IBC Management: An application of WIP control
for a pharmaceutical company
MASSACHUSETTS INS TITUTE
J
by
o
TECic7
LCG
DEC 072008
He Hu
LIBRARIES
B.Eng. Industrial Engineering and Management
Shanghai Jiaotong University, China, 2007
SUBMITTED TO THE DEPARTMENT OF MECHANICAL ENGINEERING IN
PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF ENGINEERING IN MANUFACTURING
AT THE
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
SEPTEMBER 2008
© 2008 Massachusetts Institute of Technology.
All rights reserved.
Signature of Author:
Department of Mechanical Engineering
August 19, 2008
Certified by:
Stephen C. Graves
Abraham Siegel Professor of Management, Sloan School of Management
Thesis Supervisor
A
Accepted by:
Lallit Anand
Professor of Mechanical Engineering
Chairman, Department of Committee on Graduate Students
ARCHIVES
IBC Management: An application of WIP control for a
pharmaceutical company
By
He Hu
Submitted to the Department of Mechanical Engineering
on August 19, 2008
in partial fulfillment of the requirements for the Degree of
Master of Engineering in Manufacturing
Abstract
An Intermediate Bulk Container (IBC) is used to contain raw materials and
semi-products in pharmaceutical company ABC. Due to the high price of an IBC,
company ABC sought to minimize the number of IBCs needed to support the
production of four separate products. In this project, we identify five IBCs drivers that
affect or determine the number of IBCs required. The cleaning schedule for IBCs was
modified to reuse IBCs as much as possible. In this paper, we analyze the
management of 1800L IBCs. Three of the five IBC drivers were analyzed in detail.
Integer programming model, deterministic simulation method and Buzacott's formula
for zero buffer line were used to solve the problems associated with the three IBC
drivers to minimize the required number of the 1800L IBCs. Cleaning schedule for
1800L IBCs was modified under two production scenarios to reuse 1800L IBCs as
much as possible.
Thesis Supervisor: Stephen C. Graves
Title: Abraham Siegel Professor of Management, Sloan School of Management
Acknowledgement
First of all, I would like to express my heartfelt gratitude to my thesis supervisor
Professor Stephen C. Graves for his insightful guidance and patience in discussing
and modifying the thesis.
I would also like to express my thanks to all the people in ABC for supporting us on
this project. I would especially like to thank Mr, Ger, Mr, Kong and Sam for helping
us all though the project. Thanks to Chua Wei-Jiea and Kelvin Wong for taking time
off their busy schedules to give me guidance on the project. Kind thanks also goes to
Teresa Gui for her excellent administration support.
Last but not least, I would really like to thank my teammate, Chen Xiaowen. Without
your help and encouragement, I will not learn so much from this project.
Contents
A b stra c t .............................................................................................................................................
2
A cknowledgem ent ...............................................................................
....... ............
L ist o f Fig ures ................................................................................................
3
7
......................
L ist of Tables.......... . . .............................................................................................
8
Chapter 1 Introduction ....................................................................................................
1.1
6
8
B ackground ................................................................
1.1.1 Company background .............................................................. 8
1.1.2 M anufacturing Facilities and Products.................................................
.............. 8
1.1.3 Production campaign........................................
1.1.4 Intermediate Bulk Container (IBC)................................. .................... 9
1.2
9
.............................
1.1.5 Cleaning activities ...........................
Project O verview ........................................................
........................................
10
10
1.2.1 M otivation ..............................................................
1.2.2 Objective and Project O utline ............................................................................... 10
12
1.3 Organization of the Thesis .........................................
2.1 Manufacturing process ..........................
14
..................
Chapter 2 Manufacturing Operations in Company ABC ......................
.................
.............
14
16
2.1.1.1 Product A...................................................
............ 17
2.1.1.2 Product B , C ....................................................................................
..
2.1.1.3 Production campaigns for product A, B and C....................................
2.2 IB C usage..................................
17
18
2.1.2 Manufacturing process for product D .........................................
..................... 19
...............................
............................. 19
2.2.1 IB C usage in Product A ......... ............. ...............
2.2.2 IBC usage in Product B and C ...............................................
............ 20
2.2.3 IBC usage in Product D.......................................................20
21
2.3 IBC cleaning ....................................................
Chapter 3: IBC Driver and Problem diagnosis..................................23
3.1 IB C D river.....
. . . . .................................................................
3.1.1 Definition .........................
.................................. 23
23
...................
...............
..... 24
3.1.2 Allocation of Compression machines in PF .........................................
3.1.3 IBC Driver of Product B and C .......................................................... 26
3.1.4 IBC Driver of Product D .......................................
3.2 IBC cleaning activity ......
................ 27
28
.......................................
3.5.1 C leaning system ..................................................
........................................ 29
3.5.2 Problem of IBC cleaning....................................................29
Chapter 4: Modeling and Analysis for IBC drivers......................
4.1 Allocation of compression machines....................
.........
.......
....
31
.................... 31
4 .1.1 Methodology ....................................................................... 31
4.1.2 Modeling ..........................................
4.1.3 Results Evaluation......................
.
..
...........
32
...................................................... 37
4.2 IBC Turnover between PF1 and PF2 ........................
...............
4.2.1 A ssum ptions .....................................................................................................
42
...... 43
4.2.2 Deterministic Simulation .......................
43
...................................
4.3 WIP Level for Product 1)............................................................45
4 .3.1 M ethod ology ..........................................................................................................
45
4.3.2 Deterministic Simulation by Simul 8....................................................47
4.3.3 Line Efficiency defined by Buzacott........
.............................
4.3.4 Productivity considering machines down................................
50
. .................... 51
4.3.5 Results Evaluation by an Example.............................................52
Chapter 5 IB C C leaning .................................................................................................................. 54
5.1 Production campaign scenarios....................................................54
5.2 Capacity constraint and available time slots for automatic washer...................................56
5.3 Cleaning jobs generation....................................................56
56
5.3.1 M ethodology ..............................................................................
5 .3.2 E xam p le .......................................................................... 5 7
Chapter 6 Conclusions and Recommendations ......................................
6.1 Conclusions .....................................................
6.2 Limitations of the models and future work ...........................
R eferences......
A ppendix....
............................................................
................................................................
.
............. 67
67
...................... 68
70
71
List of Figures
........................ ... 11
Figure 1-1 Project outline ....................................
Figure 2-1 Manufacturing process of Product A, B and C ............................ 13, 32
Figure 2-2 Manufacturing process of Product D .................................... 17, 45
Figure 2-3 IBC usage in Product A .........................................................
18
Figure 2-4 IBC usage in Product B, C ..................................................... 19
Figure 2-5 IBC usage in Product D
.................
.
. ......... .....
.... 19
Figure 3-1 Cleaning process for 1800L IBC ....................................... 22, 28
Figure 4-1 The number of the 1800L IBCs needed versus time by using AP1 ......... 39
Figure 4-2 1 The number of the 1800L IBCs needed versus time by using AP2.......39
Figure 4-3 The number of the 1800L ICBs needed versus time by using API ......... 40
Figure 4-4 T Number of 1800L IBCs needed for product B ........................ 44, 59
Figure 4-5 Number of 1800L IBCs needed for product C ........................... 44, 62
Figure 4-6 Simulation model for product D by Simul 8 ................................. 47
Figure 5-1 Campaign sequence scenario 1..........................
Figure 5-2 Campaign sequence scenario 2 .....................
..... 54
............ 54
Figure 5-3 Number of the 1800L IBCs needed for the first product A campaign......37
Figure 5-4 Number of the 1800L IBCs needed for the second product A campaign...42
List of Tables
Table I-1 IBC information..................................................
................... 9
Table 2-1 Cycle time information for Product A ...................................... 16, 34
Table 2-2 Cycle time information for Product B and Product C ......................... 17
Table 2-3 Cycle time information for Product D ............
................. 18, 47
Table 3-1 An example for a product A campaign ..................................... 24, 31
Table 3-2 Allocation of compression machines based on API ..................... 25, 37
Table 3-3 Allocation of compression machines based on AP2 ....................... 25,38
Table 4-1 Allocation of compression machines based on AP12 .......................... 37
Table 4-2 Performance comparison among API, AP2 and AP12 ........................ 36
Table 4-3 Transportation time of the 1800L IBC for product D ......................... 48
Table 4-4 Relations between the production volume and resources available.........50
Table 5-1 Production Planning for the first product A campaign ........................ 57
Table 5-2 Production Planning for the second product A campaign .................. 58
Table 5-3 The optimal Allocation Policy for the first product A campaign ............ 59
Table 5-4 IBCs release time points for the first product A campaign ................. 59
61
Table 5-5 IBCs release time points for the product B campaign ................
Table 5-6 The optimal allocation policy for the second product A campaign...........62
Table 5-7 IBCs release time points for the second product A campaign ............
62
Table 5-8 IBCs release time points for the product C campaign .......................... 64
Table 5-9 IBC usage and cleaning scheduling.....................
...... 66
Chapter 1 Introduction
1.1 Background
1.1.1 Company background
Company ABC is a multi-national pharmaceutical company that discovers, develops,
manufactures and markets a broad range of innovative health care products. Company
ABC was incorporated in the year 1999 and produced its first batch of products in the
year 2001.
1.1.2 Manufacturing Facilities and Products
Company ABC has three separate manufacturing facilities: Pharmaceutical Facility 1
(PF1), Pharmaceutical Facility 2 (PF2) and Pharmaceutical Facility 3 (PF3). Currently,
there are two products families, Product A and B, in production and another two
products, Product C and D, are under development. Product A is completely done in
PF1. Product D is completely done in PF3. Product B and C have their first half of
process done in PFI and finish the second half in PF2. Product A has four kinds of
strengths and two batch sizes. Products B, C and D have only one kind of strength and
one batch size.
1.1.3 Production campaign
A production campaign is a period in which a pharmaceutical facility is only
producing one type of product. For example, when PF1 runs a Product A campaign,
this means that PF1 will only produce Product A for some period of time into the
future.
1.1.4 Intermediate Bulk Container (IBC)
In the pharmaceutical industry, all the Work-In-Process (WIP) must be stored in
Intermediate Bulk Containers (IBCs) to avoid exposure to the air. There are three
types of IBCs, which differ in terms of their volume: 600L IBC, 1800L IBC and
2400L IBC. For example "600L IBC" means that the volume of the IBC is 600 liters.
These IBCs are used in different places in the production process for different
purposes. Information for each type of IBC is summarized in Table 1-1.
