Six Sigma Concept

6 Sigma QUALITY

The Need

With the advent of Globalization every organization relentlessly pushes themselves for the processes that gives them error free products in a competitive environment. With the growth of competitive environment - „error free products‟ acts as a Qualifying parameter only. However the survival demands more.

What is more? More‟ is defined by the Customer and the competitive environment. - Critical to Customer - Critical to Delivery - Critical to Cost - Critical to Process etc.

6 Sigma QUALITY

Early Growth

Motorola suffered a severe downturn when a Japanese manufacturer invaded the chip market in 1985-86. In 1987, Motorola started process improvement projects to counter the invasion. Initial finding was the process cycle time, but quality fell off the radar. Later the managers began to recognized the quality problem was due to wait times, inventory mismanagement etc. Motorola‟s biggest customer Ford Motors took considerable interest in the process improvement and Six Sigma kick-off. Lots of people participated in the invention of Six sigma over a long period of time. The Six Sigma methodology package was rolled out in 1987-88. Motorola was honored with Malcolm Baldrige National Quality Award in 1988.

6 Sigma

Project Priority

Known

Solution

Unknown

QUALITY

Priority #3

Priority #1

Cause Known Solution Unknown

Cause Unknown Solution Unknown

Priority #2 Cause Known Solution Known

Cause Unknown Solution Known

Known

Unknown

Cause

6 Sigma QUALITY

Methodology

Control – to guarantee performance

Define – what‟s important

Improve – by fixing what‟s wrong

Measure – how we‟re doing

Analyze – what‟s wrong

6 Sigma QUALITY

Parallel-Lean Six Sigma

Lean Six Sigma Analyze Opportunity

Plan Improvement

Focus Improvement

Deliver Performance

Improve Performance

Lean Six Sigma is combination of Lean Method and Six Sigma. The method builds on the knowledge, methods and tools derived from decades of operational improvement research and implementation. Lean approaches focus on reducing cost through process optimizations. Its aim is effectiveness, not just efficiency. Broadly Lean Six Sigma is for operational improvements - refining existing processes to reduce cost.

Six Sigma is about meeting customer requirements, stakeholder expectation and improving quality by measuring and eliminating defects.

6 Sigma QUALITY

Six Sigma Jargon

Champion -

The Champion is a person responsible for instilling the vision of Six Sigma and communicating it across the firm. They are usually the upper management or executive officers. The assist in dedicating the resources and choosing the projects.

Master Black Belt -

The MBB people are those, who have extensive experience in Six Sigma methodology. MBBs acts as a coach to its team member in project planning and result evaluation.

Black Belt -

The BB people leads the project on fulltime basis. BBs are certified people with hands on Six Sigma projects. They have strong understanding of statistical methods of data collection and analysis. They are the project managers and are responsible for all traditional roles.

Green Belt -

The GBs acts as an assistant to BBs in their job. GBs have the basic understandings of statistic but don‟t have the expertise and experience like BBs. GBs does the legwork of BBs in project realization. However, they lead the project on a part time basis.

6 Sigma QUALITY

Define Phase

Involves: • To develop a Project Charter. Project • Objective – Develop focus and purpose of the team. Charter

VOC

• Use Voice of Customer to identify customer needs. • Objective – Identify the actual pain / opportunity.

CTQs

• Translate customer needs into Critical To Quality elements. • Objective – Identify Project CTQs.

• Translate customer needs into Critical To Quality elements. Process • Objective – Identify Project CTQs. Map

6 Sigma

Define Phase

QUALITY

Project Charter: Elements Business Case

- Why is the project worth doing - Consequence of not doing the project

Problem Statement

- Description of problem - What is not meeting customer‟s requirement

Goal Statement

- Improvement the team desires

Scope Milestone

- What lies in scope and what‟s out of scope - Project plan with timeline

6 Sigma QUALITY

Define Phase

Project Charter: RACI Model Responsibility Charting is a technique for identifying functional areas where there are process ambiguities . It brings the differences out in open and resolving them through a cross-functional collaborative effort. It defines the participation roles to different departments linked with the project.

