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CHAPTER 1Introduction

Six Sigma management is the philosophy of the reduction of variation in all critical process to achieve continuous and breakthrough improvements. Lean manufacturingseeks to provide optimal quality by eliminating waste. Lean Six Sigma is the combinationof both. Kwak and Anbari (2006) have defined Six sigma as a method of project drivenmanagement approach to improve the organizations products, service, and process bycontinually reducing defects in the organization. Lean manufacturing and Six Sigma are

powerful philosophies baked by several tools for improving quality, productivity, profitability and market competitiveness for any corporation in a holistic manner(Cudney, Mehta and Monroe, 2006). In the competitive environment, the fundamentalgoals of the company are to be able to survive in the market and for the long term.Productivity should be evaluated as one of the most important indicators of the business

performance. Product quality is now measured by the level of error in the millions. Toachieve these strict quality criteria, the whole system starting from the design step should

be constructed such that it can produce right at first time. From the last 15 years manyleader companies implementing lean six sigma methods. Lean Six Sigma is an integrationof Six Sigma and Lean Manufacturing. Six Sigma provides an integrated improvementapproach that increases quality by reducing variation, defects, and costs. Six sigmafocuses on quality rather than speed, where speed up can be resolved by Leanmanagement. Lean management is better for the improvement in the speed and processflow rather than in improvement quality. Therefore, the best result can be achieved bycombining both. Lean Management focuses on eliminating loss in process and reducingthe complexity.

Lean management can be implemented in all the fields and bring out opportunities toincrease performance. Six Sigma provides quality philosophy and is a statistical tool tomonitor process performance. It aims to reduce the variability in the process and toeliminate errors. Lean management and six sigma work together successfully. Six sigmais applied to improve the quality of the product and Lean management is to make costeffective and faster. Lean Six Sigma is a combination of certain tools and techniques to

prov ide Six Sigma practitioners another philosophy to reduce process and productiontimes while minimizing the variation and reducing waste at the same time (Khalil, Khanand Mahmood 2006). Six sigma is a philosophy and deployed by carrying outimprovement projects. Lean adds tools that increases process throughput by eliminatingwaste. The primary focus of Lean is on reducing waste, synchronizing flows and

managing variability in (process) flows. Lean and Six Sigma have complementary benefits. For integration, Lean may use the management structures that Six Sigma offers:Six Sigmas project -by-project approach provides an effective embedding framework toapply Lean principles.

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1.1 Motivation

In the modern competitive market product performance is being measured in terms ofdefect level per million opportunities. From the study of different papers and books nowit is known that in six sigma quality control the number of non-conformities per millionopportunities is only 3.4 when mean is not change. It is very few amounts as comparedwith traditional system. But in Bangladesh the use of Lean Six sigma yet have notconsiderable. Now- a days Lean tools is being used in some manufacturing industries. Forthis reason we have been motivated to work on Lean Six sigma and applied in anymanufacturing company in Bangladesh.

1.2 Research Objectives

The objectives of this work is to ensure high level of product performance in terms ofdefect level and also to reduce the cost associated with the production, improvement of

the process quality and enhancement of the companies goodwill. To achieve thesequalities Lean and Six sigma have been used in the form of integration. The use of sixsigma methodology is to reduce defect per million opportunities and to reduce processvariation sigma () and Lean tools to reduce costs, waste and speed up the process.

1.3 Problem statement

It is known to all that, there are seven wastes in any type of manufacturing industries.These are overproduction, waiting, transportation, inventory, motion, over-processing anddefective items. The purposes of this research papers is to reduce mainly two among theseven wastes and thats over -processing and defective items using Lean and Six sigma. Ifit is possible to reduce the process variability () by improving process of production,then number of defective items will be also reduced. Lean is a tool to reduce waste andSix sigma is a philosophy. If the implementation of Lean Six sigma be successful then thenumber of defect and reworks will be reduced. But in Bangladesh the implementation ofLean Six sigma yet have not considerable. Most of the manufacturing industries are notconcerned about Lean Six sigma. So that, there is big amount of loss due to defective itemand rework. It has been seen that in Pran Agro Ltd. one of the renown food productindustry in Bangladesh. In the ice-pop department in this company it has been found thatthere are mainly five types of defects. These are leakage, black particle, without coding,excess/short, cap loose. There is big amount of losses due to these defective items. Themain purpose of this thesis paper is to reduce these defective items by using Lean SixSigma tools. If this can be reduced then a surplus profit can be achieved. And it is

possible by successfully implementation of Lean Six sigma methodology.

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1.4 Methodology

This section presents the methodological framework that will be used for the subsequentempirical analysis. After conducting a literature review, the core of this research is datacollection. Prior to visit and a detailed questionnaire is developed with the aim of findingout how many defect and rework are being happened regularly and what caution theyfollow to reduce them. The research will primarily intend to be done in two stages: an onfield questionnaire survey and upon response and face to face interviews will beconducted to get closer insights and their corresponding response. Secondary materialwill also be used, literatures, research publications, trade and production data, articles inlocal news paper and internet. The study will also be included some informalcommunications and direct observation which will give added depth. After the datacollection we have to analysis the problem related to the defective item. Calculation will

be according to the DMAIC approach. In different Phase of DMAIC model analysis will be done by dif ferent types lean tools and Six sigma tools, such as control chart, Fish- bone diagram, Pareto analysis and QFD. After observing the controlling situation incontrol chart and finding out the causes of the defects by using cause effect diagram, ithave been tried to improve the condition by implementing Lean tools(5s).

1.5 Organization of the thesis

The structure of this Thesis paper has been organized in following manners. In the firstchapter of the thesis contain Introduction with subtitles motivation, research objectives,

problem definitions, methodology and organization of thesis. Literature review in thesecond chapter contains some related past Thesis work on Lean manufacturing and SixSigma and how different this paper from them. A theoretical framework of the thesis has

explained in chapter 3.All the tools and theory that have been used in this works hasexplained briefly. The case study and data collection is discussed with data about theconcerning company in chapter 4. After that, in chapter 5, all the calculations are showedwith chapter named Result. Result analysis has been conducted in chapter 6. Finally somerecommendations and future works are given in chapter 7. This paper has been completedthrough conclusion, reference and appendix successively.