Table 1-1 IBC information
IBC Type
Products involved with
Location
Total Number
600L
Product A B C
PF1
28
1800L
Product A B C D
PFI PF2 PF3
30
2400L
Product B C
PF2
18
1.1.5 Cleaning activities
Due to safety and quality requirements, all equipment in contact with one product has
to undergo a major cleaning (wet cleaning) before switching to another product. When
the equipment switches between different strengths of the same product family, a
minor cleaning (dry cleaning) is needed.
Just as with the other equipment, an IBC needs a major cleaning before switching to
another product and a minor cleaning before switching to a different strength of the
same product.
1.2 Project Overview
1.2.1 Motivation
When Company ABC launched the production in the year of 2001, it spent hundreds
of thousands of dollars on purchasing IBCs. Since ABC had many more IBCs than it
needed at the time, management did not pay close attention to the management of
IBCs.
However currently as Company ABC starts to produce more types of products, the
number of IBCs it needs keeps increasing. Based on the productivity report of 2008,
there are some machines that were shut down due to IBC unavailability; that is the
production process had to stop because an IBC was not available.
The management believes that in the future when they launch Product C and D, the
management of IBC's will be more complicated and the occurrence of "IBC
unavailable" will certainly increase. The motivation of the project is to manage the
use of IBCs to make sure they are used efficiently. By doing this, the company also
expects us to find out what is the minimum number of IBCs needed when all four
products are in production so that they know whether it is necessary to purchase extra
IBCs.
1.2.2 Objective and Project Outline
The team project is conducted for Company ABC by two MIT graduate students. The
objective of this research is to analyze and maximize the utilization of IBCs and then
to make recommendations that allow the company to decide whether to buy new IBCs
to support the production of Product C and Product D.
To help the company answer this question, we need to know whether the current
number of IBCs is enough for the company. To determine the number of IBCs
actually needed for each production activity was the main objective in the first half of
the project. In this stage, we learned about the manufacturing process of products A, B
and the coming products C and D. Five production activities were identified as the
main IBC drivers, which affect or determine the number of IBCs needed. We
collected data on each activity, analyzed the data and found out the actual number of
IBCs needed for each activity. However, we cannot just add together these numbers to
decide the total number of IBCs needed.
The reason for this is that IBCs can be shared between products and between uses.
The second half of the project was focused on the reuse of IBCs. IBC cleaning
activity affects the number of IBCs needed as well. For example if 4 IBCs are needed
for a campaign of Product A and 19 IBCs are needed for a campaign of Product B,
then 19 IBCs may be enough for both. The IBCs for the Product A campaign can be
cleaned and reused for the campaign for Product B, and vice versa.
After the two stages, we can answer the question whether the company needs to buy
new IBCs. Along the process of looking for the answer, we also identified the wastes
of IBC usage and can provide some suggestions to eliminate these wastes. This is an
integral part of the implementation of lean manufacturing in the company.
This project is divided into two individual theses. This thesis consists of two parts and
focuses on the management of the 1800L IBCs. The first part of the thesis focuses on
the three of five IBC drivers and the second part is about the cleaning activities of the
1800L IBCs. The other thesis, written by Xiaowen Chen [ , will analyze the other two
IBC drivers and cleaning activities of the 600L IBCs. Figure 1-1 shows the overview
of the project and what will be covered in this thesis.
Legend:
C
Content in this thesis
I
Content in Xiaowen
Content in both theses
Figure 1-1 Project outline
In the production, there are five IBC drivers: Stand-by for Equipment downtime is the
main determinant for the number of 600L IBCs needed; the other four IBC drivers
will affect the number of 1800L IBCs needed. In the cleaning, the effect of Product C
and Product D will be considered. Cleaning orders are generated based on the
production plan in 2009 and resource limitations such as water and manpower are
considered as the capacity constraints.
1.3 Organization of the Thesis
Chapter 2 describes in detail how does the manufacturing operate and the process
flow for each of the products in company ABC. Chapter 3 describes the problems we
faced in reaching the objective of the project by investigating IBC drivers and
cleaning activities. Chapter 4 shows the analysis and calculations we did to minimize
12
the number of 1800L IBCs needed for each IBC driver in chapter 3. Chapter 5 studies
the total number of 1800L IBCs needed by considering cleaning and reuse of IBCs.
Finally, conclusions are drawn and recommendations are made to company ABC in
chapter 6.
Chapter 2 Manufacturing Operations in Company ABC
This chapter describes how the manufacturing of four products operates and how the
Intermediate-Bulk-Containers (IBC) are used in the production.
2.1 Manufacturing process
There are three pharmaceutical facilities in Company ABC namely PF , PF2 and PF3.
Four products (Product A, B, C and D) are manufactured in these three facilities. The
manufacturing process of Product A, B and C generally follow the process shown in
Figure 2-1. The manufacturing of product D is independent of these three products
and will be described later.
2.1.1 Manufacturing process for product A, B and C
PFI
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PF2
I------------------------------
Figure 2-1: Manufacturing process of Product A, B and C
From Figure 2-1 we can see that Product A, B and C share the same process of
"Charging" and "HSM" in PF1. The "Blending" and "Compression" of Product A is
finished in PFI and those processes of Product B and C are done in PF2. The only
difference between producing A and producing B or C is that for product B and C
14
there is an extra Charging in PF2. This is because the tablet of product B and C has
two layers. The first layer's "Charging" is done in PF1 and the second layer's
"Charging" is done in PF2. The two layers get combined at the "Blending" step in
PF2. The materials in the two "Charging" are different.
Production volume in Company ABC occurs in batches. The duration of a campaign
is expressed in terms of a number of batches. The batch size is in kilograms. For
"Charging" at PF1, one subpart (equal to one quarter of a batch) is produced each
time. After subpart I to subpart 4 are finished, they are sent to "HSM" as batch 1.
"HSM" processes one subpart at a time, similar to Charging. After subpart 1 to
subpart 4 are finished, they are sent together to blending as batch 1. The production of
blending and compression are both in batches. After blending and compression, the
production of batch 1 is finished.
When PF1 starts a campaign for product A or B, C the Charging step will start one
day ahead of the other steps. During that day, three to four batches (12 to 16 subparts)
of products get charged and serve as standby inventories between the Charging and
the HSM step. This is because the company wants the HSM to run at its maximum
capacity so that it could finish the campaign for one product as quickly as possible
and switch to the campaign for another product. When all of the standby products are
ready, the HSM will start to produce and run continuously through the campaign.
Meanwhile, instead of continuously running, the Charging will produce a new batch
only when the HSM completes the production for one batch. In this way, the company
attempts to maintain a buffer of between two to four batches between Charging and
HSM.
As mentioned in section 1.1.3, the production of Company ABC runs in campaigns.
Only one product is produced during one campaign. "Product A Campaign" means
that "Charging" and "HSM" in PFI produces Product A and two compression
machines in PF1 are running to produce Product A. "Product B or C Campaign"
15
means " charging" and "HSM" in PFI produces Product B or C and two compression
machines in PF2 are running to produce Product B or C.
2.1.1.1 Product A
Product A has four levels of strengths (a, b, c and d) and two batch sizes (x and y).
Strength a and b only have one batch size x. For the other two strengths, each has two
batch sizes. The cycle time of each process in Table 2-1 is different for each level of
strength. Table 2-1 is a summary of the cycle time for four strengths of Product A. The
cycle time for compression is the cycle time of one compression machine. All of the
cycle times here are target cycle times, which are the production targets; the practical
cycle time may be longer or shorter than target, but the difference is not significant.
Table 2-1 Cycle time information for Product A
Charging
HSM
Blending
Compression
a
4h
7hr
Ih
26hr30min
b
4h
7hr
1h
9hr
Batch size x
4h
7hr
1h
8hr
Batch size y
4h
7hr
1h
8hr
Batch size x
4h
7hr
lh
9hr
Batch size y
4h
7hr
1h
9hr
Process
Strength
c
A minor changeover is needed for the HSM and Compression to switch from
producing on level of strength to another. It takes 3 hours to do the minor changeover
for the HSM and 16 hours for compression. In order to minimize the changeover of
compression, Company ABC schedules the two compression machines in PF1 so that
each compression machine is dedicated to two levels of strengths. For example,
compression machine 1 will produce strength a, b and compression machine 2 will
produce strength c, d.
2.1.1.2 Product B, C
Product B and C follow the same process. They both have one batch size and one
level of strength. The cycle times for each process of Product B and C are in Table 2-2.
The compression cycle time is the cycle time for one compression machine.
Table 2-2 Cycle time information for Product B and Product C
From Table 2-2 and Table 2-1, we can see that the compression cycle time for Product
B and C are generally longer than that of Product A. The cycle time for other
processes are more or less the same as that of Product A.
2.1.1.3 Production campaigns for product A, B and C
The longer compression cycle time for Product B and Product C determines that the
total campaign length of "Product B or C Campaign" (in PFI and PF2) will be longer
than that of "Product A Campaign" (only in PFI). When the HSM in PFI finishes
"Product A Campaign", it can run a "Product B or C Campaign". When "Product B or
C Campaign" in PF I finishes, the intermediate or semi-products of Product B or C are
sent to PF2 for blending and compression. The HSM in PFI can start another
"Product A Campaign" after a campaign changeover. The campaign changeover is
about 7 days. During these days, the equipment is major cleaned and some quality
tests are done.
At some times, the compression machines in PF1 and PF2 are running at the same
time. It is possible that the compression machines in PF1 are producing Product A and
the compression machines in PF2 are producing Product B or C.
Currently, the campaign sequence is Product A Campaign - Product B Campaign Product A Campaign - Product B Campaign in PFI. When Product C is introduced
into production, the production sequence in PF1 can either be Product A Campaign Product B Campaign - Product A Campaign - Product C Campaign (Scenariol) or
Product A Campaign - Product B Campaign - Product C Campaign - Product A
Campaign (Scenario 2).
2.1.2 Manufacturing process for product D
The manufacturing of Product D will be done in PF3. Figure 2-2 is the manufacturing
process of Product D. We can see from Figure 2-2 that the raw materials first get
charged at the First Charging step and transferred to the Pre-blending step to mix with
the solutions. After that the semi-products go to the Roller Compaction step and get
pressed. From Roller Compaction the semi-products are transferred to the Second
Charging step where a new ingredient is added. After Final Blending step where the
semi-products mix with the solutions again, the semi-products get compressed into
tablets which are the final products at the Compression step. The production in each
step is done in batches. There is only one level of strength and one batch size of
Product D. The target cycle time is listed in Table 2-3.
