IMPLEMENTATION OF LEAN SIX SIGMA METHODOLOGY TO …
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IMPLEMENTATION OF LEAN SIX SIGMA METHODOLOGY TO ELIMINATE WASTE IN THE WHITE CIGARETTE PRODUCTION LINE, CASE STUDY IN PT. ZZZ By Widyan Farisy ID No. 004201400053 A Thesis presented to the Faculty of Engineering President University in partial fulfillment of the requirements of Bachelor Degree in Engineering Major in Industrial Engineering 2018
Transcript of IMPLEMENTATION OF LEAN SIX SIGMA METHODOLOGY TO …
IMPLEMENTATION OF LEAN SIX SIGMA
METHODOLOGY TO ELIMINATE WASTE IN THE
WHITE CIGARETTE PRODUCTION LINE, CASE
STUDY IN PT. ZZZ
By
Widyan Farisy
ID No. 004201400053
A Thesis presented to the Faculty of Engineering President
University in partial fulfillment of the requirements of Bachelor
Degree in Engineering Major in Industrial Engineering
2018
i
THESIS ADVISOR
RECOMMENDATION LETTER
This thesis entitled “IMPLEMENTATION OF LEAN SIX SIGMA
METHODOLOGY TO ELIMINATE WASTE IN THE WHITE
CIGARETTE PRODUCTION LINE, CASE STUDY AT PT. ZZZ”
prepared and submitted by Widyan Farisy in partial fulfillment of the
requirements for the degree of Bachelor Degree in the Faculty of
Engineering has been reviewed and found to have satisfied the
requirements for a thesis fit to be examined. I, therefore, recommend this
thesis for Oral Defense.
Cikarang, Indonesia, March 29th, 2018
Burhan Primanintyo, B.Sc., M.Eng.
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THESIS ADVISOR
RECOMMENDATION LETTER
This thesis entitled “IMPLEMENTATION OF LEAN SIX SIGMA
METHODOLOGY TO ELIMINATE WASTE IN THE WHITE
CIGARETTE PRODUCTION LINE, CASE STUDY AT PT. ZZZ”
prepared and submitted by Widyan Farisy in partial fulfillment of the
requirements for the degree of Bachelor Degree in the Faculty of
Engineering has been reviewed and found to have satisfied the
requirements for a thesis fit to be examined. I, therefore, recommend this
thesis for Oral Defense.
Cikarang, Indonesia, March 29th, 2018
Johan K Runtuk, S.T., M.T.
iii
DECLARATION OF ORIGINALITY
I declare that this thesis, entitled “IMPLEMENTATION OF LEAN SIX
SIGMA METHODOLOGY TO ELIMINATE WASTE IN THE
WHITE CIGARETTE PRODUCTION LINE, CASE STUDY AT PT.
ZZZ”, to the best of my knowledge and belief, an original piece of work
that has not been submitted, either in whole or in part, to another university
to obtain a degree.
Cikarang, Indonesia, March 29th, 2018
Widyan Farisy
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IMPLEMENTATION OF LEAN SIX SIGMA
METHODOLOGY TO ELIMINATE WASTE IN THE
WHITE CIGARETTE PRODUCTION LINE, CASE
STUDY AT PT. ZZZ
By
Widyan Farisy
ID No. 004201400053
Approved by
Johan K Runtuk, S.T., M.T. Burhan Primanintyo B.Sc, M.Eng.
Thesis Advisor Thesis Advisor
Ir. Andira. MT.
Head of Industrial Engineering Study Program
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ABSTRACT
In every process of production, non-value added activities may lead to various problem
such as high defect rate, high lead time, overproduction, high production cost, machine
downtime, etc. Lean manufacturing comes up with the idea of decreasing the non-value
added activities through reduction production waste. There are seven type of waste
identified by Lean which are transportation, inventory, motion, waiting,
overproduction, overprocess, and defect. All types of waste can be identified and
reduce using lean tools. Six Sigma, as management philosophy is an activity
undertaken by all members of the company that become culturally and in accordance
with the vision and mission of the company. Combined, Lean six sigma is the
systematic approach that used in order to make continuous improvement upon process
or activity. Six sigma consist of approach that is known as DMAIC methodology.
DMAIC are stands for Define, Measure, Analyze, Improve and Control. In this thesis,
two significant wastes identified which are waiting and defect waste. Due to a limited
time, the concern will be only for waiting waste as it is results in high machine
downtime which affect Overall Equipment Effectiveness (OEE) of packer machine
especially. In order to reduce the waste, DMAIC methodology with the help of some
tools such as Fault Tree Analysis (FTA) and Failure Mode and Effect Analysis
(FMEA). The focused will be on packer machine’s OEE specifically for minor stop
occurrences. After all root cause of minor stops at packer machine are identified. The
improvement will be proposed and apply in the production line. The OEE of packer
machine after implementation showed an improvement about 11% as it goes from 68%
to 79% of OEE score.
Keywords: Lean manufacturing, Six Sigma, Lean Six Sigma, DMAIC, Overall
Equipment Effectiveness (OEE), Fault Tree Analysis (FTA), Failure Mode and Effect
Analysis (FMEA), Machine downtime, Minor Stop, Packer Machine
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ACKNOWLEDGEMENT
This thesis is hardly to be finished without the blessing of Allah Subhanahu wa Ta’ala,
the Almighty God, all praise be to You. Thank You for Your love and mercy towards
me. Alhamdulillah.
Incredible support and motivation from everyone, has given me great spirit in
completing this thesis. Therefore, I would like to express my sincere expression of
gratitude to:
1. Mr. Burhan Primanintyo, B.Sc, M.Eng. as my thesis advisor. Thank you for the
guidance, advice, direction and everything that enhance my knowledge in
completing this thesis.
2. Mr. Johan Krisnanto Runtuk, S.T., M.T. as my 2nd thesis advisor. Your advice
and support really helped me to reach this final stage.
3. Mrs. Ir. Andira, M.T. as Head of Industrial Engineering Department.
4. My Intern Mentor, operators and mechanics of PT.ZZZ, who are willing to
support to the fullest as I interrupt them with discussions and ask to provide me
data. Thank you for all the suggestions and guidance I received.
5. My Parents, Muhammad Syahril and Raiha. Thank you for everything. Your
love, endless support and prayers will always be the light in my heart that will
keep going forward and chasing my dream.
6. My brothers, Muftah and Canggalung and all of my cousins. Thank you for
whatever support you all gave me. It means a lot having you all in my life.
7. All my bestfriends in Pecinta Sunnah and Zahirul Ma’ala. You are all the best
that Allah gave to my in this phase of my life. Even when sometimes I thought
I don’t deserve you. I hope our friendship will go until Jannah. Oh right, and of
course Pablo Squad too. You are the best guys.
8. Everyone that I cannot mention one by one but always give me support and
motivation towards goodness.
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TABLE OF CONTENT
THESIS ADVISOR RECOMMENDATION LETTER ............................................... i
THESIS ADVISOR RECOMMENDATION LETTER .............................................. ii
DECLARATION OF ORIGINALITY ........................................................................ iii
IMPLEMENTATION OF LEAN SIX SIGMA METHODOLOGY TO ELIMINATE
WASTE IN THE WHITE CIGARETTE PRODUCTION LINE, CASE STUDY AT
PT. ZZZ ........................................................................................................................ iv
ABSTRACT .................................................................................................................. v
ACKNOWLEDGEMENT ........................................................................................... vi
TABLE OF CONTENT .............................................................................................. vii
LIST OF TABLES ........................................................................................................ x
LIST OF FIGURES ..................................................................................................... xi
LIST OF TERMINOLOGIES ..................................................................................... xii
CHAPTER I INTRODUCTION .................................................................................. 1
1.1 Background ......................................................................................................... 1
1.2 Problem Statement .............................................................................................. 2
1.3 Objectives ............................................................................................................ 2
1.4 Scope ................................................................................................................... 3
1.5 Assumptions ........................................................................................................ 3
1.6 Research Outline ................................................................................................. 3
CHAPTER II STUDY LITERATURE ........................................................................ 5
2.1 Production system ............................................................................................... 5
2.2 Lean Manufacturing ............................................................................................ 5
2.3 Six Sigma ............................................................................................................ 8
2.4 Lean Six Sigma ................................................................................................... 9
2.5 DMAIC Methodology ....................................................................................... 10
2.6 Value Stream Mapping ...................................................................................... 12
2.7 Fault Tree Analysis ........................................................................................... 13
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2.8 Pareto chart ........................................................................................................ 15
2.9 Overall Equipment Effectiveness (OEE) .......................................................... 15
2.10 Failure Mode and Effect Analysis ................................................................... 18
2.11 OEE and RPN .................................................................................................. 19
CHAPTER III RESEARCH METHODOLOGY ...................................................... 20
3.1 Theoretical Framework ..................................................................................... 20
3.1 1 Initial Observation ...................................................................................... 21
3.1.2 Problem Identification ................................................................................ 21
3.1.3 Literature Study .......................................................................................... 21
3.1.4 Data Collection and Analysis ..................................................................... 21
3.1.5 Conclusions and recommendations ............................................................ 24
3.2 Flow Process of research ................................................................................... 25
CHAPTER IV DATA COLLECTION AND ANALYSIS ........................................ 27
4.1 Product Description ...................................................................................... 27
4.1.1 Material used in Packer Machine .......................................................... 29
4.2 Flow Process ................................................................................................. 31
4.3 Machines Information .................................................................................. 31
4.4 Current Value Stream Map ........................................................................... 33
4.5 DMAIC implementation ............................................................................... 39
4.5.1 Define.......................................................................................................... 39
4.5.2 Measure ....................................................................................................... 44
4.5.3 Analyze ....................................................................................................... 45
4.5.4 Improve ....................................................................................................... 55
4.5.5 Control ........................................................................................................ 61
CHAPTER V CONCLUSIONS AND RECOMMENDATIONS ............................. 63
5.1 Conclusions ....................................................................................................... 63
5.2 Recommendations ............................................................................................. 64
REFERENCES ............................................................................................................ 65
APPENDICES ............................................................................................................ 66
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Appendix 1 Calculation Of OEE, Cycle time and defect rate of Maker (Before
improvement) .......................................................................................................... 66
Appendix 2 Calculation Of OEE, Cycle time and defect rate of Packer (Before
improvement) .......................................................................................................... 68
Appendix 3 Calculation Of OEE, Cycle time and defect rate of Cellophaner
(Before improvement) ............................................................................................. 70
Appendix 4 Calculation Of OEE, Cycle time and defect rate of Cartoner (Before
improvement) .......................................................................................................... 72
Appendix 5 Calculation Of OEE, Cycle time and defect rate of Case packer (Before
improvement) .......................................................................................................... 74
Appendix 6 Additional Task Detail Sheet for Operator .......................................... 76
Appendix 7 Campaign on Paper and Sign ............................................................... 77
Appendix 8 Calculation Of OEE, Cycle time and defect rate of Packer (After
improvement) .......................................................................................................... 79
Appendix 9 Calculation Of OEE, Cycle time and defect rate of Cellophaner (After
improvement) .......................................................................................................... 81
Appendix 10 Calculation Of OEE, Cycle time and defect rate of Cartoner (After
improvement) .......................................................................................................... 83
Appendix 11 Calculation Of OEE, Cycle time and defect rate of Case Packer (After
improvement ............................................................................................................ 85
Appendix 12 Control checklist ................................................................................ 87
Appendix 13 FMEA table and new RPN score ....................................................... 88
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LIST OF TABLES
Table 2.1 Symbols used in FTA .................................................................................. 14
Table 2.2 Rating criteria for severity, occurrence and detection ................................ 19
Table 4.1 Material Information ................................................................................... 30
Table 4.2 Machine’s Speed ......................................................................................... 32
Table 4.3 Output quantity to be equal to Maker ......................................................... 32
Table 4.4 Project charter ............................................................................................. 40
Table 4.5 Six big losses percentage in the Production line ......................................... 41
Table 4.6 six minor stop .............................................................................................. 43
Table 4.7 Minor stops in the production line .............................................................. 44
Table 4.8 Minor Stops ................................................................................................. 47
Table 4.9 Scaling of severity ....................................................................................... 48
Table 4.10 Scaling of Occurence ................................................................................ 49
Table 4.11 Scaling of detection ................................................................................... 50
Table 4.12 FMEA for Packer Machine ....................................................................... 53
Table 4.13 RPN score for minor stops ........................................................................ 54
Table 4.14 Proposed improvement for Minor stops.................................................... 56
Table 4.15 Action Plan 5W 1H ................................................................................... 58
Table 4.16 OEE of Machines after improvement (exclude maker) ............................ 59
Table 4.17 Minor stop in the production line (after) ................................................... 61
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LIST OF FIGURES
Figure 2.1 Seven Wastes of Lean .................................................................................. 7
Figure 2.2 Integration of Lean and Six Sigma ............................................................ 10
Figure 2.3 DMAIC Cycle ............................................................................................ 12
Figure 2.4 Symbols used in Value Stream Mapping .................................................. 12
Figure 2.5 Pareto chart example.................................................................................. 15
Figure 2.6 Six Big Losses addressed by OEE ............................................................. 17
Figure 3.1 Research Framework ................................................................................. 20
Figure 3.2 Detail process............................................................................................. 25
Figure 4.1 Product A ................................................................................................... 27
Figure 4.2 Maker ......................................................................................................... 27
Figure 4.3 Packer ........................................................................................................ 28
Figure 4.4 Cellophaner ................................................................................................ 28
Figure 4.5 Cartoner ..................................................................................................... 29
Figure 4.6 Case Packer ................................................................................................ 29
Figure 4.7 Flow Process of Product A ........................................................................ 31
Figure 4.8 Current Value Stream Map (CVSM) ......................................................... 36
Figure 4.9 Takt time vs Cycle time graph ................................................................... 38
Figure 4.10 Pareto Diagram of six big losses ............................................................. 42
Figure 4.11 Fault tree analysis of downtime in packer machine................................. 46
Figure 4.12 Pareto Diagram for RPN .......................................................................... 55
Figure 4.13 OEE of Packer Machine comparison ....................................................... 59
Figure 4.14 Future Value Stream Map (FVSM) ......................................................... 60
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LIST OF TERMINOLOGIES
Maker : Machine that process the raw material and create it into a
cigarette stick with speed of 7500 cigarette sticks per minute.
Packer : Machine that process the cigarette stick from maker and wrap it
into pack which contain 20 cigarette sticks per pack.
Cellophaner : Machine in which the process is to give stamp for every pack
that passes through it.
Cartoner : Machine in which the process is to wrap the pack that has been
stamp into a bundle. Each bundle is contain of 10 cigarette pack.
Case Packer : Machine in which the process is to input the bundles of cigarette
pack into a box. Each box contain of 25 bundles from cartoner.
Minor stop : Machine failure that occur during the process of production. It is
different than breakdown since it only took approximately 4
minutes to solve.
Task Detail Sheet : TDS is a module prepared for operators that contains list of
instructions and procedures for operating the machine. TDS
consist of cleaning instruction, machine’s part adjustment and
minor stop prevention procedure.
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CHAPTER I
INTRODUCTION
1.1 Background
Manufacturing industry, has a purpose to produce goods economically to gain
advantage and deliver the products just in time. Effectiveness and efficiencies issue in
the production process might be cause by the production line that is not smooth. Thanks
to the spearheading achievement of Toyota, the idea of a "lean" operating system has
been executed in endless Manufacturing companies and even adapted for industries as
diverse as insurance and healthcare (Hanna, Julia 2007). Therefore, the industry can
continue existing and serve customer needs.
In order to achieve effectiveness and efficiencies, a company should be ready to deal
with many factors. Factor that has many branches is about production problem. It is a
very important matter for a company as it is highly affect the profit of the company
itself. If the production process run well, target can be achieved. However, when
production process is in trouble, target may get disturbed. Hence it is very important to
keep the production process be as smooth as possible.
PT. ZZZ produce cigarette as their product, and they have been implementing
continuous improvement in their daily activities. Currently, the production process has
met several problems, especially in the production line of Product A. Specifically in
the production line of product A, the current lead time is 24.1 minutes. It can be
considered quite good as the value added time is 5.1 minutes. However, mapping of
the current process showed a low score for machine’s uptime (based on OEE
calculation) and it still have not meet the required standard from the company which is
80%. Thus, to make the production more effective and the uptime of the machine
increase, implementing lean six sigma is the right decision to make.
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Lean Six Sigma is a two arranged business approach with continual improvements
which centers around decreasing waste and product variation of service and
manufacturing processes. Lean itself, it refers on maximizing value and minimize any
existing waste. Six Sigma is the on-going effort to continually reduce process and
product variation through an establishment project approach. Combining the two
approaches, a continuous improvement is promoted (Matt, 2008).
For this research, the production line of product A will be observed and analyzed to
create the value stream mapping of the current condition. After that, deep analysis will
be conduct with the help of some other tools to detect waste and then choose the best
strategy of lean six sigma to reduce it all and propose the improvement that can be a
solution to the existing problems. In this case, the most critical waste will be the main
concern to get eliminated.
1.2 Problem Statement
Problem background leads to the following statements:
1. In current VSM (Value Stream Mapping), what are the waste that can be find
in the product A’s production line?
2. How to eliminate the waste that exist in the product A’s production line?
3. What kind of improvement that can be proposed to control and eliminate the
waste in the product A’s production line?
1.3 Objectives
The research objectives are:
1. To create CVSM and figure out the waste in the production of product A.
2. To find a proper method in eliminating the waste in production of product A.
3. To proposed the new improvement and control for eliminating the waste.
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1.4 Scope
In doing this research, the scope for the observation is as follows:
1. Production data are based on data that has been collected for 3 months (October
– December) observation along with the interviews and discussion with
Operator, Production technician and mechanic in the production line.
2. Main concern will be the machine and the process, so inventory is ignored.
3. This observation is only in the secondary department where the product A are
produce.
4. The improvement will be focused on the most dominant production waste.
5. For value stream mapping of the production line, data of production is collected
for only 40 shifts. The current VSM data taken on November 2017, while for
future VSM taken in January 2018.
1.5 Assumptions
Assumptions are given in order for this research to be conduct properly:
1. The interview and discussion result from operator and mechanic are accurate
and reliable.
2. Setup and cleaning machine are done simultaneously at break time, so as not to
interfere with the planned production time.
3. In all seven waste, waiting and defect waste are two wastes in which can easily
be found in the system.
1.6 Research Outline
Steps of composition in this research study are as follows:
Chapter I Introduction
This chapter provide the background of the occurred problem, problem
statements, research objectives, scope, assumptions and the descriptions
of the research outline.
Chapter II Literature Study
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This chapter contains theories related to the subject of research which
becomes the basis of thinking and the basis of research preparation. The
theories are derived from reference books as well as other sources of
information related to the research discussion.
Chapter III Research Methodology
This chapter delivers detail process flow and description of every step
conducted in this research, starting from problem identification to
conclusion.
Chapter IV Data Collection and Analysis
This chapter contains collected information that needed to perform data
process to get result that correspond with the research objectives. Data
analysis will be perform using several methods until the expected result
obtained.
Chapter V Conclusion
This chapter gives the summary of obtained result of the research to
answer the problem statements and achieved problem objectives as well.
It then continue to provides the recommendation for future research if
any similar topic are attempted.
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CHAPTER II
STUDY LITERATURE
2.1 Production system
Production system is an integrated system that has structural and functional
components. In the modern production system there is a value added process from the
input material to the output finished goods.
The production system has components or structural and functional elements that play
an important role in supporting the operational continuity of the production system.
Components or structural elements that make up the production system consist of: raw
material, machine and equipment, labor, capital, energy, information, factory etc. as
for the functional elements consist of: supervise, planning, controlling, coordination
and leadership, all of them connect to management and organization (Vincent
Gaspersz, 1998).
In addition to the production system that has been known in general, specifically known
also the term manufacturing system. Manufacturing system include processes from raw
materials to finished products through a series of operations. The manufacturing
process can be divided into two types of processes namely the operation process and
assembly process (Groover, 2000).
