail is added. On the

other hand, vendors of simulators have attempted to make their software moreflexible by allowing programming in certain model locations using an internal pseudo-language.

In at least one simulator, it is now possible to modify existing modeling constructs and to create

new ones. Thus, the distinction between simulation languages and simulators has really become

blurred. Based on the above discussion, we will now say that there are two types of simulation

packages. Ageneral purposesimulation package can be used for any application, but might have

special features for certain ones (e.g., for manufacturing or process reengineering).

Examples of general-purpose simulation packages are Arena, AweSim, Extend, GPSS/H, Micro

Saint, MODSIM III, SIMPLE++, SIMUL8, SLX, and Taylor Enterprise Dynamics Developer. On

the other hand, an application sorientedsimulation package is designed to be used for a certain

class of applications such as manufacturing, health care, or call centers. Examples of

manufacturing-oriented simulators are Arena Packaging Edition, AutoMod, AutoSched, Extend +

MFG, ProModel, QUEST, Taylor Enterprise Dynamics Logistics Suite, and WITNESS.

DEVELOPING VALID AND CREDIBLE SIMULATION MODELS

A simulation model is a surrogate for actually experimenting with a manufacturing system, which

is often infeasible or not cost-effective. Thus, it is important for a simulation analyst to determine

whether the simulation model is an accurate representation of the system being studied, i.e.,

whether the model is valid. It is also important for the model to be credible; otherwise, the results

may never be used in the decision-making process, even if the model is valid.

The following are some important ideas/techniques for deciding the appropriate level of model

detail (one of the most difficult issues when modeling a complex system), for validating a

simulation model, and for developing a model with high credibility:

o State definitively the issues to be addressed and the performance measures for

evaluating

a system design at the beginning of the study.

o Collect information on the system layout and operating procedures based on

conversations

with subject-matter experts (SMEs).

o Delineate all information and data summaries in an assumptions document, which

becomes the major documentation for the model.

o Interact with the manager (or decision-maker) on a regular basis to make sure that

the correct problem is being solved and to increase model credibility.

o


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o Perform a structured walk-through (before any programming is performed) of the

conceptual simulation model as embodied in the assumptions document before an audience

of SMEs, managers, etc.

o

Use sensitivity analyses to determine important model factors, which have to be modeled carefully.

o Simulate the existing manufacturing system (if there is one) and compare model

performance measures (e.g., throughput and average time in system) to the

comparable measures from the actual system.

STATISTICAL ISSUES IN SIMULATING MANUFACTURING SYSTEMS

Since random samples from input probability distributions drive a simulation model of a

manufacturing system through time, basic simulation output data (e.g., times in system of parts)

or an estimated performance measure computed from them (e.g., average time in system from the

entire simulation run) are also random. Therefore, it is important to model system randomness

correctly and also to design and analyze simulation experiments in a proper manner. These topics

are briefly discussed in this section.

Simulation of Manufacturing Systems

Modeling System Randomness

following are some sources of randomness in

simulated manufacturing systems:

Arrivals of orders, parts, or raw materials

Processing, assembly, or inspection times

Machine times to failure

Machine repair times

Loading/unloading times

Setup times


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In general, each source of system randomness needs to be modeled by an appropriateprobability

distribution, not what is perceived to be the mean value. Note that sources of randomness

encountered in practice are rarely normally distributed. A detailed discussion of simulation input

modeling is given in Chapter 6 of Law and Kelton (1999).

Design and Analysis of Simulation Experiments

Because of the random nature of simulation input, a simulation run produces a statistical estimate

of the (true) performance measure not the measure itself. In order for an estimate to be

statistically precise (have a small variance) and free of bias, the analyst must specify for each

system design of interest appropriate choices for the following:

o

Length of each simulation run

o Number of independent simulation runs

o Length of the warmup period, if one is appropriate

We recommend always making at least three to five independent runs for each system

design, and using the average of the estimated performance measures from the individual runs as

the overall estimate of the performance measure. (Independent runs means using different random

numbers for each run, starting each run in the same initial state, and resetting the models

statistical counters back to zero at the beginning of each run.) This overall estimate should be

more statistically precise than the estimated performance measure from one run. Note that

independent runs (as compared to one very long run) are required to obtain legitimate andsimple

variance estimates and confidence intervals.

For most simulation studies of manufacturing systems, we are interested in the long-run (or

steady-state) behavior of the system, i.e., its behavior when operating in a normal manner. On

the other hand, simulations of these kinds of systems generally begin with the system in an

empty and idle state. This results in the output data from the beginning of the simulation run not

being representative of the desired normal behavior of the system. Therefore, simulations are

often run for a certain amount of time, the warmup period, before the output data are actually

used to estimate the desired performance measure. Use of the warmup-period data would bias the

estimated performance measure. A comprehensive treatment of simulation output-data

analysis can be found in Chapter 9 of Law and Kelton

(1999).


