MINOR PROJECT

A Study onSix Sigma Techniques

AndIts application in reduction of seat rejection

At BOSCH LTD.

Submitted bySuyog Gholap(107269)

R.Rahul(107254)Chandra shekhar.L(107266)

Sudip Pal(107237)K.Seshi Kiran Reddy(107249)

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Introduction to Six Sigma:

Sigma (σ) is a letter in the Greek alphabet that has become the statistical symbol and metric of

process variation. The sigma scale of measure is perfectly correlated to such characteristics as

defects-per-unit, parts-per-million defectives, and the probability of a failure. Six is the number of

sigma measured in a process, when the variation around the target is such that only 3.4 outputs out of

one million are defects under the assumption that the process average may drift over the long term by

as much as 1.5 standard deviations. Six sigma may be defined in several ways. Tomkins defines Six

Sigma to be “a program aimed at the near-elimination of defects from every product, process and

transaction.” Harry (1998) defines Six Sigma to be “a strategic initiative to boost profitability, increase

market share and improve customer satisfaction through statistical tools that can lead to breakthrough

quantum gains in quality.”

Six sigma was launched by Motorola in 1987. It was the result of a series of changes in the quality

area starting in the late 1970s, with ambitious ten-fold improvement drives. The top-level management

along with CEO Robert Galvin developed a concept called Six Sigma. After some internal pilot

implementations, Galvin, in 1987, formulated the goal of “achieving Six-Sigma capability by 1992” in a

memo to all Motorola employees. The results in terms of reduction in process variation were on-track

and cost savings totaled US$13 billion and improvement in labor productivity achieved 204% increase

over the period 1987–1997.In the wake of successes at Motorola, some leading electronic companies

such as IBM, DEC, and Texas Instruments launched Six Sigma initiatives in early 1990s. However, it

was not until 1995 when GE and Allied Signal launched Six Sigma as strategic initiatives that a rapid

dissemination took place in non-electronic industries all over the world. In early 1997, the Samsung

and LG Groups in Korea began to introduce Six Sigma within their companies. The results were

amazingly good in those companies. For instance, Samsung SDI, which is a company under the

Samsung Group, reported that the cost savings by Six Sigma projects totaled US$150 million. At the

present time, the number of large companies applying Six Sigma in Korea is growing exponentially,

with a strong vertical deployment into many small- and medium-size enterprises as well. Six sigma

tells us how good our products, services and processes really are through statistical measurement of

quality level. It is a new management strategy under leadership of top-level management to create

quality innovation and total customer satisfaction. It is also a quality culture. It provides a means of

doing things right the first time and to work smarter by using data information. It also provides an

atmosphere for solving many CTQ (critical-to-quality) problems through team efforts. CTQ could be a

critical process/product result characteristic to quality, or a critical reason to quality characteristic.

Defect rate, PPM and DPMO:

The defect rate, denoted by p, is the ratio of the number of defective items which are out of

specification to the total number of items processed (or inspected). Defect rate or fraction of defective

items has been used in industry for a long time. The number of defective items out of one million

inspected items is called the ppm (parts-per-million) defect rate. Sometimes a ppm defect rate cannot

be properly used, in particular, in the cases of service work. In this case, a DPMO (defects per million

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opportunities) is often used. DPMO is the number of defective opportunities which do not meet the

required specification out of one million possible opportunities.

Standard Deviation:

In probability theory and statistics, standard deviation is a measure of the variability or dispersion of

a population, a data set, or a probability distribution. A low standard deviation indicates that the data

points tend to be very close to the same value (the mean), while high standard deviation indicates that

the data are “spread out” over a large range of values.

For example, the average height for adult men in the United States is about 70 inches, with a

standard deviation of around 3 inches. This means that most men (about 68%, assuming a normal

distribution) have a height within 3 inches of the mean (67 inches – 73 inches), while almost all men

(about 95%) have a height within 6 inches of the mean (64 inches – 76 inches). If the standard

deviation were zero, then all men would be exactly 70 inches high. If the standard deviation were 20

inches, then men would have much more variable heights, with a typical range of about 50 to 90

inches.

Fig: A data set with a mean of 50 (shown in blue) and a standard deviation (σ) of 20.

Fig: A plot of a normal distribution (or bell curve).

Each colored band has a width of one standard deviation.

In addition to expressing the variability of a population, standard deviation is commonly used to

measure confidence in statistical conclusions. For example, the margin of error in polling data is

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determined by calculating the expected standard deviation in the results if the same poll were to be

conducted multiple times. (Typically the reported margin of error is about twice the standard deviation,

the radius of a 95% confidence interval.) In science, researchers commonly report the standard

deviation of experimental data, and only effects that fall far outside the range of standard deviation are

considered statistically significant. Standard deviation is also important in finance, where the standard

deviation on the rate of return on an investment is a measure of the risk.

Consider a population consisting of the following values

There are eight data points in total, with a mean (or average) value of 5:

To calculate the standard deviation, we compute the difference of each data point from the mean,

and square the result:

Next we average these values and take the square root, which gives the standard deviation:

Therefore, the population above has a standard deviation of 2.

Note that we are assuming that we are dealing with a complete population. If our 8 values are

obtained by random sampling from some parent population, we might prefer to compute the sample

standard deviation using a denominator of 7 instead of 8.

The standard deviation of a discrete random variable is the root-mean-square (RMS) deviation of its

values from the mean.

If the random variable X takes on N values   (which are real numbers) with equal

probability, then its standard deviation σ can be calculated as follows:

1. Find the mean, , of the values.

2. For each value xi calculate its deviation ( ) from the mean.

3. Calculate the squares of these deviations.

4. Find the mean of the squared deviations. This quantity is the variance σ2.

5. Take the square root of the variance.

This calculation is described by the following formula:

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Where   is the arithmetic mean of the values xi, defined as:

If not all values have equal probability, but the probability of value xi equals pi, the standard deviation

can be computed by:

and

Where

And N' is the number of non-zero weight elements.

For example

Suppose we wished to find the standard deviation of the data set consisting of the values 3, 7, 7, and

19.

Step 1: find the arithmetic mean (average) of 3, 7, 7, and 19,

Step 2: find the deviation of each number from the mean,

Step 3: square each of the deviations, which amplifies large deviations and makes negative values

positive,

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Step 4: find the mean of those squared deviations,

Step 5: take the non-negative square root of the quotient (converting squared units back to regular

units),

So, the standard deviation of the set is 6. This example also shows that, in general, the standard

deviation is different from the mean absolute deviation (which is 5 in this example).Note that if the

above data set represented only a sample from a greater population, a modified standard deviation

would be calculated to estimate the population standard deviation, which would give 6.93 for this

example.

Rules for normally distributed data

Dark blue is less than one standard deviation from the mean. For the normal distribution, this

accounts for 68.27 % of the set; while two standard deviations from the mean (medium and dark blue)

account for 95.45%; three standard deviations (light, medium, and dark blue) account for 99.73%; and

four standard deviations account for 99.994%. The two points of the curve which are one standard

deviation from the mean are also the inflection points.

The central limit theorem says that the distribution of a sum of many independent, identically

distributed random variables tends towards the normal distribution. If a data distribution is

approximately normal then about 68% of the values are within 1 standard deviation of the mean

(mathematically, μ ± σ, where μ is the arithmetic mean), about 95% of the values are within two

standard deviations (μ ± 2σ), and about 99.7% lie within 3 standard deviations (μ ± 3σ). This is known

as the 68-95-99.7 rule, or the empirical rule.

For various values of z, the percentage of values expected to lie in the symmetric confidence

interval (−zσ,zσ) are as follows:

zσ percentage

1σ 68.2689492%

1.645σ 90%

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1.960σ 95%

2σ 95.4499736%

2.576σ 99%

3σ 99.7300204%

3.2906σ 99.9%

4σ 99.993666%

5σ 99.9999426697%

6σ 99.9999998027%

7σ 99.9999999997440%

In Fig: Dark blue is less than one standard deviation from the mean. For the normal distribution, this

accounts for 68.27 % of the set; while two standard deviations from the mean (medium and dark blue)

account for 95.45%; three standard deviations (light, medium, and dark blue) account for 99.73%; and

four standard deviations account for 99.994%. The two points of the curve which are one standard

deviation from the mean are also the inflection points.

The standard deviation of a data set is the same as that of a discrete random variable that can

assume precisely the values from the data set, where the point mass for each value is proportional to

its multiplicity in the data set.

The term "standard deviation" was first used in writing by Karl Pearson in 1894 following use by him in

lectures. This was as a replacement for earlier alternative names for the same idea: for

example Gauss used "mean error". A useful property of standard deviation is that, unlike variance, it

is expressed in the same units as the data.

Sigma quality level

Specification limits are the tolerances or performance ranges that customer's demand of the products

or processes they are purchasing. Figure 1.8 illustrates specification limits as the two major vertical

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lines in the figure. In the figure, LSL means the lower specification limit, USL means the upper

specification limit and T means the target value. The sigma quality level (in short, sigma level) is the

distance from the process mean (μ) to the closer specification limit. In practice, we desire that the

process mean to be kept at the target value. However, the process mean during one time period is

usually different from that of another time period for various reasons. This means that the process

mean constantly shifts around the target value. To address typical maximum shifts of the process

mean, Motorola added the shift value ±1.5 s to the process mean. This shift of the mean is used when

computing a process sigma level. From this figure, we note that a 6 sigma quality level corresponds to

a 3.4ppm rate.