Figure 2-2 Manufacturing process of Product D
Table 2-3 Cycle time information for Product D
First
Pre-
Final
Compression
Charging blending
4h
Second
Roller
Compaction
Charging blending
4h
4.5h
Ih
45min
4.5h
2.2 IBC usage
2.2.1 IBC usage in Product A
600L IBC and 1800L IBC are used in Product A. Figure 2-3 shows IBC usage in
Product A.
--- I
Figure 2-3 IBC usage in Product A
One 600L IBC is needed at the start of "Charging" in PFI as well as the product is
being charged. Each time "Charging" produces one subpart. After "Charging", this
subpart is sent to "HSM" by this 600L IBC. After unloading the material, it can be
sent back to "charging" to reuse.
Once the HSM starts to process the first subpart of one batch, one 1800L IBC is
needed. After four subparts (equal to one batch), are finished, they will be sent to
Blending by this 1800L IBC. After the Blending step the semi-product is transferred
by this 1800L IBC to one of the two compression machines to do the compression.
19
When "compression" finishes the batch, thel800L IBC is released and will be sent
back to the HSM for reuse.
2.2.2 IBC usage in Product B and C
600L IBC, 1800L IBC and 2400L IBC are used in Product B and C. Figure 2-4 shows
IBC usage in Product B and C.
- -
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PF2
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Figure 2-4 IBC usage in Product B, C
The usage of 600L IBC and 1800L IBC in PF 1 are the same as the usage in Product A.
After "HSM", the 1800L IBC is sent to PF2, where it is matched at "blending" with
one 2400L IBC charged of materials in PF2. After "compression", the empty 1800L
IBC will be sent back to PF1 for reuse.
2.2.3 IBC usage in Product D
1800L IBC is used in Product D. Figure 2-5 shows IBC usage in Product D.
w
I
Figure 2-5 IBC usage in Product D
At the start of "First Charging", one 1800L IBC is needed. After the First Charging
step, this 1800L IBCs is used to transport the product to Pre-blending to mix with the
solution. It is empty after the completion of roller compaction and can be sent back to
"First Charging" for reuse. At the start of roller compaction, a second 1800L IBC is
needed. During "Roller Compaction", materials are transferred from one 1800L IBC
to another. This second 1800L IBC can be released after compression and sent back to
"Roller Compaction" for reuse.
2.3 IBC cleaning
IBC requires a minor cleaning (dry cleaning) before switching to another level of
strength of the same product and a major cleaning (wet cleaning) before switching to
another product. Since minor cleaning can be done very quickly, in 15 minutes, in this
thesis we only study IBC major cleaning issues. IBC's major cleaning requires
manpower, water, solution and washer.
The company's production runs in campaign. During one campaign, only one product
is manufactured in each facility. After the campaign, equipment such as HSM needs
to be cleaned as well. The major cleaning of HSM consumes a lot of water; IBC
cannot be cleaned during the HSM major cleaning period (2 days).
In regard to manpower, two people are needed to load IBCs onto the washer, and then
cleaning can be done automatically by the washer for about three hours. After the
cleaning, IBCs need to be unloaded by two people. Currently in the company, there
are no people specifically responsible for IBC cleaning. People who help clean IBCs
are quite flexible. Whoever is idle in the manufacturing process can do the IBC
cleaning.
The 600L and 1800L IBCs are currently cleaned in PF1 and share the same washer
WA-900. The 2400L IBCs are cleaned in PF2 using two washers WA-2900 and
WA-2940; one is for part washing, the other is for body washing. The WA-2900 and
WA-2940 washers can be redesigned to clean 1800L IBCs if necessary.
Chapter 3: IBC Driver and Problem diagnosis
To support the production for four types of products with a minimum number of IBCs,
we need to achieve two goals. First, use a minimum number of IBCs to support
production for each of the product. Second, schedule the cleaning of IBCs well so that
IBCs can be shared as much as possible among different products. This chapter
describes in detail what are the problems we will address to achieve the two goals.
This chapter contains two parts. First, we introduce the concept of an IBC Driver.
Details of three IBC Drivers are provided and each IBC driver corresponds to a
problem. Solving the three problems enables us to achieve the first goal mentioned
above. Second, we describe the IBC cleaning system and the problem we face. The
answer to this problem builds on the answers to the problems of the three IBC Drivers
and it is the key to achieve the second goal mentioned above.
3.1 IBC Driver
3.1.1 Definition
We define an "IBC Driver" to be the element that we can control in production and
that affects or determines the number of IBCs needed. For example, in a production
campaign for product B, the number of batches that need to be produced determines
the number of IBCs needed. However, we do not treat this as an IBC Driver. This is
because we cannot control the production requirement which is driven by the
customer demands.
After investigating the manufacturing operations in company ABC, we found out five
IBC Drivers in total. Since this thesis focuses on the management of the 1800L IBC,
we only describe three of the five IBC Drivers. Please refer to my teammate Xiaowen
Chen' thesis ['l for the other two IBC Drivers.
For the use of the 1800L IBC, the three IBC Drivers are: Allocation of Compression
Machines in PF1, IBC Turnover between PFI and PF2, WIP Level for Product D.
3.1.2 Allocation of Compression machines in PF1
The IBC Driver of Product A is "Allocation of Compression Machines in PFI".
Figure 2-1 in chapter 2 shows the manufacturing process of product A and we know
that there are two compression machines in PFI to produce product A. Product A has
four levels of strengths; a production plan for a product A campaign is shown in Table
3-1. Table 3-1 means that this product A campaign will start by producing 4 batches of
strength a, followed by 14 batches of strength b and so on. Given this production plan
we consider two ways to schedule the production to the two compression machines
and the way of using them will affect the number of 1800L IBCs needed.
Table 3-1: An example for a product A campaign
Production Sequence
Strength
Number of batches
I
a
4
2
b
14
3
c
2
4
d
31
5
c
15
6
b
16
7
a
2
The first Allocation Policy (AP1) which is also what the company executes currently
is that one kind of strength is only assigned to the same compression machine during
the whole production campaign. Under the guide of API, the two compression
machines will be used in the way shown in Table 3-2. Table 3-2 shows that for this
24
product A campaign, the strength a and c are assigned to compression machine I and
the strength b and d are assigned to compression machine 2.
Table 3-2: Allocation of compression machines based on API
Production Sequence
Strength
Number of batches
Compression machines
assigned
1
a
4
Compression machine 1
2
b
14
Compression machine 2
3
c
2
Compression machine 1
4
d
31
Compression machine 2
5
c
15
Compression machine 1
6
b
16
Compression machine 2
7
a
2
Compression machine 1
The second Allocation Policy (AP2) uses two compression machines to do each of the
strengths in one campaign. Under the guide of this policy, the two compression
machines will be used in the way shown in Table 3-3. Unlike AP1, Table 3-3 shows
that the two compression machines are used to produce all the strength. The entire
production requirement is approximately equally distributed to the two compression
machines.
Table 3-3: Allocation of compression machines based on AP2
Production
Sequence
1
2
Assignment for compression
machine 1
2 batches of strength a
7 batches of strength b
Assignment for compression
machine 2
2 batches of strength a
7 batches of strength b
3
5
6
1 batch of strength c
16 batches of strength d
7 batches of strength c
8 bathes of strength b
1 batch of strength c
15 batches of strength d
8 batches of strength c
7
1 batch of strength a
4
8 batches of strength b
1 batch of strength a
The two allocation policies differ in terms of the utilization of the two compression
machines. We know that Product A has four levels of strengths and the compression
machines need a minor changeover, which takes about 16 hours to switch to another
25
strength. API
minimizes the number of changeovers needed for compression
machines during the whole campaign. Since each changeover means a 16 hours'
capacity loss for compression machines, API minimizes the capacity loss due to
changeover. This is why the company currently executes API. However, if we use the
API schedule, there will be a lot of idling of the compression machines. One of the
compression machines could be idle when the HSM is producing the strength which is
assigned to the other compression machine. We do not want the machines to idle and
from this perspective AP2 is better at minimizing the capacity loss of compression
machines due to idling.
We want a high utilization of compression machines. This is because higher
utilization means overall the two compression machines release the 1800L IBCs faster
which means fewer 1800L IBCs (WIPs) will be needed between the HSM and
Compression.
We can see that both API and AP2 are extreme policies. One is to minimize
changeover with the cost of more idling and the other one is to minimize idling with
the cost of a lot of changeovers. The optimal policy is probably a mix of these two
policies. Given a production plan, one problem is to decide how to use the two
compression machines to minimize the number of 1800L IBCs needed. In chapter 4,
we propose an integer programming model to solve this problem. We also use a
deterministic discrete event simulation to compare the new mixed policy AP12 with
both API and AP2.
3.1.3 IBC Driver of Product B and C
The IBC Driver of Product B and C is "IBC Turnover between PFI and PF2". From
section 2.1.1 we know that the production of A, B and C shares the same HSM in PF1
and therefore the company tries to keep HSM running, even though sometimes it is
not the bottleneck of the process. The benefit of doing so is that HSM can finish one
product campaign as quickly as possible and switch to another production campaign.
When PFI and PF2 runs a campaign for product B or C, the compression step in PF2
is the bottleneck; this can be seen by checking the cycle time information for product
B and C in Table 2-2. This is because two of the four compression machines in PF2
are dedicated for product B and the other two are for product C. In this situation, since
the company still does not want to lose the capacity for HSM, we need many 1800L
IBCs to store the intermediate product between the HSM and the compression
machines in order for HSM to run continuously. However, even though compression
step in PF2 is much slower than HSM in PFI, it could still release some of the 1800L
IBCs during the production. Therefore we can infer that the number of 1800L IBCs
needed would be less than the number of batches in the campaign produced, because
of the reuse of some 1800L IBCs.
In the past, the company was not aware of the importance of estimating how many
1800L IBCs are needed between PFI and PF2. This was because the company had
more 1800L IBCs than it needed and thus it would just use whatever it had regardless
of how many were actually needed. However, as the production rates increase, 1800L
IBCs gradually become a critical issue and therefore the company has asked a
question: Can we reduce the number of IBCs used for product B and C? How many
can we reduce?
In chapter 4, we will analyze how many 1800L IBCs are needed to support an X batch
campaign of product B or C.
3.1.4 IBC Driver of Product D
The IBC Driver of Product D is "WIP Level for Product D". Product D is a new
product which will be launched in the year 2009. One problem the company faces
now is to determine how many 1800L IBCs are needed for the production of D. As we
know from Figure 2-2 which shows the process flow of product D, the manufacturing
process for product D requires at least two 1800L IBCs, since the step called Roller
compaction transfers the product from one 1800L IBC to another. All the 1800L IBCs
involved in this process will be divided into two groups. One group of IBCs will serve
the first three steps and the other group will serve the last four steps.