Responsible (R) – “doer”

The doer(s) is responsible for action/implementation. The doers are those who actually completes the task.

Accountable (A) – “the buck stops here”

The accountable persons are those who are actually answerable for activity. This includes “yes” or “no” authority. Only one “A” is assigned to an action.

Consult (C) – “in the loop”

The consultants are consulted prior to a final decision or action.

Inform (I) – “keep in the picture”

The communication to this individual is a one-way. They are just informed about the changes and are expected to take action as a result of the changes.

6 Sigma

Define Phase

QUALITY

RACI Charting: Example Mother

Father

Sam

Feed the Dog

A

C

R

Play with Dog

I

I

A

Morning Walk

C

A/R

Evening walk

C

A/R

Clean the Dog

C

A/R

Clean the Table

C

RACI Closing Guideline: 1. 2. 3. 4. 5.

A

Jenny

Clark

Kids

R R

R

R

Place the Accountability & Responsibility at lowest feasible level. There can be only one accountable individual per activity. Authority must accompany accountability. Minimize the number of Consults & Informs. Horizontal & Vertical Analysis of RACI chart indicates flaws in roles/responsibilities.

6 Sigma QUALITY

Define Phase

VOC / CTQs “if we can find it, we can focus on it” Customer Surveys

Complaints

Executive level discussions

Benchmark Data

Voice of customer (internal/external) is tracked through various means. Customer Surveys and Complaints are generally used for external customers while executive level discussions are intended for refining/improving internal processes. Benchmark data is essential for process developments/innovations with respect to peers.

Example- CTQs Critical To Quality (CTQ)

Critical To Delivery (CTD)

Critical To Cost (CTC)

Critical To Process (CTP)

• Dimensions

• JIT Compliance

• RM/Scrap/Inventory

• Process Cycle Time

Definition of CTQs changes as per projects, However, it should be very clear and straightforward as it forms the base of project.

6 Sigma

Define Phase

QUALITY

Process Mapping: “y = f(x₁, x₂, x₃ …..)” Supplier

Input

S

I

Process

P

Output

Customer

O

C

High level Process Map

Process Mapping begins with SIPOC map. Once SIPOC is prepared, Process part is again fragmented into a high level process diagram involving all work centers that affects Output. Majority of this process comes under Measure Phase, but to prioritize and link CTQs to various process inputs.

6 Sigma QUALITY

Measure Phase

Tools:

Process Mapping

YX Matrix

Measurement Systems Analysis (MSA)

Capability Analysis

Measure Phase is a pioneer stage in quantifying, qualifying and validating the Six Sigma needs. It is in this phase that a more refined data evolves in terms of CTQs. And the phase translates the physical behavior into statistical problem. The phase also makes an assessment of current process performance. Many companies have their own methodology of recording day-to-day process data however, methods of measurement revolves around above stated five tools.

6 Sigma

Measure Phase

QUALITY

High Level Process Mapping: Example-Drilling Work Order QTY/Part

Start

Jig Required ?

Refer the Drawing

No

Go for Marking

Yes Rework Area

Rework Report

Yes

Rework Possible

No

Use Jig

Prepare Tooling

Is Drilling OK

Start Drilling

No Rejection

Rejection Report

Yes End

A high level process mapping is very important in understanding the interaction of various process interaction and linkage of CTQs with the process centers. Majority of concentration is put on in eliminating the “excess” or the “wastage” of cycle time as against to the optimal requirement.

6 Sigma

Measure Phase

QUALITY

YX Matrix:

Input Variable (X)

Output Variable (Y) # Rank= Score x Weight

Weld Strength

Weld Appearance

Pinhole Density

Weld Flexibility

Weight

10

8

7

4

Weld Frequency

8

4

7

5

181

Power Amplitude

8

8

4

9

208

Initial Gap

5

2

3

4

103

Contact Pressure

2

7

10

2

154

Rank

The YX Matrix is an exhaustive process study to zero in on to the process characteristic that has greater impacts on CTQs. Hence, we deduce the weight of different process impacting CTQs.