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CHAPTER 2Literature Review

Six sigma has been implemented by Motorola in the early in 1980s and Leanmanufacturing has emerged towards the end of the 1990s. It has become so popularmethods and become prevalent in all business areas over time. There are lot of work onsix sigma and Lean mana gement and combination of both. For the last one decadeshistory in the literature, a number of academic terms has found about this methodology.ONeil and Duvall (2003) in their worked they have focused on post occupancyevaluation (POE) research method and discussed about the application in six sigmaquality frameworks. They have used POE to create and manage the optimal space for theoffice workers. The team began to process of tracking these data using Minitab, whichcontain six sigma quality tools- such as control chart. Does and Koning (2006) explainedthat the use of lean six sigma in a hospital to provide the healthcare to the patient atlowest possible cost and time. They solved the most important problem of their patients

(long waiting time) and cost as well. They had tried to reduce the lead time which is perhaps one of the most important quality indicators from the perspective of their patientsin healthcare. How Lean Six sigma can be applied in non-manufacturing organization istheir future work. Kwak and Anbari (2006) have identified the benefits, obstacles andfuture of six sigma approach. They focused on examination of the evaluation, benefitsand challenges of six sigma practices and finding outs the key factors which leads thesuccess of six sigma. Factors influencing successful six sigma projects includemanagement involvement and organizational commitment, project management andcontrol skills, cultural change, and continuous training. However, integrating the datadriven, structured six sigma processes in organizations still has room for improvementwhich is further research objective.

In the recent years, the manufacturing industry has successfully applied the six sigmamethodology to project. However, due to insufficient data or a misunderstanding of thesix sigma methodology, some of the project failed. Aimed to discuss define, measure,analyze, improve and control (DMAIC) phases of integrating a manufacturing executionsystem (MES) and six sigma methodology (Hawang 2006). Chang and Wang (2007)explained six sigma methodology is a method that can lead to a continuous decrease in

process variance. In their work they applied six sigma methodologies and proposed acontinuous improvement model on different phase of collaborative planning, togetherwith forecasting and replenishment (CPFR). Future work should pull data from longer

period of time and investigate how to manage demand smoothing and supply arrangementin CPFR. Fairbanks et al. (2007) discussed about the improving patient flow in the

preoperative environment is challenging, but it has positive implications for both staffmembers and for the facility. Improve patient throughput by incorporating six sigma andlean methodologies for patients undergoing elective procedures.

Ditahardiyani et.al (2008) has presented the six sigma methodology and itsimplementation in a primer packaging process of Cranberry drink. DMAIC approacheshave used to analyze and to improve the primer packaging process, which have highvariability and defects output. Hekmatpanah et al (2008) in their works they has surveyedthe six sigma process and its impact on the organizational productivity. And they havestudied key concepts, problem solving process of six sigma as well as the survey ofimportant fields such as; DMAIC, six sigma and productivity applied program, and otheradvantage of six sigma. Amer et al. (2008) in their works they focused mainly on the

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customer requirement and controlling supply chain variables. By optimizing supply chain processes and meeting customers requi rement order can be fulfilled. Here fuzzy settheory provides a way of monitoring supply chain performance. They recommended thatfurther work should address the strategic outcomes of improved collaborative

performance that the methodology could potentially deliver.

Chakravorty (2009) provided a model to effectively guide the implementation of sixsigma is to reduce variation or waste from the operation. The purpose of this research wasto develop an effective implementation model which consists of six steps. The first step isto perform strategic analysis driven by the market and customer. The second step is toestablish a high level, cross-functional team to drive the improvement initiative. The thirdstep is to identify overall improvement tool. The fourth step is to perform high-level

process mapping and to prioritize improvement opportunities. The fifth is to develop adetailed plan for low-level improvement teams, and the sixth step is to implement,document, and revised as needed. Yang and Hsieh (2009) suggested that six sigma is atactical tool of significant value in achieving operational excellence. The project selectiondecision, under a resources constraint, is the early stage of implementation for a six sigmaintervention. They also proposed to adopt national quality award criteria as the six sigma

project selection criteria and proposed a hierarchical criteria evaluation process. Schon,Bergquist and Kiefsjo (2010) presented a study of how six sigma influences jobsatisfaction among employees at three large companies with manufacturing units inSweden that have used different implementation strategies. Barac, Milovanovic andAndjelkovic (2010) have applied Lean six sigma methodologies in supply chainmanagement in manufacturing products. They have tried to eliminate non-value added

process and waste in terms of time, cost or inventory. Waste reduction and the removal ofunnecessary process can save companies millions of dollars a year. Getting the right

product at the right price, at the right time to the end customer is not only key to thesuccess of companies in competitive markets, but also the key to their survival.

Six sigma focuses on quality more than speed. Lean management removes the weaknessof six sigma by speeding process. The Lean six sigma methodology developed by usingthese two techniques together is presented. The goal of application is making processLean and increasing the Level of sigma. Implementation studies can be carried out inservice and public sectors where Lean six sigma practices are rather inconsiderable istheir future research objective (Atmaca and Girenes, 2011). Padhy and Sahu (2011)

proposed two-stage methodology to identify and select of six sigma project based on (1)Real Option Analysis for evaluating the value of the project to improve the managerial

flexibility (2) a zero-one integer programming model for selecting and scheduling anoptimal project portfolio, based on the organizations objectives and constraints. Hisfurther research can be carried out in the area for quantifying the impact of the project andorganizational specific risks on project payoffs, which is very much prevalent in the sixsigma projects. The reliability estimate of project cash flow and investment calculation isanother research area. Brun A. (2011) explained the result of a research project focusedon six sigma implementation processes, with a particular attention to understand whichthe situation of the enterprises operating in Italy is and consequently, which are themanagerial implications of a six sigma implementation in the typical Italian company. Healso said that any six sigma implementation aims at improving customer satisfaction, bymean of improved process capability. This, in turn, is made possible by focusing on

Critical to Quality (CrQ) characteristics and implementing improvement action seekingto continuously reduce process variability in terms of CrQ. These actions are carried out

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by involving every employee. His further research objective was validation andReorganizing it could constitute a road map for implementation of six sigma.Johannsen, Leist and Zellner (2011) in their work aimed to systematically identify key

problems of the six sigma application in service and to assign them to the phases to beable to determine the exact moment of their beginning. The lacking process orientation

within the framework of the definition of key performance indicator was the mostimportant problem. The second most important problem was seen in the insufficient dataquality in services which makes the design of adequate measurement systems difficult.Their further research have recommended that about answering the following questions a)What are the effects of a missing process orientation for a Six Sigma project in services?

b) How can the degree of process orientation be measured in an organization and whichdegree is sufficient for a Six Sigma project?