2.2 Lean Manufacturing
Lean manufacturing is a manufacturing or production system best described as a
persistent waste elimination from all of its activities and operations. It intend to
improve and create a smooth process flow within the production line (Morgan and
Brenig-jones, 2012). While numerous specialists and experts examined and remarked
on lean manufacturing, it is hard to locate a compact definition on which everybody
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concurs. According to Abdulmalek and Rajgopal (2007), lean manufacturing is oftenly
correlate with elimination of seven significant wastes to lighten variability in supply,
processing time or demand. Herron and Braident (2007), describe it as a deliberate
waste removal for every products or services that take after common process paths to
the consumer (value stream).
Nowadays, Lean manufacturing has become an integrated system that includes highly
inter-related elements and immense management practices. Lean intend to boost
productivity, cut down/shorten the lead time and cost and also improving a quality.
Lean thinking has been widely adopted in many manufacturing operations. Even, lean
thinking has successfully been applied towards various practices including healthcare
(Abdelhadi and Shakoor, 2014).
2.2.1 Seven waste
Within every process, “Things that adds no Value” is define as waste. The seven wastes
came from Japan and known as term of “muda”. “The seven wastes” which was
basically developed by Toyota’s Chief Engineer Taiichi Ohno as an essence of Toyota
Production System, and also known as lean manufacturing is a tool to categorize these
“muda”. Eliminating existing waste, is consider one of the most effective ways to raise
profitability in any business. In order to eliminate the waste, it is really necessary to
learn what exactly the waste is and where does it exists in the process. While the
products between companies / factories is significantly different between one another,
the type of wastes found are somehow similar (Mcbride 2003).
Figure 2.1 shows the types waste that can be found within manufacturing environment.
The wastes has an acronym of TIMWOOD which stands for Transportation, Inventory,
Motion, Waiting, Overproduction, Over Processing and Defect.
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Figure 2.1 Seven Wastes of Lean
The seven wastes are:
Transportation
Moving products, excessive movement and handling that are not necessary can
lead to an accident and create a slit for quality to get degrade.
Inventory
Fat inventory might result into high lead times and devour space.
Motion
Any undesired and unimportant movement performed by people or equipment.
Waiting
On any occasion where the goods are not moving or being processed, the waste of
waiting arise.
Overproduction
Manufacture a goods before it actually needed and makes the production exceed
the demand.
Over processing
Adding an extra steps in the process, produce goods with a higher quality than its
required are categorized into this waste. Incorrect used of the equipment, failure in
rework, or even a bad process design may be the cause of it.
Defect
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Any produced part or goods that does not meet the specification and might as well
caused by poor manufacturing processes (human or machine errors).
2.3 Six Sigma
Six Sigma aim to eliminating defects. In many sources Six Sigma is called the “Zero
defect philosophy”. It requires data-driven decisions (statistical approach) and
perceives that variety impedes high quality service delivery. To have the capacity to do
as such, it offers a set of quality tools and a framework for viable problem-solving
where arrangements ought to be founded on data. Business process that get modified
using six sigma tools bring continuous outcomes. Seeking for a stable and capable
processes to fulfil the requirements of customer is one of the most critical principles in
Six Sigma. Lee and Choi (2012) define Six Sigma as an improvement methodology to
enlarge competitiveness of an organization.
Six Sigma is a top-down business strategy that require attention and support from every
member of organizations. It described as a “project driven management approach to
enhance organization’s products, services and processes by continuously minimizing
defects in the organization”. Six Sigma is more comprehensive compare to some earlier
quality initiatives like Total Quality Management (TQM) and Continuous Quality
Improvement (CQI) (Anbari and Kwak 2004, 1). To implement the Six Sigma projects,
DMAIC (Define-Measure-Analyze-Improve-Control) methodology is discussed.
According to Nur Metasari (2009), Six Sigma has 2 important meaning, which are:
Six Sigma as Management philosophy
Six sigma is an activity undertaken by every member in the organization that
becomes a positive culture within it and in accordance with the vision and
mission of the organization. The goal is to improve efficiency of the business
process and satisfy customer requirements, thereby increasing the value of the
company/organization
.
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Six Sigma as a Measurement system
Six sigma in accordance to the meaning of sigma, is a distribution or dispersion
(variation) of the mean of a process or procedure. Six sigma is applied to
minimize variation (sigma).
Six sigma as a measurement system using Defect per Million Oppurtunities
(DPMO) as the unit of measurement. DPMO is a good measure of product
quality or process, because it associates directly with costs, time, and defects
wasted. Sigma level can be obtain using ppm and sigma conversion table.
2.4 Lean Six Sigma
Lean is a methodical way to determine and eliminate waste by means of continuous
improvement. Meanwhile, Six Sigma is a classified and systematic method for strategic
process improvement, new product and service development in which depend on
statistical and scientific method to generate a dramatic minimization in customer
defined defect rates. Lean Six Sigma is the systematic approach that used in order to
make continuous improvement upon process or activity. Six sigma consist of approach
that is known as DMAIC methodology. DMAIC is an acronym for Define, Measure,
Analyze, Improve, and Control.
The term of “Lean Six Sigma” is used to define the integration of Lean and Six Sigma
(Sheridan, 2000). Lean and Six Sigma might actually have lots of things in common.
Both method focus to satisfy customers while using different tools in doing so. Lean
concern on eliminating waste by seeing through customer inputs. Six Sigma give more
concern on the processes’s variation, then, decrease it by applying statistical tools
(Esteban Berty, 2011). For both method to be applied in a proper way, it will surely
give an impact to the mindset of every member within the organizations. Once it has
become a mindset, a culture of continuous improvement is created and awareness of
process efficiency developed.
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Lean and Six Sigma are both consider as a very efficient tools for improvements,
combining both method will surely bring more benefits and betterment to the
organization/company. “Lean gives stability and repeatability in many basic processes.
Once stability has taken hold, many variations cause by human processes removed.
The data collected to support six sigma activities thereby becomes much more reliable
and accurate”. (Crabtree, 2004).
Many companies have combined both methods into a Lean Six Sigma approach. Figure
2.2 below shows the example of improvement percentage when implement lean, six
sigma, and the combined Lean Six Sigma.
Figure 2.2 Integration of Lean and Six Sigma
As it is shown in figure 2.2, using only one method result in 6% of improvement. When
both methods combined, percentage of improvement is increase into 12% as it is
obtained from Lean and also Six Sigma.
2.5 DMAIC Methodology
DMAIC is the methodology used in Lean Six Sigma for the realization of a project. It
provides the framework to improve existing processes in a systemic way. In DMAIC,
each step will have its own tools which are utilized to cover all parts of the project.
1. Define
Define is a first step for improving the quality based on Six Sigma method. In
Define step, the problem and possible improvement should be identified,
determine scope of process analysis, find out Critical to Quality (CTQ) and Process
mapping.
2. Measure
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Measure is the next step after the problems defined in the Six Sigma method. The
aims of this step is to understand how the processes work and perform that it
describe the problem more effectively. This phase require an accurate
measurements, as it will be compare with future measurements after the
improvement. In this phase, data related to the identified problem are collected.
3. Analyze
The third step in Six Sigma is Analyze step. In this phase, types of defect are
identified and root causes analysis of problem is done. After all the possible causes
list down, the most significant cause of defect should be determined. The tools for
this step can use the Ishikawa diagram, fault tree analysis etc to find out the root
cause of the overall problem. After all the possible causes are gathered, it is a must
to make priority, which one the most effective to be improve, by considering high
impact and low effort.
4. Improve
The fourth step of Six Sigma is Improve step. In this step, the propose improvement
should be made. The improvement consists of designing, giving solution, and
improvement idea to eliminate the factors that cause the defective product and
giving recommendation for production process in order to reduce the defect in
production. The improvement plan in this step should be evaluated its effectiveness
through achievement of target in the quality improvement of six sigma, which is
decreasing the DPMO (Defect per Million Opportunity) or CPMO (Complaints per
Million Opportunities) to a target of zero defect oriented or achieving the process
capability is higher or equal to 6-sigma, also convert.
5. Control
Control is the last step in the DMAIC process of Six Sigma implementation in order
to do improvement of quality. In this step, it will be design control system which
able to maintain and monitor the process, so that the process is running as a
proposed standard or procedure and will not go back to its original state once the
project is done.
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Following figure 2.3 will give the overview steps of DMAIC for improving the quality
of process.
Figure 2.3 DMAIC Cycle
2.6 Value Stream Mapping
Value stream mapping (VSM) is a technique for outwardly mapping a product’s
production path from “door to door”. VSM is a critical tool of Lean manufacturing.
VSM can be a good option for any company/organization that wants to be lean. VSM
can serve as a starting point to support management, engineers, production associates,
schedulers, suppliers, and customers in recognizing waste and identify its causes as it
depict all of the step in the process. VSM expose every actions, both value-added and
non value-added within the process.
There are some symbols used in VSM. There are process symbols, material symbols,
information symbols and general symbols.
Figure 2.4 Symbols used in Value Stream Mapping
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James P. Womack (2006) states that in particular, we have to know whether each action
is:
Valuable, means whether it creates value or not from customer’s point of view.
Capable, the extent to which a result with a decent quality achieved at all times.
Available, meaning how much the progression can work when it is required.
Adequate, meaning how much limit is set up to react to customer orders as
required.
Flexible, implies how much a procedure step can switch over rapidly and
requiring little to no effort from one member of a product family to another.
2.7 Fault Tree Analysis
Fault tree analysis (FTA) is a logical, graphical diagram that begin with unwanted
condition in a system. The diagram then lays out the many possible faults, and
combinations of faults, within the subsystems, components, assemblies, software, and
parts comprising the system that may lead to the top-level unwanted fault condition
(Fred Schenkelberg, 2016).
Because of its flexibility, it can be connected in various territories, especially in the
field of risk, quality, and security management. It is appropriate as a preventive
strategy, and in addition the technique for investigation of existing issue (e.g. accident).
FTA technique usually follow the FMEA analysis and it is planned for complex
frameworks (Vit Kraus, 2015).
Element used in creating an FTA called gates and events. Gates describe outcome,
while events described the input for gates. FTA has several functions which are:
To investigate potential failure
To investigate its mode and causes
And to measure the contribution of the system's unreliability to product design
goals.
14
2.7.1 Steps for FTA
According to Hank Marquis (2006), there are six steps to conduct the fault tree analysis,
which are:
1. Select a top level event for analysis.
2. Identify fault that could lead to top level event.
3. For each fault, list as many as possible in boxes below the related fault.
4. Draw a diagram of the Fault Tree.
5. Continue identifying causes for each fault until you reach a root cause (reactive
FTA), or one that you can do something about (proactive FTA).
6. Consider countermeasure.
Table 2.1 Symbols used in FTA
Event
Basic event
Conditional event
A specific condition or restriction
House event
Event regularly anticipated to appear.
Transfer symbol
Indicates a transfer continuation to a
sub tree.
AND gate
Appear if all input appear.
OR gate
Appear if at least one input appear
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2.8 Pareto chart
According to Margaret Rouse (2011), a Pareto chart, additionally called a Pareto
distribution diagram, is a vertical structured presentation in which esteems are plotted
in diminishing request of relative recurrence from left to right. Pareto charts are
amazingly helpful for breaking down what problems require attention first in light of
the fact that the taller bars on the chart, which speak to recurrence, unmistakably
delineate which variables have the best aggregate impact on a given system.
Pareto chart is one of tools for determine the problem areas. Several items/problems
are identified and measured on certain scale and then the data are ordered in decending
order, as a cumulative distribution. in the Pareto chart there is a principle of 80/20 rule,
this principle tells that 80% of the overall effects/output comes from 20% of the
cause/input. Therefore, the 20 percent of population of problems will be given greater
effort be solved rather than the other problems.
Figure 2.5 Pareto chart example
2.9 Overall Equipment Effectiveness (OEE)
OEE (Overall Equipment Effectiveness) is the best quality level for estimating
manufacturing productivity. An OEE score of 100% means that the production is
16
perfect, running at its best, without machine stop or any defect produced. Estimating
OEE is an assembling best practice. By estimating OEE and the hidden misfortunes,
you will increase imperative bits of knowledge on the best way to methodicallly
enhance your assembling procedure. OEE is the absolute best metric for recognizing
misfortunes, benchmarking progress, and enhancing the profitability of assembling
hardware (i.e., taking out waste).
OEE is helpful as both a benchmark and a standard:
As a benchmark it can be utilized to analyze the execution of a given creation
advantage for industry models, to comparable in-house resources, or to comes
about for various movements dealing with a similar resource.
As a standard it can be utilized to track advance after some time in killing waste
from a given creation resource.
OEE calculation depends on the three OEE Factors which are availability,
performance, and quality.
Availability shows the percentage of time available for the machine to run. It takes into
account all events that stop planned production long enough where it make sense to
track a reason for being down (typically several minutes).
The formula to calculate availability is given in the equation (2-1):
𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦 = 𝑃𝑙𝑎𝑛𝑛𝑒𝑑 𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝑇𝑖𝑚𝑒 − 𝐷𝑜𝑤𝑛𝑡𝑖𝑚𝑒
𝑃𝑙𝑎𝑛𝑛𝑒𝑑 𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝑇𝑖𝑚𝑒
(2-1)
Performance considers anything that cause the manufacturing process to run at less
than the maximum possible speed when it is running (including both slow cycle and
small stops). The formula to calculate the performance is given in the equation (2-2):
𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 = 𝐴𝑐𝑡𝑢𝑎𝑙 𝑂𝑢𝑡𝑝𝑢𝑡
𝐼𝑑𝑒𝑎𝑙 𝑂𝑢𝑡𝑝𝑢𝑡
(2-2)
Quality takes into account Quality Loss, it compares the good product with the total
product produce. The formula for quality is given in the equation (2-3):
𝑄𝑢𝑎𝑙𝑖𝑡𝑦 = 𝐺𝑜𝑜𝑑 𝐶𝑜𝑢𝑛𝑡
𝑇𝑜𝑡𝑎𝑙 𝐶𝑜𝑢𝑛𝑡
17
(2-3)
OEE takes into account all losses (Stop Time Loss, Speed Loss, and Quality Loss),
which give result in a measure of truly productive manufacturing time.
It is calculated as the ratio of Fully Productive Time to Planned Production Time. The
formula for OEE is given in the equation (2-4):
𝑶𝑬𝑬 = 𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦 × 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 × 𝑄𝑢𝑎𝑙𝑖𝑡𝑦
(2-4)
According to Abdus Samad (2012), the Six Big Losses, which fall under three OEE
loss categories, are discussed at the following figure:
Figure 2.6 Six Big Losses addressed by OEE
As an additional, the formula on how to calculate machine’s design speed, cycle time
and takt time are given. The formula can be seen in the equation (2-5), (2-6) and (2-7)
respectively.
18
𝐷𝑒𝑠𝑖𝑔𝑛 𝑆𝑝𝑒𝑒𝑑 = 𝑇𝑜𝑡𝑎𝑙 𝑂𝑢𝑡𝑝𝑢𝑡
𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝑡𝑖𝑚𝑒 − 𝐷𝑜𝑤𝑛𝑡𝑖𝑚𝑒
(2-5)
𝐶𝑦𝑐𝑙𝑒 𝑇𝑖𝑚𝑒 = 𝐸𝑥𝑝𝑒𝑐𝑡𝑒𝑑 𝑂𝑢𝑡𝑝𝑢𝑡
𝐴𝑐𝑡𝑢𝑎𝑙 𝐷𝑒𝑠𝑖𝑔𝑛 𝑆𝑝𝑒𝑒𝑑
(2-6)
𝐷𝑒𝑓𝑒𝑐𝑡 𝑅𝑎𝑡𝑒 = 𝑅𝑒𝑗𝑒𝑐𝑡 𝑝𝑖𝑒𝑐𝑒𝑠
𝐴𝑐𝑡𝑢𝑎𝑙 𝑂𝑢𝑡𝑝𝑢𝑡
(2-7)
𝑇𝑎𝑘𝑡 𝑇𝑖𝑚𝑒 = 𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝑇𝑖𝑚𝑒
𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟 𝐷𝑒𝑚𝑎𝑛𝑑
(2-8)
2.10 Failure Mode and Effect Analysis
Failure Mode and Effects Analysis (FMEA) is an organized way to deal with finding
potential failures that may exist within the design of a product or process. Failure
modes are the manners by which a procedure can come up short. Effects are the ways
that these failures can prompt waste, deserts or destructive results for the client. Failure
Mode and Effects Analysis is intended to recognize, organize and restrict these failure
modes. FMEA isn't a substitute for good engineering. Rather, it enhances great
engineering by applying the learning and experience of a Cross Functional Team (CFT)
to audit the outline advance of an item or process by evaluating its risk of failure.
Risk Priority Number (RPN) is a measure utilized while evaluating risk to help
distinguish basic failure modes related with your plan or process. The RPN esteems
extend from 1 (absolute best) to 1000 (absolute worst). The FMEA RPN is usually
utilized as a part of the car business and it is fairly like the criticality numbers utilized
as a part of Mil-Std-1629A. The realistic beneath demonstrates the variables that make
up the RPN and how it is computed for every failure mode.
When performing a Process or Design FMEA, the Risk Priority Number (RPN) is a
calculation to sort the risks from highest to lowest.
19
The RPN is calculated by multiplying the three scoring columns: Severity, Occurrence
and Detection. It can be seen in the equation (2-8).
𝑅𝑃𝑁 = 𝑆𝑒𝑣𝑒𝑟𝑖𝑡𝑦 × 𝑂𝑐𝑐𝑢𝑟𝑟𝑒𝑛𝑐𝑒 × 𝐷𝑒𝑡𝑒𝑐𝑡𝑖𝑜𝑛
(2-8)
Severity (S), is a numerical subjective estimate of how severe the customer or end user
will perceive the EFFECT of a failure. Occurrence (O), is a numerical subjective
estimate of the LIKELIHOOD that the cause of a failure mode will occur during the
design life, or during production in the case of a Process FMEA. Detection (D), is
sometimes termed EFFECTIVENESS. It is a numerical subjective estimate of the
viability of the controls to avoid or recognize the cause or failure mode before the
failure reach the customer. The suspicion is that the cause has happened.
Table 2.3 in below will show the example of rating criteria for severity, occurrence and
detection.
Table 2.2 Rating criteria for severity, occurrence and detection
Severity Occurrence Detection
Effect Score Effect Score Effect Score
No 1 Almost never 1 Almost certain 1
Very slight 2 Remote 2 Very high 2
Slight 3 Very slight 3 High 3
Minor 4 Slight 4 Moderately High 4
Moderate 5 Low 5 Medium 5
Significant 6 Medium 6 Low 6
Major 7 Moderately high 7 Slight 7
Extreme 8 High 8 Very slight 8
Serious 9 Very high 9 Remote 9
Hazardous 10 Almost certain 10 Almost Impossible 10
2.11 OEE and RPN
According to study of correlating OEE and RPN by Chandrajit and Anand (2012),
which determine the effect of fluctuation of OEE factors (availability, performance and
quality) on RPN has a result which concluded that low RPN will result into high OEE
vice versa.
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CHAPTER III
RESEARCH METHODOLOGY
3.1 Theoretical Framework
Figure below is the flowchart and the description of the research methodology for this
thesis.
Initial Observation
Problem
Identification
Literature Study
Data Collection
Conclusion &
Recommendation
Initial Observation
Observe the production process of Product A
Problem Identification
Identify the problem background of project
Determine the project’s objectives, scopes
and assumptions
Literature Study
Value Stream Mapping
Six Sigma DMAIC
Failure mode effect analysis
Data Collection
Collecting data of production of product A
and draw Current state map
Current Value Stream Mapping analysis
Analyze machine performance
Conclusion and recommendation
Create a conclusion based on the calculation
and analysis
Provide a recommendation for further
research with the similar observation.
Data Analysis
Determine which tools to use for
improvement
Proposed improvements and control plan to
eliminate identified waste
Data Analysis
Figure 3.1 Research Framework
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3.1 1 Initial Observation
The initial observation is done in the production floor secondary process.