Obviously as we increase the number of 1800L IBCs involved in the production, we
will increase the productivity of the whole production line. However there is a
tradeoff between the cost of WIP inventory and the benefit of production line's
productivity. How to distribute the 1800L IBCs in the system is also a problem that
needs to be addressed. Do the first three steps need more IBCs than the last four steps?
Or putting more IBCs for the last four steps will bring us more benefits in terms of
productivity of the whole line.
In chapter 4, we will describe how to use simulation to approach this problem and get
to know the relationship between the WIP level and productivity of the production
line in PF3. We also present a way to use Buzacott's Line Efficiency [2] concept to
estimate the productivity considering machines' failure.
3.2 IBC cleaning activity
An IBC requires a minor cleaning (dry cleaning) before switching to another strength
of the same product and a major cleaning (wet cleaning) before switching to another
product. Since minor cleaning can be done very quickly, in 15 minutes, in this thesis
we only study IBC major cleaning issues.
3.5.1 Cleaning system
Cleaning system for IBCs includes only one machine called automatic washer. Both
600L IBC and 1800L IBC are cleaned by this automatic washer in PFI. Since
company ABC faces more pressures from 1800L IBC currently, the cleaning priority
of 1800L IBC is higher than that of 600L IBC. Therefore in this thesis, we will not
consider the impact due to 600L IBCs on cleaning section.
The cleaning process for one 1800L IBC is shown in Figure 3-1. This system can only
clean one 1800L IBC at a time. Since there are some manual operations in the process,
the total processing time varies and the mean of the total processing is 3.3 hours. The
automatic washer can run at anytime that it is not down. However, due to the labor
and water constraints in PFI, the cleaning system cannot process cleaning jobs all the
time. Details about the capacity and available slots for the system to handle cleaning
jobs are described in chapter 5.
-
ug
pd
W
t
i
as
Maulula
Figure 3-1: Cleaning process for 1800L IBC
3.5.2 Problem of IBC cleaning
Currently there is no IBC cleaning management system in company ABC and no one
on the floor is specifically in charge of IBC cleaning. This impedes the ability to share
IBCs among different products as much as possible. To understand this, let's assume
that IBC number 1 finishes its production assignment for product A at time ti. If it
could be cleaned before time t2 it can join the production for B. However, there is no
awareness of this opportunity and therefore the company may choose to clean IBC
number I when they have less workload.
To have a clear IBC cleaning schedule, two things need to be further investigated. The
first thing is the details about the pattern of cleaning jobs. For example, how many
IBCs are needed to be cleaned per campaign? Is that different for different product
campaigns? When are the IBCs released from production and ready to be cleaned?
The second thing is automatic washer's capacity and available time slots to clean
IBCs. In chapter 5, we first give the available slots for automatic washer to clean
IBCs and analyze its capacity. After that we present a deterministic discrete events
simulation method to show the cleaning jobs' pattern. This builds on the results of
chapter 4. Finally we give the cleaning scheduling by an example to achieve the
objective of the thesis: support the production of four types of products with a
minimum number of 1800L IBCs.
Chapter 4: Modeling and Analysis for IBC drivers
From chapter 3 we know that there are three IBC Drivers and each of them
corresponds to a problem. To solve the three problems enables us to answer the
question for each of the products, what is the minimum number of IBCs needed. In
this chapter we describe the models we used to solve the three problems respectively.
Results analyses are also provided to show what we can learn from the outputs of the
models.
4.1 Allocation of compression machines
4.1.1 Methodology
From section 3.1.2, we know that the problem corresponding to the IBC Driver
"Allocation of Compression Machines in PF 1" is that given the production plan for a
product A campaign, how should we use the two compression machines in PF1 to
minimize the number of the 1800L IBCs needed.
Table 3-1: An example for a product A campaign
Production Sequence
Strength
Number of batches
I
a
4
2
b
14
3
c
2
4
d
31
5
c
15
6
b
16
7
a
2
Given a production plan as shown in Table 3-1 (reproduced and shown above), we
31
know that one product A campaign consists of several sub-campaigns
(7
sub-campaigns for the case shown in Table 3-1). In each sub-campaign, only one kind
of strength is produced. For example, for the case shown in Table 3-1, the first
sub-campaign is to produce 4 batches of strength a.
Next we will first introduce an integer programming model to solve this problem. The
decision variables of our model are the manufacturing state for the two compression
machines for each sub-campaign of product A. The machine has to choose either to
join this sub-campaign (1) or not to join this sub-campaign (0); when a machine "joins"
the sub-campaign then it will be setup to produce the strength for the campaign and
will continue to produce until the sub-campaign is finished. The objective of the
model is to minimize the number of 1800L IBCs needed in this process. After that, we
will present a deterministic discrete event simulation method to evaluate the solution
obtained from the integer programming model. We will compare the mixed allocation
policy that is determined by the integer programming model with both API and AP2
mentioned in section 3.1.2; we will then provide the analyses of the results.
4.1.2 Modeling
4.1.2.1 Input and output
The input for the model is the production plan for a product A campaign including the
product strength and volume for each sub-campaign. An example can be seen in Table
3-1. The output of the model is a plan about how to use the two compression
machines in PF1 to complete this campaign for product A, which we call the
Allocation Policy 12 (API2).
4.1.2 .2 Assumptions
We make the following assumptions to solve this problem. Figure 2-1 and Table 2-1
are reproduced and shown below for convenience.
1. All the machines in the process of producing A (shown in Figure 2-1) are reliable
which means machines' failures will not happen.
2. Cycle time for each production step is fixed as shown in Table 2-1.
3. Transportation and setup time is ignored in the model.
4. The compression machine can start the production for one batch whenever it is
available for production and does not need to wait for HSM to finish that batch.
Compression machines are not available for production when they are doing minor
changeover for strength change.
5. Changeover of compression machine is only considered when it does two
consecutive sub-campaigns.
6. The compression machines are fixed for each of the sub-campaigns. This means
once a compression machine joins a sub-campaign, the machine is dedicated to this
production assignment until the sub-campaign is finished.
7. The product strength for two consecutive sub-campaigns is different. This
assumption is valid because if two consecutive sub-campaigns produce the same
product strength the two sub-campaigns can be combined into one sub-campaign.
PFI
I
I
I
I
I
I
I
I
I
1
I
I
i
I
I
I
i
i
I
I
I
I
I
Figure 2-1: Manufacturing process of Product A, B and C
Table 2-1 Cycle time information for Product A
Charging
HSM
Blending
Compression
a
4h
7hr
1h
26hr30min
b
4h
7hr
1h
9hr
Batch size x
4h
7hr
1h
8hr
Batch size y
4h
7hr
Ih
8hr
Batch size x
4h
7hr
1h
9hr
Batch size y
4h
7hr
lh
9hr
Process
Strength
c
d
The assumptions are made to make this problem easier to model without sacrificing
the performance of the solutions. We note here that by making the assumption number
four, we allow the WIP between HSM and the compression machines to be negative.
We know that in reality the compression machines cannot start to produce a batch
until the HSM finishes the production of the batch. Therefore, the WIP we calculate in
this problem is not the actual WIP. Nevertheless, the WIP, as calculated by the model,
provides a relative measure of what WIP might be needed in reality. As such, we
expect that this assumption will be fine, given the intent of the model is to determine
the schedule of the two compression machines that can minimize the amount of WIP.
The evaluation of the solution is given in section 4.1.2.5.
4.1.2.3 Variables
xi
h
Manufacturing state for compression machine 1 for the i sub-campaign
xi=1 if
the compression machine 1 joins the ith sub-campaign and xi=O if not
yi
Manufacturing state for compression machine 2 for the ith sub-campaign
if the compression machine 2 joins the ith sub-campaign and yi=O if not
Ci
Cycle time of one compression machine for the ith sub-campaign in hours
It is known when the strength for the ith sub-campaign is given as input.
Hi
Cycle time of the HSM for the ith sub-campaign in hours
ni
th
Number of batches that need to be produced for the i sub-campaign
34
yi= 1
This is given as input.
k
Number of sub-campaigns in the whole product A campaign
t
Time needed for a minor changeover of compression machine in hours
Pi
WIPs accumulated during the production period for the ith sub-campaign in
batches
Oi
WIPs accumulated during the minor changeover period after the ith sub-campaign
in batches
Wj
WIPs accumulated after the jth sub-campaign
4.1.2.4 Modeling
Min z
s.t.
Pi= (~- Hi
Ci
i --
Ci
*Yi)*
ni[xi * yi * Ci/2 + (1 - xi * Yi) * Hi]
(1)
for i=1,2,..., k --------------------------------------Oi=[± -(l-xi * xi+ 1 ) * - - (1 - yi * Yi+1) * ]*t*[L1-(1-xi*xi+l)*(1-yi*yi+l)]
Hi
C1
Ci
(2)
for i= 1,2,...,k ----------------------------------------Wj =
Pi +
i=, Oi for j=1,2,...,k-------------------------------------
z_ Wj for all j=1,2,3,...,k --------------------------------------xi+yi> 1----------------------------------------------
(3)
---------------- (4)
--------------------------------------
(5)
xi=O0,1 ----------------------------------------------------------------
(6)
yi=0,1--------------------------------------------------------------------
(7)
The objective function is to minimize the maximum amount of WIP that is
accumulated after any sub-campaigns.
Constraint (1) defines the WIP that is accumulated during the production period for
the ith sub-campaign. (
-
* xi-
* Y) is the difference between the HSM
production rate and the compression machine production rate, which is the WIP
35
accumulating rate. According to cycle time information in Table 2-1, when two
compression machines both produce the same strength (except for a strength 10/10),
the WIP accumulating rate is negative. This is valid because of the assumption 4
which assumes that the compression machines can start producing a batch even when
the HSM has not finished that batch. ni[xi * Yi * 2 + (1 - xi * Yi) * Hi] is the time
needed for the faster step between HSM and compression to finish the ith
sub-campaign. From the expression we know that when two compression machines
are running for the same sub-campaign, compression is the faster step and therefore
the time needed for the i'' sub-campaign is ni*Ci/2. Otherwise, HSM is the faster step
and therefore the time needed is ni*Hi.