DMAIC (define measure, analyze, improve and control) method in six sigma is oftendescribed as an approach for problem solving. DMAIC is applicable to empirical

problems ranging from well-structured to semi-structured, but not to ill-structured problems or pluralistic messes of subjective problems. Six sigma is generic method. Theadvantage of such methods is that they are very versatile. The disadvantage is that task-domain specific method can be more powerful because they can be more specific andoperational in the guidance they can provide. Mast and Lokkerbol (2012) has highlightedthe characteristics of the DMAIC approach and its limitation; specifically from problemsolving perspectives. Their future work is to find out direction where the approach may

be improved.

There are lots of papers or works on Lean six sigma methodology in the history ofliterature. In this thesis work lean six sigma methodology have been implemented throughDMAIC model in a manufacturing industry to reduce waste. The difference of this workfrom the others is in terms of tools used in conducting the thesis and its perspectives.Here Lean tools have used to reduce the defective items and reworks and Six sigma toolslike control chart, fish-bone diagram, Pareto analysis etc are used to analyze anddetermine what is to be controlled. In the previous works these type tools have not used toreduce waste in terms of defects and reworks in a manufacturing industry in Bangladesh.

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CHAPTER 3

Theoretical Framework

In this chapter has discussed all the theories and tools that have been used in this thesiswork. This work has been carried out on two philosophies and their related tools. One isSix sigma management and another is Lean manufacturing. All the TQM tools can beused as a Six Sigma tools. Some of their have been used in this work likes control chart,Pareto analysis, fish-bone diagram etc. MCD approach and QFD also has used to identifythe problem. 5S tool of Lean manufacturing have used to recommend the solution. Alltools and philosophies are discussed individually in the below.

3.1 Six Sigma Management

Six Sigma is a short-cut for saying six standard deviations from the mean, which specifies

a tolerable range. The common definition of Six Sigma management is that it is anorganizational initiative designed to create manufacturing, service, and administrative processes that produce a high rate of sustained improvement in both defect reduction andcycle time. By using it Motorola has been successful to reduce 50% cycle time anddefects within two years. This company is the first one who gets Malcolm Baldrige

National Quality Award in 1988. The award strives to identify those excellent firms thatare worthy role models for other business. The innovation of Motorola which leads to getthe award that was Six Sigma program. The term Six Sigma is derived from the normaldistribution used in statistics. Many observable phenomena can be graphicallyrepresented as a bell shaped or a normal distribution as illustrated in figure 3.1.

LSLUSL

-6 -3 0 3 6

Figure 3.1: Normal distribution of 6 quality control (Mean , standard deviation )

A statistic is used to measure the typical value of output of a process is called mean oraverage, . Another statistic is which is used to measure variability of the output of a

process. In the normal distribution of Six Sigma quality control the interval is calculated by mean plus or minus 6 standard deviations thats mean 6. This interval contains99.9999998% of the data. That means 2 data per billion of data values are outside of thearea created by the mean plus or minus 6 standard deviations (2=1,000,000,000x[0.0000002%=100%- 99.9999998%]). Motorola presume that mean can be drift by 1.5 in

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either direction due to the natural causes. A process which is operating on Six Sigmaquality control system per million non- conformities will be 3.4 if mean shifted by 1.5 ineither direction. In the 3 quality control system the interval is obtained similarly bymean plus or minus 3 standard deviations that means 3. It contains 99.73% of thedata. This means 2700 defects per million opportunities will be outside the area created

by mean plus or minus 3 standard deviations (2,700=1,000,000 x [0.27%=100%-99.73%]). If mean is shifted by 1.5 in either direction from the mean than number ofnon-conformities per million will be 66,807.

The difference between the 3 quality control management and 6 quality controlmanagement is on VoP (voice f process) and VoC (voice of customer). In 3 qualitycontrol VoP is equal to VoC. But in the 6 quality control system VoP is half of the VoC.So that the value of in Six Sigma Quali ty control system is half of the value in the threesigma quality control system. Six Sigma promotes the idea that the distribution of outputfor a stable normally distributed process (Voice of the process) should be designed to takeup no more than half of the tolerance allowed by the specification limits (Voice of thecustomer). Although process may be designed to be at their best, practically it has beenseen that the process variation may increase over time. This variation may caused bysmall variation in inputs, changing in monitoring system, changing conditions. This

process variation caused the shift of process mean in either direction. It has been seen thatin practice the average shift in mean is about 1.5 standard deviations from target due tothe increase in process variation. In the 1980s, Motorola demonstrated that in practice, a1.5 standard deviation shift was what was observed as the equivalent increase in processvariation for many processes that were benchmarked. Three Sigma quality controlmanagements graphically can be shown in normal curve in the following figure 3.2.

LSL USL

Figure 3.2: Normal curve of Three Sigma quality control

In 3 quality management system the upper specification limit is calculated byUSL=+3 and lower specification limit is calculated by LSL= -3. Where is themean and is the standard deviation of the process. Voice of customer is equal to thevoice of process in this control management. Large number of product is gone out of

control if mean shift in either direction from the origin.

-3 3

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Similarly in the Six Sigma quality control system upper specification limit is calculated by USL=+6 and lower specification limit is calculated by LSL= - 6. Here also and is expressing similar meanings as three sigma quality control management. It is knownthat in 6 management VoP is half of the VoC. VoC gives the specification intervalamong which customer will accept the product satisfactorily. Outside this specification

limit product is considered as a non-conformity product. Since VoP is half of VoC so thatthe process will try to control their product within half of the specification limit, thatmeans within 3 limits. But it should be remind again that the value of sigma in 6 ishalf of the value in the 3 quality control management. The specification limit in bothcase is same and given by the customer. That means 3 specification limit in the 3quality control management and 6 specification limit in the 3 quality controlmanagement contains same limit. The difference between two is on the VoP which isdesigned by the company. In the 3 quality control VoP is designed equal to VoC and inthe 6 quality control VoP is designed equal to the half of the VoC. The mean or averageis generally situated in the middle position in the interval. But due to the natural deviationmean may shift either direction even process is well designed. The graphical presentationof normal distribution with mean shift in 6 management is shown in figure 3.3.

-1.5 1.5

LSL USL

-6 -4.5 -3 -1.5 0 1.5 3 4.5 6

Figure 3.3: Six Sigma process with 1.5-Sigma shift in the mean

The mean shifted by 1.5 results in a 3.4 non -conformities or defects per millionopportunities at the nearest specification limit, or one or late monthly report in 24,510years [1/0.0000034/12]. This is the definition of 6- Sigma level of quality. Whereas in the3- Sigma process if mean shift by 1.5 then results in 66,807 defects per millionopportunities at the nearest specification limits. The difference between the 3-Sigma

process and 6-Sigma process is dramatic enough to certainly believe that 6-Sigma level of performance matters, especially with more complex processes with a greater number ofsteps or components.