3.1.2 Problem Identification
After the initial observation finished, the problem will be identified. In this research,
the goal is to make production line of product A become more effective and efficient
by reducing the waste that exist in it. In addition, the scopes and assumptions of this
research are determined, the purpose of scopes and assumptions is to limit the research,
so that the outcome of research is valid and can be acknowledge.
3.1.3 Literature Study
In this step of research, the researcher seek for the literature references that can be
utilized as a supporting theories for this research. The theories will help and guide the
researcher to find the essential objective of the research in which to reduce waste that
exist in the production of product A. The resource can be gathered from journals,
books, website and previous research that related to this topic. Some fundamental
theories expected to explained and support the research observation. The first is about
the production system. The next is about lean manufacturing and six sigma theory.
Then it continues to seven waste analysis. Then, all the tools that will be used to support
the analysis will be explained too.
3.1.4 Data Collection and Analysis
In this step, the data will be collected and then will continue to be analyze. The data
that will be collect are the process in each work station of product A. In the secondary
department, there are about five workstations. The process in each workstation
includes: cycle time, uptime, inventory, work in process and lead time.
3.1.4.1 Current Value Stream
In order identify wastes in the production line of product A, a Value Stream Mapping
of a current condition needs to be made. Value stream mapping a method of visually
22
mapping a product’s production path (material and information) from “door to door”.
To create the current value stream mapping, data related to production needs to be
collected from all machines in the production line the data were collected in a normal
condition of production. Because it is a fast moving production, 40 shift of data is
considered sufficient to be the basis for creating CVSM. After all the data are collected,
the current state map will be drawn. The waste will be identified from the CVSM.
When the waste already identified, the cycle time needs to be sure that it does not
exceed the takt time. If it is exceeded, then it is unacceptable because it means that the
production line will not able to satisfy the customer demand. The acceptable condition
is only if the cycle time did not exceed the takt time. If there is no problem with cycle
time, it then continue to DMAIC implementation.
3.1.4.2 Define
The define phase start after the waste in the value stream are identified. The project
charter is made to define the objective, goals of the project, who are involve in it and
the timeline of the project. Then, the data of six big losses are collected to show which
losses that has the highest percentage. It turns out that the idling and minor stops
category is the big losses with the highest percentage in the production line. After that,
from all of the minor stop in the packer machine, only six was chosen. The six minor
stops are chosen based on the time spent to deal with it.
3.1.4.3 Measure
In the measure phase, the detail of the time spent for every minor stops are given as the
historical condition of minor stops time spent. Based on the historical data, most of the
minor stop are occurred in the packer machine. The time spent to deal with the minor
stop are also the highest in the packer machine.
3.1.4.4 Analyze
In this phase, the six minor stop are dismantled through discussion with operator and
mechanic. The purpose is to identify what are the root cause for each analyzed minor
23
stops. To find the root cause, the author used the deductive failure analysis which is
fault tree analysis. By using fault tree analysis, the understanding of how the minor stop
occur can be learn.
After the root cause identified, Failure mode and effect analysis is used. Failure Mode
and Effects Analysis is designed to identify, prioritize and limit the failure modes/minor
stops. The failure mode is prioritize by the Risk Priority Number (RPN) score. In order
to find the RPN score, the assessment of severity, occurrence and detection needs to be
done. The rating is given from scale 1 to 10. For severity and occurrence, the best score
is 10 while for detection the best score is 1.
It started by scaling the severity of the potential effect of failure. The severity scale is
assessed by operator and mechanic. To give the severity scale, operator and mechanic
assessed it based on the duration of treatment when the effect of failure occurs. The
longer the time required to overcome the severity, the severity scale given will be
higher. The next scaling is for occurrence. For this scaling, it decided to see it from
how many time does the minor stop occur in one month. The higher the occurrences,
the higher the occurrence scale given to it. However, some of the minor stop has more
than one cause of failure. Last, for detection scaling, operator and mechanic give an
assessment based on the pre-existing current control to prevent minor stop events. The
better the current control, the detection will be easier and the scaling will be smaller.
After rating of severity, occurrence and detection are given, Risk Priority Number
(RPN) can be compute. The calculated RPN then will be rank in order from the highest
to the lowest. Minor stop with high RPN value will be prioritized to be addressed. This
relates to what has been written on CH 2 which, if the RPN value decreases, the OEE
will increase.
24
3.1.4.5 Improve
In this phase, all the possible improvement is proposed based on the current situation
that has been analyzed. The proposed improvement was discussed also with operator
and mechanic. To be applied to the production line, the proposed improvements will
be sorted and then included in addition to the operator's task details sheet, cleaning task
or warning / reminder sign around the machine.
After the proposed improvements are applied, the data in the product A production line
are collected once again. This activity aims to see the result after the improvements are
applied whether it gives a good result or not. The data were also taken during normal
condition in 40 shifts. Then, the packer machine’s OEE are calculated. The result of
calculation shows that there is improvements of OEE score in the packer machine
which indicates a good result of the research. Minor stop data was also taken to see if
the minor stops that was analyze has decreased in its consumed repair time.
3.1.4.6 Control
In the final phase of DMAIC methodology, the control plan needs to be given in order
to keep the achieved target always on track. The control plan is given in form of
checklist to make it easier for operator to fill it. The new RPN from FMEA of are also
has been assessed by operator and mechanic which shows that the RPN has decreased.
The RPN score from FMEA will also help the operator if there is any project related to
minor stop is conduct in the future.
3.1.5 Conclusions and recommendations
The last step in this research The final step of the research is to present conclusions and
recommendations. The conclusion contains the summary of the entire process of the
research until research objectives are met. In conclusion, the stated problems in chapter
1 will be answered. When the researcher arrived to the conclusion, it means that the
research objectives had been accomplished.
25
The conclusion part will then be followed by the recommendations given by the
researcher. The recommendation is the part where the suggestion and advise are given
for the readers or those who would conduct the research with a resemblant topic as this
research. All the recommendations is given in order to achieve better research in the
future.
3.2 Flow Process of research
Figure 3.2 Detail process
Start
Initial Observation
Data Collection
Current Value Stream Mapping
Problem Identification
Six Big Losses
Machine historical performance
Fault tree
Failure Mode and Effect Analysis
Proposed Improvement
Measure
Analyze
Control
After Improvements
Control Plan
Done
Conclusions and recommendations
Improve
Project charter
Define
26
In order to create the current value stream map (CVSM), data of production is collected.
The value stream map will show what are the waste that can be find in the production
line of product A. The dominant waste will be chosen to analyze. As the dominant
waste identified as the problem in the production line, the DMAIC methodology is
applied for improvement in packer machine. In the define phase, the project charter is
created to define the objectives, timeline and team involve in the project. Then, the data
of six big losses are collected to show which losses that has the highest percentage.
Idling and minor stops turns out to be the highest losses in the category with the highest
percentage in the production line. Out of all occurred minor stops, only 6 minor stops
chosen.
In measure phase, the historical data will be given to show the time loss due to six
chosen minor stop. Next, the analyze phase will provide all the root cause of six minor
stop using the fault tree. Then, after the root cause are identified, failure mode and
effect analysis is performed. Scale of severity, occurrence and detection needs to be
done to find the Risk Priority Number (RPN) for each minor stop. Minor stop with high
RPN will be prioritize to get the improvement.
In improvement phase, the improvement for each minor stop is given. For minor stop
with higher RPN, the improvement includes creating new task list for operator while
for minor stop with lower RPN, the improvement is given through visual instruction
and signs attached around the machine. After improvement, some data will be taken
once again to see if there any significant changes after the improvements are applied.
It also include creating the future value stream map (FVSM).
As the last phase of DMAIC methodology, control phase will provide the control
checklist in order to keep the applied improvements stays on track. Lastly, the research
is closed by giving conclusions and provide recommendations.
27
CHAPTER IV
DATA COLLECTION AND ANALYSIS
4.1 Product Description
Figure 4.1 below shows physical look of the product that studied in this research. The
finished goods however will contain the product into a box. Here is the look of the
product:
Figure 4.1 Product A
Below are the machines used for the production of product A. One production line
consist of several machines that operate together. The machines are Maker, Packer,
Cellophaner, Cartoner and Case packer. The production starts with maker and will end
with the output of the case packer. The picture of each machine are shown in the figure
4.2 - 4.6.
Figure 4.2 Maker
28
The maker machine produce cigarette sticks and pass it to the packer machine through
cigarette transportation.
Figure 4.3 Packer
Packer machine collect the cigarette sticks from the cigarette transportation. Then, the
cigarette sticks will be group into 20 sticks each so that the cigarette sticks make into
1 pack.
Figure 4.4 Cellophaner
Cellophaner machine receive packs of cigarette and gives stamp to every one of it.
29
Figure 4.5 Cartoner
Cartoner machine receive stamped cigarette packs and wrapped in 10 of it into one
bundle.
Figure 4.6 Case Packer
Case packer receive bundles of cigarette pack and then store it into one box.
4.1.1 Material used in Packer Machine
Materials information are provided as additional information to make it easier to
understand the future explanation as it might be related to the problem. The materials
which can be seen in the table 4.1, are the material for the packer machine in which
this research will put concern on.
30
Table 4.1 Material Information
Image Information
Cigarette
The formula-fed tobacco and a filter are then wrapped in
tipping paper and cigarette paper
Aluminium Foil / Inner Liner
Material that serves to wrap the cigarette and to maintain
the texture or aroma of cigarette from the influence of the
outside environment
Inner Frame
Material that serves to protect the cigarette from impact or
damage. If the cigarette produced has a menthol aroma,
then the aroma comes from the inner frame
Self Adhesive Label
Material that serves as a cigarette protector when the pack
is about to open and close, keep the flavor and cleanliness
of the filter cigarette
Blank (etiket)
Material that serves as a packaging to protect products and
as a means for delivery of product brand (Logo)
Tear Tape Pack
Material that serves to facilitate open OPP pack on the
product
31
4.2 Flow Process
The flow process of product A describe in symbols and represent the processing
activities. The figure below will explain the process of making product A. The
flowchart shown in visual will make the process can easily be understood rather than
explaining the whole process in sentences.
Figure 4.7 Flow Process of Product A
In the figure 4.7 from maker machine until case packer machine, all process has two
output which are good product and reject product. Reject product can be found start
from maker product. All rejected products from each machine, will be send to ripping
machine to get the remaining that still can be process and then send again to the primary
process.
4.3 Machines Information
As can be seen in the figure 4.7, there are five machines in the production line that
involves in making finished product of product A. Since each machines have a different
Start
Primary Process
Maker (production)
Packer
Cellophaner
Cartoner
Case packer
Finished Goods
Delivery
Warehouse
End
Ripping machine
32
quantity of output, it also has a different machine speed. Table 4.2 below shows the
machine speed of each machine.
Table 4.2 Machine’s Speed
Machine Speed
Maker 7500 cpm
Packer 370 ppm
Cellophaner 365 ppm
Case Packer 36 cspm
Cartoner 1 bpm
Based on table 4.2, maker machine has speed of 7500 cigarettes per minute (cpm),
packer has speed of 370 pack per minute (ppm), cellophaner has speed of 365 pack per
minute (ppm), case packer has speed of 36 case per minute (cspm) and cartoner has
speed of 1 box per minute (bpm). Because the output of each machine has different
unit, it needs to be equalized to make it easier later when constructing a current value
stream map (CVSM). Basically, packer, cellophaner, case packer and cartoner will be
convert into same unit as maker. Maker machine output is in cigarette sticks, so the
other machine unit should be in cigarette sticks too. Table 4.3 below shows the amount
of unit that should be produced by each machine to be equal to maker machine that
produced 5,000 cigarette sticks.
Table 4.3 Output quantity to be equal to Maker
Machine Unit In sticks
Maker 5,000 5,000 sticks
Packer 250 5,000 sticks
Cellophaner 250 5,000 sticks
Case Packer 25 5,000 sticks
Cartoner 1 5,000 sticks
From the table 4.3 in the previous page, it was found that 250 units produced by the
packer will be equal to 5,000 sticks produced by the maker and same goes with
cellophaner machine. 25 units that produced by case packer will be equal to 5,000 sticks
produced by the maker also. Last, for cartoner, it should produce 1 unit to get equal
with maker.
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4.4 Current Value Stream Map
To give a description of a current situation in the production line, Value Stream
Mapping is used. The following value stream mapping consist of all information that
was obtained during the research. The information obtained from the actual condition
in the production line of product A. Because the entire production line has a very fast
speed, the cycle time calculation of product A cannot be calculated per stick as the
speed of the maker machine itself is 7,500 cpm (cigarette per minute), the calculation
of the cycle time will be count per five thousand (5,000) sticks. The next machine will
follow the number of the maker machine. The finished product from case packer is one
box contain of 250 pack that has 5,000 amount of cigarette sticks. So, it is fair to
calculate cycle time per 5,000 cigarette sticks.
In creating value stream map, the data of production is taken for 40 shifts in a normal
condition. Each shift has an available production time of 450 minutes. For the
calculation of uptime in each machine, the OEE formula is used. OEE calculated with
three factors that involves in it which are availability, performance and quality. To
calculate OEE, availability, performance and quality factors needs to be find first.
Below is the example on how to calculate OEE, it based on the shift number 1 in the
appendix 2. The calculation shows the OEE score of packer machine in the current
condition.
Availability is the total time available for the machine after it reduced by machine’s
downtime. To find machine’s availability, equation (2-1) is used. t
𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦 =𝑃𝑙𝑎𝑛𝑛𝑒𝑑 𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝑇𝑖𝑚𝑒 − 𝐷𝑜𝑤𝑛𝑡𝑖𝑚𝑒
𝑃𝑙𝑎𝑛𝑛𝑒𝑑 𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝑇𝑖𝑚𝑒× 100%
𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦 =450 𝑚𝑖𝑛 − 55 𝑚𝑖𝑛
450 𝑚𝑖𝑛× 100%
𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦 = 87.8%
Performance can be calculated using equation (2-2).
𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 =𝐴𝑐𝑡𝑢𝑎𝑙 𝑂𝑢𝑡𝑝𝑢𝑡
𝐼𝑑𝑒𝑎𝑙 𝑂𝑢𝑡𝑝𝑢𝑡× 100%
34
𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 =133,023
146,150 × 100%
𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 = 91.02%
Quality point out the percentage of good product compares to total production. The
equation (2-3) is used.
𝑄𝑢𝑎𝑙𝑖𝑡𝑦 =𝐺𝑜𝑜𝑑 𝐶𝑜𝑢𝑛𝑡
𝑇𝑜𝑡𝑎𝑙 𝐶𝑜𝑢𝑛𝑡× 100%
𝑄𝑢𝑎𝑙𝑖𝑡𝑦 =131,671
133,023× 100%
𝑄𝑢𝑎𝑙𝑖𝑡𝑦 = 99.0%
After availability, performance and quality factors has been calculated, the OEE of the
machine can be find by multiplying these three factors. Equation (2-4) is used as can
be seen below:
𝑂𝐸𝐸 (𝑃𝑎𝑐𝑘𝑒𝑟) = 𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦 × 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 × 𝑄𝑢𝑎𝑙𝑖𝑡𝑦
𝑂𝐸𝐸 = 87.8% × 91.02% × 99.0%
𝑂𝐸𝐸 = 79.1%
For the calculation of cycle time in each machine, the actual design speed of each
machine must be calculated first. In PT.ZZZ, machine’s design speed is not always
stable, since the machine experience many stops and the machine itself is not in good
condition. All the machines has its own design speed. For example, packer machine
has a run rate of 370 ppm. However, by seeing the output and its operation time that
has been reduce by downtime, the overall design speed within a shift might not the
same as its ideal design speed. Thus the actual design speed needs to be calculated.
After actual design speed is obtained, the cycle time can be calculated by dividing the
unit produce with its design speed. Design speed is calculated using the equation (2-5).
The example of the calculation is:
𝐴𝑐𝑡𝑢𝑎𝑙 𝐷𝑒𝑠𝑖𝑔𝑛 𝑆𝑝𝑒𝑒𝑑 (𝑃𝑎𝑐𝑘𝑒𝑟) =𝐴𝑐𝑡𝑢𝑎𝑙 𝑂𝑢𝑡𝑝𝑢𝑡
𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛 𝑡𝑖𝑚𝑒
𝐴𝑐𝑡𝑢𝑎𝑙 𝐷𝑒𝑠𝑖𝑔𝑛 𝑆𝑝𝑒𝑒𝑑 (𝑃𝑎𝑐𝑘𝑒𝑟) =133,023 𝑝𝑎𝑐𝑘
395 𝑚𝑖𝑛
𝐴𝑐𝑡𝑢𝑎𝑙 𝐷𝑒𝑠𝑖𝑔𝑛 𝑆𝑝𝑒𝑒𝑑 (𝑃𝑎𝑐𝑘𝑒𝑟) = 336.7 𝑝𝑝𝑚
35
Cycle time of packer machine is judged by how long does the machine takes to produce
5,000 cigarette sticks. Output of the packer machine is 1 wrapped pack which contain
20 cigarette sticks. So for packer machine, 250 units of packs is equal to 5,000 cigarette
sticks. Cycle time used the equation (2-6).
𝐶𝑦𝑐𝑙𝑒 𝑡𝑖𝑚𝑒 (𝑃𝑎𝑐𝑘𝑒𝑟) =5,000 𝑠𝑡𝑖𝑐𝑘𝑠
𝐴𝑐𝑡𝑢𝑎𝑙 𝐷𝑒𝑠𝑖𝑔𝑛 𝑆𝑝𝑒𝑒𝑑
𝐶𝑦𝑐𝑙𝑒 𝑡𝑖𝑚𝑒 (𝑃𝑎𝑐𝑘𝑒𝑟) =250 𝑝𝑎𝑐𝑘
336.7 𝑝𝑝𝑚
𝐶𝑦𝑐𝑙𝑒 𝑡𝑖𝑚𝑒 (𝑃𝑎𝑐𝑘𝑒𝑟) = 0.74 𝑚𝑖𝑛
So, to produce as equal to 5,000 cigarette stick, packer need to produce 250 unit pack.
In one of the shift, the cycle time of packer machine per 250 packs is 0.74 min.
For defect rate, the calculation is by dividing the reject piece with the actual output
from the machine. To calculate the defect rate, equation (2-7) is used. The example of
calculation can be seen in the next page.
𝐷𝑒𝑓𝑒𝑐𝑡 𝑟𝑎𝑡𝑒 (𝑃𝑎𝑐𝑘𝑒𝑟) =𝑅𝑒𝑗𝑒𝑐𝑡 𝑝𝑖𝑒𝑐𝑒𝑠
𝐴𝑐𝑡𝑢𝑎𝑙 𝑂𝑢𝑡𝑝𝑢𝑡× 100%
𝐷𝑒𝑓𝑒𝑐𝑡 𝑟𝑎𝑡𝑒 (𝑃𝑎𝑐𝑘𝑒𝑟) =133,023
1,352× 100%
𝐷𝑒𝑓𝑒𝑐𝑡 𝑟𝑎𝑡𝑒 (𝑃𝑎𝑐𝑘𝑒𝑟) = 1.02%
The complete calculation of uptime, (OEE), cycle time and defect percentage of each
machine can be seen in appendix 1-5. In addition, the production line consists of 3
operators and 2 assistants, daily production target is 700 box, it works in 3 shifts and
machine break for 30 minutes in each shift. OEE, cycle time and defect rate shown in
the mapping are the average in 40 shifts of operation in normal condition.
After the calculation completed, next step is to input it into data table below each
process. From the following value stream mapping, major problem can be detected and
room for improvement could be defined easier as the map shows the details of
information in each process of the production. The Current Value Stream Map can be
seen in the next page.