Constraint (2) defines the WIP that is accumulated during the minor changeover
period after the it h sub-campaign. According to the assumption 5, changeover is only
considered when the compression machine does two consecutive sub-campaigns. The
factor 1-(1-xi*xi+l)*(1-yi*yi+1) means that if neither of the two compression machines
has a changeover (xi*xi+l=0 and yi*yi+1=O) there is no WIP accumulated due to
1
1
changeover (Oi=O). t is the time needed for changeover and - -(1-xi * xi+1) * - Hi
1
(1 - yi * Yi+1) *
Ci
is the difference between the HSM production rate and the
compression machine production rate. We can see that if the compression machine I
does two consecutive sub-campaigns (xi * xi+, = 1), it loses the production capacity
for t hours.
Constraint (3) defines the WIP that is accumulated after the j sub-campaigns and
constraint (4) introduces a variable z which is larger than or equal to Wj for all j.
Constraint (5) shows that for the
ith
sub-campaign, at least one compression machine
should be assigned to it. Constraint (6) and (7) show that xi and yi are binary
variables.
4.1.3 Results Evaluation
4.1.3.1 Example
Assume that the production schedule for a product A campaign is given in Table 3-1.
For this example, fixed variables include:
Hi= 7 for i=1,2,...,7
[Ci, C2, C 3, C 4, C5, C6 , C7] T= [26.5, 9, 8, 9, 8, 9, 26.5]
[ni, n2, n 3 , n4 , n 5, n 6]
T=
T
[4, 14, 2, 31, 15, 16, 2]
k=7
Let the time needed for one compression machine to undergo a minor changeover be
16 hours, which means t= 16 hours. Decision variables are xi and yi.
We built the integer programming model in Excel and solve the problem by the Excel
Solver. The optimal solution AP12 is shown in Table 4-1.
Table 4-1: Allocation of compression machines based on AP12
Production
Sequence
Strength
Number of batches
Compression machines assigned
1
a
4
Compression machine 2
2
b
14
Compression machine I and 2
3
c
2
Compression machine 2
4
d
31
Compression machine I and 2
5
c
15
Compression machine land 2
6
b
16
Compression machine I and 2
7
a
2
Compression machine 1 and 2
We also know that under the guide of API and AP2, the two compression machines
will be used in the way shown in Table 3-2 and Table 3-3 respectively. Table 3-2 and
3-3 are reproduced and shown below for convenience to compare.
Table 3-2: Allocation of compression machines based on API
Production Sequence
Strength
Number of batches
Compression machines
Compression machines
I
a
4
Compression machine 1
2
b
14
Compression machine 2
3
c
2
Compression machine 1
4
d
31
Compression machine 2
5
c
15
Compression machine 1
6
b
16
Compression machine 2
7
a
2
Compression machine 1
assigned
Table 3-3: Allocation of compression machines based on AP2
Production
Sequence
1
2
3
4
5
6
7
Assignment for compression
machine I
2 batches of strength a
7 batches of strength b
1 batch of strength c
16 batches of strength d
7 batches of strength c
8 bathes of strength b
1 batch of strength a
Assignment for compression
machine 2
2 batches of strength a
7 batches of strength b
I batch of strength c
15 batches of strength d
8 batches of strength c
8 batches of strength b
I batch of strength a
In section 3.1.2, we mentioned that currently the company executes API to guide how
to use the two compression machines to produce product A. However, both AP2
shown in Table 3-3 and API2 shown in Table 4-1 are feasible to implement. First, the
minor changeover of compression machines costs nothing other than the 16 hours'
capacity lost. If the people on the floor know that the compression machine would
join the i+ls' sub-campaign, the machine can be cleaned after the ith sub-campaign.
Second, sometimes when the number of batches of a sub-campaign is so large, such as
more than 30 batches, then the people on the floor do use two compression machines
during one sub-campaign to release the 1800L IBCs faster. Otherwise the 1800L IBCs
would not be enough to support the production of product A.
Next we will compare the performance of the three Allocation Policies on saving the
1800L IBCs.
4.1.3.2 Deterministic Discrete Event Simulation
We will use a Deterministic Discrete Event Simulation method to compare for this
example, how these three policies (API, AP2 and AP12) perform.
We make the following assumptions to do the simulation:
1. All the machines in this process are reliable which means machines' failure will
not happen.
2. Cycle time for each of the step is fixed as shown in Table 2-1.
3. Transportation and setup time is ignored.
4. The HSM starts at time zero for this campaign
Given the production plan shown in Table 3-1, we know that the first sub-campaign is
to produce four batches of strength a. According to the assumption number 4, the
HSM starts at time zero. Since the cycle time for the HSM is 7 hours, we know the
HSM finishes the first batch of strength a at time 7 (0+7). Meanwhile, one of the
compression machines will start to produce the first batch of strength a and finish it at
time 33.5 (7+26.5). The HSM of the second batch finishes at time 14 (7+7). If both
the two compression machines join this sub-campaign, the other compression
machine will start the compression at time 14 and finishes at time 40.5 (14+26.5). If
only one of the compression machines join the sub-campaign, it can only start the
compression for the second batch at time 33.5 and finishes at the time 60 (33.5+26.5).
We simulate the production for all the batches of this product A campaign under the
guide of API, AP2 and AP12 for how to use the two compression machines. We
determine the number of the 1800L IBCs needed as follows: the number increases by
one whenever the HSM starts to produce a batch and it decreases by one when one of
the compression machines completes the compression. We can plot the number of
1800L IBCs needed over time for API, AP2 and AP12. The plot is shown in Figure
4-1, 4-2, and 4-3 respectively.
AP1
en
M
~ ll0<UiL O0
~LM 1N"
LA
zr4,IN
O
r-
m
4-4 F14o
4
4q
N
00e
w
0'7%
r
0 W
Ln
0
0
n
0
t
Time (h)
Figure 4-1: The number of the 1800L IBCs needed versus time by using AP1
AP2
a
-4
M
0
"
r"
f4
M
M
M
A
1-4
V
N
4
4
0vM
r
4
(4
0
M
r"
4
M
4
Mw
rn
v
e
-w
n
N
Ln
Time (h)
Figure 4-2: The number of the 1800L LBCs needed versus time by using AP2
(D
AP12
7
m Lnr
-________
-
-
'r
Oii~~
k6
0
T
-4
r
4
t".4enL
m
(h )
-
m
U
-k
Time (h)
Figure 4-3: The number of the 1800L ICBs needed versus time by using AP12
4.1.3.3 Results Analysis
The maximum number of the 1800L IBCs needed and the campaign length for this
production plan under the guide of AP1, AP2 and AP12 are summarized in Table 4-2.
The campaign length starts from time zero and finishes when the compression of the
last batch of the campaign completes.
Table 4-2: Performance comparison among AP1, AP2 and AP12
Campaign Length
Maximum Number of the 1800L IBCs
Allocation
(hours)
needed
Policies
656
10
API
640.5
8
AP2
640.5
6
AP12
We can see from Table 4-2 that the minimum maximum number of the 1800L IBCs
needed for this production plan is 6, which can be achieved by implementing AP12.
Comparing Figure 4-1, 4-2 and 4-3, we can find that the plot corresponding to AP12
actually combines the better part of API and AP2 together. In terms of campaign
length, for this production plan, AP2 and API2 perform equally. Therefore, we can
conclude that for this production plan, AP12 performs best among the three allocation
policies.
According to the company's production plan for the year 2008, during a product A
campaign, there may be more than one sub-campaigns producing the same strength.
For example, in the example shown in Table 3-1, the strength a appears twice and the
strength d only appears once. We compared the performances of API, AP2 and AP12
by using five production plans available from company for this year and obtained the
following observations.
1. For the strengths b, c and d the maximum number of the 1800L IBCs will not
exceed a certain value if we use two compression machines, regardless of the
number of batches required to be produced in a sub-campaign. According to the
cycle time information in Table 2-1, for the strength b, c and d if we use two
compression machines the bottleneck for the system will be the HSM step.
Therefore the WIP does not accumulate between the HSM and compression
machines as the production volume increases for the sub-campaign.
2. It is better to not use two compression machines to produce the strength a together.
We know that the cycle time of compression for the strength a is 26.5 hours which
is much longer than that of the HSM. If we use two compression machines to
produce the strength a together, this will result in many 1800L IBCs accumulating
between the HSM and the compression step. Under this situation, it is better to
assign one of the compression machines to start producing the next sub-campaign
for some strength other than strength a. This reduces the maximum number of the
1800L IBCs needed.
4.2 IBC Turnover between PF1 and PF2
From section 3.1.3 we know that the problem corresponding to the IBC Driver "IBC
Turnover between PFI and PF2" is how many 1800L IBCs are needed to support an
X batch campaign of product B or C.
From Figure 2-1 we know that product B and C follow the same manufacturing
process. According to the cycle time information in Table2-2 when PF1 and PF2 run a
product B or C campaign, the Compression step in PF2 is the bottleneck. Therefore to
prevent the HSM in PFI from being blocked, we need some amount of the 1800L
IBCs to store the product between HSM and the compression machines. However, we
know that during the process, the compression machines in PF2 can release some
1800L IBCs; hence the number of 1800L IBCs required will be less than the batch
size of X. We now provide a deterministic simulation method to analyze this problem.
4.2.1 Assumptions
We make the following assumptions to run the deterministic simulation.
1. All the machines in this process (shown in Figure 2-1) are reliable which means
machines' failures will not happen.
2. Machines' process time is constant
3. The 1800L IBC will be sent to PF2 immediately after the HSM in PF 1I
completes
the production of each a batch. The situation is the same for PF2, which means the
1800L IBC will be sent back to PFI immediately after the compression is
completed and the IBC is released.
4. Both PF1 and PF2 are running at 24 hours/day and 7 days/week.
4.2.2 Deterministic Simulation
The deterministic simulation in this section is similar with what we have done in the
section 4.1.3.2. We know that the cycle time of the HSM is 7 hours/batch for both
product B and C. The cycle time of one compression machine for product B is 22
43
hours and 35 hours for product C. We assume that the transportation time of the
1800L IBC between PF1 and PF2 is 0.75 hours. In the following we use a product B
campaign as an example to illustrate the process of simulation we have done.
Assume the product B campaign starts at time zero. The HSM step for first batch of
product B is finished at time 7 (0+7). One 1800L IBC will transport the semi-product
to PF2 and the compression of the first batch will start at 7.75 (7+0.75) and finish at
29.75 (7.75+22). The HSM starts the second batch at 7 and finishes it at 14 (7+7).
Since there are two compression machines in PF2 for product B, compression for the
second batch will start at 14.75 (14+0.75) and finish at 36.75 (14.75+22). We can see
that the third batch will arrive at PF2 at the time 21.75 (14+7+0.75); however the
compression can only start at 29.75 when the compression of the first batch finishes.