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3.1.1 DMAIC Model

DMAIC model is an acronym for Define, Measure, Analyze, Improve, and Control. Itcontains five individual steps through which Six Sigma can be implemented. Therelationship between voice of customer and voice of process and DMAIC models is

explained in figure 3.4. The output distributions of 3-Sigma process and its old flowcharthave been shown in the left side in the figure 3.4. The right side shows a new flowchartwith its 6-Sigma output distribution. The model utilized in Six Sigma management tomove from the old flowchart to the new flowchart through impro vement of a process iscalled the DMAIC model.

Figure 3.4: Relationship between the VoC, the VoP, and the DMAIC model

The Define phase of a six Sigma DMAIC model is used to identify the product qualitycharacteristics which is critical to customer (called CTQs). In this thesis paper we havetried to identify this though QFD model and questionnaires that means voice of customer.The second phase named measure is involves defining the CTQs operationally.

Start Start

Stop

Ye

Stop

No

No

Ye

DMAICModel

LSLUSL

-3 0 3

OLD

LSLUSL

-6 0 6

NEW

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The analyze phase involves identifying input and output variables that affect each CTQs( Xs) using process map or flowchart, creating a cause-effect-diagram to understand therelationship between the CTQs and Xs. In this phase normally finding out of critical workor major problem which is responsible to the large part of the problem is identified. Theimprove phase deals with the activity related to the improvement of the project. This

phase is involved designing the appropriate experiments to understand the relationships between the Xs. The final phase of the model named control phase involves avoiding potential problems in Xs with risk management and mistake proofing, standardizingsuccessful process changes, controlling the critical Xs, development the process plan anddocumentation of the process plan.

3.1.2 Control chart

Control chart is one of the most important, popular and widely used tool of the seventh basic tool of TQM. This chart shows the output variation of the process graphically. Itcontains prefix upper and lower specification limits. In 1924, Walter Shewhart of BellLaboratory introduced the concept of control chart as a tool of showing the variation ofthe quality characteristics. To manage the process economically and to bring the processin statistical control system Shewhart had stressed. The variation of the output of thequality characteristic may caused by several reason or source. The source of variationscan be machine, operators, materials, etc. These types of reason bring a certain amount ofnatural variation in the entire production system. The main purpose of control chart is toshow the variation of output and subsequently control it. There are some randomlyoccurred causes which are uncontrollable, natural, and result in small variation. Thesevariations are referred to as a stable system of chance cause. There is another type ofcauses of variations which are assignable, with non-random pattern of occurrence. Thistype of cause may take place in large amount which may result in the out of controlsituation. This type assignable cause may be associated with the machine, method, man,material etc. assignable cause do not occur frequently if the process control is good andstable.

The prior signal about the possible out of control situation may also be obtained throughthe control chart. So this chart can be acted as a diagnostics of the possible out of controlstate of the process. The process which is in control situation is considered as a stable

process and the process which is out of control situation is considered as a unstable process. When the out of control situation is observed in the control chart then it is up tothe operators, Engineers or management of the process to find out which reason behindthis out of control situation and trying to solve that. At last we can say that the main

purpose of control chart is to reduce the process variability through eliminating the reasonwhich is for out of control situation. The purposes of Six Sigma quality controlmanagement philosophy is same as control chart so that this chart can be used as aneffective tool of Six Sigma.

A control chart is basically a graphical monitoring tool to observe the pattern of variation.There are two control limits exist for this purpose:

1. Upper Control Limit (UCL)2. Lower Control Limit (LCL).

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A central line also exists between the control limit and it represents the target mean of thequality characteristics. It is expected that all the data values of the output qualitycharacteristics will be fallen within this two limits named UCL and LCL. If any data goesout of this limit then it will be considered as an out of control situation. There need tomake an investigation to find out the reason for which that value is out of the control limit

and after that need to solve it. To draw a control chart the data value of qualitycharacteristics have to be taken and should have to plot in the control chart. A typicalcontrol chat shown in the figure 3.5.

Rejection

UCL

CL

LCL Rejection

Sample number

Figure 3.5: Basic structure of control chart

All the point of data value fall within this control limit then the process will be consideredas an acceptable. The point beyond this limit is unacceptable and this region is rejectionregion. Although all the points fall within the limits then the process may be out ofcontrol. Thats depends on two important criteria. The criteria which indicate that the

process is out of control are: 1) the pattern of plots of points must be random. If the last

10 point falls all below or above the central line that mean there is assignable cause behind this. This cannot be occurred by a natural cause. 2) There must not be anyincreasing or decreasing pattern, or any specific trend. If occur, there is also anyassignable cause behind.

3.1.3 Pareto Analysis

This is one of the most important tools of TQM basic seven tools. In the early nineteenthcentury, the famous Italian Economist Vifredo Pareto observed and stated that about 80%of the countrys wealth is occupied by about 20% of the population. This famousobservation was later named as 80 -20 rule. Although the observation was concentrated

on only wealth distribution but later the researcher has found that this rule can be equallyapplicable to the other fields of knowledge. In quality control, it has been seen that 80%

Q u a

l i t y c h a r a c

t e r i s

t i c v a

l e

A c c e p

t a n c e

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defects or out of control situation is due to the 20% causes. If these 20% cause can be possible to remove then the out of control situation will be reduced by 80%. This 20%cause is known as vital few, whereas the rest many are known as Trivial many.Pareto chart looks like a cumulative bar graph. The length of the bar represents thefrequency or cost and arranged longest bar to the left and shortest to the right. It is also

known as Pareto chart or Pareto diagram.

3.1.4 Cause-Effect Diagram

One of the famous quality gurus named Kaoru Ishikawa had introduced the cause-effectdiagram. Every adverse effect of product related to the quality characteristics which must

be result in the some specific reason or cause. Cause-effect analysis is a tool for analyzingand illustrating a process by showing the main causes and sub-causes leading to an effect.Drawing a Cause-effect diagram for a quality effect with all possible causes looks like askeleton of a fish. For this reason its called fishbone diagram. This diagram generallyis drawn by brainstorming step by step. At first need to draw a horizontal line align with

the quality effect then possible causes are drawn to the both side of line with an arrowline. All the sub-causes are drawn to every possible cause.