36
Maker Packer Cellophaner Case PackerCartoner
Production Scheduler
Primary Process
Sales & Marketing
CT: 0.61 min
Uptime: 69%
Defect: 0.44%
3 shift
CT: 0.79 min
Uptime: 68%
Defect: 1.4%
3 shift
CT: 0.90 min
Uptime: 63.5%
Defect: 0.36%
3 shift
CT: 0.89 Min
Uptime: 63.5%
Defect: 0.72%
3 shift
CT: 1.91 min
Uptime: 48.9%
Defect: 4.1%
3 shift
Warehouse
CustomerTim
Leader
Daily Daily Daily Daily Daily
Weekly Order
1 Operator 1 Operator 1 Operator 1 Assistant1 Assistant
0.61 min
2.53 min
0.79 min 0.90 min 0.89 min 1.91 min
0.42 min 0.05 min 1 min 15 minLead time = 24.1 min
Value added = 5.1 min
1 shift: 8 hrs
Break: 30 min
Available time: 450 min
Figure 4.8 Current Value Stream Map (CVSM)
37
After seeing the Current Value Stream Map (CVSM) in the previous page, some wastes
are identified and possibilities of improvements are obtained. Below are the lists of
what were found after seeing the CVSM:
High defect rate. As can be seen in the map, in the case packer process, there is
4.1% rate of defect. Compare to the other processes, case packer machine gives
the highest defect rate in the entire value stream. This situation indicates that the
case packer process needs to be examine in order to reduce the defect rate in it.
Low Uptime. It indicates high downtime which causes excessive waiting waste.
Based on OEE calculation, the Current Value Stream Map (CVSM), tells that all
the process in the entire production line of product A had an uptime below 70%.
The worst uptime once again, found in the case packer machine which only has
48.9%. The company has target of 80% uptime. Seeing the current condition, it
can be say that the uptime percentage of all the process are really bad and should
be taken care of. However, to deal with all the uptime in each process would
consume lot of time for the research. This research will only concern on increasing
the uptime of the certain process to boost all other process’s uptime as well.
The machines in the production line of product A actually cannot run if the
previous machine is stopped. It is because the machines are waiting the work in
process from previous machine. So all the machines are linked. Packer machine
however, did not stop if the maker machine stop. It is caused by there is tray
machine that already load with the output of maker machine which always ready
to supply the packer machine whenever the maker machine had problem. Still, it
only applies in Maker-packer machine. All the machine after packer will stopped
if the packer stopped because it has to wait. Waiting then cause the machine uptime
becomes low. Based on this situation, it can be concluded that the packer machine
is the major cause that in the next machines or processes, the uptime are also low.
Thus, increasing the uptime of the packer machine will surely affect and gives a
good impact to the uptime of every process after it.
38
After the waste are identified, next step is to check whether the cycle time for each
process is acceptable or not. To know this, the calculation of takt time would help to
determine it. TAKT time is the maximum acceptable time to meet the demand of the
customers. As it were, TAKT Time is the speed with which an item or product should
be made so as to fulfill the requirements of the customers. The cycle time is considered
acceptable if it is below the takt time. Otherwise the cycle time should be reduced
below the takt time as well because cycle time that exceed the takt time is not
acceptable. Takt time calculation uses the equation (2-7) stated in chapter 2. The
calculation is as follows:
𝑇𝑎𝑘𝑡 𝑡𝑖𝑚𝑒 = 𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑡𝑖𝑚𝑒
𝑑𝑎𝑖𝑙𝑦 𝑑𝑒𝑚𝑎𝑛𝑑
= 7.5 ℎ𝑜𝑢𝑟𝑠 × 3 𝑠ℎ𝑖𝑓𝑡 × 60 𝑚𝑖𝑛𝑢𝑡𝑒𝑠
700 𝑏𝑜𝑥
= 1.92 𝑚𝑖𝑛
The comparison of each cycle time and the takt time can be seen in the graph below:
Figure 4.9 Takt time vs Cycle time graph
As it all shown in the graph, there is no cycle time of a process that exceed the takt
time. So, there will be no problem with the cycle time.
0
0.5
1
1.5
2
2.5
Maker Packer Cellophaner Cartoner Case Packer
TAKT time vs Cycle time
Cycle Time (min) Takt Time (min)
39
Considering with the limited time, from the two problems encountered (defect and
uptime), there will only be one problem on the production line of product A which is
preferred to be completed. From the results of discussions with operators and
mechanics, overcoming or increasing uptime on the packer machine is the main choice
compared to overcome the defect on the case packer machine. This is because if there
is an increase of uptime on the packer machine, waiting time on the next machine will
decrease. When waiting time is decreased, uptime on the machine after packer
(cellophaner, cartoner and case packer) is expected to increase. Increasing uptime
means having to deal with downtime problem. In PT.ZZZ, minor stops has the highest
contribution for downtime.
4.5 DMAIC implementation
As this research aims to find out and eliminate the dominant waste that exist in the
production line of product A, the DMAIC methodology with the help of lean tools will
be used.
4.5.1 Define
Define is the initial phase in the DMAIC method. As it was identified in the current
value stream map, uptime of machine is low because of too many downtime occurred.
Downtime occurrences create so much time loss and make the production has to wait.
Out of many causes of machine’s downtime, minor stop is the most often problem to
happen.
In this first phase, the project charter will be made in order to define the project, goals,
team members and the duration of the project. It also serves as a clear direction and
focus for the team about goals to be achieved in the project. The project charter can be
seen in the table 4.4 in the next page:
40
Table 4.4 Project charter
Project Title Minimizing waste in Cigarette Production machine
(Packer machine)
Department Secondary Process
Analyzed process Cigarette production
Business Case Problem/Opportunity Statement
Minor stop is a condition where the machine stops
caused by the state of the material, something happens
to the machine or the fault of the operator in operating
the machine. It is happened mostly in the packer
machine. The occurrences of a minor stop that too
often can lead to high downtime waste that leads to
low production performance and uptime also damage
to parts on the machine that breakdown cannot be
avoided. If the improvement is not done, the frequent
occurrence of minor stop will affect the time of
production up to the condition that the daily demand
cannot be fulfill.
Of all machines on the production
line, the packer machine raises the
minor stop most often. The entire
machine after the packer machine
will stop when the packer machine
is stopped. By overcoming minor
stop problems in the packer
machine, downtime waste can be
reduced so performance increases
as well as whole machine
afterwards. Current uptime (OEE)
packer machine still below
company standard which is 80%.
Goal Statement Project Scope
Reduce the occurrence of minor stop on the packer
machine so that machine’s uptime (based on OEE
calculation) increased to 80%.
Only in production of product A,
Secondary process and in Packer
machine.
Team Member Project Timeline
Name Department Key Milestone Target Date Revised
Date
Trainer Technical Training Project start date Oct-17
Mechanic Operations Define Nov-17
Operator 1 Operations Measure Dec-17
Operator 2 Operations Analyze Dec-17
Author - Improve Jan-18
Control Feb-18
Completion date Feb-18
After the project charter completed, the define phase then continue. In defining which
minor stop to be analyze, the six big losses data are collected from the production line.
Six big losses percentage can be seen in Table 4.5 in the next page.
41
Table 4.5 Six big losses percentage in the Production line
Six big losses Total time losses (min) Percentage
Breakdowns 1,993.8 10.2%
Planned downtimes 3,180.1 16.3%
Idling and Minor stop 9,104.3 46.7%
Speed loss 4,427.5 22.7%
Production rejects 463.4 2.4%
Reject start/stop 308.8 1.6%
Total 19,477.90 100%
Table 4.5 shows the amount of minutes spend in all of the category of six big losses.
The six big losses are the average historical data. As it is stated in the chapter two, the
six big losses in the table can be categorized for three value that contribute to OEE.
Breakdown and planned downtimes affect the availability, minor stops and speed loss
to machine performance and the both reject category affect the quality. For minor stop
however, Abdus Samad (2012) also categorized the idling and minor stop into the
availability as well. It might because when there are too many idling and minor stops
that occurred, the time consumed to fix those small problems will result in a big number
if the all the minor stops are combined. It also can be seen in the table that in fact, in
the last two months of production, idling and minor stops shows the highest time losses
about 9,104.3 minutes, followed by the speed loss 4,427.5 minutes and the planned
downtime 3,180.1 minutes.
To make it more clear, from the six big losses above, the Pareto diagram can be drawn
to show the obvious influence of those six big losses to the machine effectiveness. In
the Pareto diagram, idling and minor stops loss is the main factor that cause the OEE
score of the machine low.
42
Figure 4.10 Pareto Diagram of six big losses
In PT. ZZZ, more than 10 minutes reparation is consider breakdown. However, idling
and minor stops that did not get serious attention could lead to breakdowns. In the
previous 2 months, figure 4.10 shows that over 80% of six big losses have come from
minor stop, speed losses and planned downtime. Among the three big losses, the minor
stop has the highest percentage of 46.7%. As already known, speed losses affect the
percentage of performance in OEE along with minor stop. The information obtained
from mechanics that, by fixing the causes of minor stop appearances, it is certain that
speed losses will also decrease. It is because the speed losses usually occur caused by
the condition that also has the potential to bring up a minor stop. While for planned
downtime, significant change cannot be made as it is a fix schedule for the machine.
As it tells in the Pareto chart, idling and minor stop gave the biggest time lost for the
packer machine. So idling and minor stop will then be the focus to get the
improvements. According to the historical performance, there are some minor stop that
has highest rate of occurrences. However, the minor stop that will be analyze will be
Idling
and
Minor
stop
Speed
loss
Planne
d
downti
mes
Breakd
owns
Product
ion
rejects
Reject
start/sto
p
Total time losses (min) 9,104.3 4,427.5 3,180.1 1,993.8 463.4 308.8
Cummulative 46.7% 69.5% 85.8% 96.0% 98.4% 100.0%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
90.0%
100.0%
-
1,000.0
2,000.0
3,000.0
4,000.0
5,000.0
6,000.0
7,000.0
8,000.0
9,000.0
10,000.0
MU
NIT
ES
Six Big Losses
43
limited only for six minor stops. The six minor stop with the highest rate of occurrences
can be seen in the table 4.6 below.
Table 4.6 six minor stop
No Minor Stops
1 CH Tear Tape Missing
2 Inner Frame Absence
3 Packet hopper: open/jam
4 Sheet misaligned on the transport belt f1/f2
5 CH Spider; Packed out of position
6 No Blank on transport belt
The explanation of the six chosen minor stops are given.
1. CH Tear tape missing is minor stop that indicates by the sensor which detect
the tear tape, machine stop will be directly initiate when the sensor did not
detect the tape.
2. Inner frame absence indicates by sensor 20B1638 which detects the existence
of inner frame pieces in the inner frame pocket, machine stop will be directly
initiate when the sensor did not detect inner frame.
3. Packet hopper: open/jam. Occurs when the packet on the hopper unit is open or
cannot flow normally (buildup) to the next process. Machine will stop will be
initiate immediately.
4. Sheet misaligned on the transport belt F1/F2. Occurs when sensor 20B3230 and
20B3231 detected that the foil sheet is not in the alignment position to meet
with cigarette bundle. When it happened, machine stop is initiated.
5. CH spider, packed out of position, is a minor stop initiated when the sensor in
the machine detected the packed is not in center position.
6. No blank on transport belt is minor stop that initiates when sensor 20B4959 did
not detect the blank presence.
As it is stated in the project charter, the packer machine is the main concern of this
project. All the six minor stop with the highest occurrence are also from the packer
machine. In the production line of product A, machines after packer machine is linked
to packer machine. It means, all the machine after packer will stop if the packer
stopped. All machine has to wait if the packer machine stopped. Waiting then cause
44
the machines uptime becomes low. Based on this situation, it can be said that the packer
machine is the major cause that in the next machine or process, the uptime is low. Thus,
increasing the uptime of the packer machine will surely affect and gives a good impact
to the uptime of every process after it.
The minor stops in the packer are identified. The six minor stop from table 4.6 will
then proceed to measure phase to know its current situations. With the minor stops as
the specific problem to improve, target of improvements is generated. The target is to
analyze what are the causes of minor stops occurrences in packer machine and what
are possible solutions to overcome it. By overcome the minor stop, the uptime of packer
machine is expected to increase so it can fulfil the company target.
4.5.2 Measure
The second phase of DMAIC is Measure. Measurement of the actual current condition
of occurrences of the minor stops are provide so that it can be compared with the
measurement after implementations, this later will tell if the improvements bring in the
desired result. However, as it an individual project of the author, not all improvement
can be directly implemented in the production line of product A.
For measurement, historical minor stop data are collected. Although the minor stop
data is the overall production line data, almost all minor stops that often appear are
minor stop belonging to the packer machine. So the minor data stop production line
can be used as reference for minor stop packer machine. In the next page, the Table 4.7
shows the list of minor stops that happen in the packer machine in minutes in the
production of product A.
Table 4.7 Minor stops in the production line
No Minor Stops Min Percentage
1 Service button pressed 1,361 14.9%
2 CH Tear Tape Missing 1,096 12.0%
3 Inner Frame Absence 1,064 11.7%
4 Packet hopper: open/jam 981 10.8%
5 Sheet misaligned on the transport belt f1/f2 935 10.3%
45
Table 4.7 Minor stops in the production line (continued)
No Minor Stops Min Percentage
6 CH Spider; Packed out of position 931 10.2%
7 No Blank on transport belt 901 9.9%
8 Blank absence at exit belt entry 321 3.5%
9 Undesired splice presence 225 2.5%
10 Others 1,289 14.2%
9,104 100.0%
Table 4.7 showed the minor stop that occurred in the packer machine. Number 1-7
covered 79% of the minutes spent to dealt with the minor stop. However, the service
button pressed is actually a preventive step taken by the operator consciously when
they realize something is wrong with the machine. For example, the operator sees an
undue material on the machine or when the operator realizes that the adjustment part is
starting to loose. Then, the service button will be pressed and the machine will stop.
Operator may also press the service button if he detects a glitch that can be seen directly
which potentially leads to a minor stop itself. It is the reason why service button pressed
is not included to get to the analyze phase.
For minor stop from number 8 to number 21, the consumed time are somewhat distant
with number 2 to number 7. So, the minor stop that will be analyzed are only from
number 2-7 as it is already state in the define phase.
4.5.3 Analyze
After the measure phase, the next step is analyze. In this step, the selected minor stops
will be analyze to find out what is/are the root cause which cause the machine stops.
The minor stops will be analyzed using the fault tree analysis. In generating the fault
tree, the discussion with operator and mechanic are required. Discussion start by asking
why all the minor stops occurred, what are the effect of failure and continue to until the
root cause are identified. After observing and conduct discussion with operator and
mechanic in the respective production line, the minor stops might have some
similarities in its cause. The fault tree can be seen in the next page.
46
Downtime in
Packer Machine
CH tear tape missing Inner frame absencePacket hopper: open/
jam
Sheet misaligned on
the transport belt
CH spider: packed out
of position
No blank on transport
belt
Tear tape not on
track
Slicer inner frame
settingTear tape reject
Nozzle, guide,
drying belt are
dirty
Foil sheet not
parallel
Suction power
reduced
Blank undetected
by sensor
Guide roller
tear tape dirty
Guide roller
misposition
Slicer
mispositionInner liner
misposition
Choke on
vacuum
Inner frame not
detected
Transfer inner
frame dirty
Cover inner
frame
misplacedGlue bleed out
Improper
folding
Foil sheet messy
Improper inner
liner cut
Vacuum release
too soon
Blank
arrangement not
tidy
Figure 4.11 Fault tree analysis of downtime in packer machine
47
4.5.3.1 Failure Mode and Effect Analysis
After the root cause are identified using fault tree diagram, FMEA chosen is then used.
FMEA is a method used to determine which problem that is more dominant that should
be the focus to be addressed. As the Fault tree has shown, the information in it can be
used to create FMEA table. Before continue to assess the severity, occurrence and
detection to find the Risk Priority Number (RPN) in each problem, identifying the
potential minor stops in the packer machine should be done. The potential downtime
are:
Table 4.8 Minor Stops
No Minor Stops Occurrences
1 CH Tear Tape Missing 363
2 Inner Frame Absence 374
3 Packet hopper: open/jam 294
4 Sheet misaligned on the transport belt f1/f2 304
5 CH Spider; Packed out of position 247
6 No Blank on transport belt 328
From the fault tree shown on the previous page, it can be seen that the lowest part of
the fault tree is the root cause causing the emergence of a minor stop. Referring to
FMEA, the root cause of the fault tree can be interpreted as the cause of failure of the
minor stop that appears. This root cause will be an occurrence scale to judge how often
it appears. Furthermore, from the cause of failure, the impact of the error arises. It turns
out before it ends up being a minor stop, root cause will cause effect of failure first
before reaching minor stop. Therefore, the severity scale is done at those level which
is level 2 of the fault tree instead of level 1. Scale for detection is done at level 1 of the
fault tree.
To give the scaling of severity, occurrence and detection, the data is taken from system
and through assessing it with the operator and mechanic of the packer also. The scale
that will be used is from 1 to 10 based on what has been explained in the Chapter II of
this research. For minor stops, operator is the most reliable source as they are the one
48
who witness anything that happen in the machine. As for support, the scale that has
given by the operator was also check by the mechanic.
4.5.3.2 Severity
All the minor stops that are mostly occur in the packer machine has been identified.
The next step is to identify the potential effect(s) of failure. Based on the potential
effect(s) of failure, the assessment of severity will be done to each of the failure.
For severity assessments, experienced operators gives the score based on how much
time spend to performed the reparation when a minor stop occurs. It later will also be
check by mechanic. So, the more serious the effect of failure, the longer the reparation
time is needed. Based on the discussion, scale 1 means severity can be quickly
overcome and only takes below 1 minutes to solve. Scale 2, 3, 4 are given when the
reparation only took below 2 minutes. Then, scale 5, 6 are for the reparation that takes
1 until 2 minutes. For 7, 8, 9 means severity is overcome in a longer period of time and
takes 2 to 3 minutes of reparation. Last, severity scale of 10 is for reparation that takes
above 4 minutes to deal. For example, minor stop reparation that often took longest
time to deal is suction power reduced, where the operator requires mechanical
assistance to check and open the machine which handling time is more than 3 minutes.
Thus, the longer the minor stop fix is made, the greater the severity scale is given.
Table 4.9 Scaling of severity
No Failure Mode Potential Effect(s) of Failure Severity
1 CH tear tape missing Tear tape reject 6
Tear tape not on track 7
2 Inner frame absence Slicer inner frame setting 3
Inner frame not detected 5
3 Packet hopper: open/jam Nozzle, guide, drying belt are dirty 8
4 Sheet misaligned on the transport
belt
Foil sheet not parallel 7
Foil sheet messy 6
5 CH spider: packed out of position Suction power reduced 9
6 No blank on transport belt Blank undetected by sensor 5
49
As it was also showed in the fault tree, some of the failure mode could came from more
than one potential effect of failure. From table 4.9, it shows that the potential effect of
failure Suction power reduced have the highest severity scale which is 9. It then
followed by Nozzle, guide, drying belt are dirty with severity scale of 8. The nozzle, guide
and drying belt are often cover with dirt and glue. To clean the dirt is easy, operator
only use air gun. However, when it covered by glue, it takes time to clean it. Then, tear
tape not on track and foil sheet not parallel with the severity scale 7. The third potential
effect of failure is tear tape reject and foil sheet messy has severity scale of 6. Potential
effect of failure Inner frame not detected and Blank undetected by sensor has severity
scale of 5. Last, the lowest severity scale for the potential effect(s) of failure is 3 which
is possessed by Slicer inner frame setting.
4.5.3.3 Occurrence
Occurrence scaling can be judged by how often the potential cause of failure occurred.
Occurrence scaling of the potential cause of failure can be seen in the table 4.10 below.
For the occurrence scaling, operator and mechanic scale it by data of the failure mode
occurrence itself. However, some failure mode has more than one cause of failure.