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6.2.Modeling Perspective


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Figure 8 : Arena Simulation Model Draft

Variables;

Rated speed of machines Xi;

The amount of spring water Y;

Finished goods P

Parameters;

Machines(Line 1) Rated Speed per hour

Inflating 12000

Filling 14000

Labeling 12000

Shrinking 14400

Palletizing 14400

Machines(Line 1) Rated Speed per hour

Inflating 10000

Filling 14500

Labeling 14500

Shrinking 30 pallet/min

Table6: Parameters of machines in Line 1

Note: One pallet is formed by 7 floor and one pallet is including 2016 bottle. ( for

0.5lt pet bottle )

These data were taken from the company. And we observed these machines

and they suit the standard numbers. Because of the inflating machines be a first

machine and its rated speed is the lowest rated speed, there is no any capacity

problem for other machines.


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7. SOLUTION METHODOLOGY

In order to solve this case, we made some literature research then we decided

to make simulation model. Within this simulation software, we could know that

actual rate of water which is transferring from spring water and how many tones of

water should transfer to which lines. And also we could do throughput analysis,

time-in-system analysis and bottleneck analysis. We had to follow some principles to

reach our goal. These principles are arrivals of orders, parts, process times of

machines in lines, machine times to failure, machine repair times and setup times of

machines. Our research showed us that we had to use discrete event simulation for

our project. Discrete event simulation has three main advantages in our project.

Risk avoidance Hypothetical or potentially dangerous systems can be

studied without the financial or physical risks that may be involved in

building and studying a real system

Repeatability The ability to study different systems in identical

environments or the same system in different environments

ControlEverything in a simulated environment can be precisely monitored

and exactly controlled

After the stating our parameters, we clarified our variables for the model. Our

three critical variables are rated speed of machines, the amount of spring water, and

produced goods per lines. These 3 variables are significative factors of simulation

model. Defining one of these variables, produced goods would also explain our result

of project. Cause by this way, we can easily state throughput of lines.


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In order to complete these requirements, we needed to fill some blanks of

simulation model. First goal was collecting data of production lines. As its

mentioned in 4.2, our first step was defining breakdown rates of production lines. We

collected all daily production reports of February, March and June months. These

daily production data are including breakdowns, machine setups, total amount of

water which used in production lines and amount of water extravagance as shown

Figure 9.

Figure 9: Daily Production Report


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Figure 10: Daily Breakdown Report

In this reports, our priority was find out breakdowns rate. This would help us

in simulation model because we put them to model as machines failures and within

the failures, we could find real throughput level. We selected all breakdowns fromdaily production reports and copied them to blank worksheet. This separation process

would help us to analyze data more easily. We opened all single days and shifts of

reports and completed separation process. After the separation, we separated them

again and classified them as processes as shown Figure 11.

Figure 11: Production Report Review


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We did this separation process for 8 production lines and separate them into

their processes as 7 worksheets. One worksheet includes process, reason and time of

breakdowns. These worksheets classified as process names (Bottle Fabrication,

Ozonation, Labeling, Shrinking, Inflating, Palletizing and Line). We classified

general breakdowns (which not related to specific processes) as Line. These

worksheets breakdown frequencies are more than other processes so we eliminated

processes which happened at most 5 times in 3 months. Finally, we draw a chart of

these downtimes analysis for Line 1 and Line 2. We picked first two lines because

their downtime ranges are more than other lines. These charts show us percentages of

downtimes of Line 1 and Line 2 as seen as Figure 12.

Figure 12: Percentages of Downtimes.

Our next step was calculating up times and down times. Up time means time

between first breakdown and next breakdown of each process. On the other hand;

Downtime is repair time of each process. (Figure 13) These analyzes are playing

very critical role in simulation model. Before inserting our breakdown data to model,

we had one more step to go. We had to convert this data to arena simulation

softwares language. Input analyzer tool was eligible way to this step. We put our up

time and down time data to input analyzer and we got a histogram of our data.

0510

1520253035404550


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After following next steps, we found distributions of our data. (Arena ->

Tools -> Input analyzer -> Fit -> Fit all).Then, we collected all input analyzer data of

production lines in one excel sheet. That excel sheet shows distributions and p-values

of data according to chi-square test as shown as Figure 14 and we showed input

analyzer results in figures 15 and 16.

Figure 13: Down time and Up Time Excel Sheet


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Figure 14: General distributions of breakdowns of lines.

Figure 15: Input Analyzer for Line 3 ( Down Time)


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Figure 16: Input Analyzer for Line 3 ( Up Time)

During the input analyzer work, determining p-value was very important

because p-values are giving an idea about our data. We only needed to reach ( p

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