Fig: Sigma quality levels of 6σ and 3σSigma level for discrete data:

Suppose two products out of 100 products have a quality characteristic which is outside of

specification limits. Then in one million parts 20,000 parts will be defects so, sigma level will be

between 3 & 4.Preciously it will come as 3.51σ. The broad classification of sigma level is shown

below-

PPM Defectives Sigma level

6,91,000 1

8

3,09,000 2

67,000 3

6,200 4

230 5

3.4 6

Product Definition:

Fig: DSLA Nozzle Assembly

Fig: Injector Assembly

Fig: Body of DSLA type nozzle

Step Turning

Guide Bore Drilling

Seat Profile Grinding

Inlet hole Drilling

Dowel hole drilling

Shoulder Turning

Pressure Chamber machining

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Sack Hole

Seat Surface

Seat- seen under Microscope

DMAIC Process in Six Sigma methodology: The most important methodology in Six Sigma management is perhaps the formalized improvement methodology characterized by DMAIC (define-measure-analyze-improve control) process. This DMAIC process works well as a breakthrough strategy. Six Sigma companies everywhere apply this methodology as it enables real improvements and real results.

Fig: Flow diagram of DMAIC methodology adopted

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Literature Survey

Case study of manufacturing Industry

Identification of problem Industry

Create solution statement

Create improvement Ideas

Implement improvement solutionsImprove

Data Collection

Define Define customer Requirements

Identify Specific problem

Set Goals

SIPOC diagram

Data Collection Plan

Measurement System Analysis

Identify variation due to measurement system

SIPOC diagram

Measure

Analyze Process Capability Analysis

Draw conclusion from data verification

Determine root causes

Map cause & effect diagram

Make needed adjustments

Monitor Improvement progress

Establish standard measures to maintain performance

Control

Scope of future work

Improvement Results

Conclusions

DEFINE PHASE:

1. Why the project? (The Business case) DSLA nozzle parts are hardened at UDA (Hardening

process) and after subsequent chamfer grinding they come at UVA (High precision internal grinding)

machines for Guide bore and Seat grinding. The seat and guide bore surface grinding is done on UVA

and then they are sent to inspection for seat visual checking. At seat visual checking section the no. of

parts getting rejected are quite high. From Jan08 to July08 average 22600 ppm (parts per million)

were rejected due to Bad seat problem (Rejection due to other reasons are not included in the scope

of the project).

Due to these rejections the first pass yield and type wise fulfillment of parts decreases. Also Due to

added seat repair operation at UVA the m/c utilization decreases and at the same time it increases

the defect cost associated with it. By successfully implementing the project we can save up to 1, 50

TINR.per month.

2. Initial project charter

Due to this current project scenario an 8 member team was formed for this project. Source of the

project is Seat repair monthly data available with inspection dept. Enclosures include Rejection

analysis of W174.

Characteristics of the project is: Seat Repair

Measures : Monthly PPM

Defect Definition : Seat visually not OK.

3. SIPOC (Supplier-Input-Process-Output-Customer):

SIPOC is a six sigma tool. The acronym SIPOC stands for Suppliers, inputs, process, outputs, and

customers. A SIPOC is completed most easily by starting from the right ("Customers") and working

towards the left.

Suppliers to UVA process are Company, TEF1, TEF2, PLP, and MSEB.

Inputs to UVA process are Man, Machine, Electricity, Drawings, and H.T. over parts, Gauges, Tooling

Compressed air, JML, Cutting oil, Check list , Instruction charts, Program etc.

Process taking place at UVA process is Internal grinding of seat surface.

Output of the UVA process are Seat Grinding over parts, Worn out tooling, Grinding muck, PMI chart,

Re-release chart.

Customers of the UVA process are Inspection, Repair process, Stores, Scrap yard, Etamic check,

Honing, Profile Grinding.

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Using this data a SIPOC diagram is created.

Fig: SIPOC for UVA (Internal grinding) process.

4. CTQ (Critical to Quality) Identification:

A CTQ tree (Critical-to-quality tree) is used to decompose broad customer requirements into more

easily quantified requirements. CTQ Tree is often used in the Six Sigma methodology.

CTQs are derived from customer needs. Customer delight may be an add-on while deriving Critical to

Quality parameters. For cost considerations one may remain focused to customer needs at the initial

stage. CTQs (Critical to Quality) are the key measurable characteristics of a product or process

whose performance standards or specification limits must be met in order to satisfy the customer.

They align improvement or design efforts with customer requirements. CTQs represent the product or

service characteristics that are defined by the customer (internal or external). They may include the

upper and lower specification limits or any other factors related to the product or service. A CTQ

usually must be interpreted from a qualitative customer statement to an actionable, quantitative

business specification. To put it in layman's terms, CTQs are what the customer expects of a

product... the spoken needs of the customer. The customer may often express this in plain English,

but it is up to us to convert them to measurable terms using tools such as DFMEA, etc.

The requirements of the output of the process and measures of Critical Process Issues are collected

as CTQ’s. They have to be derived from customer/business requirements, risks, economics, and

regulations. CTQ’s could be the combination of CTB’s or CTC’s, where CTB means Critical to

Business and CTC means Critical to Customer.

The CTQ tree is generated because it-

SUPPLIER INPUT PROCESS OUTPUT CUSTOMER

CompanyElectricity

MaintenanceTEF1

Purchase

ManMachineElectricityDrawingsH.T. over partsGauges, ToolingCompressed airJML ,Cutting oilCheck list Instruction chartsProgram

UVA process

High Precision InternalGrindingProcess

Seat Grinding over partsWorn out toolingGrinding muckPMI chartRe-release chart

InspectionRepair process

Stores Scrap yard

Etamic check,Honing

Profile Grinding

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Soft Stage Operations

Hardening UVA process(High Precision

Internal Grinding)

Profile Grinding

Seat Visual Inspection

– Translates broad customer/business requirements into specific critical-to-quality (CTQ)

requirements.

– Helps the team to move from high-level (Big Y) to Specific Measurable CTC/CTB (Small “y”).

– Ensures that all aspects of the need are addressed.

CTQ tree is generated when there are Unspecific customer/business requirements or complex, broad

needs from the customer.

Steps to create CTQ diagram?

1. List customer/business needs.

2. Identify the major drivers for these needs (major means those which will ensure that the need

is addressed).

3. Break each driver into greater detail.

3. Stop the breakdown of each level when you have reached sufficient detailed information that

enables you to measure whether you meet the customer/business need or not.

Fig: CTQ tree for UVA process.

By the reference of CTQ tree there are 5 elements in UVA process seat repair. To select the right

CTQ for the project Pareto Analysis was performed on the data gathered from Jan’08 to July’08.

Pareto Analysis:

The Pareto chart was introduced in the 1940s by Joseph M. Juran, who named it after the Italian

economist and statistician Vilfredo Pareto, 1848–1923. It is applied to distinguish the “vital few from

the trivial many” as Juran formulated the purpose of the Pareto chart. It is closely related to the so

called 80/20 rule – “80% of the problems stem from 20% of the causes,” or in Six Sigma terms “80%

of the poor values in Y stem from 20% of the Xs.”

In the Six Sigma improvement methodology, the Pareto chart has two primary applications. One is for

selecting appropriate improvement projects in the define phase. Here it offers a very objective basis

for selection, based on, for example, frequency of occurrence, cost saving and improvement potential

in process performance. The other primary application is in the analyze phase for identifying the vital

few causes (Xs) that will constitute the greatest improvement in Y if appropriate measures are taken.

A procedure to construct a Pareto chart is as follows:

1) Define the problem and process characteristics to use in the diagram.

Seat repair

Guide bore repair

Taper repair

Repair

Scrap

Seat scrap

Guide bore scrap

To reduce UVA

processRepair

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2) Define the period of time for the diagram – for example, weekly, daily, or shift. Quality

improvements over time can later be made from the information determined within this step.

3) Obtain the total number of times each characteristic occurred.

4) Rank the characteristics according to the totals from step 3.

5) Plot the number of occurrences of each characteristic in descending order in a bar graph along with

a cumulative percentage overlay.

6) Trivial columns can be lumped under one column designation; however, care must be exercised

not to omit small but important items.

From this Analysis we clearly see that Seat repair is the most critical of all rejections.

Kano model of Quality:

The Kano model is a theory of product development and customer satisfaction developed in the 80's

by Professor Noriaki Kano which classifies customer preferences into five categories:

Attractive

One-Dimensional

Must-Be

Indifferent

Less the better

The Kano model offers some insight into the product attributes which are perceived to be important to

customers. The purpose of the tool is to support product specification and discussion through better

development team understanding. Kano's model focuses on differentiating product features, as

opposed to focusing initially on customer needs. Kano also produced a methodology for mapping

consumer responses to questionnaires onto his model.

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As per Kano model of Quality A CTQ specification table is generated for giving the specifications of

rejections.

CTQ MEASURE SPECIFICATION DEFECT DEFINITION KANO STATUS

G.B. Repair Monthly PPM --G.B. size out of

specificationMust Be

Seat Repair Monthly PPMSeat Damage/

Finish BadSeat visually not O.K. Less the Better

Taper bad

RepairMonthly PPM --

Taper out of

specificationLess the Better

G.B. Scrap Monthly PPM --G.B. size out of

specificationLess the Better

Seat Scrap Monthly PPM Seat Damage Seat visually not O.K. Less the Better

Fig: CTQ table

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5. FINAL PROJECT CHARTER:

BOSCH Six sigma project charterProject: To reduce UVA process (High pressure Internal grinding process) seat repair.1. Background & reasons for selecting the project :Average seat repair from Jan.08 to May.08 is 22,600 ppm.2. Aim of the project :To reduce defect cost.To increase first pass yield.To increase m/c utilization.To increase type wise fulfillment.To reduce process seat repair from 22,600 to 11,300 ppm.3. Sponsor: Mr. Klaassen Enno. 4.Team Leader : Kulkarni Rahul G.5.Team members: 6.Mr.Khiratkar Ajay 1. Mr.Ganesh B. 7. Mr.Vidwans Devendra2. Mr.Mishra Vipin 8.W174 Associates3. Mr. Deshak rahul 9.W174 Foremen4. Mr.Singhal K.P 10.W172 Associates5. Mr.Jadhav Kailash 11.W172 Foremen

6.Characteristics of output of product / process & it’s measures:

Characteristics Measures Defect Identification

Seat Repair Monthly PPM Seat visually not OK.