Table 4.10 Scaling of Occurence
No Failure Mode Frequency Potential Cause(s) of
Failure Occurrence
1 CH tear tape missing 363 Guide roller tear tape dirty 7
Guide roller misposition 8
2 Inner frame absence 374
Slicer misposition 5
Transfer inner frame dirty 7
Cover inner frame misplaced 7
3 Packet hopper: open/jam 294 Glue bleed out 6
Improper folding 7
4 Sheet misaligned on the
transport belt 304
Inner liner misposition 7
Improper inner liner cut 8
5 CH spider: packed out
of position 247 Choke on vacuum 9
6 No blank on transport
belt 328
Vacuum release too soon 7
Blank arrangement not tidy 5
50
For the occurrence, scale used is from 1 to 10. Scale 1 means that the cause of failure
is rarely to happen while the scale 10 indicates that the cause of failure happened very
often. Table 4.10 shows there are potential 12 root causes leads to minor stops/failure
mode. The frequency of 6 minor stops are shown also to support the occurrence scaling.
From table 4.10, Choke on vacuum has the highest occurrence rate which is 9. Guide
roller misposition and Improper inner liner cut has the same occurrence rate of 8. The
guide roller tear tape dirty, transfer inner frame dirty, cover inner frame misplaced,
improper folding, inner liner misposition and vacuum release too soon also has the
same occurrence rate of 7. Glue bleed out has the occurrence rate of 6. Last, the lowest
occurrence rate score is 5 which are the scoring for Slicer misposition and Blank
arrangement not tidy
4.5.3.4 Detection
Assessment of detection in FMEA aims to determine the possibility of process control
undertaken to detect the next mode of failure, so that the assessment is done on the
ability to control the process to prevent the occurrence of the machine stops functioning
or machine breakdown. In other words, scaling for detection is based on the prevention
of failure mode. The used scale for detection rating is 1 to 10. Scale 1 means that the
current control for prevention is almost certain to detect while scale 10 means almost
impossible to detect. Scaling for detection can be seen in the Table 4.11 below.
Table 4.11 Scaling of detection
No Potential Failure
Mode Current controls, Prevention
Level of
Effectiveness Detection
1 CH tear tape missing Cleaning sensor 3S255 Low 6
2 Inner frame absence Cleaning vacuum chamber Moderately
high 4
3 Packet hopper:
open/jam
Adjust fix folder and flap
folder. Cleaning nozzle, guide
and drying belt.
Slight 7
4 Sheet misaligned on
the transport belt Adjust inner liner blade setting Very slight 8
5 CH spider: packed out
of position Cleaning vacuum channel High 3
6 No blank on transport
belt
Material preparation, Operator
awareness High 3
51
Scaling of detection in the table 4.11 shows that there are some potential failure mode
that its current control considered as high with scale of 3. The high/effective current
controls are for CH spider: packed out of position and no blank on transport belt which
has scale of 3. Inner frame absence has detection scale of 4 which is moderately high.
Lastly, there are three potential failure mode that its detection level are not good enough
which are CH tear tape missing, packed hopper: open/jam and sheet misaligned on the
transport belt with the detection scale of 6, 7 and 8 respectively.
After the scaling for severity, occurrence and detection are made, it then continue to
the next step which is to calculate the Risk Priority Number (RPN).
4.5.3.5 Risk Priority Number (RPN)
After rate of severity, occurrence and detection for each potential failure mode has been
rate, the RPN can be calculated using the equation (2-8):
𝑅𝑃𝑁 = 𝑆 × 𝑂 × 𝐷
The minimum and maximum score for RPN are 1 and 1000 respectively. Those score
will be the limit of RPN. Therefore, the RPN score for each failure mode are:
1. CH tear tape missing has severity scale of 6, occurrence 7 and detection 6 for
the potential effect of failure for tear tape rejection which leads to the RPN
score of 252. For tear tape not on track, the severity scale is 7, occurrence scale
of 8 and detection scale of 6. The RPN for the second potential effect of failure
is 336. With the two score combined, the RPN score for CH tear tape missing
is 588.
2. Inner frame absence, the slicer inner frame setting has the severity scale 3 and
its occurrence 5. For inner frame not detected, the severity scale is 5 and it has
two occurrence scale in which both are 7. The scale of detecting the failure
mode is 4. This failure mode has three RPN score. By summed up three RPN,
the total RPN score is 340 points.
52
3. Packet hopper: Open/jam has scale for severity of 8. For its occurrence, it has
two score which are scale of 6 for glue bleed out and scale of 7 for improper
folding. For detection, the scale of 7 is given. Then, for this failure mode, it
also has two RPN score which are 336 and 392. To get total score of RPN, two
score are summed up. Hence, this failure mode has the total RPN score of 728
points.
4. Sheet misaligned on the transport belt has two severity scale from foil sheet
not parallel and foil sheet messy which are scored 7 and 6 respectively. For its
occurrence scale, it also has two score which are 7 for inner liner misposition
and 8 for improper inner liner cut. Detection scale is 8. This failure mode also
has two RPN score which are 392 and 384. Both score are then summed up.
The total calculated RPN score is 776.
5. CH spider: packed out of position only has one severity scale which is 9. It
also only has one occurrence scale and one detection scale. Its occurrence scale
is 9 and detection scale is 3. The calculated RPN comes by multiply the
severity, occurrence and detection. The RPN score is 243 points.
6. No blank on transport belt has severity scale of 5. For its occurrence, this
failure mode has two scale that comes from two root causes which are 7 and 5
respectively. For detection of this failure mode, scale of 3 is given. It then leads
to its total RPN score of 180 points.
After the RPN calculated, FMEA score is completed for the six minor stop. It then
input to the FMEA table. Minor stop that has more than one RPN score, the RPN score
is summed up as it is already mention above. The FMEA result for minor stops in
packer machine can be seen in the table 4.12 in the next page.
53
Table 4.12 FMEA for Packer Machine
No Potential Failure
Mode
Potential effect(s)
of Failure (S)
Potential Cause(s) of
Failure (O) Current Controls, Prevention (D) RPN Total RPN
1 CH tear tape
missing
Tear tape reject 6 Guide roller tear tape dirty 7 Cleaning sensor 3S255 and
guide roller 6
252
588 Tear tape not on
track 7
Guide roller misposition 8 336
2 Inner frame
absence
Slicer inner frame
setting 3
Slicer misposition 5
Cleaning vacuum chamber 4
60
340 Inner frame not
detected 5
Transfer inner frame dirty 7 140
Cover inner frame misplaced 7 140
3 Packet hopper:
open/jam
Nozzle, guide,
drying belt are
dirty
8
Glue bleed out 6 Adjust fix folder and flap folder.
Cleaning nozzle, guide and
drying belt.
7
336
728
Improper folding 7 392
4 Sheet misaligned
on the transport belt
Foil sheet not
parallel 7
Inner liner misposition 7
Adjust inner liner blade setting 8 392
776
Foil sheet messy 6 Improper inner liner cut 8 384
5 CH spider: packed
out of position
Suction power
reduced 9 Choke on vacuum 9 Cleaning vacuum channel 3 243 243
6 No blank on
transport belt
Blank undetected
by sensor 5
Vacuum release too soon 7 Material preparation, Operator
awareness 3
105 180
Blank arrangement not tidy 5 75
54
Based on the data that has been analyze using FMEA method, the Risk priority number
(RPN) for each minor stop are found. RPN score is affected based on how much
influence the breakdown of the reliability of the machine seen from the time of the
machine failure (severity). In addition, the frequency of the machine experiencing a
stop caused by a particular failure mode also affects the RPN score of an occurrence
mode. Last is how the control or detection that has been done by the company against
the failure mode (detection). Whether the control and detection performed by the
company against the failure mode is effective or not. The minor stops (failure mode)
with the highest RPN to the lowest are then arranged in the table 4.13 below.
Table 4.13 RPN score for minor stops
Minor Stops RPN Cumm.
RPN Percentage
Cumm.
Percentage
Sheet misaligned on the
transport belt 776 776 27.2% 27.2%
Packet hopper: open/jam 728 1,504 25.5% 52.7%
CH tear tape missing 588 2,092 20.6% 73.3%
Inner frame absence 340 2,432 11.9% 85.2%
CH spider: packed out of
position 243 2,675 8.5% 93.7%
No blank on transport belt 180 2,855 6.3% 100.0%
Based on Table 4.13, Sheet misaligned on the transport belt, Packet hopper: Open/jam
and CH tear tape missing, shows RPN above 500 which can be consider as the main
failure mode that gave the highest contribute to time loss in the packer machine. Based
on the RPN score also, as it is stated in chapter II, Sheet misaligned on the transport
belt can be said significantly affected the OEE score in the current condition. Hence, if
the RPN of these six minor stop reduced, OEE is expected to increase.
Pareto diagram of minor stops (failure mode) can also be seen in the figure 4.12 to
clarify the RPN score from the table 4.13. Based on the table 4.13 and figure 4.12, the
impact of these three potential failure modes strongly affects the reliability of the
machine because 80% stops on the machine is caused by the three potential failure
modes. This situation indicates that the improvement should given more concern on
55
those three potential failure mode, certainly without ignoring the other failure modes.
The improvement given will be based on the causes of failure that has been analyze
based on fault tree analysis and failure mode and effect analysis.
Figure 4.12 Pareto Diagram for RPN
4.5.4 Improve
After analyzing the root cause for each downtime with the Failure Mode and Effect
Analysis, the next step is to propose the improvement to overcome the root cause. The
purpose of the improvement is to eliminate the occurrence of the minor stop that has
been analyze, so that the overall equipment effectiveness of packer machine will
increase. Then, as the OEE calculation serve as valuation of machine’s uptime, an
increase from OEE itself can be interpreted as a decrease in downtime waste of the
packer machine. As the result of discussion with the mechanic, operator and also using
FMEA, improvements are provided.
Based on the RPN obtained through the FMEA method, the prioritized failure are
identified. Then, the proposed improvement to deal with failures that happened are
given. The proposed will be given to all of the minor stops. However, sheet misaligned
on the transport belt, CH tear tape missing, Packet hopper: open/jam are more
Sheetmisaligned on thetransport
belt
Packethopper:
open/jam
CH teartape
missing
Innerframe
absence
CHspider:packedout of
position
No blankon
transportbelt
RPN 776 728 588 340 243 180
Cumm. Percentage 27.2% 52.7% 73.3% 85.2% 93.7% 100.0%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
90.0%
100.0%
- 200 400 600 800
1,000 1,200 1,400 1,600 1,800 2,000 2,200 2,400 2,600 2,800 3,000
RPN Cumm. Percentage
56
prioritized. The proposed improvement for these minor stops is because the failure is
highly affect the machine reliability as it occurs many times during the production
process. Proposed improvements given will base on the causes of the failure that has
been stated before in the FTA and FMEA is shown at table 4.14.
Table 4.14 Proposed improvement for Minor stops
No
Potential
Failure
Mode
Potential
Cause(s) of
Failure
Proposed activity for improvement
1
Sheet
misaligned
on the
transport
belt
Inner liner
misposition
Need to check the transport belt and 2nd rake sealer
before the engine starts at the beginning of the shift.
Transport belt and 2nd rake sealer is very susceptible to
where the inner liner stuck, so when a minor stop occurs,
always check if there are inner liners. If the blade has
felt dull should be replaced. It is better to replace the
inner liner blade at the beginning of the shift.
Improper
inner liner
cut
2 Packet
hopper:
open/jam
Glue bleed
out
Perform cleaning sensor 20S8902 before running
machine that stop. Tooth on 2nd wheel also needs to be
cleaned to avoid folds in processed material.
Improper
folding
Fix folder and flap folder should be adjust to the right
position. Adding more task list specified in doing so
would help and the improper folding of the film can be
avoided.
3
CH tear
tape
missing
Guide roller
tear tape
dirty
Cleaning and adjusting the position of the tear tape
against the pack should be done to prevent dust or
cigarette particles contaminating the guide roller. The
operator should take samples periodically to check the
position of the tear tape pack.
Guide roller
misposition
Checking the axial guide roller position at the initial
distance (+ -38mm) from the bracket surface before
restarting the machine that has stop so that the tear tape
is always on its track. Then, make sure the 3S255 sensor
has been cleaned to keep detecting tear tape.
4 Inner frame
absence
Slicer
misposition
Check and cleaning the vacuum chamber periodically.
The slicer for inner frame should be check and adjust so
that the poor inner frame cut can be avoid. Transfer
inner frame
dirty
Cover inner
frame
misplaced
Mechanic should educate the operator to pay more
attention on the cover inner frame position and know if
it is installed correctly. Cleaning cover inner frame is
also needs to be done periodically.
57
Table 4.14 Proposed improvement for Minor stops (continued)
No
Potential
Failure
Mode
Potential
Cause(s) of
Failure
Proposed activity for improvement
5 No blank on
transport belt
Vacuum
release too
soon
Doing the material check before input it to the
machine. Vacumm release adjustment should become
a consern also. It has to be check and set before starting
the machine and when the machine stops because of
other factors
Blank
arrangement
not tidy
For this matter, human factor is really important. The
operator should check and warn the helper when they
arrange the blank beside the packer machine.
6 CH spider:
packed out of
position
Choke on
vacuum
Check the vacuum channel every time the stops
happen. Check the belt infeed in the CH spider, make
sure that there is no particle or anything sticks on the
belt and the belt surface still rough. Replace the belt if
there is any part of it that has worn out.
After the improvements given, it then proposed in the production line. The proposed
improvement in the production is converted to Task Detail Sheet (TDS) as a standard
of the company and campaign on paper and warning/reminder sign placed in the
production line. The TDS module contains instructions and procedures for doing
something related to the machine. For example, cleaning machine instructions,
arrangements on specific parts of the machine, instructions for solving machine errors
(minor stop) and so on. The example of task detail and warning sign is can be seen in
the appendix 6-7. The prioritize minor stop will be given more concern of
improvements. For minor stop with high RPN, the improvement applied by adding
more instructions for cleaning and adjustment machine’s part in the task detail sheet.
Some of the improvement will also be a warning/reminder sign attached around the
production line. While for minor stop with smaller RPN, the improvements will be
bound to paper campaign, warning/reminder sign attached around the production line.
For all the improvements proposed, the action plan is created. The action plan is created
using 5Ws and 1H. This action plan answers what, why, where, when, who, and how
the improvement implemented in the production line. Table 4.15 in the next page shows
the action plan and it specific steps.
58
Table 4.15 Action Plan 5W 1H
What
(action to be taken)
Why
(justification)
Where
(location) When
Who
(responsible)
How
(steps)
Add more adjustment instruction for transport belt and
2nd rake sealer in minor stop TDS and cleaning task
No detail for transport belt
and 2nd rake sealer
adjustment and cleaning task
Packer
Machine
Jan-
18
Technical
trainer
Discussed with mechanic and
operator
Add more instruction in TDS
Add Cleaning task for sensor 20S8902, tooth belt, fix
folder and flap folder adjustment.
Create a reminder sign and stick it near the machine as
a cleaning reminder
No cleaning taks for sensor
and toot belt yet
No warning/reminder sign
near the machine
No TDS yet for fix folder
and flap folder
Packer
machine
Jan-
18
Technical
trainer
Add more cleaning
instruction and additional
adjustment in TDS
create warning/reminder sign
Tear tape adjustment and cleaning task must be added
in task detail sheet. Reminder sign to tell operator to
take sample.
Initial distance of axial guide roller has to be check
every stops occurred. Add cleaning instruction for
sensor 3S255
No detailed task and cleaning
instruction for the respective
matters yet
Packer
Machine
Jan-
18
Technical
trainer
Discussed with mechanic and
operator
Add more instruction in TDS
Put visual instruction near the machine for a correct
installation of cover inner frame
Reminder sign to check and clean vacuum chamber
and cover inner frame
Not yet applied in the
machine area
Packer
Machine
Jan-
18
Technical
trainer
Discussed with mechanic to
create the visual instruction
Create reminder sign for material check and stick it
near material station
Add vacuum release adjustment on task detail
Helper are sometimes
unaware of the condition of
the material in which it is
prepared
Production
line
Jan-
18
Technical
trainer
Discussed with mechanic and
operator to create
warning/reminder sign and
add more instruction in task
detail sheet
Reminder sign for vacuum channel and belt infeed
check, also for belt surface
There is no
warning/reminder sign yet
for the specific matters
Production
line
Jan-
18
Technical
trainer
Discussed with mechanic and
operator to create
warning/reminder sign
59
4.5.4.1 After improvements
The improvement applied in the end of January 2018 as an added task detail and
warning sign or paper campaign. The production line once again observed to gather the
data of after improvement. The data was taken in a normal condition which consist of
40 shifts. Then, the OEE score of packer machine are calculated once again the way it
was calculated before when creating the current value stream map in section 4.4 in this
chapter. As it was predicted, after the improvement are implemented, uptime (based on
OEE calculation) shows an improvement. The average of 40 shifts OEE shows the
score of 79%. If compared to the company standard it still below 80%. However, many
of the shifts data that was taken shows an OEE 80% or above if it was not calculated
as an average. The uptime improvements of packer machine can be seen in figure 4.10.
Figure 4.13 OEE of Packer Machine comparison
As Figure 4.13 shows, it improved by 11% after the research conducted. The detailed
calculation of OEE after improvements can be seen in the appendix 8-11. For
comparison of OEE for machines after packer, table 4.16 will shows the result.
Table 4.16 OEE of Machines after improvement (exclude maker)
Machines OEE
Before After
Packer 68.0% 79.8%
Cellophaner 63.5% 78.1%
Cartoner 63.5% 71.6%
Case Packer 48.9% 56.0%
To show it in details, future value stream map has been created and can be seen in the
figure 4.14 in the next page.
68%
79%
BEFORE AFTER
OEE before and after improvements
60
Maker Packer Cellophaner Case PackerCartoner
Production Scheduler
Primary Process
Sales & Marketing
CT: 0.61 min
Uptime: 69.0%
Defect: 0.44%
3 shift
CT: 0.70 min
Uptime: 79.8%
Defect: 1.18%
3 shift
CT: 0.79 min
Uptime: 78.1%
Defect: 0.27%
3 shift
CT: 0.87 Min
Uptime: 71.6%
Defect: 0.50%
3 shift
CT: 1.58 min
Uptime: 56%
Defect: 3.5%
3 shift
Warehouse
CustomerTim
Leader
Daily Daily Daily Daily Daily
Weekly Order
1 Operator 1 Operator 1 Operator 1 Assistant1 Assistant
0.61 min
2.53 min
0.70 min 0.79 min 0.87 min 1.58 min
0.42 min 0.05 min 1 min 15 minLead time = 23.55 min
Value added = 4.55 min
1 shift: 8 hrs
Break: 30 min
Available time: 450 min
Figure 4.14 Future Value Stream Map (FVSM)
61
Table 4.16 in the previous page shows that after improvements, all machine’s OEE
start from packer are increased. It means, waste of waiting from packer to case packer
machine are reduced and the downtime in the production line is decreased. Hence, the
uptime that are based on OEE calculation are increased. Figure 4.14 also shows the
detail improvement happened the production line. Total lead time in the production line
is reduced.
The minor stops data was also collect again to see any changes in the percentage and
wasted time. Table 4.17 shows the minor stops percentage after the improvements. The
data of minor stops time is within a month period.
Table 4.17 Minor stop in the production line (after)
No Minor Stops Min Percentage
1 Service button pressed 1,124 14.9%
2 Inner Frame Absence 812 10.8%
3 Empty pocket check: Undesired pocket 682 9.1%
4 CH Tear Tape Missing 647 8.6%
5 Upper packet flap jam 623 8.3%
6 Sheet misaligned on the transport belt f1/f2 589 7.8%
7 Packet hopper: open/jam 576 7.7%
8 No Blank on transport belt 462 6.1%
9 Foil splice check failure 412 5.5%
10 Others 1,596 21.2%
7,523 100.0%
It can be seen also that the amount of time wasted to deal with the minor stop was
reduced. As the total of minutes wasted before the improvement was 9,104 minutes. It
then reduced to 7,523 minutes. Last, the research will reach its final step which is
control.