Using this method, we can simulate an X batch campaign of product B and determine
the number of the 1800L IBCs required for the campaign. We can do the simulation
for any value of X; by varying the value of X, we can get the plot that shows how the
IBC requirements vary with the size of the campaign. This is shown in Figure 4-4.
33
-
-
----
--
__
31-
-o )
29
--
-
-
-
--
27
r
25
I, 00
-
0
-
-----------~---------------
-
--~-----~------~-~----------~-------
21
S19
17
4041424344454647484950515253545556575859606162636465666768697071
727374757677787980
Campaign Size (number of batches)
Figure 4-4: Number of 1800L IBCs needed for product B
Figure 4-4 shows that as we vary the campaign size from 40 to 80, the 1800L IBC
requirements increase from 17 to 32. Currently company ABC has 30 1800L IBCs
44
and therefore the company needs to purchase extra 1800L IBCs to support a product
B campaign whose size is larger than 76 batches.
Since product C follows exactly the same manufacturing process as product B, we can
also generate the same plot for product C which is shown in Figure 4-5.
i
a
31
a)
29
U
S27
25
c
23
00
0
21
19
d 17
Z
25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
Campaign Size (number of batches)
Figure 4-5: Number of 1800L IBCs needed for product C
In Figure 4-5, we vary the campaign size from 25 to 50 and the number of 1800L
IBCs increase from 17 to 32. We can also see that the maximum campaign size the
current 30 1800L IBCs can support is 48.
4.3 WIP Level for Product D
4.3.1 Methodology
From section 3.1.4 we know that the problem corresponding to the IBC Driver "WIP
Level for Product D" is: the relation between the WIP level (number of the 1800L
IBCs) in the system and the productivity of the system shown in Figure 2-2 and how
to distribute those IBCs to gain maximum benefits in terms of productivity of the
system.
Figure 2-2 Manufacturing process of Product D
We will first describe how to use Simul 8 to build a deterministic model to study the
relation between the WIP level and productivity of the system and how to distribute
the IBCs. In the deterministic model we will assume all the machines are perfectly
reliable which is different from the real case. Therefore we will propose a method to
estimate the real productivity of the system considering the effects caused by
machines' failures by using Buzacott's Line Efficiency concept.
There are two reasons why we do not use simulation to analyze the real productivity
of the system directly accounting for the unreliability of the machines. First, product
D has not been launched yet and therefore we do not have downtime information for
machines. To use simulation to get the productivity of the system considering
machines down, we need to assume the MTTR (Mean-Time-To-Repair) and MTTF
(Mean-Time-To-Fail) for each of the machine. However, by using Buzacott's Line
Efficiency concept to estimate the productivity of the system, we only need to know
the ratio of MTTR and MTTF for each of the machines. We can get the ratio by
assuming the efficiency of each of the machines, which is easier. Second, we can put
the results obtained from the deterministic simulation model and Buzacott's
Zero-Buffer Line Efficiency formula into an Excel spreadsheet model that will be
much more flexible for the company. The company can change the efficiency of the
machines in the future when they have downtime information.
4.3.2 Deterministic Simulation by Simul 8
In this section, we use simulation to study the relation between the WIP level and the
productivity of the system shown in Figure 2-2. Following assumptions are made to
build the model.
1. All the machines in this process (shown in Figure 2-2) are reliable which means
machines' failure will not happen.
2. Machines' process time is constant as shown in Table 2-3.
3. Transportation time of the 1800L IBC is constant as shown in Table 4-3.
4. Setup time is not considered
Table 2-3 Cycle time information for Product D
First
Pre-
Roller
Second
Final
Charging
blending
Compaction
Charging
blending
4h
lh
4.5h
4h
45min
Compression
4.5h
Table 4-3: Transportation time of the 1800L IBC for product D
From
First Charging
Pre-Blending
Roller Compaction
Roller Compaction
Second Charging
Final Blending
Compression
To
Pre-Blending
Roller Compaction
First Charging
Second Charging
Final Blending
Compression
Roller Compaction
Transportation time of IBC (h)
0.5
0.5
0.5
0.5
0.5
1
1
Assumption 1 and 2 tell us that the model we are going to build is deterministic and
nothing in the simulation is subject to random variation. To simulate the process
shown in Figure 2-2, we built a system with Simul 8 shown in Figure 4-6.
Resource A
First Charging
00
Preblending
trahsportati
Resurce B
Roller Compaction
0
nspotatio2
Second Charging
Transportation3
0
Final Blending
0
Transportatior
Compression
0
Transportation5
Figure 4-6: Simulation model for product D by Simul 8
From Figure 4-6 we can see that there are six Work Centers: First Charging,
Pre-Blending, Roller Compaction, Second Charging, Final Blending and Compression.
(For the icon meaning and other details about Simul 8 such as Work Center and
Resource, please refer to the appendix.) Since in Simul 8, the transportation between
two Work Centers is set to be zero, we treat the transportation as a work center also.
The input cycle time for the transportation steps in the model is the same as shown in
Table 2-3. Based on the characteristic that the 1800L IBCs are used at the start of one
process and released after the completion of that process, we consider the impacts of
the 1800L IBCs by adding two kinds of Resources. Resource A is required for First
Charging, Pre-Blending and Roller Compaction and Resource B is required for Roller
Compaction, Second Charging, Final Blending and Compression. These Work Centers
can only operate when the resource they need is available. (Roller Compaction can
only operate when both of the two resources are available.) Resources will be released
after the work is completed.
The purpose of this simulation is to investigate the relation between the number of the
1800L IBCs used in the system and the productivity of the system. Therefore we
varied the number of each resource in the system and determine the production
volume for the system. We assume that any machine in the system will start a batch
whenever it can and the objective is to produce as many batches as possible. The
simulation length is one year and the production volume is counted in batch. The
results are summarized in Table 4-4.
From Table 4-4 we can see that when the number of resource A reaches 3, any
additional resource A does not bring benefits to the production volume of the system.
For example, the annual production volume that corresponds to (3, 4) and (4, 4) is the
same and equal to 1942 batches. The same situation happens to resource B when the
number of resource B reaches 4. For example, the production volume that corresponds
to (3,4) and (3,5) is the same, and equal to 1942 batches. Therefore, we set the
maximum number of the 1800L IBCs in the system equal to 7 since three resources A
and 4 resources B already enable the system to achieve the best performance.
Table 4-4: Relations between the production volume and resources available
Total number of
resources
2
Number of resources(Resource
A, Resource B)
(1,1)
Production Volume for one
year (batches)
635
3
(2,1)
3
4
(1,2)
635
944
(1,3)
(3,1)
944
635
4
5
5
(2,2)
1270
(1,4)
(4,1)
944
635
5
(2,3)
1884
5
(3,2)
1270
6
6
(1,5)
(5,1)
(2,4)
944
635
1884
(4,2)
(3,3)
4
6
6
6
7
(1,6)
1270
1905
944
7
(6,1)
636
7
(2,5)
1884
7
(5,2)
1270
7
(4,3)
1905
7
8
(3,4)
(3,5)
1942
1942
8
(4,4)
1942
9
(4,5)
1942
4.3.3 Line Efficiency defined by Buzacott
Buzacott (1967)
[21
defined the line efficiency for a zero buffer line.
Assume that we have a flow line with k machines. Machines are assumed to have
constant operation times. Machines can only fail while they are working. Both down
times and up times are distributed geometrically. Let ti be the operation time for
machine i.
Define the probability of Machine Mi failing during a time unit when it is operating
be pi=ti/MTTFi
Define the probability of Machine Mi being repaired during a time unit when it is
down be ri=ti/MTTRi
The efficiency of the line is defined as the ratio of the number of units produced over
some long interval to the number that would have been produced in the same time
with no stoppages. According to Buzacott (1967)
[2]
the efficiency of the line EODF can
be expressed as:
1+z
1+i=1r ii
---
----------------------------------------
(8)
In section 4.3.2 we obtained the one year's production volume of the system by
varying the WIP level (number of the Resources). Note the fact that the maximum
number of resources is 7 and one of the six machines needs two resources. This
system performs similarly to a zero buffer line. In Buzacott's model he assumed that
whenever a machine in the line fails the whole line stops. For the system shown in
Figure 4-6, although the whole line does not stop immediately when a machine fails,
since we restrict the number of the 1800L ICBs in the system, the whole line will stop
soon after any one of the machines fails. Therefore we use zero buffer line to
approximate the performance of the line shown in Figure 4-6.
4.3.4 Productivity considering machines down
We know that the availability of one machine for production is defined as:
A
E[Uptime]
[
E[Uptime]+E[Downtime ]
---------------------------------------
(9)
Therefore we define the availability for machine Mi as
Ai
MTTF i
-
T-------------------------------------------------------------------------
MTTF i + MT T R i
(10)
We have that
Pi
-r
ri
MTTRi
MTTF-------------------------------------------------------(11)
MTTFi
According to equation (8), (10) and (11), we have
EODF
k 1-Ai
1i=1 Ai
-- -- - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
(12)
Let Pij be the production volume of the system for a year with i resource A and j
resource B without considering machines down. We can find the values for Pij from
the Table 4-4.
Finally we determine the production volume of the system for a year with i resource A
and j resource B and with unreliable machines as follows:
1
Pi=PijxEODF=Pij x
k 1-A
------------------------------------------------------- (13)
1+zi=1 Ai
4.3.5 Results Evaluation by an Example
Suppose that company ABC plans to produce 150 batches of product D for the next
year. Since this is a new product, the company estimates that the effective production
time of the next year will only be 4 months. The rest of the year will be spent on
testing and experiments.
We assume the availability for the six machines in the system are the same and equal
to 80%, which means AI=A 2=...=A6 =0.8.
If we use only two 1800L IBCs in the system, based on the Table 4-4, we have
P (1,1 = 635
We use the equation (19) to get
P(1, 1 =635x
k1-A
=
254
i=1 T-
Since there are only four months available for production, the actual batches we
expect to produce is 254*4/12=84.7 which is smaller than the 150 batches' production
requirement.
Let's use four 1800L IBCs in the system. Probably we will choose that two of them
are for the first three machines and the other two are for the last four machines. From
the Table 3-3, we know that P
1
P (2,2)= 1270
-k
1-Ai
1+i=1" Ai
=
(2,2)
= 1270. We use the equation (19) to get
508
Considering the effective time for production, finally we see that we are able to
produce 508*4/12=169 batches which can meet the production requirement. None of
the combination in the Table 3-3 can meet the production requirement with less than 4
1800L IBCs; thus for this example the minimum number of the 1800L IBCs needed is
4.