7. Source of the project:Monthly repair data available with inspection.

8. Benefits / Cost impact:Process seat repair reduction from 22,600 ppm. To 11,300 ppm. (Expected saving of 150 TINR per month

9. Meeting frequency:Every Thursday at 03.30pm.

10. Enclosures:Rejection analysis of W174.

Dept: MFN2 Page: 1 Revision : 0 Date: 05/08/2008

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MEASURE PHASE:

Fig: Approach to measure phase.

Creating a data collection plan: As per the approach specified a plan for collecting the base line

data is created. It is given below.

Data Collection Plan Action: Data collection from Seat Rejection

What question do you want to answer? Body seat visually OK?

Data Operational definition and procedures

WhatMeasure type/

data type

How

measured

Related

conditions to

record

Sampling

notes

How/where

recorded

(Attached form)

Seat defects Discrete data visually lot wise 100% --

Fig: Data collection plan

During weekly project meeting it was decided to change the format for recording of parts checked at

seat visual section as it was outdated. So with the help of line foremen new format was developed by

brainstorming. It is as follows:

New format developed for Seat visual section:

Collect baseline data on defects & possible causes

Develop a sampling strategy

Validate your measurement system using Gauge R & R.

Analyze patterns in data

Determine process capability

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Ungroundseat

No sack hole

Rubbing at sack hole

PatchesRingsBad

FinishTypeLot No.Scrap

Seat DefectsItem No.

QtyRejected

Qty.OK

Qty.Inspected

Token No: Name_________________________ShiftDate

BOSCH Nashik plant

Ungroundseat

No sack hole

Rubbing at sack hole

PatchesRingsBad

FinishTypeLot No.Scrap

Seat DefectsItem No.

QtyRejected

Qty.OK

Qty.Inspected

Token No: Name_________________________ShiftDate

BOSCH Nashik plant

Segregation of defects observed at seat visual section:

Pareto Analysis of Seat rejections:

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162779219215025665058

00044331782/9/2008Day-20001483712991/9/2008Day-1901669553216330/08/08Day-180000633810128/08/08Day-1700018632119270808Day-1600016948219226/08/08Day-15

00067011318925/08/08Day-14200129020430823/08/08Day-1310089011521422/08/08Day-120100957417020/08/08Day-11720056208519/08/08Day-1010004324618/08/08Day-9

280572939045017/08/08Day-81004788016314/08/08Day-7

100224204713/08/08Day-6

0102516541660712/8/2008Day-5

012412440646111410/8/2008Day-4

0029631001748/8/2008Day-3

11561721823677/8/2008Day-2

0110932773726/8/2008Day-1

Rubbing at sack hole end

due to burr

No sackhole

Ungroundseat

PatchesRingsBad finish

(rough surface)Total no. of

parts checkedDateDay count

162779219215025665058

00044331782/9/2008Day-20001483712991/9/2008Day-1901669553216330/08/08Day-180000633810128/08/08Day-1700018632119270808Day-1600016948219226/08/08Day-15

00067011318925/08/08Day-14200129020430823/08/08Day-1310089011521422/08/08Day-120100957417020/08/08Day-11720056208519/08/08Day-1010004324618/08/08Day-9

280572939045017/08/08Day-81004788016314/08/08Day-7

100224204713/08/08Day-6

0102516541660712/8/2008Day-5

012412440646111410/8/2008Day-4

0029631001748/8/2008Day-3

11561721823677/8/2008Day-2

0110932773726/8/2008Day-1

Rubbing at sack hole end

due to burr

No sackhole

Ungroundseat

PatchesRingsBad finish

(rough surface)Total no. of

parts checkedDateDay count

Measurement System Analysis:

A Measurement System Analysis, abbreviated MSA, is a specially designed experiment that seeks

to identify the components of variation in the measurement.

Just as processes that produce a product may vary, the process of obtaining measurements and data

may have variation and produce defects. A Measurement Systems Analysis evaluates the test

method, measuring instruments, and the entire process of obtaining measurements to ensure the

integrity of data used for analysis (usually quality analysis) and to understand the implications of

measurement error for decisions made about a product or process. MSA is an important element

of Six Sigma methodology and of other quality management systems.

MSA analyzes the collection of equipment, operations, procedures, software and personnel that

affects the assignment of a number to a measurement characteristic.

A Measurement Systems Analysis considers the following:

Selecting the correct measurement and approach

Assessing the measuring device

Assessing procedures & operators

Assessing any measurement interactions

Calculating the measurement uncertainty of individual measurement devices and/or

measurement systems

Common tools and techniques of Measurement Systems Analysis include: calibration studies, fixed

effect ANOVA, components of variance, Attribute Gage Study, Gage R&R, ANOVA Gage R&R,

Destructive Testing Analysis and others. The tool selected is usually determined by characteristics of

the measurement system itself.

Accuracy and Precision:

In the fields of engineering, industry and statistics, accuracy is the degree of closeness of

a measured or calculated quantity to its actual (true) value. Accuracy is closely related to precision,

also called reproducibility or repeatability, the degree to which further measurements or calculations

show the same or similar results. The results of calculations or a measurement can be accurate but

not precise, precise but not accurate, neither, or both. A measurement system or computational

method is called valid if it is both accurate and precise. The related terms are bias (non-random or

directed effects caused by a factor or factors unrelated by the independent variable)

and error (random variability), respectively.

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Fig: Accuracy indicates proximity to the true value, precision to the repeatability or reproducibility of

the measurement.

Accuracy versus precision:

Accuracy is the degree of veracity while precision is the degree of reproducibility. The analogy used

here to explain the difference between accuracy and precision is the target comparison. In this

analogy, repeated measurements are compared to arrows that are shot at a target. Accuracy

describes the closeness of arrows to the bull’s eye at the target center. Arrows that strike closer to the

bull’s eye are considered more accurate. The closer a system's measurements to the accepted value,

the more accurate the system is considered to be.

To continue the analogy, if a large number of arrows are shot, precision would be the size of the arrow

cluster. (When only one arrow is shot, precision is the size of the cluster one would expect if this were

repeated many times under the same conditions.) When all arrows are grouped tightly together, the

cluster is considered precise since they all struck close to the same spot, if not necessarily near the

bull’s eye. The measurements are precise, though not necessarily accurate.

Fig: High accuracy, but low precision Fig: High precision, but low accuracy

However, it is not possible to reliably achieve accuracy in individual measurements without precision-

if the arrows are not grouped close to one another, they cannot all be close to the bull’s eye.

(Their average position might be an accurate estimation of the bull’s eye, but the individual arrows are

inaccurate.) See also Circular error probable for application of precision to the science of ballistics.

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Factors affecting MSA include:

Equipment: measuring instrument, calibration, fixturing, etc

People: operators, training, education, skill, care

Process: test method, specification

Samples: materials, items to be tested (sometimes called "parts"), sampling plan, sample

preparation, etc

Environment: temperature, humidity, conditioning, pre-conditioning,

Management: training programs, metrology system, support of people, support of quality

management system, etc.

ANOVA Gauge Repeatability & Reproducibility: (GRR study)

ANOVA Gauge R&R (or ANOVA Gauge Repeatability & Reproducibility) is a Measurement Systems

Analysis technique which uses Analysis of Variance (ANOVA) model to assess a measurement

system. The evaluation of a measurement system is not limited to gauges (or gages) but to all types

of measuring instruments, test methods, and other measurement systems.

ANOVA Gauge R&R measures the amount of variability induced in measurements that comes from

the measurement system itself and compares it to the total variability observed to determine the

viability of the measurement system. There are several components affecting a measurement system

including:

Measuring instruments, the gauge or instrument itself and all mounting blocks, supports,

fixtures, load cells etc. The machine ease of use, sloppiness among mating parts, "zero" blocks

are examples of sources of variation in the measurement system;

Operators (people), the ability and/or discipline of a person to follow the written or verbal

instructions.

Test methods, how to setup your devices, how to fixture your parts, how to record the data, etc.

Specification, the measurement is reported against a specification or a reference value. The

range or the engineering tolerance does not affect the measurement, but is an important factor

affecting the viability of the measurement system.

Parts (what is being measured), some items are easier to measure than others. A measurement

system may be good for measuring steel block length but not for measuring rubber pieces.

21

There are two important aspects on a Gauge R&R:

1. Repeatability is the variation in measurements taken by a single person or instrument on the

same item and under the same conditions. A measurement may be said to be repeatable when this

variation is smaller than some agreed limit. Repeatability conditions include:

the same measurement procedure

the same observer

the same measuring instrument, used under the same conditions

the same location

Repetition over a short period of time.

The repeatability coefficient is a precision measure which represents the value below which the

absolute difference between two repeated test results may be expected to lie with a probability of

95%. The standard deviation under repeatability conditions is part of precision and accuracy.

2. Reproducibility is the variability induced by the operators. It is the variation induced when

different operators (or different laboratories) measure the same part.

Reproducibility is one of the main principles of the scientific method, and refers to the ability of a test

or experiment to be accurately reproduced, or replicated, by someone else working independently.

The results of an experiment performed by a particular researcher or group of researchers are

generally evaluated by other independent researchers who repeat the same experiment themselves,

based on the original experimental description. Then they see if their experiment gives similar results

to those reported by the original group. The result values are said to be commensurate if they are

obtained (in distinct experimental trials) according to the same reproducible experimental description

and procedure. Reproducibility is different from repeatability, which measures the success rate in

successive experiments, possibly conducted by the same experimenters. Reproducibility relates to

the agreement of test results with different operators, test apparatus, and laboratory locations. It is

often reported as a standard deviation.