4.5.5 Control
As the last phase of DMAIC, control phase aims to keep the improvements that have
been applied will always be applied and will not lost through time. This activity is done
because, if after the project is completed and no monitoring is given, the process will
slowly return to where it was before the project. It is critical to give track to the
62
implementations, especially during the first months so we can get an idea if these
improvements are working. In this phase, control plan will be provided to support the
proposed improvement so that the same waste can be reduce. Control Plan will be in
form of control checklist and will be provided in appendix 12.
FMEA table also has been checked and rate by operator and mechanic with the new
RPN score for the minor stops. As can be seen in appendix 13, the new RPN score is
lower than the previous which indicates the increasing in machine’s uptime (based on
OEE calculation). With the FMEA table, it is expected to also help the production line
to maintain the improvement. FMEA table should be helpful later if there is any further
research regarding the minor stops. FMEA table are also provided in the appendix.
63
CHAPTER V
CONCLUSIONS AND RECOMMENDATIONS
5.1 Conclusions
The objectives of this research in PT.ZZZ regarding waste elimination has been
achieved. Below are the conclusion gained after the research completed.
1. After Current value stream mapping of product A is made, it was found that all the
machines uptime (based in OEE calculation) was below the company standard of
80%. Packer machine however, were consider gave big contribution in low uptime
performance because when the packer machine stops, the whole next machine
cannot be operated. Low OEE happened when there are so many stops or
breakdowns occur in the machine.
2. To reduce waste in the production line, the most suitable method is lean six sigma.
Lean tools combine with six sigma steps of DMAIC gives a good support in order
to eliminate the current problem in the production line. The DMAIC then used to
analyze and identified the root cause of the problem. In define phase, value stream
mapping showed the detail condition of the production line. In analyzing the
problem, fault tree analysis(FTA) and fault mode and effect analysis(FMEA) are
used.
3. In purpose of waste elimination, the improvement given was proposed in the
production line and it was converted into form of task detail sheet for operator and
campaign on paper and warning sign. The improvements consist of keeping parts of
the machine which always caused the machine failure to be always clean and in fix
adjustment. Then, to support the given improvements, control plan is provided.
After the improvements are applied, it was proved to have improvements in the
production line. The Packer machine uptime increased by 11% from 68% to 79%
and almost reach the company target of 80% OEE. The improvements showed a
64
good result since all the machine that rely on the packer machine are also get the
improvements in its uptime.
5.2 Recommendations
For further research in the future, some recommendations are given:
1. Build a strong leadership and commitment within the operators.
2. For the future project, breakdowns should become another concern after minor stops
as it is also has a big contribution in affecting OEE.
65
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Morgan, John. Bregnig-Jones, Martin. Lean Six Sigma for Dummies. Great Britain.
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Muthukumaran.Venkatachalapathy. Impact on integration of Lean Manufacturing and
Six Sigma in various applications – a review, IOSR Journal of Mechanical and Civil
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Najib, Halim. Choiri, Mochamad. Implementation of Lean Six Sigma to minimize
waste on Webb production process in PT Temprina Media Grafik Nganjuk, Jurnal
Rekayasa dan Manajemen Sistem Industri. Vol:2, No. 5. Malang. 2014.
Qualitydigest, Fault tree analysis and its common symbol, June 28, 2016.
[https://www.qualitydigest.com/inside/lean-article/062816-fault-tree-analysis-and-its-
common-symbols.html]. accessed February 5th, 2018.
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Effectiveness of the CNC Cutting Section of a Shipyard, ARPN Journal of Science and
Technology, Vol:2, No. 11. Bangladesh. 2012.
66
APPENDICES
Appendix 1 Calculation Of OEE, Cycle time and defect rate of Maker (Before improvement)
Shift
Planned
Prod
time
(min)
Down
time
(min)
Operation
time (min)
Ideal
design
speed
Ideal
Output
Actual
output
Reject
Pieces
Good
Pieces
Availa
bility
Perfor
mance Quality OEE
Design
Speed
Cycle
time
Defect
Rate
1 450 111 339 7500 2,542,500 2,752,855 13,142 2,739,713 75.3% 108% 99.5% 81.2% 8120.52 0.62 0.48%
2 450 132 318 7500 2,385,000 2,598,982 11,855 2,587,127 70.7% 109% 99.5% 76.7% 8172.90 0.61 0.46%
3 450 132 318 7500 2,385,000 2,530,546 11,434 2,519,112 70.7% 106% 99.5% 74.6% 7957.69 0.63 0.45%
4 450 137 313 7500 2,347,500 2,558,103 14,424 2,543,679 69.6% 109% 99.4% 75.4% 8172.85 0.61 0.56%
5 450 76 374 7500 2,805,000 3,014,083 13,347 3,000,736 83.1% 107% 99.6% 88.9% 8059.05 0.62 0.44%
6 450 155 295 7500 2,212,500 2,428,493 12,701 2,415,792 65.6% 110% 99.5% 71.6% 8232.18 0.61 0.52%
7 450 210 240 7500 1,800,000 2,007,427 9,328 1,998,099 53.3% 112% 99.5% 59.2% 8364.28 0.60 0.46%
8 450 127 323 7500 2,422,500 2,623,203 9,253 2,613,950 71.8% 108% 99.6% 77.5% 8121.37 0.62 0.35%
9 450 177 273 7500 2,047,500 2,267,806 7,845 2,259,961 60.7% 111% 99.7% 67.0% 8306.98 0.60 0.35%
10 450 162 288 7500 2,160,000 2,365,498 7,311 2,358,187 64.0% 110% 99.7% 69.9% 8213.53 0.61 0.31%
11 450 123 327 7500 2,452,500 2,652,763 11,601 2,641,162 72.7% 108% 99.6% 78.3% 8112.43 0.62 0.44%
12 450 218 232 7500 1,740,000 1,957,610 12,133 1,945,477 51.6% 113% 99.4% 57.6% 8437.97 0.59 0.62%
13 450 147 303 7500 2,272,500 2,487,070 7,395 2,479,675 67.3% 109% 99.7% 73.5% 8208.15 0.61 0.30%
14 450 146 304 7500 2,280,000 2,491,186 8,411 2,482,775 67.6% 109% 99.7% 73.6% 8194.69 0.61 0.34%
15 450 143 307 7500 2,302,500 2,518,418 7,381 2,511,037 68.2% 109% 99.7% 74.4% 8203.32 0.61 0.29%
16 450 188 262 7500 1,965,000 2,178,399 7,062 2,171,337 58.2% 111% 99.7% 64.3% 8314.50 0.60 0.32%
17 450 176 274 7500 2,055,000 2,244,491 9,846 2,234,645 60.9% 109% 99.6% 66.2% 8191.57 0.61 0.44%
18 450 125 325 7500 2,437,500 2,644,694 7,736 2,636,958 72.2% 109% 99.7% 78.1% 8137.52 0.61 0.29%
19 450 170 280 7500 2,100,000 2,321,838 7,613 2,314,225 62.2% 111% 99.7% 68.6% 8292.28 0.60 0.33%
20 450 157 293 7500 2,197,500 2,415,358 6,349 2,409,009 65.1% 110% 99.7% 71.4% 8243.54 0.61 0.26%
21 450 220 230 7500 1,725,000 1,938,596 26,521 1,912,075 51.1% 112% 98.6% 56.7% 8428.68 0.59 1.37%
22 450 138 312 7500 2,340,000 2,556,605 7,414 2,549,191 69.3% 109% 99.7% 75.5% 8194.25 0.61 0.29%
67
23 450 175 275 7500 2,062,500 2,277,771 14,862 2,262,909 61.1% 110% 99.3% 67.0% 8282.80 0.60 0.65%
24 450 198 252 7500 1,890,000 2,098,258 9,861 2,088,397 56.0% 111% 99.5% 61.9% 8326.42 0.60 0.47%
25 450 124 326 7500 2,445,000 2,659,310 11,970 2,647,340 72.4% 109% 99.5% 78.4% 8157.39 0.61 0.45%
26 450 150 300 7500 2,250,000 2,475,923 8,596 2,467,327 66.7% 110% 99.7% 73.1% 8253.08 0.61 0.35%
27 450 111 339 7500 2,542,500 2,761,487 8,197 2,753,290 75.3% 109% 99.7% 81.6% 8145.98 0.61 0.30%
28 450 211 239 7500 1,792,500 2,009,599 7,672 2,001,927 53.1% 112% 99.6% 59.3% 8408.36 0.59 0.38%
29 450 211 239 7500 1,792,500 2,006,610 8,269 1,998,341 53.1% 112% 99.6% 59.2% 8395.86 0.60 0.41%
30 450 192 258 7500 1,935,000 2,155,744 6,179 2,149,565 57.3% 111% 99.7% 63.7% 8355.60 0.60 0.29%
31 450 214 236 7500 1,770,000 1,987,652 6,179 1,981,473 52.4% 112% 99.7% 58.7% 8422.25 0.59 0.31%
32 450 157 293 7500 2,197,500 2,411,101 14,752 2,396,349 65.1% 110% 99.4% 71.0% 8229.01 0.61 0.61%
33 450 205 245 7500 1,837,500 2,047,619 10,288 2,037,331 54.4% 111% 99.5% 60.4% 8357.63 0.60 0.50%
34 450 155 295 7500 2,212,500 2,427,207 8,337 2,418,870 65.6% 110% 99.7% 71.7% 8227.82 0.61 0.34%
35 450 200 250 7500 1,875,000 2,093,419 7,103 2,086,316 55.6% 112% 99.7% 61.8% 8373.68 0.60 0.34%
36 450 203 247 7500 1,852,500 2,068,364 13,107 2,055,257 54.9% 112% 99.4% 60.9% 8373.94 0.60 0.63%
37 450 182 268 7500 2,010,000 2,212,315 7,839 2,204,476 59.6% 110% 99.6% 65.3% 8254.91 0.61 0.35%
38 450 198 252 7500 1,890,000 2,112,486 13,758 2,098,728 56.0% 112% 99.3% 62.2% 8382.88 0.60 0.65%
39 450 177 273 7500 2,047,500 2,263,364 11,684 2,251,680 60.7% 111% 99.5% 66.7% 8290.71 0.60 0.52%
40 450 217 233 7500 1,747,500 1,960,032 10,738 1,949,294 51.8% 112% 99.5% 57.8% 8412.15 0.59 0.55%
69.0% 0.61 0.44%
68
Appendix 2 Calculation Of OEE, Cycle time and defect rate of Packer (Before improvement)
Shift
Planned
Prod
time
(min)
Down
time
(min)
Operation
time (min)
Ideal
design
speed
Ideal
Output
Actual
output
Reject
Pieces
Good
Pieces
Availa
bility
Perfor
mance Quality OEE
Design
Speed
Cycle
time
Defect
Rate
1 450 55 395 370 146,150 133,023 1,352 131,671 87.8% 91% 99.0% 79.1% 336.77 0.74 1.02%
2 450 53 397 370 146,890 126,855 994 125,861 88.2% 86% 99.2% 75.6% 319.53 0.78 0.78%
3 450 54 396 370 146,520 126,685 1,446 125,239 88.0% 86% 98.9% 75.2% 319.91 0.78 1.14%
4 450 65 385 370 142,450 131,536 1,475 130,061 85.6% 92% 98.9% 78.1% 341.65 0.73 1.12%
5 450 46 404 370 149,480 142,019 861 141,158 89.8% 95% 99.4% 84.8% 351.53 0.71 0.61%
6 450 83 367 370 135,790 122,309 2,036 120,273 81.6% 90% 98.3% 72.2% 333.27 0.75 1.66%
7 450 80 370 370 136,900 128,491 1,740 126,751 82.2% 94% 98.6% 76.1% 347.27 0.72 1.35%
8 450 110 340 370 125,800 113,494 1,596 111,898 75.6% 90% 98.6% 67.2% 333.81 0.75 1.41%
9 450 93 357 370 132,090 115,494 1,625 113,869 79.3% 87% 98.6% 68.4% 323.51 0.77 1.41%
10 450 67 383 370 141,710 128,942 1,515 127,427 85.1% 91% 98.8% 76.5% 336.66 0.74 1.17%
11 450 80 370 370 136,900 129,758 1,435 128,323 82.2% 95% 98.9% 77.1% 350.70 0.71 1.11%
12 450 101 349 370 129,130 113,209 1,545 111,664 77.6% 88% 98.6% 67.1% 324.38 0.77 1.36%
13 450 93 357 370 132,090 125,559 1,254 124,305 79.3% 95% 99.0% 74.7% 351.71 0.71 1.00%
14 450 88 362 370 133,940 110,513 1,487 109,026 80.4% 83% 98.7% 65.5% 305.28 0.82 1.35%
15 450 111 339 370 125,430 106,056 1,055 105,001 75.3% 85% 99.0% 63.1% 312.85 0.80 0.99%
16 450 51 399 370 147,630 128,994 1,126 127,868 88.7% 87% 99.1% 76.8% 323.29 0.77 0.87%
17 450 53 397 370 146,890 115,163 1,058 114,105 88.2% 78% 99.1% 68.5% 290.08 0.86 0.92%
18 450 67 383 370 141,710 122,658 1,082 121,576 85.1% 87% 99.1% 73.0% 320.26 0.78 0.88%
19 450 99 351 370 129,870 105,468 1,235 104,233 78.0% 81% 98.8% 62.6% 300.48 0.83 1.17%
20 450 67 383 370 141,710 130,826 1,039 129,787 85.1% 92% 99.2% 78.0% 341.58 0.73 0.79%
21 450 106 344 370 127,280 108,489 1,596 106,893 76.4% 85% 98.5% 64.2% 315.38 0.79 1.47%
22 450 109 341 370 126,170 106,595 1,693 104,902 75.8% 84% 98.4% 63.0% 312.60 0.80 1.59%
23 450 53 397 370 146,890 131,263 1,183 130,080 88.2% 89% 99.1% 78.1% 330.64 0.76 0.90%
24 450 70 380 370 140,600 123,939 1,669 122,270 84.4% 88% 98.7% 73.4% 326.16 0.77 1.35%
25 450 41 409 370 151,330 139,300 938 138,362 90.9% 92% 99.3% 83.1% 340.59 0.73 0.67%
26 450 134 316 370 116,920 98,804 2,173 96,631 70.2% 85% 97.8% 58.0% 312.67 0.80 2.20%
69
27 450 132 318 370 117,660 91,317 2,120 89,197 70.7% 78% 97.7% 53.6% 287.16 0.87 2.32%
28 450 95 355 370 131,350 117,230 1,285 115,945 78.9% 89% 98.9% 69.6% 330.23 0.76 1.10%
29 450 118 332 370 122,840 91,990 1,561 90,429 73.8% 75% 98.3% 54.3% 277.08 0.90 1.70%
30 450 90 360 370 133,200 119,781 1,751 118,030 80.0% 90% 98.5% 70.9% 332.73 0.75 1.46%
31 450 115 335 370 123,950 96,996 1,425 95,571 74.4% 78% 98.5% 57.4% 289.54 0.86 1.47%
32 450 113 337 370 124,690 118,132 1,461 116,671 74.9% 95% 98.8% 70.1% 350.54 0.71 1.24%
33 450 113 337 370 124,690 102,392 2,046 100,346 74.9% 82% 98.0% 60.3% 303.83 0.82 2.00%
34 450 100 350 370 129,500 93,853 2,566 91,287 77.8% 72% 97.3% 54.8% 268.15 0.93 2.73%
35 450 123 327 370 120,990 102,479 1,682 100,797 72.7% 85% 98.4% 60.5% 313.39 0.80 1.64%
36 450 135 315 370 116,550 99,945 1,938 98,007 70.0% 86% 98.1% 58.9% 317.29 0.79 1.94%
37 450 92 358 370 132,460 103,012 1,959 101,053 79.6% 78% 98.1% 60.7% 287.74 0.87 1.90%
38 450 112 338 370 125,060 98,014 1,832 96,182 75.1% 78% 98.1% 57.8% 289.98 0.86 1.87%
39 450 112 338 370 125,060 95,790 1,757 94,033 75.1% 77% 98.2% 56.5% 283.40 0.88 1.83%
40 450 120 330 370 122,100 96,343 2,227 94,116 73.3% 79% 97.7% 56.5% 291.95 0.86 2.31%
68.0% 0.79 1.40%
70
Appendix 3 Calculation Of OEE, Cycle time and defect rate of Cellophaner (Before improvement)
Shift
Planned
Prod
time
(min)
Down
time
(min)
Operation
time (min)
Ideal
design
speed
Ideal
Output
Actual
output
Reject
Pieces
Good
Pieces
Availa
bility
Perfor
mance Quality OEE
Design
Speed
Cycle
time
Defect
Rate
1 450 29 421 365 153,665 132,964 340 133,050 93.6% 87% 99.7% 81.0% 316.84 0.79 0.25%
2 450 50 400 365 146,000 126,715 251 126,464 88.9% 87% 99.8% 77.0% 316.79 0.79 0.20%
3 450 28 422 365 154,030 125,655 259 125,396 93.8% 82% 99.8% 76.3% 297.76 0.84 0.21%
4 450 36 414 365 151,110 131,346 291 132,038 92.0% 88% 99.8% 80.4% 319.64 0.78 0.22%
5 450 44 406 365 148,190 140,727 333 140,394 90.2% 95% 99.8% 85.5% 346.62 0.72 0.24%
6 450 52 398 365 145,270 121,921 301 121,620 88.4% 84% 99.8% 74.0% 306.33 0.82 0.25%
7 450 33 417 365 152,205 128,210 300 127,910 92.7% 84% 99.8% 77.9% 307.46 0.81 0.23%
8 450 56 394 365 143,810 112,691 453 112,238 87.6% 78% 99.6% 68.3% 286.02 0.87 0.40%