6 Sigma

Measure Phase

QUALITY

Measurement System Analysis: Input

Measurement Process

Process

Output

Process Variation Actual Process Variation Variation due to Instrument

Measurement Process Variation

Variation due to Operator

Variation due to Environment

MSA is used to separate the variations of measurement system in the process.

Repeatability: Variation when one person repeatedly measures same unit with same system Reproducibility: Variation when two people measuring same unit with same system Tolerance: Expectation of error range

6 Sigma QUALITY

Measure Phase

Gage R & R Analysis: “Example”

R & R < 10% : Satisfactory

R & R ≈ 10 - 30% : May be Satisfactory

R & R > 30% : Unsatisfactory Gage R & R analysis presented above seems to be complicated , but in fact it‟s not. It is simply a process where Repeatability & Reproducibility is measured.

6 Sigma

Measure Phase

QUALITY

Capability Analysis: Process Capability Cp

Process Performance Cpk

• Cp is the ratio of tolerance width to short term process spread. • Estimates instantaneous capability of the process.

• Cpk is the ratio of the distance measured between process mean and specification limits closer to half the total process spread. • Estimates measure of capability of a process to meet established customer requirement.

LSL

LSL

USL

Cp 80 – Critical need for immediate action. Severity > 5 – Safety related defects. High RPN & Low Detection – Flaws in internal tests. High Occurrence – Poor process capability. Detection (1 to 10) – Good Control to Poor Control

Insufficient Stock 7

Insufficient Stock 7

Sushmit, 5th Padmavati, 10th Raghu, 10th July 2011 July 2011 July 2011 Plan Developed

Responsibility & Target Dates

Monthly Stock Audit

Process Inspection

Review SOP

Audit Plan Developed

FMEA is a dynamic document and each process owner keeps updating it whenever required. Revised RPN

Detection (1-10)

Occurrence (1-10)

Action Severity (1-10)

Action Taken

RPN

Process

SOP Revised

Recommended Action

315

168

Detection (1-10)

315

9

8

9

Current Control

Checked twice a year

Incoming Inspection

Standard Procedure

Occurrence (1-10)

3

7

Potential Causes

Correct Location Full

Potential Effects of Failure

Delay in Finding Severity (1-10)

Potential Failure Mode

Wrong Location

5

Product Function

Stock Inventory

QUALITY

5

Handling Error Supplier Defect

Damaged

Damaged

Stock Inventory Stock Inventory

6 Sigma

Analyze Phase

FMEA: “Example” Revised Result

6 Sigma

Analyze Phase

QUALITY

Pareto Analysis: Vilfredo Pareto, a 19th century Italian economist who discovered that 80% of the land in Italy was owned by 20% of the population, established the principle. Later it was redefined as 80% of the problems has their roots in 20% of the causes. Hence Pareto Analysis will give the vital few “20%” that accounts for massive “80%” defects in the process.

Example – Let‟s consider delays in credit card processing by a bank with following reasons and frequency of occurrences.

Category

Frequency

No Address

9

Illegible

22

Current Customer

15

No Signature

40

Other

8

6 Sigma

Analyze Phase

QUALITY

Pareto Analysis: Reorganize the frequency data in decreasing order . Then calculate the relative percentage on 100 percent level then calculate the cumulative percentage. Category

Frequency

Relative Percentage

Cumulative Percentage

No Signature

40

43%

43%

Illegible

22

23%

66%

Current Customer

15

16%

82%

No Address

9

10%

92%

Other

8

8%

100%

Run the Pareto analysis tool in Sigma XL or in XL select the graphical plotting tool that resembles the graph shown in next slide.

6 Sigma QUALITY

Analyze Phase

Pareto Analysis: Result:

The Breaking Point: The breaking point divides the vital few from trivial many in the Normally, the vital few starts fro 70% and above. In the above result it is “No Signature, Illegible”.

chart.