How to perform GR & R:

The Gauge R&R (GRR) is performed by measuring parts using the established measurement system.

The goal is to capture as many sources of measurement variation as possible, so they can all be

assessed and addressed. Please note that the purpose is not to "pass". A small variation reported on

a GRR may be because an important source of error was missed during the study.

To capture reproducibility errors, multiple operators are needed. Some (ASTM code) call for at least

ten operators (or laboratories) but others use only 2 or 3 to measure the same parts. To capture

repeatability errors, the same part is usually measured several times per operator. To capture

interactions of operators with parts (e.g. one part may be more difficult to measure than other), usually

between 5 and 10 parts are measured.

22

There is not a universal criteria for minimum requirements for the GRR matrix, being up to the Quality

Engineer to assess risks depending on how critical the measurement is and how costly they are. The

30x2x2 (30 parts, 2 operators, 2 repetitions) is acceptable for some studies, although it has very few

degrees of freedom for the operator component. Several methods of determining the sample size and

degree of replication are available

In this project GRR study I along with a quality over checker took 30 parts and checked its angle

twice. The recorded measurements were fed to standard Minitab software and the results obtained

are as follows:

If GRR <10 Gauge is acceptableIf 10<GRR<30 Gauge is conditionally acceptableIf 30<GRR Gauge is unacceptable & must be replaced/modified.

Misconceptions about GR & R:

Need only one GRR per family of gauges. It is usual to say "There is an acceptable GRR for

this caliper". This statement is false, as a GRR is for the measurement system, which includes the

part, specification, operator and method. As an example, measuring a steel block with a caliper

may be achieved with a good precision, but the same caliper may not be suitable to measure soft

rubber parts that may deform while it is being measured.

The GRR will not pass using parts, so it has to be done with standard weights and blocks.

The GRR done in this way will assess the precision while measuring standard weights. The

device might not be suitable to measure that specific type of parts. If the part "changes" while

being measured, this has to be counted as a measurement system error.

Need to report on PPAP documentation GRR results for everything that is measured. This

is not necessarily a requirement. The Quality Engineer usually makes an educated assessment. If

the characteristic is critical to safety, a valid GRR is required. Instead, if there is enough

understanding that some particular part is easy to measure with acceptable precision, a formal

GRR is not required. Customers may ask for additional GRRs during PPAP reviews. Knowing that

a GRR is not good and still uses the measurement system does not make sense. This is like

using bent calipers to get measurements, you get a number but it does not mean anything.

Performing a GRR is very expensive. To perform a GRR usually a number of parts (sometimes

between 5 to 10) is required to be measured by at least 3 operators (some suggest ten or more) 2

to 3 times. So the measurement costs are the ones associated with those additional

measurements. For simple devices this may not be very costly, and the results is a known

measurement error that can be used to assess all measurements subsequent to that. The costs

can be higher for destructive testing.Measuring Table-20249 Measuring Table-19389

Gage R & R 18.82 13.23

No. Of Distinct Categories 8 10

23

GRRs must be within 10% to pass. There are AIAG guidelines for GRR errors relative to the

specification, and what to report on a PPAP process. The final call is between the supplier and

customer, and it is a function of the criticality of the characteristic and the assessed measurement

error. GRR is a tool that helps making this assessment, but it does not give you the answer.

Process Capability Analysis

Process capability analysis was performed to find out the actual state of the process.

Minitab was used to draw a process capability analysis curve for Seat Rejections measured over a

month. As the data is discrete the Sigma level what we get is in terms of PPM (Defective Parts per

Million Opportunities)The Minitab output obtained for the Analysis is shown below.

Fig 8: Process Capability analysis of Seat visual process before

Implementing DMAIC methodology

From Results the PPM Def level is 22,624 (i.e.22, 624 Defectives in 1 Million parts.)

The below table shows different Sigma levels for PPM rejections.

PPM Defectives Sigma level

6,91,000 1

3,09,000 2

67,000 3

6,200 4

230 5

3.4 6

Fig: PPM defectives & Sigma level Comparison

By doing interpolation between 3 & 3σ levels the Sigma level of the Seat visual process comes out to

be 3.5 Sigma.

ANALYZE PHASE:

24

To analyze the defects and its generation, the tool of brainstorming is used. The suspected Sources

of variations were identified using tool of tree diagram.

Brainstorming: It is a group creativity technique designed to generate a large number of ideas for

the solution of a problem. The method was first popularized in the late 1930s by Alex Faickney

Osborn in a book called Applied Imagination. Osborn proposed that groups could double their creative

output with brainstorming. Four basic rules were followed in brainstorming. These ware intended to

reduce social inhibitions among group’s members, stimulate idea generation, and increase overall

creativity of the group.

1. To keep Focus on quantity: This rule was a means of enhancing divergent production, aiming

to facilitate problem solving through the maxim, quantity breeds quality. The assumption is that

the greater the number of ideas generated, the greater the chance of producing a radical and

effective solution.

2. To withhold criticism: In brainstorming, criticism of ideas generated was put 'on hold'. Instead,

participants focused on extending or adding to ideas, reserving criticism for a later 'critical stage'

of the process. By suspending judgment, participants feel free to generate unusual ideas.

3. Welcome unusual ideas: To get a good and long list of ideas, unusual ideas were welcomed.

They can be generated by looking from new perspectives and suspending assumptions. These

new ways of thinking may provide better solutions.

4. To combine and improve ideas: Good ideas may be combined to form a single better good

idea, as suggested by the slogan "1+1=3". It is believed to stimulate the building of ideas by a

process of association.

Method: Method followed during brainstorming is as follows-

Set the problem: Before a brainstorming session, it is critical to define the problem. The problem

must be clear, not too big, and captured in a specific question. If the problem is too big, the facilitator

should break it into smaller components, each with its own question.

Create a background memo: The background memo is the invitation and informational letter for the

participants, containing the session name, problem, time, date, and place. The problem is described

in the form of a question, and some example ideas are given. The memo is sent to the participants

well in advance, so that they can think about the problem beforehand.

Select participants: The facilitator composes the brainstorming panel, consisting of the participants

and an idea collector. A group of 10 or fewer members is generally more productive. Many variations

are possible but the following composition is suggested.

Several core members of the project who have proved themselves.

Several guests from outside the project, with affinity to the problem.

One idea collector who records the suggested ideas.

25

Create a list of lead questions: During the brainstorm session the creativity may decrease. At this

moment, the facilitator should stimulate creativity by suggesting a lead question to answer, such

as Can we combine these ideas? Or How about looking from another perspective? It is best to

prepare a list of such leads before the session begins.

The process

Participants who have ideas but were unable to present them are encouraged to write down

the ideas and present them later.

The idea collector should number the ideas, so that the chairperson can use the number to

encourage an idea generation goal, for example: We have 44 ideas now, let’s get it to 50!.

The idea collector should repeat the idea in the words he or she has written verbatim, to

confirm that it expresses the meaning intended by the originator.

When more participants are having ideas, the one with the most associated idea should have

priority. This to encourage elaboration on previous ideas.

During a brainstorming session, managers and other superiors may be discouraged from

attending, as it may inhibit and reduce the effect of the four basic rules, especially the generation

of unusual ideas.

Evaluation

Brainstorming is not just about generating ideas for others to evaluate and select. Usually the group

itself will, in its final stage, evaluate the ideas and select one as the solution to the problem proposed

to the group.

The solution should not require resources or skills the members of the group do not have or

cannot acquire.

If acquiring additional resources or skills is necessary, that needs to be the first part of the

solution.

There must be a way to measure progress and success.

The steps to carry out the solution must be clear to all, and amenable to being assigned to the

members so that each will have an important role.

There must be a common decision making process to enable a coordinated effort to proceed,

and to reassign tasks as the project unfolds.

There should be evaluations at milestones to decide whether the group is on track toward a

final solution.

There should be incentives to participation so that participants maintain their efforts.

26

Fig: Tree diagram created from brainstorming session for Input part parameters

27

Chamfer height variation.

Acqueous Cleaning not okJet broken,Pump pressure less

Uneven chamfer band

Guide to shaft TR not okGuide to shaft TR not checked after TBT as per freq.

TR more than 100 microns

Measure by gauge

Vibrations & chatter marks on seat in soft stage

Roundness, Straightness, Guide bore to seat TR

No specification in drawing

UVA PROCESS REPAIR &

SCRAP

Seat repair

Rough finish, Rings, Patches, No sack hole,Rubbing at sack hole, Unground seat

I/P parts

100% sack hole checking poka yoke on all 5 spinner

Possibility of poka yoke failure

Parts without sack hole from soft

stage

Sack hole Drill breakage on Retco

Poka yoke not working properly

Type Mix-up ( P type in DSLA & vise versa

Possibility on all operations during lot change, 80% on Benzinger, ECM(10%), Remaining 10%

Manual element

Guide bore to shaft T.R bad

Guide to shaft TR not checked after TBT as per freq.

TR more than 100 microns

Seat TR wrt guide bore

On spinner & retco m/cmore than 70 microns

Seat angle in soft stage

On spinner & retco m/cspecification 58.8° (+/- 0.2°)

More/less than spec.

Chamfer mandrel angle in hard stage

More/less than spec.

Fig: Tree diagram due to machine related parametersFrom two tree diagrams created above it is clear that there are 7 parameters related to input part

parameters & 23 machine related parameters. To know the impact of each parameter on seat

rejections it was necessary to validate each parameter using statistical methods. In Six Sigma method

used for root cause validation is Hypothesis testing.

Statistical hypothesis testing:

A statistical hypothesis test is a method of making statistical decisions using experimental data. It is

sometimes called confirmatory data analysis. In frequency probability, these decisions are almost

always made using null-hypothesis tests; that is, ones that answer the question assuming that the null

hypothesis is true, what is the probability of observing a value for the test statistic that is at least as

extreme as the value that was actually observed? The use of hypothesis testing is deciding whether

experimental results contain enough information to cast doubt on conventional wisdom.