9 450 62 388 365 141,620 115,052 560 114,492 86.2% 81% 99.5% 69.7% 296.53 0.84 0.49%
10 450 41 409 365 149,285 128,714 340 128,374 90.9% 86% 99.7% 78.2% 314.70 0.79 0.26%
11 450 93 357 365 130,305 99,755 245 99,510 79.3% 77% 99.8% 60.6% 279.43 0.89 0.25%
12 450 67 383 365 139,795 52,153 355 51,798 85.1% 37% 99.3% 31.5% 136.17 1.84 0.68%
13 450 39 411 365 150,015 124,759 353 128,830 91.3% 86% 99.7% 78.4% 314.31 0.80 0.27%
14 450 53 397 365 144,905 110,141 335 113,848 88.2% 79% 99.7% 69.3% 287.61 0.87 0.29%
15 450 60 390 365 142,350 105,321 300 123,756 86.7% 87% 99.8% 75.3% 318.09 0.79 0.24%
16 450 88 362 365 132,130 110,479 297 110,182 80.4% 84% 99.7% 67.1% 305.19 0.82 0.27%
17 450 59 391 365 142,715 114,343 693 127,456 86.9% 90% 99.5% 77.6% 327.75 0.76 0.54%
18 450 97 353 365 128,845 114,314 940 113,374 78.4% 89% 99.2% 69.0% 323.84 0.77 0.82%
19 450 85 365 365 133,225 105,112 621 121,530 81.1% 92% 99.5% 74.0% 334.66 0.75 0.51%
20 450 79 371 365 135,415 125,632 709 104,114 82.4% 77% 99.3% 63.4% 282.54 0.88 0.68%
21 450 51 399 365 145,635 107,223 578 129,664 88.7% 89% 99.6% 78.9% 326.42 0.77 0.44%
22 450 61 389 365 141,985 106,186 605 106,878 86.4% 76% 99.4% 65.1% 276.31 0.90 0.56%
23 450 77 373 365 136,145 104,876 436 104,440 82.9% 77% 99.6% 63.6% 281.17 0.89 0.42%
24 450 58 392 365 143,080 122,412 492 130,274 87.1% 91% 99.6% 79.3% 333.59 0.75 0.38%
25 450 68 382 365 139,430 122,846 332 122,514 84.9% 88% 99.7% 74.6% 321.59 0.78 0.27%
26 450 36 414 365 151,110 97,278 196 138,948 92.0% 92% 99.9% 84.6% 336.10 0.74 0.14%
71
27 450 45 405 365 147,825 90,151 299 97,718 90.0% 66% 99.7% 59.5% 242.02 1.03 0.31%
28 450 87 363 365 132,495 11,215 377 91,120 80.7% 69% 99.6% 55.5% 252.06 0.99 0.41%
29 450 58 392 365 143,080 89,482 320 89,162 87.1% 63% 99.6% 54.3% 228.27 1.10 0.36%
30 450 42 408 365 148,920 72,323 419 71,904 90.7% 49% 99.4% 43.8% 177.26 1.41 0.58%
31 450 47 403 365 147,095 95,310 292 117,128 89.6% 80% 99.8% 71.3% 291.36 0.86 0.25%
32 450 49 401 365 146,365 88,909 373 88,536 89.1% 61% 99.6% 53.9% 221.72 1.13 0.42%
33 450 77 373 365 136,145 101,251 435 118,610 82.9% 87% 99.6% 72.2% 319.16 0.78 0.37%
34 450 43 407 365 148,555 92,215 298 117,612 90.4% 79% 99.7% 71.6% 289.71 0.86 0.25%
35 450 94 356 365 129,940 102,036 344 101,692 79.1% 79% 99.7% 61.9% 286.62 0.87 0.34%
36 450 57 393 365 143,445 96,131 283 101,782 87.3% 71% 99.7% 62.0% 259.71 0.96 0.28%
37 450 39 411 365 150,015 86,599 152 86,447 91.3% 58% 99.8% 52.6% 210.70 1.19 0.18%
38 450 80 370 365 135,050 91,315 277 101,360 82.2% 75% 99.7% 61.7% 274.69 0.91 0.27%
39 450 68 382 365 139,430 93,131 328 100,300 84.9% 72% 99.7% 61.1% 263.42 0.95 0.33%
40 450 93 357 365 130,305 93,513 422 98,238 79.3% 76% 99.6% 59.8% 276.36 0.90 0.43%
68.0% 0.90 0.36%
72
Appendix 4 Calculation Of OEE, Cycle time and defect rate of Cartoner (Before improvement)
Shift
Planned
Prod
time
(min)
Down
time
(min)
Operation
time (min)
Ideal
design
speed
Ideal
Output
Actual
output
Reject
Pieces
Good
Pieces
Availa
bility
Perfor
mance Quality OEE
Design
Speed
Cycle
time
Defect
Rate
1 450 28 422 36 15,192 13,124 33 13,307 93.8% 88% 99.8% 82.1% 31.61 0.79 0.25%
2 450 49 401 36 14,436 12,643 54 12,589 89.1% 88% 99.6% 77.7% 31.53 0.79 0.43%
3 450 27 423 36 15,228 12,532 25 12,541 94.0% 83% 99.8% 77.4% 29.71 0.84 0.20%
4 450 35 415 36 14,940 13,112 23 13,216 92.2% 89% 99.8% 81.6% 31.90 0.78 0.17%
5 450 43 407 36 14,652 13,975 23 14,060 90.4% 96% 99.8% 86.8% 34.60 0.72 0.16%
6 450 51 399 36 14,364 12,159 63 12,096 88.7% 85% 99.5% 74.7% 30.47 0.82 0.52%
7 450 32 418 36 15,048 12,802 49 12,753 92.9% 85% 99.6% 78.7% 30.63 0.82 0.38%
8 450 54 396 36 14,256 11,232 45 11,225 88.0% 79% 99.6% 69.3% 28.46 0.88 0.40%
9 450 58 392 36 14,112 11,489 49 11,463 87.1% 82% 99.6% 70.8% 29.37 0.85 0.43%
10 450 39 411 36 14,796 12,868 37 12,831 91.3% 87% 99.7% 79.2% 31.31 0.80 0.29%
11 450 92 358 36 12,888 9,961 39 9,922 79.6% 77% 99.6% 61.2% 27.82 0.90 0.39%
12 450 38 412 36 14,832 5,128 49 12,855 91.6% 87% 99.6% 79.4% 31.32 0.80 0.38%
13 450 53 397 36 14,292 11,332 120 11,212 88.2% 79% 98.9% 69.2% 28.54 0.88 1.06%
14 450 59 391 36 14,076 10,925 60 12,316 86.9% 88% 99.5% 76.0% 31.65 0.79 0.48%
15 450 87 363 36 13,068 10,443 82 10,913 80.7% 84% 99.3% 67.4% 30.29 0.83 0.75%
16 450 57 393 36 14,148 10,931 45 12,794 87.3% 91% 99.6% 79.0% 32.67 0.77 0.35%
17 450 95 355 36 12,780 11,412 75 11,376 78.9% 90% 99.3% 70.2% 32.26 0.78 0.65%
18 450 83 367 36 13,212 11,236 52 12,173 81.6% 93% 99.6% 75.1% 33.31 0.75 0.43%
19 450 77 373 36 13,428 10,458 95 10,363 82.9% 78% 99.1% 64.0% 28.04 0.89 0.91%
20 450 49 401 36 14,436 12,468 39 13,004 89.1% 90% 99.7% 80.3% 32.53 0.77 0.30%
21 450 58 392 36 14,112 10,564 82 10,645 87.1% 76% 99.2% 65.7% 27.36 0.91 0.76%
22 450 76 374 36 13,464 10,423 108 10,315 83.1% 77% 99.0% 63.7% 27.87 0.90 1.04%
23 450 57 393 36 14,148 10,212 41 13,044 87.3% 92% 99.7% 80.5% 33.30 0.75 0.31%
24 450 65 385 36 13,860 12,233 85 12,148 85.6% 88% 99.3% 75.0% 31.77 0.79 0.69%
25 450 35 415 36 14,940 12,126 36 13,862 92.2% 93% 99.7% 85.6% 33.49 0.75 0.26%
26 450 44 406 36 14,616 9,723 97 9,638 90.2% 67% 99.0% 59.5% 23.98 1.04 1.00%
73
27 450 86 364 36 13,104 9,003 105 8,977 80.9% 69% 98.8% 55.4% 24.95 1.00 1.16%
28 450 57 393 36 14,148 1,113 128 8,724 87.3% 63% 98.6% 53.9% 22.52 1.11 1.45%
29 450 41 409 36 14,724 8,846 121 5,535 90.9% 38% 97.9% 34.2% 13.83 1.81 2.14%
30 450 46 404 36 14,544 7,123 56 11,659 89.8% 81% 99.5% 72.0% 29.00 0.86 0.48%
31 450 48 402 36 14,472 8,829 99 8,730 89.3% 61% 98.9% 53.9% 21.96 1.14 1.12%
32 450 76 374 36 13,464 8,789 61 11,826 83.1% 88% 99.5% 73.0% 31.78 0.79 0.51%
33 450 42 408 36 14,688 10,112 50 11,721 90.7% 80% 99.6% 72.4% 28.85 0.87 0.42%
34 450 93 357 36 12,852 9,195 83 10,072 79.3% 79% 99.2% 62.2% 28.45 0.88 0.82%
35 450 98 352 36 12,672 10,157 86 10,071 78.2% 80% 99.2% 62.2% 28.86 0.87 0.85%
36 450 57 393 36 14,148 9,535 94 10,047 87.3% 72% 99.1% 62.0% 25.80 0.97 0.93%
37 450 39 411 36 14,796 8,235 50 8,185 91.3% 56% 99.4% 50.5% 20.04 1.25 0.61%
38 450 79 371 36 13,356 9,057 165 9,862 82.4% 75% 98.4% 60.9% 27.03 0.93 1.65%
39 450 66 384 36 13,824 9,310 289 9,517 85.3% 71% 97.1% 58.7% 25.54 0.98 2.95%
40 450 93 357 36 12,852 9,297 63 9,782 79.3% 77% 99.4% 60.4% 27.58 0.91 0.64%
69.0% 0.89 0.72%
74
Appendix 5 Calculation Of OEE, Cycle time and defect rate of Case packer (Before improvement)
Shift
Planned
Prod
time
(min)
Down
time
(min)
Operation
time (min)
Ideal
design
speed
Ideal
Output
Actual
output
Reject
Pieces
Good
Pieces
Availa
bility
Perfor
mance Quality OEE
Design
Speed
Cycle
time
Defect
Rate
1 450 27 423 1 423 263 26 237 94.0% 62% 90.1% 52.7% 0.62 1.61 9.89%
2 450 29 421 1 421 268 4 264 93.6% 64% 98.5% 58.7% 0.64 1.57 1.49%
3 450 32 418 1 418 297 6 291 92.9% 71% 98.0% 64.7% 0.71 1.41 2.02%
4 450 19 431 1 431 237 9 228 95.8% 55% 96.2% 50.7% 0.55 1.82 3.80%
5 450 24 426 1 426 259 11 248 94.7% 61% 95.8% 55.1% 0.61 1.64 4.25%
6 450 28 422 1 422 233 21 212 93.8% 55% 91.0% 47.1% 0.55 1.81 9.01%
7 450 19 431 1 431 220 15 205 95.8% 51% 93.2% 45.6% 0.51 1.96 6.82%
8 450 22 428 1 428 246 4 242 95.1% 57% 98.4% 53.8% 0.57 1.74 1.63%
9 450 30 420 1 420 210 4 206 93.3% 50% 98.1% 45.8% 0.50 2.00 1.90%
10 450 25 425 1 425 172 3 169 94.4% 40% 98.3% 37.6% 0.40 2.47 1.74%
11 450 11 439 1 439 256 3 253 97.6% 58% 98.8% 56.2% 0.58 1.71 1.17%
12 450 9 441 1 441 229 3 226 98.0% 52% 98.7% 50.2% 0.52 1.93 1.31%
13 450 14 436 1 436 250 6 244 96.9% 57% 97.6% 54.2% 0.57 1.74 2.40%
14 450 31 419 1 419 228 9 219 93.1% 54% 96.1% 48.7% 0.54 1.84 3.95%
15 450 3 447 1 447 190 4 186 99.3% 43% 97.9% 41.3% 0.43 2.35 2.11%
16 450 34 416 1 416 275 14 261 92.4% 66% 94.9% 58.0% 0.66 1.51 5.09%
17 450 6 444 1 444 245 2 243 98.7% 55% 99.2% 54.0% 0.55 1.81 0.82%
18 450 21 429 1 429 214 8 206 95.3% 50% 96.3% 45.8% 0.50 2.00 3.74%
19 450 29 421 1 421 243 10 233 93.6% 58% 95.9% 51.8% 0.58 1.73 4.12%
20 450 14 436 1 436 243 25 218 96.9% 56% 89.7% 48.4% 0.56 1.79 10.29%
21 450 16 434 1 434 212 11 201 96.4% 49% 94.8% 44.7% 0.49 2.05 5.19%
22 450 21 429 1 429 225 15 210 95.3% 52% 93.3% 46.7% 0.52 1.91 6.67%
23 450 21 429 1 429 267 4 263 95.3% 62% 98.5% 58.4% 0.62 1.61 1.50%
24 450 15 435 1 435 208 13 195 96.7% 48% 93.8% 43.3% 0.48 2.09 6.25%
25 450 23 427 1 427 268 18 250 94.9% 63% 93.3% 55.6% 0.63 1.59 6.72%
26 450 22 428 1 428 216 10 206 95.1% 50% 95.4% 45.8% 0.50 1.98 4.63%
75
27 450 27 423 1 423 189 7 182 94.0% 45% 96.3% 40.4% 0.45 2.24 3.70%
28 450 8 442 1 442 170 9 161 98.2% 38% 94.7% 35.8% 0.38 2.60 5.29%
29 450 2 448 1 448 234 0 234 99.6% 52% 100.0% 52.0% 0.52 1.91 0.00%
30 450 12 438 1 438 237 0 237 97.3% 54% 100.0% 52.7% 0.54 1.85 0.00%
31 450 13 437 1 437 206 14 192 97.1% 47% 93.2% 42.7% 0.47 2.12 6.80%
32 450 6 444 1 444 230 8 222 98.7% 52% 96.5% 49.3% 0.52 1.93 3.48%
33 450 14 436 1 436 238 10 228 96.9% 55% 95.8% 50.7% 0.55 1.83 4.20%
34 450 22 428 1 428 207 9 198 95.1% 48% 95.7% 44.0% 0.48 2.07 4.35%
35 450 17 433 1 433 213 3 210 96.2% 49% 98.6% 46.7% 0.49 2.03 1.41%
36 450 31 419 1 419 200 2 198 93.1% 48% 99.0% 44.0% 0.48 2.10 1.00%
37 450 7 443 1 443 236 0 236 98.4% 53% 100.0% 52.4% 0.53 1.88 0.00%
38 450 22 428 1 428 175 28 147 95.1% 41% 84.0% 32.7% 0.41 2.45 16.00%
39 450 23 427 1 427 241 6 235 94.9% 56% 97.5% 52.2% 0.56 1.77 2.49%
40 450 20 430 1 430 213 13 200 95.6% 50% 93.9% 44.4% 0.50 2.02 6.10%
48.9% 1.91 4.1%
76
Appendix 6 Additional Task Detail Sheet for Operator
77
Appendix 7 Campaign on Paper and Sign
Reminder for cleaning sensor example for campaign sign
Visualization work instruction for proper install of inner frame
78
New list of cleaning added
79
Appendix 8 Calculation Of OEE, Cycle time and defect rate of Packer (After improvement)
Shift
Planned
Prod
time
(min)
Down
time
(min)
Operation
time (min)
Ideal
design
speed
Ideal
Output
Actual
output
Reject
Pieces
Good
Pieces
Availa
bility
Perfor
mance Quality OEE
Design
Speed
Cycle
time
Defect
Rate
1 450 83 367 370 135,790 131,864 1,259 130,605 81.6% 97% 99.0% 78.4% 359.30 0.70 0.95%
2 450 67 383 370 141,710 137,982 2,359 135,623 85.1% 97% 98.3% 81.5% 360.27 0.69 1.71%
3 450 92 358 370 132,460 128,529 1,185 127,344 79.6% 97% 99.1% 76.5% 359.02 0.70 0.92%
4 450 62 388 370 143,560 134,853 989 133,864 86.2% 94% 99.3% 80.4% 347.56 0.72 0.73%
5 450 96 354 370 130,980 122,449 2,091 120,358 78.7% 93% 98.3% 72.3% 345.90 0.72 1.71%
6 450 96 354 370 130,980 122,964 1,898 121,066 78.7% 94% 98.5% 72.7% 347.36 0.72 1.54%
7 450 78 372 370 137,640 130,135 1,752 128,383 82.7% 95% 98.7% 77.1% 349.83 0.71 1.35%
8 450 64 386 370 142,820 137,297 1,285 136,012 85.8% 96% 99.1% 81.7% 355.69 0.70 0.94%
9 450 51 399 370 147,630 139,138 1,264 137,874 88.7% 94% 99.1% 82.8% 348.72 0.72 0.91%
10 450 65 385 370 142,450 138,972 1,329 137,643 85.6% 98% 99.0% 82.7% 360.97 0.69 0.96%
11 450 63 387 370 143,190 139,236 992 138,244 86.0% 97% 99.3% 83.0% 359.78 0.69 0.71%
12 450 96 354 370 130,980 126,346 1,086 125,260 78.7% 96% 99.1% 75.2% 356.91 0.70 0.86%
13 450 63 387 370 143,190 140,973 1,875 139,098 86.0% 98% 98.7% 83.5% 364.27 0.69 1.33%
14 450 60 390 370 144,300 141,921 2,098 139,823 86.7% 98% 98.5% 84.0% 363.90 0.69 1.48%
15 450 75 375 370 138,750 135,978 1,093 134,885 83.3% 98% 99.2% 81.0% 362.61 0.69 0.80%
16 450 56 394 370 145,780 139,860 1,387 138,473 87.6% 96% 99.0% 83.2% 354.97 0.70 0.99%
17 450 86 364 370 134,680 129,648 1,985 127,663 80.9% 96% 98.5% 76.7% 356.18 0.70 1.53%
18 450 77 373 370 138,010 135,894 1,898 133,996 82.9% 98% 98.6% 80.5% 364.33 0.69 1.40%
19 450 61 389 370 143,930 139,838 1,452 138,386 86.4% 97% 99.0% 83.1% 359.48 0.70 1.04%
20 450 51 399 370 147,630 144,902 1,067 143,835 88.7% 98% 99.3% 86.4% 363.16 0.69 0.74%
21 450 53 397 370 146,890 143,289 1,326 141,963 88.2% 98% 99.1% 85.3% 360.93 0.69 0.93%
22 450 81 369 370 136,530 135,792 1,892 133,900 82.0% 99% 98.6% 80.4% 368.00 0.68 1.39%
23 450 87 363 370 134,310 132,168 1,152 131,016 80.7% 98% 99.1% 78.7% 364.10 0.69 0.87%
24 450 85 365 370 135,050 134,996 1,542 133,454 81.1% 100% 98.9% 80.2% 369.85 0.68 1.14%
25 450 98 352 370 130,240 127,985 823 127,162 78.2% 98% 99.4% 76.4% 363.59 0.69 0.64%
26 450 79 371 370 137,270 135,902 2,096 133,806 82.4% 99% 98.5% 80.4% 366.31 0.68 1.54%
80
27 450 64 386 370 142,820 140,288 2,421 137,867 85.8% 98% 98.3% 82.8% 363.44 0.69 1.73%
28 450 60 390 370 144,300 139,898 1,357 138,541 86.7% 97% 99.0% 83.2% 358.71 0.70 0.97%
29 450 77 373 370 138,010 128,796 1,739 127,057 82.9% 93% 98.6% 76.3% 345.30 0.72 1.35%
30 450 69 381 370 140,970 129,863 1,357 128,506 84.7% 92% 99.0% 77.2% 340.85 0.73 1.04%
31 450 65 385 370 142,450 135,982 1,415 134,567 85.6% 95% 99.0% 80.8% 353.20 0.71 1.04%
32 450 88 362 370 133,940 128,986 2,462 126,524 80.4% 96% 98.1% 76.0% 356.31 0.70 1.91%
33 450 65 385 370 142,450 140,986 1,095 139,891 85.6% 99% 99.2% 84.0% 366.20 0.68 0.78%
34 450 98 352 370 130,240 120,582 2,212 118,370 78.2% 93% 98.2% 71.1% 342.56 0.73 1.83%
35 450 56 394 370 145,780 142,986 1,986 141,000 87.6% 98% 98.6% 84.7% 362.91 0.69 1.39%
36 450 87 363 370 134,310 130,862 1,356 129,506 80.7% 97% 99.0% 77.8% 360.50 0.69 1.04%
37 450 95 355 370 131,350 122,692 1,562 121,130 78.9% 93% 98.7% 72.8% 345.61 0.72 1.27%
38 450 63 387 370 143,190 139,873 1,732 138,141 86.0% 98% 98.8% 83.0% 361.43 0.69 1.24%
39 450 63 387 370 143,190 131,258 1,198 130,060 86.0% 92% 99.1% 78.1% 339.17 0.74 0.91%
40 450 59 391 370 144,670 134,598 2,091 132,507 86.9% 93% 98.4% 79.6% 344.24 0.73 1.55%
79.8% 0.70 1.18%
81
Appendix 9 Calculation Of OEE, Cycle time and defect rate of Cellophaner (After improvement)
Shift
Planned
Prod
time
(min)
Down
time
(min)
Operation