6 Sigma

Analyze Phase

QUALITY

Hypothesis Testing:

Hypothesis testing is a value judgment made about a circumstance, a statement made about a population. As the name suggests, it is a technique to test the efficacy of changes in any process. Let‟s assume that we have a data of different types of crime in a city. Now, a hypothesis can be made for the trends in a span of 10 years. But to ascertain this hypothesis a standard error based hypothesis testing using is conducted, which validates the hypothesis of crime in span of 10 years. However, in actual applications when certain data tends to be normal i.e. the data follows normal distributions, then standard test called as t-test is applied to validate the hypothesis. To have a better command over this test one needs have a clear idea of right tools and have better understanding of statistical tools.

Common Tools: ANOVA

Welch ANOVA

• Normal Data • More than two group of equal Variance.

• Normal Data • More than two group of unequal Variance

Paired t-test

t-test

Chi-Square

• Normal Data • For matched two groups.

• Normal Data • For unmatched two groups

• For Discrete Data

6 Sigma QUALITY

Analyze Phase

Hypothesis Testing: “Elements” 1.

Null Hypothesis (Ho): Null hypothesis is the hypothesis to be tested. In the earlier example Ho : Crime trends in a span of 10 years.

2.

Alternate Hypothesis (H₁ or Ha): Alternate hypothesis is opposite to null hypothesis. It is assumed when null hypothesis is rejected after test. In earlier example H₁ : No trends in crime in a span of 10 years.

3.

Sample t-test: Sample t-test is conducted to reject or fail to reject null hypothesis.

4.

Level of Risk : Level of risk implies the kinds of error associated while making an inference from hypothesis test. Two types of error can be made. The experiment can falsely reject a hypothesis that is true. In this case we say the error is Type I or the α(alpha) error. If test actually fails the hypothesis that is actually false then it is Type II or β (beta) error.

5.

Decision Rule: Decision pertains to condition of rejecting or failing to reject the hypothesis. Predefined confidence level helps in decision.

6 Sigma

Analyze Phase

QUALITY

Hypothesis Testing: “ α & β error” The American Trial System In Truth, the Defendant is: H0: Innocent HA: Guilty

Verdict

Innocent

Guilty

Correct Decision

Incorrect Decision

Innocent Individual Goes Free

Guilty Individual Goes Free Type II or β Error

Incorrect Decision Innocent Individual Is Disciplined Type I or α Error

Correct Decision Guilty Individual Is Disciplined

The above example shows the condition of Type I and Type II error. The decision rule for above verdict is based on the confidence level of α and β . When p-value < 0.05: Reject H0 and when p-Value > 0.05 : Fail to Reject H0

6 Sigma

Analyze Phase

QUALITY

Chi-Square Test: “ Discrete data” Chi-Square test applies where discrete data is available for hypothesis testing. Chi-Square test is usually conducted in a situation where customer feedback/VOC trends are compared with expected trends. Example – Let‟s consider a company “X” makes customer survey for satisfaction quarterly and compares them with the previous expected surveys. Assume the sample size be 80. Category

Q3-FY10

Q4-FY10

Excellent

8

8

Very Good

36

37

Good

12

11

Fair

4

7

Poor

8

9

Very Poor

12

8

Here we will inspect the case in one variable only that is “Category”. H0 : Result Similar and Ha : Results not Similar. In this test Null & Alternate hypothesis always remains same.

6 Sigma QUALITY

Analyze Phase

Chi-Square Test: “ Result ” Chi-Square test run gives following result: Chi-Sq = 1.734, DF = 5, P-Value = 0.885 Hence, we observe that , P-Value = 0.885 i.e P-Value > 0.05. Therefore, it fails to reject Null hypothesis ( H0 : Result Similar) and the data of Q2-FY10 & Q3-FY10 are similar. Which implies that company “X” sustained its confidence level in the market.