Null hypothesis (H0) formally describes some aspect of the statistical behaviour of a set of data; this

description is treated as valid unless the actual behaviour of the data contradicts this assumption.

Thus, the null hypothesis is contrasted against another hypothesis. Statistical hypothesis testing is

used to make a decision about whether the data contradicts the null hypothesis: this is called

significance testing. A null hypothesis is never proven by such methods, as the absence of evidence

against the null hypothesis does not establish it. In other words, one may either reject, or not

reject the null hypothesis; one cannot accept it. Failing to reject it gives no strong reason to change

28

decisions predicated on its truth, but it also allows for the possibility of obtaining further data and then

re-examining the same hypothesis.

Alternative hypothesis is always set out for a particular significance test in conjunction with a null

hypothesis. Although in some cases it may seem reasonable to consider the alternative hypothesis as

simply the negation of the null hypothesis, this would be misleading. In fact, significance testing and

statements about hypotheses always take place within the context of a set of assumptions (which may

unfortunately be unstated). This provides a way of considering alternative hypotheses which are the

negation of the null hypothesis within the context of the overall assumptions. However not all

alternative hypotheses are of this "negation type": the simplest cases are directional hypotheses. An

important case arises in testing for differences across a number of different groups, where the null

hypothesis may be "no difference across groups" with the alternative hypothesis being that the mean

values for the groups would be in a certain pre-specified order. In the theory of statistical hypothesis

testing, the triple of "assumptions", "null hypothesis" and "alternative hypothesis" provides the basis

for choosing an appropriate test statistic.

Example

For example, one may want to compare the test scores of two random samples of men and women,

and ask whether or not one group (population) has a mean score (which really is) different from the

other. A null hypothesis would be that the mean score of the male population was the same as the

mean score of the female population:

H0 : μ1 = μ2

Where:

H0 = the null hypothesis

μ1 = the mean of population 1, and

μ2 = the mean of population 2.

Alternatively, the null hypothesis can postulate (suggest) that the two samples are drawn from the

same population, so that the variance and shape of the distributions are equal, as well as the means.

Formulation of the null hypothesis is a vital step in testing statistical significance. One can then

establish the probability of observing the obtained data (or data more different from the prediction of

the null hypothesis) if the null hypothesis is true. That probability is what is commonly called the

"significance level" of the results.

That is, in scientific experimental design, we may predict that a particular factor will produce an effect

on our dependent variable — this is our alternative hypothesis. We then consider how often we would

expect to observe our experimental results or results even more extreme, if we were to take many

samples from a population where there was no effect (i.e. we test against our null hypothesis). If we

find that this happens rarely (up to, say, 5% of the time), we can conclude that our results support our

experimental prediction — we reject our null hypothesis.

29

P-value: In statistical hypothesis testing, the p-value is the probability of obtaining a result at least as

extreme as the one that was actually observed, assuming that the null hypothesis is true. The fact that

p-values are based on this assumption is crucial to their correct interpretation.

The lower the p-value, the less likely the result, assuming the null hypothesis, so

the more "significant" the result, in the sense of statistical significance – one often uses p-values of

0.05 or 0.01, corresponding to a 5% chance or 1% of an outcome that extreme, given the null

hypothesis. More technically, a p-value of an experiment is a random variable defined over

the sample space of the experiment such that its distribution under the null hypothesis is uniform on

the interval [0,1]. Many p-values can be defined for the same experiment.

Generally, one rejects the null hypothesis if the p-value is smaller than or equal to the significance

level,often represented by the Greek letter α (alpha). If the level is 0.05, then results that are only 5%

likely or less are deemed extraordinary, given that the null hypothesis is true.

In the above example we have:

null hypothesis (H0) — fair coin;

observation (O) — 14 heads out of 20 flips; and

Probability (p-value) of observation (O) given H0 — p(O | H0) = 0.0577 × 2 (two-tailed) =

0.1154 (percentage expressed as 11.54%).

The calculated p-value exceeds 0.05, so the observation is consistent with the null hypothesis — that

the observed result of 14 heads out of 20 flips can be ascribed to chance alone — as it falls within the

range of what would happen 95% of the time were this in fact the case. In our example, we fail to

reject the null hypothesis at the 5% level. Although the coin did not fall evenly, the deviation from

expected outcome is just small enough to be reported as being "not statistically significant at the 5%

level".

However, had a single extra head been obtained, the resulting p-value (two-tailed) would be 0.0414

(4.14%). This time the null hypothesis - that the observed result of 15 heads out of 20 flips can be

ascribed to chance alone - is rejected. Such a finding would be described as being "statistically

significant at the 5% level".

Some common misunderstandings about p-values.

1. The p-value is not the probability that the null hypothesis is true. (This false conclusion is

used to justify the "rule" of considering a result to be significant if its p-value is very small.)

In fact, frequentist statistics does not, and cannot, attach probabilities to hypotheses.

Comparison of Bayesian and classical approaches shows that a p-value can be very close to

zero while the posterior probability of the null is very close to unity. This is the Jeffreys-

Lindley paradox.

2. The p-value is not the probability that a finding is "merely a fluke." (Again, this conclusion

arises from the "rule" that small p-values indicate significant differences.)

As the calculation of a p-value is based on the assumption that a finding is the product of

chance alone, it patently cannot also be used to gauge the probability of that assumption

30

being true. This is subtly different from the real meaning which is that the p-value is the

chance that null hypothesis explains the result: the result might not be "merely a

fluke," andbe explicable by the null hypothesis with confidence equal to the p-value.

3. The p-value is not the probability of falsely rejecting the null hypothesis. This error is a version

of the so-called prosecutor's fallacy.

4. The p-value is not the probability that a replicating experiment would not yield the same

conclusion.

5. 1 − (p-value) is not the probability of the alternative hypothesis being true.

6. The significance level of the test is not determined by the p-value.

The significance level of a test is a value that should be decided upon by the agent

interpreting the data before the data are viewed, and is compared against the p-value or any

other statistic calculated after the test has been performed.

7. The p-value does not indicate the size or importance of the observed effect (compare

with effect size).

31

Validation of all SSVs using Statistical testing: (Input part parameters)

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Vib

ratio

ns

&

chat

ter

mar

ks o

n se

at in

so

ft st

age

Sr.

No. 1 2 3

32

(Input part parameters continued..)

Con

clus

ions

The

impa

ct o

f No

sack

hol

e p

arts

on

se

at r

ejec

tions

is

Sig

nif

ican

t

The

impa

ct o

f ty

pe

mix

up

on

Sea

t rej

ectio

ns is

S

ign

ific

ant.

The

impa

ct o

f S

eat a

ngle

mor

e on

sea

t rej

ect

ions

is

Insi

gn

ific

an

t

The

impa

ct o

f ch

amfe

r m

and

rel

angl

e on

se

at

reje

ctio

ns is

In

sig

nif

ica

nt

Res

ults

obt

aine

d

No

sac

k ho

le p

art

br

eaks

the

grin

din

g

wh

eel t

ip &

m/c

ge

ts im

med

iate

ly

sto

pped

, du

ring

redr

essi

ng 5

0 pa

rts

cam

e b

ad.

p-ty

pe in

DS

LA lo

t br

eaks

the

ad

apto

r& g

rindi

ng

wh

eel,

whi

ch

resu

lts in

50

bad

in

50,w

ith n

orm

al

part

s 0

bad

in 5

0.

3 ba

d in

285

ang

le

mor

e p

arts

,0

bad

in 3

00 a

ngle

ok

par

ts

As

the

re in

no

va

riatio

n in

ou

tput

sta

tistic

al

test

can

not

be

perf

orm

ed

Tes

t use

d

2 pr

opor

tions

test

2-pr

opor

tions

test

2-pr

opor

tions

test

No

va

riatio

n in

out

put

End

date

13-J

an-

09

20-N

ov-

08

28-N

ov-

08

25-D

ec-

08

Sta

rt d

ate

13-J

an-

09

20-N

ov-

08

21-N

ov-

08

25-N

ov-

08

Tria

l tak

en

On

e no

sac

k ho

le

part

wa

s pu

t on

UV

A 2

0315

& it

's

effe

ct o

n

reje

ctio

ns w

as

obse

rved

On

e ty

pe m

ix u

p p

art

wa

s pu

t o

n U

VA

203

15

& it

's

effe

ct o

n s

eat

re

ject

ion

s is

ob

serv

ed

285

part

s w

ith s

eat

angl

e m

ore

we

re p

roce

ssed

up

to

seat

vis

ual

alon

g w

ith 3

00 a

ngle

ok

par

ts

Ch

amfe

r m

andr

el

angl

es c

heck

ed b

y S

ine

bar

met

hod

& M

icro

scop

e m

etho

d

Act

ions

take

n

Co

llect

at l

eas

t 15

No

sac

k ho

le p

art

s pr

efa

rab

ly o

f DS

LA

norm

al S

haft

Co

llect

at l

eas

t 15

mix

up

par

ts

Tria

l is

take

n w

hic

h in

volv

es

seat

an

gle

mor

e

part

s ar

e p

roce

ssed

up

to s

eat v

isua

l for

ch

ecki

ng.

4 m

andr

els

giv

en to

to

ol r

oom

fo

r ch

amfe

r an

gle

verif

icat

ion

Sus

pect

ed s

ourc

es o

f va

riatio

ns(S

SV

's)

Pok

a Y

oke

pu

t off

due

to v

ario

us

reas

ons

80%

on

75%

Ben

zing

er,

10%

on

EC

M.