time (min)
Ideal
design
speed
Ideal
Output
Actual
output
Reject
Pieces
Good
Pieces
Availa
bility
Perfor
mance Quality OEE
Design
Speed
Cycle
time
Defect
Rate
1 450 27 423 365 154,395 130,124 323 129,801 94.0% 84% 99.8% 79.0% 307.62 0.81 0.25%
2 450 32 418 365 152,570 137,569 263 137,306 92.9% 90% 99.8% 83.6% 329.11 0.76 0.19%
3 450 47 403 365 147,095 127,893 230 127,663 89.6% 87% 99.8% 77.7% 317.35 0.79 0.18%
4 450 48 402 365 146,730 134,673 196 134,477 89.3% 92% 99.9% 81.9% 335.01 0.75 0.15%
5 450 27 423 365 154,395 122,351 239 122,112 94.0% 79% 99.8% 74.3% 289.25 0.86 0.20%
6 450 52 398 365 145,270 122,758 202 122,556 88.4% 85% 99.8% 74.6% 308.44 0.81 0.16%
7 450 39 411 365 150,015 130,012 480 129,532 91.3% 87% 99.6% 78.9% 316.33 0.79 0.37%
8 450 64 386 365 140,890 137,953 432 137,521 85.8% 98% 99.7% 83.7% 357.39 0.70 0.31%
9 450 55 395 365 144,175 139,091 323 138,768 87.8% 96% 99.8% 84.5% 352.13 0.71 0.23%
10 450 72 378 365 137,970 138,219 220 137,999 84.0% 100% 99.8% 84.0% 365.66 0.68 0.16%
11 450 33 417 365 152,205 138,932 329 138,603 92.7% 91% 99.8% 84.4% 333.17 0.75 0.24%
12 450 55 395 365 144,175 123,429 321 123,108 87.8% 86% 99.7% 75.0% 312.48 0.80 0.26%
13 450 35 415 365 151,475 140,219 210 140,009 92.2% 93% 99.9% 85.2% 337.88 0.74 0.15%
14 450 66 384 365 140,160 109,613 268 109,345 85.3% 78% 99.8% 66.6% 285.45 0.88 0.24%
15 450 31 419 365 152,935 134,312 263 134,049 93.1% 88% 99.8% 81.6% 320.55 0.78 0.20%
16 450 30 420 365 153,300 138,920 221 138,699 93.3% 91% 99.8% 84.4% 330.76 0.76 0.16%
17 450 36 414 365 151,110 128,902 234 128,668 92.0% 85% 99.8% 78.3% 311.36 0.80 0.18%
18 450 38 412 365 150,380 134,354 235 134,119 91.6% 89% 99.8% 81.7% 326.10 0.77 0.17%
19 450 61 389 365 141,985 137,422 196 137,226 86.4% 97% 99.9% 83.5% 353.27 0.71 0.14%
20 450 52 398 365 145,270 142,368 328 142,040 88.4% 98% 99.8% 86.5% 357.71 0.70 0.23%
21 450 36 414 365 151,110 142,246 602 141,644 92.0% 94% 99.6% 86.2% 343.59 0.73 0.42%
22 450 52 398 365 145,270 134,342 296 134,046 88.4% 92% 99.8% 81.6% 337.54 0.74 0.22%
23 450 47 403 365 147,095 131,245 782 130,463 89.6% 89% 99.4% 79.4% 325.67 0.77 0.60%
24 450 52 398 365 145,270 133,214 492 132,722 88.4% 92% 99.6% 80.8% 334.71 0.75 0.37%
25 450 57 393 365 143,445 126,432 235 126,197 87.3% 88% 99.8% 76.8% 321.71 0.78 0.19%
26 450 32 418 365 152,570 135,236 509 134,727 92.9% 89% 99.6% 82.0% 323.53 0.77 0.38%
82
27 450 55 395 365 144,175 140,012 439 139,573 87.8% 97% 99.7% 85.0% 354.46 0.71 0.31%
28 450 45 405 365 147,825 124,357 398 123,959 90.0% 84% 99.7% 75.5% 307.05 0.81 0.32%
29 450 56 394 365 143,810 97,387 214 97,173 87.6% 68% 99.8% 59.2% 247.18 1.01 0.22%
30 450 40 410 365 149,650 125,320 352 124,968 91.1% 84% 99.7% 76.1% 305.66 0.82 0.28%
31 450 55 395 365 144,175 135,623 335 135,288 87.8% 94% 99.8% 82.4% 343.35 0.73 0.25%
32 450 43 407 365 148,555 127,422 454 126,968 90.4% 86% 99.6% 77.3% 313.08 0.80 0.36%
33 450 59 391 365 142,715 97,427 235 97,192 86.9% 68% 99.8% 59.2% 249.17 1.00 0.24%
34 450 29 421 365 153,665 112,603 512 112,091 93.6% 73% 99.5% 68.2% 267.47 0.93 0.45%
35 450 57 393 365 143,445 138,935 313 138,622 87.3% 97% 99.8% 84.4% 353.52 0.71 0.23%
36 450 57 393 365 143,445 120,622 228 120,394 87.3% 84% 99.8% 73.3% 306.93 0.81 0.19%
37 450 69 381 365 139,065 120,951 429 120,522 84.7% 87% 99.6% 73.4% 317.46 0.79 0.35%
38 450 65 385 365 140,525 132,359 359 132,000 85.6% 94% 99.7% 80.4% 343.79 0.73 0.27%
39 450 67 383 365 139,795 94,372 628 93,744 85.1% 68% 99.3% 57.1% 246.40 1.01 0.67%
40 450 39 411 365 150,015 123,062 230 122,832 91.3% 82% 99.8% 74.8% 299.42 0.83 0.19%
78.1% 0.79 0.27%
83
Appendix 10 Calculation Of OEE, Cycle time and defect rate of Cartoner (After improvement)
Shift
Planne
d Prod
time
(min)
Down
time
(min)
Operation
time (min)
Ideal
design
speed
Ideal
Output
Actual
output
Reject
Pieces
Good
Pieces
Availa
bility
Perfor
mance Quality OEE
Design
Speed
Cycle
time
Defect
Rate
1 450 27 423 36 15,228 12,983 49 12,934 94.0% 85% 99.6% 79.8% 30.69 0.81 0.38%
2 450 30 420 36 15,120 13,359 58 13,301 93.3% 88% 99.6% 82.1% 31.81 0.79 0.43%
3 450 46 404 36 14,544 12,639 71 12,568 89.8% 87% 99.4% 77.6% 31.28 0.80 0.56%
4 450 47 403 36 14,508 13,242 57 13,185 89.6% 91% 99.6% 81.4% 32.86 0.76 0.43%
5 450 27 423 36 15,228 12,093 27 12,066 94.0% 79% 99.8% 74.5% 28.59 0.87 0.22%
6 450 50 400 36 14,400 12,190 26 12,164 88.9% 85% 99.8% 75.1% 30.48 0.82 0.21%
7 450 36 414 36 14,904 12,802 80 12,722 92.0% 86% 99.4% 78.5% 30.92 0.81 0.62%
8 450 63 387 36 13,932 11,270 72 11,198 86.0% 81% 99.4% 69.1% 29.12 0.86 0.64%
9 450 53 397 36 14,292 11,512 63 11,449 88.2% 81% 99.5% 70.7% 29.00 0.86 0.55%
10 450 71 379 36 13,644 12,868 44 12,824 84.2% 94% 99.7% 79.2% 33.95 0.74 0.34%
11 450 32 418 36 15,048 10,986 89 10,897 92.9% 73% 99.2% 67.3% 26.28 0.95 0.81%
12 450 55 395 36 14,220 11,249 70 11,179 87.8% 79% 99.4% 69.0% 28.48 0.88 0.62%
13 450 34 416 36 14,976 11,332 54 11,278 92.4% 76% 99.5% 69.6% 27.24 0.92 0.48%
14 450 62 388 36 13,968 10,241 28 10,213 86.2% 73% 99.7% 63.0% 26.39 0.95 0.27%
15 450 30 420 36 15,120 10,995 26 10,969 93.3% 73% 99.8% 67.7% 26.18 0.95 0.24%
16 450 32 418 36 15,048 12,839 32 12,807 92.9% 85% 99.8% 79.1% 30.72 0.81 0.25%
17 450 36 414 36 14,904 11,451 80 11,371 92.0% 77% 99.3% 70.2% 27.66 0.90 0.70%
18 450 39 411 36 14,796 12,225 69 12,156 91.3% 83% 99.4% 75.0% 29.74 0.84 0.56%
19 450 65 385 36 13,860 10,458 59 10,399 85.6% 75% 99.4% 64.2% 27.16 0.92 0.56%
20 450 51 399 36 14,364 13,043 74 12,969 88.7% 91% 99.4% 80.1% 32.69 0.76 0.57%
21 450 35 415 36 14,940 10,727 68 10,659 92.2% 72% 99.4% 65.8% 25.85 0.97 0.63%
22 450 51 399 36 14,364 10,423 33 10,390 88.7% 73% 99.7% 64.1% 26.12 0.96 0.32%
23 450 46 404 36 14,544 13,085 31 13,054 89.8% 90% 99.8% 80.6% 32.39 0.77 0.24%
24 450 51 399 36 14,364 12,233 59 12,174 88.7% 85% 99.5% 75.1% 30.66 0.82 0.48%
25 450 59 391 36 14,076 12,411 66 12,345 86.9% 88% 99.5% 76.2% 31.74 0.79 0.53%
26 450 34 416 36 14,976 12,759 63 12,696 92.4% 85% 99.5% 78.4% 30.67 0.82 0.49%
84
27 450 56 394 36 14,184 13,876 24 13,852 87.6% 98% 99.8% 85.5% 35.22 0.71 0.17%
28 450 44 406 36 14,616 11,326 70 11,256 90.2% 77% 99.4% 69.5% 27.90 0.90 0.62%
29 450 52 398 36 14,328 9,645 72 9,573 88.4% 67% 99.3% 59.1% 24.23 1.03 0.75%
30 450 41 409 36 14,724 11,715 80 11,635 90.9% 80% 99.3% 71.8% 28.64 0.87 0.68%
31 450 54 396 36 14,256 13,323 69 13,254 88.0% 93% 99.5% 81.8% 33.64 0.74 0.52%
32 450 44 406 36 14,616 11,887 90 11,797 90.2% 81% 99.2% 72.8% 29.28 0.85 0.76%
33 450 61 389 36 14,004 8,192 39 8,153 86.4% 58% 99.5% 50.3% 21.06 1.19 0.48%
34 450 27 423 36 15,228 10,155 35 10,120 94.0% 67% 99.7% 62.5% 24.01 1.04 0.34%
35 450 58 392 36 14,112 10,157 38 10,119 87.1% 72% 99.6% 62.5% 25.91 0.96 0.37%
36 450 59 391 36 14,076 10,141 76 10,065 86.9% 72% 99.3% 62.1% 25.94 0.96 0.75%
37 450 67 383 36 13,788 11,243 81 11,162 85.1% 82% 99.3% 68.9% 29.36 0.85 0.72%
38 450 66 384 36 13,824 12,905 65 12,840 85.3% 93% 99.5% 79.3% 33.61 0.74 0.50%
39 450 67 383 36 13,788 9,210 46 9,164 85.1% 67% 99.5% 56.6% 24.05 1.04 0.50%
40 450 38 412 36 14,832 11,357 61 11,296 91.6% 77% 99.5% 69.7% 27.57 0.91 0.54%
71.6% 0.87 0.50%
85
Appendix 11 Calculation Of OEE, Cycle time and defect rate of Case Packer (After improvement)
Shift
Planned
Prod
time
(min)
Down
time
(min)
Operation
time (min)
Ideal
design
speed
Ideal
Output
Actual
output
Reject
Pieces
Good
Pieces
Availa
bility
Perfor
mance Quality OEE
Design
Speed
Cycle
time
Defect
Rate
1 450 25 425 1 425 283 4 279 94.4% 67% 98.6% 62.0% 0.67 1.50 1.41%
2 450 30 420 1 420 251 4 247 93.3% 60% 98.4% 54.9% 0.60 1.67 1.59%
3 450 42 408 1 408 301 8 293 90.7% 74% 97.3% 65.1% 0.74 1.36 2.66%
4 450 41 409 1 409 256 7 249 90.9% 63% 97.3% 55.3% 0.63 1.60 2.73%
5 450 22 428 1 428 293 2 291 95.1% 68% 99.3% 64.7% 0.68 1.46 0.68%
6 450 40 410 1 410 254 5 249 91.1% 62% 98.0% 55.3% 0.62 1.61 1.97%
7 450 31 419 1 419 246 24 222 93.1% 59% 90.2% 49.3% 0.59 1.70 9.76%
8 450 41 409 1 409 295 6 289 90.9% 72% 98.0% 64.2% 0.72 1.39 2.03%
9 450 52 398 1 398 247 8 239 88.4% 62% 96.8% 53.1% 0.62 1.61 3.24%
10 450 61 389 1 389 253 3 250 86.4% 65% 98.8% 55.6% 0.65 1.54 1.19%
11 450 29 421 1 421 273 17 256 93.6% 65% 93.8% 56.9% 0.65 1.54 6.23%
12 450 53 397 1 397 292 2 290 88.2% 74% 99.3% 64.4% 0.74 1.36 0.68%
13 450 30 420 1 420 273 6 267 93.3% 65% 97.8% 59.3% 0.65 1.54 2.20%
14 450 59 391 1 391 271 13 258 86.9% 69% 95.2% 57.3% 0.69 1.44 4.80%
15 450 25 425 1 425 252 6 246 94.4% 59% 97.6% 54.7% 0.59 1.69 2.38%
16 450 39 411 1 411 252 3 249 91.3% 61% 98.8% 55.3% 0.61 1.63 1.19%
17 450 33 417 1 417 254 2 252 92.7% 61% 99.2% 56.0% 0.61 1.64 0.79%
18 450 39 411 1 411 215 9 206 91.3% 52% 95.8% 45.8% 0.52 1.91 4.19%
19 450 59 391 1 391 271 18 253 86.9% 69% 93.4% 56.2% 0.69 1.44 6.64%
20 450 48 402 1 402 248 7 241 89.3% 62% 97.2% 53.6% 0.62 1.62 2.82%
21 450 30 420 1 420 286 3 283 93.3% 68% 99.0% 62.9% 0.68 1.47 1.05%
22 450 49 401 1 401 279 5 274 89.1% 70% 98.2% 60.9% 0.70 1.44 1.79%
23 450 41 409 1 409 267 22 245 90.9% 65% 91.8% 54.4% 0.65 1.53 8.24%
24 450 48 402 1 402 277 19 258 89.3% 69% 93.1% 57.3% 0.69 1.45 6.86%
25 450 56 394 1 394 199 24 175 87.6% 51% 87.9% 38.9% 0.51 1.98 12.06%
26 450 30 420 1 420 242 11 231 93.3% 58% 95.5% 51.3% 0.58 1.74 4.55%
86
27 450 40 410 1 410 291 3 288 91.1% 71% 99.0% 64.0% 0.71 1.41 1.03%
28 450 38 412 1 412 266 9 257 91.6% 65% 96.6% 57.1% 0.65 1.55 3.38%
29 450 48 402 1 402 241 3 238 89.3% 60% 98.8% 52.9% 0.60 1.67 1.24%
30 450 29 421 1 421 251 12 239 93.6% 60% 95.2% 53.1% 0.60 1.68 4.78%
31 450 39 411 1 411 249 13 236 91.3% 61% 94.8% 52.4% 0.61 1.65 5.22%
32 450 33 417 1 417 257 19 238 92.7% 62% 92.6% 52.9% 0.62 1.62 7.39%
33 450 60 390 1 390 246 4 242 86.7% 63% 98.4% 53.8% 0.63 1.59 1.63%
34 450 20 430 1 430 255 21 234 95.6% 59% 91.8% 52.0% 0.59 1.69 8.24%
35 450 47 403 1 403 241 3 238 89.6% 60% 98.8% 52.9% 0.60 1.67 1.24%
36 450 50 400 1 400 275 5 270 88.9% 69% 98.2% 60.0% 0.69 1.45 1.82%
37 450 67 383 1 383 287 9 278 85.1% 75% 96.9% 61.8% 0.75 1.33 3.14%
38 450 61 389 1 389 278 2 276 86.4% 71% 99.3% 61.3% 0.71 1.40 0.72%
39 450 66 384 1 384 219 13 206 85.3% 57% 94.1% 45.8% 0.57 1.75 5.94%
40 450 28 422 1 422 247 6 241 93.8% 59% 97.6% 53.6% 0.59 1.71 2.43%
56.0% 1.58 3.5%
87
Appendix 12 Control checklist
Operator’s name :
Date :
Shift :
No Material (√) Notes
1 Check all the material for packer in the
material station
2 Check the installation of material on all
units on the packer machine
3 Check if there is any folded material
No Cleaning (√) Notes
1
No leftover material in transport belt, 2nd
rake, tooth on 2nd wheel guide roller and
infeed belt
2 Cleaning guide roller
3 Cleaning vacuum chamber when stops
occur
4 Cleaning sensors when stops occur
5 Clean glue nozzle when stops occur
No Adjustment (√) Notes
1 Check fix folder and flap folder adjustment
during stops
2 Adjust Tear tape and guide roller
3 Adjust axial guide roller position at initial
distance (± 38m)
4 Check slicer inner frame adjustment during
stops
5 Check cover inner frame position
6 Check vacuum release adjustment and
vacuum channel (ask mechanic)
7 Check blade/slicer and belt during stops
88
Appendix 13 FMEA table and new RPN score
No
Potential
Failure
Mode
Potential
effect(s) of
Failure
Potential
Cause(s) of
Failure
Current
Controls,
Prevention
(S) (O) (D) RPN Total
RPN
Recommended
Actions
Respo
nsibili
ty
Action
taken (S) (O) (D)
RP
N
Total
RPN
1
CH tear
tape
missing
Tear tape
reject
Guide roller
tear tape
dirty Cleaning
sensor
3S255 and
guide roller
6 7
6
252
588
Cleaning and
adjust tear tape
position. Adjust
guide roller
position.
Cleaning sensor
3S255
Opera
tor
Tear tape
and guide
roller
position
adjusted
4 6
5
120
320
Tear tape not
on track
Guide roller
misposition 7 8 336 5 8 200
2
Inner
frame
absence
Slicer inner
frame setting
Slicer
misposition
Cleaning
vacuum
chamber
3 5
4
60
340
Check and
adjust slicer
inner frame
when stops
occurred.
Check Inner
frame
installation
Opera
tor
Slicer
inner
frame
adjusted.
Cover
innerframe
installation
instruction
3 5
4
60
396 Inner frame
not detected
Transfer
inner frame
dirty 5
7 140
6
7 168
Cover inner
frame
misplaced
7 140 7 168
3
Packet
hopper:
open/jam
Nozzle,
guide, drying
belt are dirty
Glue bleed
out
Adjust fix
folder and
flap folder.
Cleaning
nozzle,
guide and
drying belt.
8
6
7
336
728
Cleaning sensor
20S8092.
cleaning tooth
on 2nd wheel.
Opera
tor
cleaning
Sensor and
tooth on
2nd wheel
5
6
5
150
300
Improper
folding 7 392 6 150
4 Sheet
misaligned
Foil sheet not
parallel
Inner liner
misposition
Adjust
inner liner 7 7 8 392 776
Check and
adjust tranport
Opera
tor
Transport
belt and 5 6 6 180 306
89
No
Potential
Failure
Mode
Potential
effect(s) of
Failure
Potential
Cause(s) of
Failure
Current
Controls,
Prevention
(S) (O) (D) RPN Total
RPN
Recommended
Actions
Respo
nsibili
ty
Action
taken (S) (O) (D)
RP
N
Total
RPN
on the
transport
belt Foil sheet
messy
Improper
inner liner
cut
blade
setting
6 8 384
belt and 2nd
rake also clean
if there any
inner liner
stuck hen stops
occure.
2nd rake
adjustment
3 7 126
5
CH spider:
packed out
of position
Suction
power
reduced
Choke on
vacuum
Cleaning
vacuum
channel
9 9 3 243 243
Check belt
infeed surface
and clean it.
Opera
tor
Cleaning
belt infeed
and
vacuum
channel
6 6 2 72 72
6
No blank
on
transport
belt
Blank
undetected
by sensor
Vacuum
release too
soon Material
preparation,
Operator
awareness
5
7
3
105
180
Check and
adjust vacuum
release
adjustment
when stops
occurred.
Mech
anic
Vacuum
release
adjusted
by
mechanic
3
6
4
72
120 Blank
arrangement
not tidy
5 75 4 48