Chapter 5 IBC Cleaning
In this chapter we focus on the cleaning issues of the 1800L IBCs. First, capacity
constraint and available time slots for automatic washer are provided to determine the
upper bound on cleaning capacity. Second, a deterministic simulation method is
proposed to generate the cleaning jobs for two different production scenarios. Finally,
we use an example to show how we can determine the minimum number of the 1800L
IBCs to support the production for four types of products.
5.1 Production campaign scenarios
In chapter 2, we have described the manufacturing process for product A, B, C and D.
We know that the manufacturing process for product D is independent of the other
three products. Moreover currently the production planning for product D has not
been fixed. Therefore we choose to analyze product A, B and C first and later will add
the impact of product D.
From the description in section 2.1.1 we know that product A shares the same
Charging and HSM steps with product B and C. From the perspective of the HSM
step, there are two production campaign scenarios which are shown in Figure 5-1 and
5-2. In Figure 5-1, the HSM follows a campaign sequence which is Product A-Product
B-Product A-Product C. In Figure 5-2, it follows the sequence Product A-Product
B-Product C-Product A.
Time
Charging & HSM
in PF1
Compression
Machines in
PF1
Compression
Machines
Numberl&2
in PF2
Compression
Machines
Number3&4
in PF2
In campaign for one product
SMajor changeover between campaign
*
Machine idling period
In PF2, compression machines 1 & 2 are dedicated for product B
compression machines 3 & 4 are dedicated for product C
Figure 5-1: Campaign sequence scenario 1
Time
Charging & HSM
in PF1
Compression
Machines in
PF1
Compression
Machines
Numberl&2
in PF2
Compression
Machines
Number3&4
in PF2
SIn campaign for one product
[-'
Major changeover between campaign
l
Machine idling period
In PF2, compression machines 1 & 2 are dedicated for product B
compression machines 3 & 4 are dedicated for product C
Figure 5-2: Figure 5-1: Campaign sequence scenario 2
5.2 Capacity constraint and available time slots for automatic washer
In section 3.5.1, we described the cleaning system in PF1. The whole cleaning process
includes manual setup, operation in automatic washer and manual unload. The system
can only handle one cleaning job (one IBC) at a time and the whole processing time
lasts approximately 3.3 hours for each IBC.
There are some time slots that the system cannot process cleaning jobs due to the
labor and water constraint of PF1. These slots include:
1. When PF1 is in campaign for one product, there are no extra people to clean IBCs.
These slots are indicated with blue color in the first row of Figure 5-1 and 5-2.
2. When the HSM in PF1 is undergoing a major changeover, there is not enough
water to clean IBCs. These slots are included in the major changeover between
campaign periods which are indicated with yellow color in the first row of Figure
5-1 and 5-2.
The major changeover between campaign periods for PF1 is 7 days. During these 7
days, the first two days are spent on the major changeover for the HSM. Therefore we
can conclude that each time when PFI finishes the campaign for one product, there
are 5 days=120 hours available to clean the IBCs. Let e be the availability of the
automatic washer which means the percent of the time that the washer is ready to use.
Let n be the maximum number of the 1800L IBCs that can be cleaned during each
available slot. Then we have:
n= 120 hours * e / 3.3 hours ----------------------------------------
------ (14)
5.3 Cleaning jobs generation
5.3.1 Methodology
We use the Deterministic Discrete Event Simulation method which is used in chapter
56
4 to calculate the following time points:
1. When does one production campaign start (end)?
2. When is the 1800L IBC released from production?
To achieve this we need to use the models mentioned in section 4.1.2 and 4.2 in
following ways:
1. Given the production plan for product A, use the model in section 4.1.2 to find the
best way to use the two compression machines in PF1 to minimize the IBC
requirements.
2. Given the production requirement for product B or C, use the model in section 4.2
to get the minimum number of the 1800L IBCs needed to support the production.
5.3.2 Example
5.3.2.1 First scenario: A-B-A-C
We assume that the company runs the production campaign in the way Figure 5-1
shows. The HSM in PFI first runs a product A campaign (The production plan is
shown in Table 5-1.), switches to a product B campaign (63 batches), switches to
another product A campaign (The production plan is shown in Table 5-2) and then
runs a product C campaign (39 batches).
Let the availability of the automatic washer
be e=0.9.
Table 5-1: Production Planning for the first product A campaign
Production Sequence
Strength
Number of batches
1
10/80
8
2
10/40
17
3
10/20
25
4
10/10
2
Table 5-2: Production Planning for the second product A campaign
Production Sequence
Strength
Number of batches
1
10/80
14
2
10/40
34
3
10/20
17
4
10/10
2
5.3.2.1.1 The first product A campaign
For the first product A campaign, we use the integer programming model in section
4.1.2 to find the way to use the two compression machines in PF1 to minimize the
number of the 1800L ICBs needed. The optimal way to use compression machines is
summarized in Table 5-3. We assume that for this campaign, the HSM starts at time
zero. By doing the same deterministic simulation as 4.1.3.2 did, we can obtain the plot
of the number of 1800L IBCs needed versus time shown in Figure 5-3. The simulation
can also tell us that the HSM finishes this campaign at time 373=0+7 hours/batch * 52
batches+3 hours/minor changeover * 3 minor changeovers. After the HSM finishes
this campaign, PF1 is in the major changeover between campaign periods. The first
48 hours (from 373 to 421) will be spent on the major changeover of the HSM, and
the rest 7*24-48=120 hours (from 421 to 541) is the available slot to clean the IBCs.
From Figure 5-3 we can see that in this campaign 4 1800L IBCs are involved in
production. Assume these four IBCs are called IBCI, IBC2, IBC3 and IBC4. Table
5-4 shows the time points when these four IBCs are released from production.
Table 5-3: The optimal Allocation Policy for the first product A campaign
Assignment for compression
Assignment for compression
Production
machine 2
machine 1
Sequence
4 batches of 10/80
4 batches of 10/80
1
8 batches of 10/40
9 batches of 10/40
2
batches of 10/20
13
12 batches of 10/20
3
1 batch of 10/10
1 batch of 10/10
4
0M
LAn
L' 000
Ch 0
0
(i
V-1
NNNN
M
NM
LA
'-4
4
r1
N
M
Time (h)
Figure 5-3: Number of the 1800L IBCs needed for the first product A campaign
Table 5-4: IBCs release time points for the first product A campaign
IBC
IBC1
Release Time points
211
IBC2
351
IBC3
IBC4
400.5
407.5
Notice that the available slot to clean IBCs is from time 421 to 541. According to
equation (14), the maximum cleaning capacity in this slot is 120 hours* 0.9/ 3.3
hours=32.7. Therefore IBCI to IBC4 can be easily cleaned in this slot and used in the
next campaign.
5.3.2.1.2 Product B campaign
The HSM will start the product B campaign from time 541 and end at the time
982=541+63*7. Assume two compression machines are used to produce B and the
number of working days per week in PF2 is 7. According to Figure 4-4 in section 4.2,
to support a 63 batches of product B campaign, 25 1800L IBCs are required. Figure
4-4 is reproduced and shown below for convenience. In this campaign 25 1800L IBCs
are involved in production, which are denote as IBC1 to IBC25. The deterministic
simulation can also tell us when these IBCs are released which is shown in Table 5-5.
33
o
0
31
29
27
U
-
-------
S25
C
23
00
-
21 4
0
-
-.--
_-
____
_______
-
~--
-
19
E
Z:
17
15
- I
-,
T-7-
1F
7- -----
-T--
T-
r7-7
I
-1
4041 42 434445464748 49 50 51 52 53 54 55 56 57 58 596061 6263 6465 66 67 68 69 7071 72 73 74 75 76 77 78 79 80
Campaign Size (number of batches)
Figure 4-4: Number of 1800L IBCs needed for product B
Table 5-5: IBCs release time points for the product B campaign
IBC
IBC 1
Release Time points
989.5
IBC2
996.5
IBC3
IBC4
IBC5
IBC6
1011.5
1018.5
1033.5
1040.5
IBC7
1055.5
IBC8
1062.5
IBC9
1077.5
IBC10O
IBC 11
1084.5
1099.5
IBC12
IBC13
1106.5
1121.5
IBC14
1128.5
IBC15
IBC16
1143.5
1150.5
IBC17
1165.5
IBC18
1172.5
IBC 19
1187.5
IBC20
1194.5
IBC21
1209.5
IBC22
1216.5
IBC23
1231.5
IBC24
1238.5
IBC25
1253.5
The HSM finishes this campaign at the time 982. The available slot to clean IBCs is
from 982+48=1030 to 1030+120=1150. Notice that IBC1 to IBC 15 are released and
can be cleaned during this slot. Therefore these 15 1800L IBCs can be used in the
next campaign. The other 10 IBCs, which are IBC16 to IBC25, have to wait and get
cleaned the next time that it is available to clean IBCs.
5.3.2.1.3 The second product A campaign
The production plan for this campaign is shown in Table 5-2. We repeat what we have
61
done in section 5.3.2.1.1. The optimal way to use compression machines is
summarized in Table 5-6. The plot of the number of 1800L IBCs needed versus time
shown in Figure 5-4. From Figure 5-4, we can see that four 1800L IBCs are involved
in production. We assume we use IBC1 to IBC4 to support the production. Table 5-7
shows the time points when these four IBCs are released from production
Table 5-6: The optimal allocation policy for the second product A campaign
Assignment for compression
Assignment for compression
Production
machine 2
machine I
Sequence
7 batches of 10/80
7 batches of 10/80
1
17 batches of 10/40
17 batches of 10/40
2
8 batches of 10/20
9 batches of 10/20
3
1 batch of 10/10
1 batch of 10/10
4
o
0
00
0
0
0
0
0
0
0
00000
0
0
0 0
0
0
a
000
0 0
0O0
0
0
00
0
0.0R0
0
0 0
0
0
0 0
000
0
0
0
0
0
0
0
Time (h)
Figure 5-4: The number of the 1800L IBCs needed for the second product A campaign
Table 5-7: IBCs release time points for the second product A campaign
Release Time points
IBC
1522
IBC1
1606
IBC2
1655.5
IBC3
1662.5
IBC4
The HSM finishes this campaign at the time 1628=1150 + 7 hours/batch *67 batches
62
+ 3 hours / minor changeover * 3 minor changeovers. Therefore the available slot
starts from 1628+48=1676 to 1676+120=1796. During this slot the maximum
cleaning capacity is 32.7 as mentioned in section 5.3.2.1.1; therefore we can clean
IBC1 to IBC 4 that were used in this product A campaign and IBC 16 to IBC 25 that
were used in the previous product B campaign. At this time, IBCI to IBC 25 are all
ready to be used in the next campaign.