Man

ual e

lem

ent

may

be

pre

sent

,E

leva

tor

cond

ition

Ang

le n

ot

chec

ked

as

per

freq

uen

cy/D

rill l

ife

over

, Dri

ll re

shar

peni

ng

impr

oper

Ch

amfe

r m

andr

el

angl

e to

be

veri

fied

in to

ol

room

sub

caus

e

Pok

a y

oke

fa

ilure

on

spin

ner

mac

hin

e

Pok

a y

oke

fa

ilure

on

Re

tco

mac

hin

e

Pos

sibi

lity

on

all o

pera

tions

On

spi

nner

&

Ret

co

mac

hin

es

Mor

e o

r le

ss th

an

spec

ifica

tion

Roo

t cau

se

Par

ts

with

out

sack

hol

e fr

om s

oft

sta

ge

Par

t typ

e m

ix u

p

Sea

t ang

le

in s

oft

sta

ge

Ch

amfe

r m

andr

el

angl

e in

sof

t st

age

Sr.

N

o. 4 5 6 7

33

Actions taken for machine related parameters

conc

lusi

ons

The

impa

ct o

f wor

khea

d vi

brat

ion

on s

eat

reje

ctio

ns is

Insi

gn

ific

ant

The

impa

ct o

f Wor

khea

d rp

m

on s

eat r

ejec

tions

is

Insi

gn

ific

ant

The

impa

ct o

f Spi

ndle

he

ight

re

peat

abili

ty o

n S

eat

reje

ctio

ns is

Insi

gn

ific

ant

The

impa

ct o

f fem

ale

cent

er

grin

ding

on

seat

re

ject

ions

is In

sig

nif

ican

t

The

impa

ct o

f Job

cl

ampi

ng p

ress

ure

on

seat

rej

ectio

ns is

In

sig

nif

ican

t.

Res

ults

obt

aine

d

Wor

khea

d vi

brat

ion

valu

es

of a

ll m

achi

nes

are

with

in 3

m

m/s

ec.

At b

oth

rpm

val

ues

all

50 p

arts

cam

e vi

sual

ly o

k

At b

oth

repe

atab

ility

leve

ls a

ll pa

rts

cam

e vi

sual

ly o

k

All

part

s be

fore

doi

ng

fem

ale

cent

er g

rindi

ng

cam

e ok

, als

o al

l par

ts a

fter

doin

g fe

mal

e ce

nter

gr

indi

ng c

ame

ok

At 5

bar

pre

ssur

e 0

bad

in

50,

at 4

bar

pre

ssur

e 29

bad

in

50 p

arts

.

Tes

t use

d

No

varia

tion

outp

ut

obse

rved

No

varia

tion

in

outp

ut

obse

rved

2-pr

opor

tions

te

st

2-pr

opor

tions

te

st

2 pr

opor

tions

te

st

End

date

16-F

eb-0

9

16-F

eb-0

9

12-M

ar-0

9

12-M

ar-0

9

30-J

an-0

9

Sta

rt d

ate

13-F

eb-0

9

13-F

eb-0

9

12-M

ar-0

9

12-M

ar-0

9

30-J

an-0

9

Tria

l tak

en

Wor

khea

d vi

brat

ion

valu

es o

f al

l mac

hine

s ar

e ch

ecke

d w

ith

help

of v

ibra

tom

eter

Tak

e 50

par

ts w

ith 2

150

rpm

,ta

ke 5

0 pa

rts

with

175

0 rp

m

50 p

arts

eac

h w

ere

proc

esse

d w

ith r

epea

tabi

lity

of 1

0µ &

at

20µ

.

50 p

arts

wer

e pr

oces

sed

be

fore

doi

ng fe

mal

e ce

nter

gr

indi

ng &

50

part

s w

ere

proc

esse

d af

ter

doin

g fe

mal

e ce

nter

grin

ding

The

job

clam

ping

pre

ssur

e w

as v

arie

d ti

4 ba

r &

5 b

ar &

it'

s im

pact

on

seat

rej

ectio

ns is

ob

serv

ed.

Act

ions

take

n

Che

ck w

orkh

ead

vibr

atio

n v

alue

s of

all

mac

hine

s

Rat

ed R

PM

val

ue is

215

0

RP

M

Che

ck r

epea

tabi

lity<

20µ

, ta

ke tr

ial w

ith p

roce

ssin

g pa

rts

with

diff

eren

t re

peat

abili

ty v

alue

s.

we

chec

ked

part

s be

fore

&

afte

r do

ing

fem

ale

cent

er

grin

ding

for

chec

king

di

ffere

nce

Air

supp

ly to

job

clam

ping

is

var

ied

to d

iffer

ent l

evel

s &

it's

effe

ct w

as o

bser

ved

Sus

pect

ed

sour

ces

of

varia

tions

(SS

V's

)

Ear

lier

not

know

n

valu

e-18

00

rpm

Rep

eata

bilit

y be

low

20µ

Grin

ding

fr

eq. n

ot

deci

ded

Chu

ck c

lam

p gr

indi

ng

sub

caus

e

Vib

ratio

n

RP

M

Spi

ndle

he

ight

Fem

ale

ce

nter

Job

clam

ping

pr

essu

re

Roo

t cau

se

Wor

khea

d

Sr.

No. 1 2 3 4 5

34

(Machine related parameters continued…)

conc

lusi

ons

The

impa

ct o

f loa

ding

sp

ring

brok

en o

n se

at

reje

ctio

ns is

Sig

nif

ican

t

The

impa

ct o

f Loa

ding

al

ignm

ent o

f com

pone

nt

on s

eat r

ejec

tions

is

Insi

gn

ific

ant.

The

impa

ct o

f Air

cylin

der

on s

eat r

ejec

tions

is

Insi

gn

ific

ant.

The

impa

ct o

f Ang

le

mas

ter

on s

eat r

ejec

tions

is

Insi

gn

ific

ant.

The

impa

ct o

f sea

t vi

sual

mic

rosc

ope

cond

ition

on

seat

re

ject

ions

is

Sig

nif

ican

t.

The

impa

ct o

f Air

supp

ly

for

part

s cl

eani

ng o

n S

eat r

ejec

tions

is

Sig

nif

ican

t

Res

ults

obt

aine

d

with

ok

sprin

g al

l 50

part

s ca

me

ok, w

ith b

roke

n sp

ring

35 b

ad in

50.

with

& w

ithou

t che

ckin

g lo

adin

g al

ignm

ent a

ll 50

pa

rts

cam

e vi

sual

ly o

k

The

qui

ck h

it ac

hiev

ed

GR

R fo

und

to b

e ok

Whe

n 50

par

ts c

heck

e w

ith fa

ulty

mic

rosc

ope

35

cam

e ba

d, w

hen

they

are

ch

ecke

d w

ith o

k sc

ope

only

50

cam

e ba

d.

With

air

clea

ning

10

par

ts b

ad in

50,

w

ithou

t air

clea

ning

22

par

ts b

ad in

50.

Tes

t use

d

2 pr

opor

tions

te

st

2 pr

opor

tions

te

st

No

hypo

thes

is

test

per

form

ed

No

test

per

form

ed

2 pr

opor

tions

te

st

2 pr

opor

tions

te

st

End

dat

e

30-J

an-0

9

30-J

an-0

9

30-J

an-0

9

30-J

an-0

9

18-D

ec-0

8

30-J

an-0

9

Sta

rt d

ate

30-J

an-0

9

30-J

an-0

9

30-J

an-0

9

30-J

an-0

9

18-D

ec-0

8

30-J

an-0

9

Tria

l tak

en

Cha

ngin

g fr

eq. o

nce

in tw

o m

onth

s.

Tak

e a

tria

l with

out c

heck

ing

load

ing

alig

nmen

t of

com

pone

nt.

No

prob

lem

of a

ir le

akag

e

take

GR

R o

f sea

t an

gle

mas

ter

Sco

pe c

ondi

tion

stud

y sc

hedu

le to

be

prep

ared

A w

orks

hop

on m

icro

scop

e h

andl

ing

to b

e ar

rang

ed

50 p

arts

take

n w

ith a

ir cl

eani

ng

& 5

0 pa

rts

take

n w

ithou

t air

clea

ning

Act

ions

take

n

Load

ing

sprin

g w

as

chan

ged

with

a b

roke

n on

e &

it's

effe

ct o

n se

at

reje

ctio

ns w

as o

bser

ved

Whi

le s

ettin

g m

achi

ne

chec

k a

lignm

ent f

or o

k / N

ot o

k

Ele

ctric

al s

ervo

mot

or u

sed

Che

ckin

g fr

eq. t

o be

re

duce

d

Che

ck r

equi

rem

ent o

f fr

eque

nt v

erifi

catio

n of

m

icro

scop

e co

nditi

on

Ass

ocia

tes

awar

enes

s ab

out m

icro

scop

e ad

just

men

t to

be d

one.

Par

ts to

be

chec

ked

with

ai

r cl

eane

d &

with

out a

ir cl

eani

ng

Sus

pect

ed

sour

ces

of

varia

tions

(SS

V's

)

Cha

ngin

g fr

eq.

Vis

ual c

heck

Air

leak

age

Mas

ter

show

ing

wro

ng

read

ing

Alig

nmen

t of

bot

h ey

es

not t

here

Fre

quen

t ch

ecki

ng b

y as

soci

ates

No

supp

ly

prov

ided

sub

caus

e

Load

ing

sp

ring

wor

nout

Load

ing

al

ignm

ent o

f co

mpo

nent

Load

ing

cy

linde

r

Ang

le m

aste

r

Vis

ual

insp

ectio

n

mic

rosc

ope

Air

supp

ly

for

part

s cl

eani

ng

Roo

t cau

se

Load

ing

/ U

nloa

ding

Che

ckin

g be

nch

Sr.