Figure 3.6: Basic structure of Cause-effect diagram

There are two types of cause-effect diagram. One is cause enumeration and another is process analysis. Cause enumeration is the most commonly used CE diagrams inindustries. This identifies one-by-one all possible causes from brainstorming sessions andthen classifies into groups. Figure 3.6 shows the general Example of cause effect (CE)diagram.

3.1.5 MCDM Approach

Multi criterion decision making approach is one of the popular approaches for decisionmaking. Decision is made depends on various criteria so the named as. There are manymethods in the category of MCDM approach. These are Analytic Hierarchical Process(AHP ), Outranking Method, Multi-Attribute Utility Approach (MAUT), Linear WeightedPoint, Judgmental Modeling, Interactive Selection Model (ISM), Categorical Method andFuzzy Sets. AHP indeed makes use of hierarchical structure to cope with Multi-criterionDecision Making (MCDM) process with data from the real world.

Effect

Material

Type/grade

Method

Machine

Man

SOPSkill

Accurac

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3.1.6 Quality Function Deployment (QFD)

Quality Function Deployment (QFD) is a structured approach to defining customer needsor requirements and translating them into specific plans of product, parts/components,

process and production plan, such that those needs are met. Voice of customer (VoC) is

the term to describe these stated or unstated customer needs or requirements. Thisunderstanding of the customer needs is then summarized in a product planning matrix orHouse of Quality (HOQ). These matrices are used to translate higher level whats orneeds into lower level hows product requirements or technical characteristics tosatisfy these needs.

3.2 Lean Manufacturing

Now- a days Lean manufacturing has become a popular waste minimization tool used inindustries. Lean manufacturing is a manufacturing strategy that seeks to produce a high

level of throughput with a minimum of inventory. Lean Production originated from theJapanese Toyota Motor Corporation, it developed from the management of the FordMotor Company that Toyota Motor Corporation is studying. Its core idea is to remove alllinks of enterprises of non-value-added activities, with less manpower, less equipment, inshorter time and smaller site to create as much as possible the value to meet customerrequirements of the product and service. It emphasis on the reduction of the wastage, sothat the overall cost of production process are reduced. Lean Production follows the mass

production (mass production, MP) mode, this production mode have a greater impact tohuman society, is a symbol of the new age of industrialization.

The main aspects of the Lean manufacturing are:

1. High level of throughput with minimum amount of decentralized stockpiles.2. SMED technique to minimize setup time, which allows small lot production; even

one-of-a-kind production is possible when setup time become zero.3. Poka-yoke technique to prevent mistake.4. 5S Program to eliminate unnecessary materials, in order to avoid mixing up of

good with bad, and make place clean and safe.5. Total Productive Manufacturing (TPM) and its part Total Productive Maintenance

(TPM), etc.

3.2.1 The 5S Philosophy

The 5S is one of the important tools in Lean manufacturing. Based on Japanese wordsthat begins with S, the 5S Philosophy focuses on effective work place organization andstandardized work procedures. 5S simplifies work environment, reduce waste and non-value activity while improving quality efficiency and safety. 5S is the Japanese conceptfor House Keeping.The 5 Ss are:

1. Sort (Seiri) - The first S focuses on eliminating unnecessary items, removing broken tools, getting rid of dust and oil, etc. from the work place. To identify theseunneeded items an effective visual method is used which is known as red tagging.

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2. Set In order/ Systematize (Seiton) This is the second of the 5Ss and focuses onefficient and effective storage methods. This phase of 5S is all about keepingthings in their rightful place.

3. Shine (Seiso) This S is concern about the thoroughly cleaning the workplace.Daily follow-up cleaning is necessary in order to sustain this improvement.

Workers take pride in a clean and clutter free work area and shine step will helpcreate ownership in the equipment and facility.

4. Standardize/Systematize (Seiketon) When above 3S have been implementedthen next step is to concentrate on standardizing best practice in the work area. Itis necessary to allow employees to participate in the development of suchstandards.

5. Sustain (Shitsuke) This is most difficult S to implement and achieve. Humannature is to resist change. Sustain focuses on defining a new status quo andstandard of work place organization. Precisely speaking, the system moves intothe area of Kaizen or ongoing improv ement.

3.3 The Seven Wastes in the Industrial Production

The concept of 7 wastes was popularized by Womack and Jones in their book namedThe Machine that changed the world . After that Taiichi Ohno in his book namedToyota Production system explained the main foundation of Lean Manufacturing .Taiichi Ohno devised 7 categories which cover virtually all of the means by whichmanufacturing organizations waste or lose money; these have become known as the7wastes.

The 7 wasted described by Ohno are:

1. Overproduction Overproduction means producing more than customerorders, producing unordered material/goods. It is often cause by quality

problems.2. Waiting Products waiting around in factories either as finished goods or

work in process (WIP) another major cause of waste.3. Transportation This type of waste concern with transportation handling

more than once, delays in moving materials, unnecessary moving or handling.4. Inventory This type of waste is associated with the unnecessary raw

materials, work in process (WIP), & finished stocks.5. Motion Those parts of the motion is considered as a waste which is concern

with the movement of people and equipment that does not add any value to thefinal product.

6. Over-Processing Rework is a typical example of over processing asdiscussed earlier reducing the root cause of the quality problem is solutioneliminating rework. Unnecessary processing or procedure that adds no value isassociated with this waste.

7. Defective Units The finished product which does not fulfill the customerrequirement or desired characteristics is known as defective products.

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CHAPTER 4

Case Study & Data Collection

For the thesis purpose we had gone to a leading food-product manufacturing company inBangladesh named Pran Agro Ltd. It is situated at Natore district. There are many typesof product and individual department for the individual product. Some of the majordepartments are Jam-Jelly, Spice, Choco-bean, Ice-pop, Plastic, Mango juice etc. Amongthem we had chosen ice-pop department and their products for collecting data. In thisdepartment all product are being produced in the same procedure with different shape,color, and flavor. There were three flavored and that are Mango, orange and litchi. The

production layout of the department is shown in figure 4.1.

Figure 4.1 the production layout of ice-pop department

We have collected data related to the quality characteristics of the product such as whattypes of defects normally occur, how frequently occur, what is the reason behind these,what procedure they follow etc. There are found five types of defect named Leakage,Black particle, without coding, Cap loose, and Short/Excess. By asking somequestionnaire about what are the causes for these defects to the worker and qualityassurance department we have found the following causes for respective defects.

Sugar syrupTank

Press filter

Reservetank

Cooking tank Sterilizer

CoolerDryerShortingconveyor

Fine filter

Fine filter

Keep 18-20min at temp.80-85 degree

to makegerm free

Check randomly

Addchemical

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1. Leakage If the thickness of the tube is not uniform Unbalanced heat External hit Operators skill etc.