5.3.2.1.4 Product C campaign
The HSM will start the product C campaign from time 1796 and end at time
2083=1796+41*7. We repeat what we have done in section 5.3.2.2.2. According to
Figure 4-5, we know that to support a 39 batches product C campaign, 25 1800L IBCs
are required. Figure 4-5 is reproduced and shown below for convenience. We assume
IBC1 to IBC 25 are used in this campaign. Table 5-8 shows when these IBCs are
released.
Q)
31
)
29
tA
27
_
25
_
_
_
_
23
O
19
17
15
Z
.
.
..
_
__
I-
_
_--_---T--
25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
Campaign Size (number of batches)
Figure 4-5: Number of 1800L IBCs needed for product C
Table 5-8: IBCs release time points for the product C campaign
Release Time points
2084.5
IBC
IBC 1
2090.75
IBC2
IBC3
IBC4
2119.5
2125.75
IBC5
IBC6
2154.5
2160.75
IBC7
2189.5
IBC8
IBC9
2195.75
2224.5
IBC10
IBC 11
2230.75
2259.5
IBC12
IBC13
2265.75
2294.5
IBC14
2300.75
IBC15
IBC16
2329.5
2335.75
IBC17
2364.5
IBC18
2370.75
IBC19
2399.5
IBC20
IBC21
2405.75
2434.5
IBC22
2440.75
2469.5
2475.75
IBC23
IBC24
Since the HSM ends the campaign at time 2083, the available cleaning slot is from
2083+48=2131 to 2131+120=2251. Notice that Table 4-8 shows that IBC1 to IBC 10
can be cleaned in this slot and be ready for the next campaign.
5.3.2.1.5 Product D
We assume the company plans to produce 150 batches of the product D in the whole
year. Assume the effective production time during the year is four months and the
availability for the six machines in the system are the same and equal to 80%, which
means AI=A 2 =...=A6 =0.8. According to the analysis we did in the section 4.3.5, at
64
least four 1800L IBCs are needed to support the production. Since the planning for
production D is very flexible, these four IBCs can be cleaned whenever the automatic
washer is available.
5.3.2.1.6 Summary
Given the production requirement of product A, B and C in section 5.3.2.1, with the
help of the models described in chapter 4, we can calculate that product A, B and C
require 4, 25 and 25 1800L IBCs respectively. Given the production requirement for
product D is section 5.3.2.1.5, according to the analysis in section 4.3.5, four 1800L
IBCs are needed. However, the total number of the 1800L IBCs needed is 29=25+4,
instead of the 4+25+25+4=58, as we have shown the feasibility of this by checking
the capacity of the cleaning system and the available slot to clean IBCs. In section 1.4
we mentioned that the company has 30 1800L IBCs in total and therefore we find that
the company could support this production scenario I with the current amount of the
1800 IBCs given our assumptions.
Till now we have achieved the objective mentioned in chapter 1 which is to support
the production of four products with the minimum number of the 1800L IBCs.
5.3.2.2 Second scenario: A-B-C-A
We assume that the company runs the production campaign in the way Figure 5-2
shows. The HSM in PF1 first runs a product A campaign (The production plan is
shown in Table 5-1.), switches to a product B campaign (63 batches), switches to a
product C campaign (39 batches) and then runs another product A campaign (The
production plan is shown in Table 5-2) Again, let the availability of the automatic
washer e=0.9. We repeat what we have done in section 5.3.2.1 and the summary is
shown in Table 5-9.
Table 5-9: IBC usage and cleaning scheduling
Production campaign
First product A campaign
Product B campaign
1800L IBCs used
IBC 1 to IBC4
IBC1 to IBC15
IBC 16 to IBC25
Product C campaign
IBC I to IBC 10
IBC11 to IBC15
IBC26 to IBC36
Second product A campaign
IBC1 to IBC4
When to clean IBCs
After the first product A campaign
After the product B campaign
After the product C campaign
After the product C campaign
After the second product A campaign
After the second product A campaign
From Table 5-9, we can see that in this production scenario, the total number of the
1800L IBCs needed is 36, which is more than that of the production scenario one. If
we assume the production requirement for product D is the same as in production
scenario one, the total number of the 1800L IBCs needed is 36+4=40. We know that
the company only has 30 1800L IBCs which means the company has to buy extra 10
1800L IBCs to support a production plan like this.
Chapter 6 Conclusions and Recommendations
6.1 Conclusions
The objective of the thesis is to support company ABC's production with the
minimum number of the 1800L IBCs. There are four types of products whose
manufacturing process requires the usage of the 1800L IBCs and therefore we divide
the thesis into two parts. The first part focused on studying the manufacturing process
of four types of products and three IBC drivers which affect or determine the number
of the IBCs needed. The second part addressed the cleaning issues of the 1800L IBCs
to see how and which parts of the 1800L IBCs can be reused. Following are the key
findings of this thesis and the recommendations we made to company ABC:
1. How to use the two compression machines in PFI influences the number of the
1800L IBCs needed to produce product A. Currently company ABC executes
Allocation Policy 1 to guide how to use these two compression machines. We
showed an integer programming model in section 4.1.2 to get a new policy called
AP12 which may provide better performance in terms of saving the 1800L IBCs
and shortening the campaign length. Therefore we recommend that after making
the production plan for a product A campaign, company ABC could use the model
in section 4.1.2 to generate the proposed policy AP12 and use the deterministic
simulation method in section 4.1.3.2 to compare the performance of API, AP2 and
AP12. Choose the best policy of the three to minimize the number of the IBCs
needed for product A.
2. Section 4.2.4 proposed a deterministic simulation method to calculate how many
1800L IBCs are required to support an X batch campaign of product B or C.
Currently company ABC uses whatever 1800L IBCs they have to support the
production of B or C regardless of how many are actually needed. Therefore we
propose that company ABC could use the model in section 4.2.4 to get an
understanding of how many 1800L IBCs are needed to support a X batch
campaign of product B or C and use the amount of the 1800L IBCs they need. The
extra 1800L IBCs could be used for the new product D or could serve as stand by.
When something unexpected happens to the in use 1800L IBCs such as IBC
broken, those stand by IBCs could be used.
3. Section 4.3 proposed a deterministic simulation method plus Buzacott's Formula
to estimate the relation between the WIP level of the system producing product D
and the productivity of the system. Since product D has not launched yet, we
recommend that company ABC could estimate the machines' efficiency in the
system based on the historical data of other machines in PFI or PF2 and set the
WIP level according to the forecasted demands by using models in section 4.3.4.
4. In chapter 5, we examined under two different production scenarios how many
1800L IBCs are needed for the production of four products considering the
cleaning and reuse. According to the labor and water constraint in PF1 not all the
1800L IBCs can be cleaned in time to join the next campaign that requires them.
We found that given the same production volume of each product, the campaign
sequence of the HSM for the four products influences the total number of the
1800L IBCs. The total required number of the 1800L IBCs is less for the
campaign sequence: Product A-Product B(Product C)-Product A-Product C
(Product B) than the sequence:
Product A-Product
B(Product C)-Product
C(B)-Product A. Therefore we recommend that if possible, it is better for the
company ABC not to run the campaign for product B and C consecutively.
6.2 Limitations of the models and future work
Following are the limitations of the analyses in the thesis and the future work we
recommend.
1. The analyses of the IBC driver Allocation of Compression Machines and IBC
Turnover between PFI and PF2 were built on the assumption that all the machines
in the process are perfectly reliable and all process times are known constants.
68
Because of this assumption, the deterministic model shown in section 4.2 and the
deterministic simulation we used to evaluate the results in both section 4.1.3.2 and
4.2.5 are valid. However, in practice the machines are not reliable and the process
time also varies from batch to batch. Thus there is a difference between the
number of the 1800L IBCs needed from our calculation and that of the real case.
Therefore we recommend that in the future, we consider the fact that machines
could be down and the process times are not constant so that the results obtained
will be more meaningful to the company.
2. In section 4.3.4 we multiplied the results we obtained from the deterministic
model by the Buzacott's Line Efficiency formula of the zero buffer line. However
the system we simulated in section 4.3.2 does not perform exactly the same as the
zero buffer line, as defined by Buzacott. Buzacott's model assumes that whenever
a machine fails the whole system stops. However for our system, when one
machine fails, the other machines will keep running till all of the 1800L IBCs in
the system are filled. Since there are only a few 1800L IBCs in the system (from 2
to 7), the performance of our system, although not the same as, should not be far
away from the zero buffer line. In the future, when product D is launched, we
recommend company ABC to collect the machine downtime information and use
simulation to study the relation between the WIP level in the system and the
productivity of the system.
References
[1] Chen, Xiaowen, Master of Engineering Thesis, August 19, 2008, unpublished work;
[2] J.A.Buzacott(1967), "Automatic Transfer Lines with Buffer Stocks," International Journal of
Production Research, Vol.5, No.3, pp 183-200
Appendix
Introduction to Simul 8
SIMUL8 is a computer package for Discrete Event Simulation. It allows you to create
a visual model of the system under investigation by drawing objects directly on the
screen. Typical objects may be queues or service points. The characteristics of the
objects can be defined in terms of, for example, capacity or speed.
Once the system has been modeled a simulation can be undertaken. The flow of Work
Items around the system is shown by animation on the screen so that the
appropriateness of the model can be assessed.
When the structure of the model has been confirmed, then a number of trials can be
run and the performance of the system described statistically. Statistics of interest
may be average waiting times, utilization of Work Centers or Resources, etc.
Shalliker and Ricketts [2002].
Following are some icon meanings for you to better understand the model in section
4.3.2.
Work entry point
This is where Work Items (Job orders) arrive into the system. How they arrive can be
controlled by the arrival distribution and parameters associated. The Work Items can
arrive singly or in batches (multiple Work items together).
'
Work Center
This is where the Work is performed on the Work Items by either machines or
Workers. You control the time and distribution that the Work takes at each machine,
and can collect a certain number of Work Items from different areas within the
simulation and give probabilities or specific Routing out after processing. It is used
mainly to change the state of the Work item.
8
Resources
These are only necessary when processes at Work stations compete for Resources,
such as when there is only one operator for several machines and can only operate one
at a time.
Work Exit Point
This is where the Work leaves the system. There can be multiple Exit points for
different produced Work Items, i.e., scrap and finished products or happy and
unhappy customers.