No. 6 7 8 9 10   11

35

(Machine related parameters continued…)

conc

lusi

ons

The

impa

ct o

f Spi

ndle

co

olin

g o

n S

eat r

ejec

tion’

s is

In

sig

nif

ican

t

The

impa

ct o

f Spi

ndle

co

olin

g s

yste

m o

n S

eat

reje

ctio

ns is

In

sig

nif

ican

t.

The

impa

ct o

f Ini

tial

setti

ng o

n se

at r

ejec

tions

is

Sig

nif

ican

t

The

impa

ct o

f new

sea

t w

heel

set

ting

on S

eat

reje

ctio

ns is

Sig

nif

ican

t.

The

impa

ct o

f Ada

ptor

T

R o

n S

eat r

ejec

tions

is

Insi

gn

ific

ant.

The

impa

ct o

f Dre

ssin

g rin

g w

orn-

out o

n se

at

reje

ctio

ns is

Sig

nif

ican

t.

Res

ults

obt

aine

d

3 pa

rts

in b

ad 1

00 w

ith

60,0

00 r

pm,1

bad

in 1

00 w

ith

50,0

00 r

pm

Spi

ndle

coo

ling

syst

ems

of a

ll m

achi

nes

are

foun

d to

be

wor

king

ok.

Whe

n in

itial

set

ting

ok 0

bad

in

50,

whe

n in

itial

set

ting

di

stur

bed

25 b

ad in

50.

Whe

n ne

w s

eat w

heel

set

ting

ok

0 b

ad in

50,

whe

n in

itial

se

tting

not

ok

30 b

ad in

50.

whe

n T

R<

10µ

0 b

ad in

50,

whe

n T

R>

10µ

0 b

ad in

50

with

wor

nout

spr

ing

45 b

adin

50

, with

ok

ring

2 ba

d in

50.

Tes

t use

d

2 pr

opor

tions

te

st

No

test

per

form

ed

2 pr

opor

tions

te

st

2 pr

opor

tions

te

st

2 pr

opor

tions

te

st

2 pr

opor

tions

te

st

End

dat

e

30-J

an-0

9

30-J

an-0

9

30-J

an-0

9

30-J

an-0

9

30-J

an-0

9

30-J

an-0

9

Sta

rt d

ate

15-J

an-0

9

30-J

an-0

9

30-J

an-0

9

30-J

an-0

9

30-J

an-0

9

30-J

an-0

9

Tria

l tak

en

100

part

s pr

oces

sed

with

60

,000

rpm

, 100

par

ts

proc

esse

d w

ith 5

0,00

0 rp

m

All

syst

ems

chek

ced

with

Mai

nten

ance

peo

ple

Initi

al s

ettin

g pa

ram

eter

s w

ere

dist

urbe

d &

tria

l is

take

n.

The

new

sea

t whe

el

heig

ht w

as s

et a

t 3.1

5mm

&

it's

effe

ct o

n se

at

reje

ctio

ns w

as o

bser

ved.

take

50

part

s w

ith a

dapt

or

TR

<10

µ

& a

gain

take

50

part

s w

ith

adap

tor

TR

>10

µ

One

wor

n ou

t rin

g w

as

plac

ed &

whe

el w

as

dres

sed

with

that

rin

g.

Par

ts a

re ta

ken

for

tria

l.

Act

ions

take

n

Tak

e a

tria

l with

di

ffere

nt R

PM

val

ues

Che

ck w

heth

er s

pind

le

cool

ing

sys

tem

s of

all

mac

hine

s ar

e ru

nnin

g ok

Initi

al s

ettin

g w

as

dist

urbe

d &

it's

impa

ct

on s

eat r

ejec

tions

was

ob

serv

ed

New

sea

t whe

el h

eigh

t to

be

set 3

.1 m

m, t

ake

tria

l with

mor

e he

ight

.

Ada

ptor

Tr

chec

ked

ev

ery

time

mac

hine

is

dist

urbe

d &

it's

impa

ct

on s

eat r

ejec

tions

obs

erve

d

Tria

l tak

en w

hich

in

volv

es p

laci

ng a

wor

n ou

t rin

g on

Mac

hine

&

taki

ng p

arts

Sus

pect

ed

sour

ces

of

varia

tions

valu

e to

be

60,0

00 R

PM

To

be a

sked

to

mai

ntai

nanc

e

Whe

el fo

rm w

ear

Ens

ure

posi

tive

cutti

ng a

fter

dres

sing

If T

R o

ut o

f sp

ecifi

catio

n se

at

bad

com

es

If dr

essi

ng r

ing

is

wor

n ou

t, th

e gr

indi

ng w

heel

fo

rm g

ets

dam

aged

. Due

to

whi

ch p

art c

omes

se

at b

ad.

sub

caus

e

RP

M

Spi

ndle

co

olin

g

Initi

al s

ettin

g

New

sea

t w

heel

se

tting

TR

<10

µ

Per

iodi

c re

plac

emen

t &

TR

Roo

t cau

se

Grin

ding

sp

indl

es

Set

ting

para

met

ers

Ada

ptor

s

Dre

ssin

g rin

g

Sr.

No. 12 13 14 15 16 17

(Machine related parameters continued…)

36

conc

lusi

ons

The

impa

ct o

f coo

lant

sy

stem

s on

Sea

t re

ject

ions

is

Insi

gn

ific

ant.

The

impa

ct o

f pok

a yo

ke o

n S

eat

reje

ctio

ns is

S

ign

ific

ant.

The

impa

ct o

f dre

ssin

g de

pth

of c

ut o

n S

eat

rejc

tions

is

Insi

gn

ific

ant.

The

impa

ct o

f dre

ssin

g fr

eq. o

n S

eat r

ejec

tions

is

Insi

gn

ific

ant.

The

impa

ct o

f fee

d ra

te

on S

eat

reje

ctio

ns is

In

sig

nif

ican

t.

The

impa

ct o

f Ope

rato

r eq

ualiz

atio

n on

sea

t re

ject

ions

is

Sig

nif

ican

t.

Res

ults

obt

aine

d

The

coo

lant

sys

tem

pa

ram

eter

s ar

e w

ithin

lim

its

Pok

a yo

ke o

tip

1 ba

d in

50,

whe

n po

ka y

oke

not o

n tip

16

bad

in 5

0.

0 ba

d in

50

with

3µ

de

pth

of c

ut.

0 ba

d in

50

with

2µ

de

pth

of c

ut.

0 ba

d in

50

with

6

part

s fr

eq. 0

bad

in 5

0 w

ith 8

par

ts fr

eq.

with

100

% fe

ed r

ate

all

50 p

arts

okw

ith 5

0 %

fe

ed r

ate

all 5

0 pa

rts

ok a

gain

.

Due

to c

ontin

uous

re

ject

ions

from

as

sem

bly

sect

ion

fear

is

set

in v

isua

l op

erat

ors.

Tes

t use

d

2 pr

opor

tions

te

st

2 pr

opor

tions

te

st

2 pr

opor

tions

te

st

2 pr

opor

tions

te

st

2 pr

opor

tions

te

st

2 pr

opor

tions

te

st

End

dat

e

30-J

an-0

9

30-J

an-0

9

30-J

an-0

9

30-J

an-0

9

30-J

an-0

9

30-J

an-0

9

Sta

rt d

ate

30-J

an-0

9

30-J

an-0

9

30-J

an-0

9

30-J

an-0

9

30-J

an-0

9

30-J

an-0

9

Tria

l tak

en

Onl

y ch

ecki

ng is

invo

lved

as

taki

ng a

tria

l is

very

da

nger

ous.

50 p

arts

take

n w

hen

poka

yo

ke o

n tip

, aga

in 5

0 pa

rts

take

n w

ith p

oka

yoke

in

back

swor

d po

sitio

n.

Tak

e pa

rts

with

3µ

dep

th o

f cu

t.T

ake

part

s w

ith 2

µ d

epth

of

cut.

Tak

e 50

par

ts w

ith 8

par

ts

dres

sing

freq

. Aga

in ta

ke 5

0 pa

rts

with

6 p

arts

dre

ssin

g fr

eq.

Tak

e pa

rts

with

50%

feed

ra

te,

Tak

e pa

rts

with

100

% fe

ed

rate

.

50 b

orde

r ca

se p

arts

wer

e sh

own

to o

pera

tors

& th

ey

wer

e sh

own

to a

ssem

bly

oper

ator

s.

Act

ions

take

n

Che

ck p

ress

ure,

te

mpe

ratu

re

of c

oola

nt s

yste

m

Pok

a yo

ke w

as s

hifte

d to

ba

ckw

ard

posi

tion

& it

s ef

fect

on

seat

rej

ectio

ns

was

obs

erve

d.

Dre

ssin

g de

pth

of c

ut is

var

ied

& tr

ial i

s ta

ken

Dre

ssin

g fr

eq. c

hang

ed &

tria

l is

take

n

The

feed

rat

e w

as

chan

ged

man

ually

& it

's

effe

ct o

n se

at r

ejec

tions

is

obse

rved

.

Dai

ly r

ejec

tions

at s

eat

visu

al is

che

cked

for

verif

icat

ions

Sus

pect

ed s

ourc

es

of v

aria

tions

(SS

V's

)

The

dre

ssin

g/

Grin

ding

pr

essu

re v

arie

s

Con

firm

atio

n of

po

ka y

oke

once

in

a s

hift

3 m

icro

ns

6 pa

rts

Man

ual k

nob

pres

ent

Inco

rrec

t dec

isio

n du

e to

fear

of g

ettin

g re

ject

ed

from

ass

embl

y.

sub

caus

e

3.5

to 4

bar

gr

indi

ng/

dres

sing

co

olan

t

Tip

bre

akag

e se

nsin

g po

ka

yoke

Dre

ssin

g de

pth

of c

ut

Dre

ssin

g fr

eq.