2. Black particle Dirty Environment/ work area Dirty tube Unclear raw materials Unclear machine.

3. Without coding Operators insincerity Unskillful worker Machine etc.

4. Cap loose Time maintaining of worker

5. Excess/Short Operators insincerity.

From the quality assurance department it has been found the data of month of 26 workingdays related to the question, what type of defects occur how frequently such as samplesize, number of non conformities, fraction non-conformities.

All data of 26 working days collected from quality assurance department are given in theAppendix A (at the last of the paper).

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CHAPTER 5

Calculations

5.1 Define: There are five types of defects in the product of ice-pop department of PranAgro Ltd. In the define phase QFD have been used to identify the relationship betweenthe defects and the factors that affect these defects.

5.1.1 Quality Function Deployment (QFD): Here QFD shows the relationship betweenthe defects and possible causes shown in figure 5.1. Here 1, 3, 9 denote

1=weak relation

3=Moderate relation

9=Strong relation

Figure 5.1 QFD model showing the relationship between defects and possible causes

In the diagram it has been seen that lack of worker attention have the highest score, thenmachine setup, dust etc respectively. Here importance means is the numbering the defectsamong 10.

U n s k

i l l e d o p e r a t o r

R a w m a t e r

i a l

H e a t

b a l a n c e

M / C s e

t u p

D u s t

N o i s e

L a c k o f a t

t e n t

i o n

M e a s u r i n g a c c u r a c y

I m p o r t a n c e (

1 0 )

W e

t o d a y (

1 0 )

T a r g e t

i n f u t u r e

( 1 0 )

I m p r o v e m e n

t r a t

i o

S a l e s p o

i n t

S c o r e s

P e r c e n

t s c o r e

( 1 0 0 )

Leakage 3 1 9 3 1 1 10 8 9 1.1 1.2 13 28Black particle 3 3 9 3 8 6 7 1.2 1.1 11 24Without coding 3 9 1 3 4 2 3 1.5 1.3 8 17Cap loose 1 3 3 9 3 3 4 1.3 1.2 5 11Short/Excess 1 1 9 3 6 4 5 1.2 1.2 9 20

Score(Sum1664)

166 100 252 309 244 70 364 159

Percentscore(100)

10 6 15 19 15 4 22 10

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5.2 Measure : Here to measure how many data are out of control limits P control charthave been used.5.2.1 Control Chart: Since the quality characteristics data is attributes type so P chart is

preferable.Table 5.1: Data table of non-conformities of 26 days with sample size n=810.

Sampleno

(day) No of

abnormalitiesFraction nonconforming

Sample no(day)

No ofabnormalities

Fraction nonconforming

1 5 0.0061 14 9 0.01112 4 0.0049 15 6 0.00743 6 0.0074 16 5 0.00614 9 0.0111 17 7 0.00865 3 0.0037 18 12 0.01486 6 0.0074 19 5 0.0061

7 4 0.0049 20 3 0.00378 11 0.0135 21 4 0.00499 6 0.0074 22 6 0.0074

10 3 0.0037 23 3 0.003711 5 0.0061 24 5 0.006112 6 0.0074 25 7 0.008613 4 0.0049 26 4 0.0049

f

Figure 5.2: P chart

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

F NC

UCL1

LCL1,2

UCL2

CL1

CL2

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5.2.2 Control limits calculation:

Lets

is the fraction non-conforming. Then (1- ) is fraction conforming. Mean fraction nonconforming for the data of table 5.1 can be found from the following equation. =0.1819/26=0.00699

Since population fraction non-conforming is not known, the value can be used tocalculate the upper and lower control limits.

UCL 1 = 0.0157CL 1= =0.00699

LCL 1= - -0.00179=0The LCL is a negative value, which is infeasible, because fraction non-conforming cannot

be negative, thereby justifying to be taken as zero.

The control limits are drawn in figure 5.2, and subsequently 26 fraction non-conformingvalues are plotted.

It has been seen in the figure that the sample no. 8 and 18 are out or so closed to the uppercontrol limits. So there might have some specific reason behind this and investigationneeds to identify. We can neglect the two data. After neglecting the two data of out ofcontrol, calculate becomes

UCL 2= 0.0148CL 2=

LCL 2= - -0.0084=0Then the control limits have been plotted in the same figure. Then all data are randomlydistributed.

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5.3 Analysis: Three tools named cause-effect diagram, Pareto chart, MCDM has used toanalyze the defects. Individual calculation shown in below;5.3.1 Causes Effect Diagram: The possible causes for individual defects are shown infigure 5.3(a, b, c, d, and e) respectively. The brainstorming data have been used to drawthese diagrams.

Man Machine Material

Supplier

Grad of

Raw material

Method Management Environment

Figure 5.3(a) Cause-effect diagram for leakage

Man Machine Material

Supplier

Method Management Environment

Figure 5.3(b) Cause-effect diagram for black particle

Leakage

Nonuniformity

Lack of attention

TemperatureSetup

Rules

Support

Noise Safety

Dust Temp. &Humidity

Lack ofskill

Lack ofTraining

System

Blackparticle

Unclean

Lack of attention

Tooling

Setup

RulesSupport

Noise

Safety

Dust Temp. &Humidity

Lackof skillLack of

Training

System

Composition

Shortage

HeatBalance

Update

Manual

Manual

Update

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Man Machine

Method Managemen t Environment

Figure 5.3 (c) Cause-effect diagrams for without coding

Man Measurement

Method Managemen t Environment

Figure 5.3(d) Cause-effect diagram for shortage/excess

WithoutCoding

Lack of attention

Tooling

Setup

RulesSupport Noise

Safety

Temp. &

Humidity

Lackof skill

Lack ofTraining

System

ShortageAccuracy

Shortage/Excess

Lack of attention

Tooling

Time

RulesSupport

Noise

Safety

Temp. &Humidity

Lackof skill

Lack ofTraining

System

Shortage Accuracy

Update

Appropriatenes

Update

Appropriatenes

Manual

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Man Machine

Method Supplier Environment

Figure 5.3(e) Cause-effect diagram for loose caps

In the above all cause-effect diagram possible causes have been divided into some majorfactors by brainstorming data. Then also some specific factors related to those major

factors have been identified.