Fee

d ra

te

Lack

of

oper

ator

eq

ualiz

atio

n

Roo

t cau

se

Coo

lant

sy

stem

s

Grin

ding

w

heel

Grin

ding

pr

ogra

m

Ope

rato

r

Sr.

No. 18 19 20 21 22 23

Ishikawa Diagram for Major defects:

37

Ishikawa diagrams (also called fishbone diagrams or cause-and-effect diagrams) are diagrams that

show the causes of a certain event. Ishikawa diagrams were proposed by Kaoru Ishikawa in the

1960s, who pioneered quality management processes in the Kawasaki shipyards, and in the process

became one of the founding fathers of modern management. It was first used in the 1960s, and is

considered one of the seven basic tools of quality management, along with the histogram, Pareto

chart, check sheet, control chart, flowchart, and scatter diagram. It is known as a fishbone diagram

Causes in the diagram are often based on a certain set of causes, such as the 6 M's, described

below. Cause-and-effect diagrams can reveal key relationships among various variables, and the

possible causes provide additional insight into process behavior. Causes in a typical diagram are

normally grouped into categories, the main ones of which are:

The 6 M's

Machine, Method, Materials, Maintenance, Man and Mother Nature (Environment): Note: a more

modern selection of categories is Equipment, Process, People, Materials, Environment, and

Management.

Causes should be derived from brainstorming sessions. Then causes should be sorted through

affinity-grouping to collect similar ideas together. These groups should then be labeled as categories

of the fishbone. They will typically be one of the traditional categories mentioned above but may be

something unique to our application of this tool. Causes should be specific, measurable, and

controllable. Most Ishikawa diagrams have a box at the right hand side, where the effect to be

examined is written. The main body of the diagram is a horizontal lines from which stem the general

causes, represented as "bones". These are drawn towards the left-hand side of the paper and are

each labeled with the causes to be investigated—often brainstormed beforehand—and based on the

major causes listed above.

Off each of the large bones there may be smaller bones highlighting more specific aspects of a certain

cause, and sometimes there may be a third level of bones or more. These can be found using the '5

Whys' technique. When the most probable causes have been identified, they are written in the box

along with the original effect. The more populated bones generally outline more influential factors, with

the opposite applying to bones with fewer "branches". Further analysis of the diagram can be

achieved with a Pareto chart.

38

Fig: Cause & Effect diagram for majority of defects

The Five elements of Fish bone diagram generated during Brainstorming session are:

Man:

Motivation less in workmen due to incentive less.

New operator working in area

Negligence during night shift

Lack of Awareness among operators

Machine:

Frequent Breakdowns, causing increase in vibration level

Detection of Defects is not effective

Coolant pressure varies abruptly

No Poka Yoke present to detect Drill breakage which causes ring formation

Material:

Tool quality not up to the mark, drill life less

Drill breakage due to drill overuse

In coming quality of parts not ok (Part bend which causes drill breakage)

Checking frequency is less

Method:

Gauges are not calibrated on daily basis

Elevator which lifts the part to chuck gets jammed causing part damage

Work instructions are over dated

Program corrections are complex during type change

Environment:

Machine is near to open window which causes dirt accumulation on part which damages

surface during grinding.

Bar chart

39

The ideas generated during Brainstorming session were verified by Process Experts and the causes

having positive impact on rejections were listed out. Bar chart analysis was performed on these

parameters to know the causes which have significant impact on rejections.

Causes & their contribution in Rejections

21

45

158 11

05

101520253035404550

Drill overuse No PokaYoke presentto detect Drill

breakage

Gauges notcalibrated on

time

Coolantpressure

varies

Others

Causes

% R

ejec

tio

ns

% wise causes

Fig 11: Bar Chart for Significant parameters

Chart clearly indicates that some system for early detection of Drill breakage needs to be

developed.

Causes & their contribution in Rejections

21

45

158 11

05

101520253035404550

Drill overuse No PokaYoke presentto detect Drill

breakage

Gauges notcalibrated on

time

Coolantpressure

varies

Others

Causes

% R

ejec

tio

ns

% wise causes

Fig: Bar chart for causes & their contribution

IMPROVE PHASE:

40

A) Detection of drill breakage on machine:

To reduce rejections which were caused by drill breakage, a new Laser sensor was installed on

machine and its feedback was given to PLC logic of machine. When tip of drill is Ok Laser falls on drill

& gets distracted, ensuring the machine to run continuously. This Tip Breakage Sensor (TBS) was

installed such that it overlaps with part loading, so change in cycle time due to Sensor installation is

zero.

Fig 12: Tool breakage sensing Poka Yoke with OK drill mounted on machine

Fig 13: Tool breakage sensing Poka Yoke when tip of drill is broken

After successfully implementing this on one pilot machine, there was horizontal deployment of this

Poka yoke on all 8 machines.

B) Drill overuse by operator:

When 5 why Analysis was done for this problem, it was found that the new drills were issues from

stores on monthly basis, so at the end of every month drill overuse was a common problem. It was

decided to top-up drill shortage on every Saturday of week so as to maintain drill float on the line. Line

foremen were given clear instructions about drill records maintenance. Accurate drill

breakage/obsolescence is maintained and this point is added to Surprise audit committee.

C) Gauges & Microscopes are not calibrated on time:

For this cause a team of operators was formed to escalate the matter immediately when gauges are

not calibrated. Also calibration work was equally divided among quality people who calibrate gauges

once in three days.

D) Coolant pressure varies:

For this cause complete hydraulic circuit was checked for leakage. The team found that on Flow

control valve was faulty (worn out). The team insisted to change every valve of the circuit and

41

complete hydraulic circuit connections were changed with new one. Due to this major action the

leakage completely stopped. The coolant pressure variation problem is completely eliminated.

E) Others:

For all other causes following actions are taken-

Window responsible for dirt accumulation was permanently closed & one exhaust fan was

installed at that place.

For new operator coming in area training sessions & supervision by skilled operators was

made compulsory.

Warning letters were issued for negligence from operators.

New & updated work instructions were put on machine boards.

CONTROL PHASE:

This phase defines control plans specifying process monitoring and corrective actions. It ensures that

the new process conditions are documented and monitored. All possible causes of specific identified

problems from the analysis phase were tackled in the control phase. Control solutions to identified

problems have been prepared in sequence to the improvements as explained above. This will prevent

the problems from recurring. The proposed control solutions to improve the previous solutions are

listed in sequence as follows.

A) Drill breakage Poka Yoke:

A Poka yoke monitoring sheet is maintained by shop. One shop Forman daily checks that all Poka

Yoke are working correctly & records it on a check sheet. A clear escalation model for problem

reporting is prepared for Poka Yoke failure.

B) Drill overuse by operators:

As weekly drill quantity top-up is done, it automatically ensures that every week drill quantity is verified

for shortage. A record sheet is maintained to keep all drills records.

C) Gauges calibration:

This issue was taken seriously by quality department & they have assigned special audit team to

ensure that gauges are calibrated on time.

D) Coolant pressure:

For all hydraulic circuits in shop, one preventive maintenance program is prepared. Operators are

given authorities to stop machine if leakage is found on it.

E) Operator related issues:

All operator related issues were taken to Worker Union and after their consent it is decided to take

strict action against the operator negligence is company.

42

RESULTS:

After completing the DMAIC methodology of Six Sigma, again the process capability Analysis was

done to know the improvement in Sigma level. One month data on Control phase was taken for the

Analysis.

Figure 14: Process capability of seat visual process after applying DMAIC methodology

From the Minitab output it is clear that PPM defect level is reduced from 22,624 ppm to 11,031 ppm.

And Sigma level is Improved from 3.5σ to 3.79 σ.

Rejections in PPM

11039

22,624

0

5,000

10,000

15,000

20,000

25,000

PPM rejections before Project PPM rejections after DMAICProject

PP

M l

evel

Rejections in PPM

Fig 15: Results showing improvement in Sigma level of the process

A few more agreed recommendations are still to be implemented during plant shut down. The

estimated savings from the project after the implementation of all recommendations are expected to

be 1, 50,000Rs per Annum.

43

Sigma level-3.5σ

Sigma level improved to 3.79σ

CONCLUSIONS:

The immediate goal of Six Sigma is defect reduction. Reduced defects lead to yield improvement;

higher yields improve customer satisfaction. The ultimate goal is enhanced net income. The money

saved is often the attention getter for senior executives. It has a process focus and aims to highlight

process improvement opportunities through systematic measurement. Six Sigma defect reduction is

intended to lead to cost reduction. Six sigma is a toolset, not a management system and can be used

in conjunction with other comprehensive quality standards present in the industry. The application of

Six Sigma technique for this project shows that company has taken a small step towards Six Sigma

Implementation on Company wide basis. Once Six Sigma finds its rightful place in the minds of higher

management, enormous gains can always be expected from its application. It is clear that the Six

Sigma methodology is highly beneficial to improve the performance of any manufacturing plant.

44

References

1) Kumar, P. (2002) “Six Sigma in manufacturing”, Productivity Journal, Vol. 43, No. 2, pp.196–

202.

2) Harry, M.J. and Schroeder, R. (1999) “Six Sigma: The Breakthrough Management Strategy

Revolutionizing the Worlds Top Corporations”, New York, NY: Double Day.

3) Henderson, K.M. and Evans, J.R. (2000) “Successful implementation of Six Sigma:

benchmarking: General Electric Company”, Benchmarking: An International Journal, Vol. 7,

No. 4, pp.260–281.

4) Mathew.H, Barth.B, and Sears.B, (2005) “Leveraging Six Sigma discipline to drive

improvement”, Int. J. Six Sigma and Competitive Advantage, Vol. 1, No. 2, pp.121–133.

5) Park, S.H. (2002) “Six Sigma for productivity improvement: Korean business corporations”,

Productivity Journal, Vol. 43, No. 2, pp.173–183.

45