LooseCaps

Lack of attention

Tooling

Setup

Responsibility

BadQuality Noise Safety

Temp. &Humidity

Lackof skill

Lack ofTraining

ShortageAccurac

Heat

Appropriateness

Manual

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5.3.2 Pareto Analysis

In our survey time, we have found among 64 defective items the no. of individual defectsis shown in table 5.2. The individual defects, its number and percentage of frequency aregiven bellow:

Table 5.2: Different types of defects percentage

Defect Num. of abnormalities Frequency(percentage)

Leakage 31 48Black particle 22 35

Without coding 05 8Cap loose 04 6

Short/Excess 02 3

Figure 5.4 Pareto Chart

In the figure 5.4 shows that 48% defects is leakage, 35% black particle and remain percents other. So here vital few is these two defects. If it possible to remove then about80%defects will be minimized.

4835

8 6 30

10

20

30

40

50

60

70

80

90

100

Leakage Black particle Without coding Cap loose Short/Excess

Series1

Defect

F r e q u e n c y

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5.3.3 MCDM approach: AHP methods have been used to rank these defects dependingon some factor denoted by C 1, C 2, C 3, and C4 shown in figure 5.5. Here Goals denotewhich one is more critical.

Figure 5.5 Proposed AHP model

Table 5.3 : Level of preference weights

Level of preference/importance

weights

Definition Explanation

1 Equally preferred Two activity contribute equally to theobjective

2 Moderately Experience and judgment slightly favorone activity over another

3 Strong Importance Experience and judgment strongly or

essentially favor one activity overanother

4 Extreme Importance The evidence favoring one activity overanother is of the highest degree possibleof affirmation

Reciprocals Reciprocals for inverse comparison

Goals

Method,C1

Environment/ Workplace,

C2

Man,C3 Management,

C4

Leakage,A1

BlackParticle,

A2

WithoutCoding,

A3

CapLoose,

A4

Excess/Short,

A5

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Table5.4 : Evaluation at level 1

Attribute C 1 C2 C3 C4 Geometricmean

Normalizedweight

C1 1 3 1/2 4 1.57 0.3298

C2 1/3 1 1/3 2 0.686 0.144C3 2 3 1 3 2.06 0.432C4 1/4 1/2 1/3 1 0.452 0.0949Sum 3.583 7.5 2.167 10 4.76

Note: Here in the Table 5.4 Geometric Mean is being calculated by this Formula: [a 1*a2*a3*.a n]

1/n where a 1,a2,a n are the elements of any particular row and n is the number of elements in thatrow. Then the Normalized Weights are being calculated as dividing each element ofGeometric mean by the summation of all the geometric means.

Example: For first row, Geometric Mean = [1*3*1/2*4] 1/4= 1.57Then the Geometric Mean = 4.76

Now the Normalized Weight for first row = 1.57/4.76 = 0.3298

Sample CalculationEigenvector max =(Normalized weight of each row*sum of respective column) = (3.583*0.3298+ 7.5*0.144+2.167*0.432+10*0.0949) = 4.1469As follows: Consistency index CI = ( max n) / (n 1 ) = 0.0489; Where n is thenumber of criteria are being considered. Here n= 4

Now the consistency ratio C.R = C.I/R.I = 0.0489/0.89 = 5.49%

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Eigen Vector max =(11.33*0.0947+3.533*0.365+3.167*0.274+7.7*0.1318+6.867*0.135) = 5.1721. For n=5;R.I= 1.11 (From Table5.5)Consistency Index (C.I) = ( max -n)/ (n-1) = 0.043 Consistency Ratio (C.R) = C.I/R.I = 3.87% < 10%, so acceptable

Table 5.7 : For the factor environmentAttribute A 1 A2 A3 A4 A5 Geometric

mean Normalizedweight

A1 1 1/3 1/3 3 2 0.8705 0.167A2 3 1 2 2 1/2 1.35 0.26A3 3 2 1 1/4 1/2 0.9028 0.174A4 1/3 1/2 4 1 2 1.173 0.226A5 1/2 2 2 1/2 1 0.903 0.174Sum 8 4.7 9.033 6.2 6 5.2

Eigen Vector max = (8*0.167+4.7*0.26+9.033*0.174+6.2*0.226+6*0.174) = 5.575. Forn=5; R.I= 1.11 (From Table5.5)Consistency Index (C.I) = ( max -n)/ (n-1) = 0.143 Consistency Ratio (C.R) = C.I/R.I = 1.29% < 10%, so acceptable

Table 5.8 : For the factor manAttribute A 1 A2 A3 A4 A5 Geometric

mean Normalizedweight

A1 1 1/2 3 1/2 2 1.084 0.215A2 2 1 2 1/3 1/2 0.92 0.183

A3 1/3 1/2 1 3 4 1.148 0.228A4 2 3 1/3 1 1/2 1.00 0.199A5 1/2 2 1/4 2 1 0.87 0.173Sum 5.83 7 6.58 6.83 8 5.022

Eigen Vector max = (5.83*0.215+7*0.183+6.58*0.228+6.83*0.199+8*0.173) = 5.327.For n=5; R.I= 1.11 (From Table5.5)Consistency Index (C.I) = ( max -n)/ (n-1) = 0.082 Consistency Ratio (C.R) = C.I/R.I = 7.36% < 10%, so acceptable

Table 5.9 : For the factor managementAttribute A 1 A2 A3 A4 A5 Geometric

mean Normalizedweight

A1 1 1/3 1/3 3 2 0.92 0.18A2 3 1 1/2 2 1/2 1.084 0.22A3 3 2 1 1/4 1/2 0.944 0.18A4 1/3 1/2 4 1 2 1.059 0.21A5 1/2 2 2 1/2 1 1 0.199Sum 7.83 5.83 7.83 6.75 6 5.01

Eigen Vector max = (7.83*0.18+5.83*0.22+7.83*0.18+6.75*0.21+6*0.199) = 5.432. For

n=5; R.I= 1.11 (From Table5.5)

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Consistency Index (C.I) = ( max -n)/ (n-1) = 0.108 Consistency Ratio (C.R) = C.I/R.I = 9.7% < 10%, so acceptable

Table 5.10 : Final Evaluation

Alternative Attribute & their weight Compositeweight

RankMethod0.3298

Environment0.144

Man0.432

Management0.0949

Leakage 0.0947 0.167 0.215 0.18 0.165 4Black

particle0.365 0.26 0.183 0.22 0.257 1

Withoutcoding

0.275 0.174 0.228 0.18 0.23 2

Cap loose 0.1318 0.226 0.199 0.21 0.182 3Excess/Short 0.135 0.174 0.173 0.199 0.163 5

Note: Here Composite weight for the first row:(0.3298*0.0947+0.144*0.167+0.432*0.215+0.0949*0.18) = 0.165From the above result it can be said black particle has the highest Composite weight0.257.

There is no calculation in the Improve and Control phase. So these have been discussedin the next chapter.

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CHAPTER 6

Result Analysis

6.1 Define: In this phase problem have been identified. QFD diagram shown in figure 5.1

represent the relation between the defects and possible causes. In the diagram it indicatesthat the lack of attention of the worker has the highest relation with the defects.

6.2 Measure: P control chart have been used to measure the condition of the processshown in figure 5.2. It shows that data on the 8 th and 18 th out or near the upper controllimits. There must have some specific assignable reason behind this data. Investigationmust need to do for what such type of result is coming.

6.3 Analysis: In this phase, fish-bone diagram, Pareto chart, MCDM approach has beenused to analyze. Figure 5.3 shows the possible causes for the individual defect. All the

possible causes are from the brainstorming session. It does not show the actual cause for

defects. Pareto chart have been used to identify which defects are occurring morefrequently that means vital few. Figure 5.4 of Pareto chart shows the leakage and black

particle is the vital defects. If these two defects can be possible to remove then defectswill be reduced up to 80%. In the table 5.10 of the MCDM it has been seen that black

particle is rank one defects. The causes of black particle need to be eliminated first.

6.4 Improve: In the analysis phase noticed that black particle and leakage is the main problem. 5S philosophy can be implemented for reduce black particle since work area/Environment is the main cause for it. Method needs to be update and operators also needto train up to reduce leakage problem. The tubes of ice-pop department from the supplierneed to inspect properly because thickness of the tube is one of main factor for leakage.In necessary supplier can be changed.

6.5 Control: This solution and continuous improvement process must need to maintainover time. For this purpose continuous training schedule for the worker need to setup andupdate new standard of documents (i.e. procedure, work inspection) must be established.

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CHAPTER 7

Recommendations and Future Works

The key objective of this study was to identify the quality problem of a product of a

manufacturing industry such as defects and its possible causes. And also the applicationof Lean and Six Sigma tools for the purpose of decreasing the defects. In this study,various Six Sigma and Lean tools such as Pareto analysis, cause-effect diagram, controlchart and also AHP technique of MCDM approach, QFD have been used. Othertechniques of MCDM approach such as Grey relational analysis (GRA), fuzzy sets may

be applied here. Data have been taken over one month only. If more data was taken itwould give more precise results. Here only defective items and their causes have beendescribed and have tried to overcome these. Other types waste such as motion; inventory,transportation etc. also can be solved by this technique. Only p chart have been used tomeasure the problem, other type likes u, c, np etc, also can be applied and use of morethan one would more precise results. Value process map can be used for the purpose of

identification the activity which does not add value. Non value added activity will need to be identified to apply 5S philosophy or elimination of other type of defects. But here nonvalue added activity have not identified although 5S tools have been suggested toevaluate the work place area. This is one of the lacks of this study. If there is relation

between the defects to each other or dependency then regression analysis may be used tounderstand the relationship.

In the future work, implementation of Lean Six Sigma can be carried out other type ofwaste like motion, inventory, transportation etc. The application of Lean Six sigma in theservice sectors is inconsiderable yet now. This may be another further research objective.

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CONCLUSION

Six sigma is a powerful method that gathered many important aspects of various tools toimprove the process. Lean and Six Sigma both have been implemented as integrated formin this study to obtain better results and support to each others. Lean Six Sigma can be

ap plied easily in any kind of business areas likes service, production, marketing, sales, procurements etc. The major advantages are reduced cost, reduced time, maximization of profits, quality of the products, increased customer satisfaction etc. The factors whichinfluence the successful implementation of Lean Six Sigma are managementinvolvements, organizational commitment, control skill, continuous training and culturalchange. In this thesis work, Lean Six Sigma have been implemented and recommendedfor decreasing the defects of products, ultimately for reducing the process variation.Although all preventive and corrective action has defined but for this moment it cannot besaid that the number of defective items is decreasing because till now no further analysishave been performed. But it can be assured that the successful implementation of thismanagement technique must be beneficial to the organization.

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REFERENCES

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2. Atmaca, E & Girenes, SS 2011, Lean six sigma methodology and Applications , TheRed Crescent Society, Turkey.

3. Barac,N, Milovanovic,G & Andjelkovic,A 2010 , Lean production and six sigmaquality in lean supply chain management, Economics and Organization, Vol.7, PP.319-334.

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5. Chakravorty, SS 2009, Six sigma programs: An implementation model , Int. J.Production Economics, Vol.119, PP.1-16.

6. Chang, KK & Wang, FK 2008 , Applying Six sigma methodology to collaborative forecasting, Int J Adv Manuf Technol, Vol.39, PP.1033-1044.

7. Cudney,E.A, Mehta M & Monroe R 2006, Combining lean and sox sigma for optical results , Society of manufacturing engineers, California.

8. Ditahardiyani, P, Ratnayani, & Angwar, M 2008, The quality improvement of primer

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9. Does, RJ & Koning, HD 2006, Lean Six Sigma in a hospital , Int. J. Sig ma andCompetitive Advantage, Vol.2, No. 2.

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Appendix A

Data table of the defects of ice-pop in Pran Agro Ltd.

Date Sample

size

Leakage Black

particle

Without

coding

Loose

caps

Excess/

shortage

Total

01.04.2012 810 2 1 0 1 1 502.04.2012 810 2 1 0 1 0 403.04.2012 810 3 2 1 0 0 604.04.2012 810 5 2 1 1 0 905.04.2012 810 1 0 0 1 1 307.04.2012 810 2 2 1 1 0 608.04.2012 810 1 1 2 0 0 409.04.2012 810 4 4 2 0 1 1110.04.2012 810 2 2 1 1 0 611.04.2012 810 1 1 0 0 1 312.04.2012 810 2 2 1 0 0 514.04.2012 810 3 1 1 1 0 615.04.2012 810 1 2 0 0 1 416.04.2012 810 4 2 1 2 0 917.04.2012 810 2 2 2 0 0 618.04.2012 810 2 2 1 0 0 519.04.2012 810 3 1 1 1 1 721.04.2012 810 3 6 2 0 1 1222.04.2012 810 3 1 1 0 0 523.04.2012 810 0 1 2 0 0 3

24.04.2012 810 1 1 0 1 1 425.04.2012 810 2 1 2 1 0 626.04.2012 810 2 1 0 0 0 328.04.2012 810 2 2 1 0 0 529.04.2012 810 3 1 1 1 1 730.04.2012 810 1 2 1 0 0 4