6 Sigma

Analyze Phase

QUALITY

Analysis of Variance (ANOVA): In 1920, Sir Ronald A. Fisher invented a statistical way to compare the data sets. Fisher called this method as Analysis of Variance which is popular as ANOVA. The F-ratio produced by ANOVA is named after Fisher. The t-test mentioned earlier had a limitation i.e. only two data sets can be analyzed. However, in ANOVA minimum of two data sets and maximum of infinite data sets can be analyzed. Scope of ANOVA: Whenever we want to compare more than two sets of data in order to conclude the better one then ANOVA plays great role. Example: Let‟s consider a case where a company wants to analyze the efficiency of drilling machine. Company collects the total time taken to complete similar jobs in three locations. Drilling time in minute in a span of 5 days A

B

C

15

28

26

17

25

23

18

24

20

19

27

17

24

25

21

6 Sigma QUALITY

Analyze Phase

Analysis of Variance (ANOVA): ANOVA Test: When ANOVA test is run, we get numerical output and graphical output. Former critically examines the individual data for differences and later plots them for comparison. One-way ANOVA: A, B, C Source DF SS MS F P Factor 2 131.73 65.87 7.81 0.007 Error 12 101.20 8.43 Total 14 232.93 S = 2.904 R-Sq = 56.55% R-Sq(adj) = 49.31%

Numerical data examines the relation of data with each other and gives the P-value that indicates the confidence of test. In above case it is 0.007 and hence P-value < 0.05 which implies that Null hypothesis is rejected and data given are different. However, in what way the data differ is explained by box plot in next step.

6 Sigma QUALITY

Analyze Phase

Analysis of Variance (ANOVA): ANOVA Test: “Understanding differences” In a box plot circle marks the average, horizontal line is median, box reaches +/- one standard deviation and vertical line is range of data. A close analysis of graphical plot reveals a lot. Location A & C are statistically similar but B differs from A & C. Location A is more efficient in drilling. Location B requires special attention. In one particular day at location A the drilling is least efficient and if rectified it will be very efficient. Location C has less consistency. Hence, we can conclude in a similar manner for different data.

6 Sigma QUALITY

Analyze Phase

Regression Analysis: Regression analysis is a statistical tool to analyze the relationship between quantitative variable. It helps in predicting the process behavior when the variables are change, for instance how JIT is affected when inventory is reduced. However, the analysis does not provides the optimal level of inventory rather it gives the level of inventory required to maintain the desired JIT compliance. Regression transforms the physical process into a mathematical model that will establish the relation between dependent and independent variable. In above case: JIT = A (Inventory) + B , value of A and B decides how strong the relationship exist between JIT and Inventory. When the independent variable is more than one then it is called as multiple regression analysis. Example: Let‟s consider a company “X” is a vendor of company „Y”. However, company “X” is poor in managing its optimal inventory level to support JIT. But since the purchase trends of company “Y” quarterly varies so they could not find the optimal inventory requirement. With poor inventory management the operational cost started increasing. To reduce the financial pain, top management wanted to analyze the effect of inventory over operational cost. They collected data of past one year, and wanted to know the optimal inventory level for particular operational cost.

6 Sigma

Analyze Phase

QUALITY

Regression Analysis:

Data: Operational cost against inventory level in a one year span. Month

Operational Cost 00000’ `

Inventory MT

Jan

22

10

Feb

20

8

Mar

30

24

Apr

28

20

May

26

19

Jun

29

23

Jul

31

26

Aug

37

28

Sep

38

30

Oct

40

34

Nov

42

37

Dec

54

45

6 Sigma QUALITY

Analyze Phase

Regression Analysis:

Result: Regression Analysis gives following result. R-Sq indicates strength of relation. The regression equation is

Operational Cost = 11.5 + 0.882 Inventory (Relation) Predictor Coef SE Coef T P Constant 11.474 2.512 4.57 0.001 Inventory 0.88202 0.09518 9.27 0.000 S = 3.23748 R-Sq = 89.6% R-Sq(adj) = 88.5% Normal Probability Plot

Action: Let the management fixes

(response is Operational Cost)

the operational cost to be 25 lakh.

99

Equation:

95 90

25 = 11.5 + 0.882 Inventory Inventory ≈ 15.3 MT

Percent

Solution:

80 70 60 50 40 30 20 10

Note: The red dots represents the

5

variation of relation form the regression model adopted.

1

-3

-2

-1

0 1 Standardized Residual

2

3

6 Sigma QUALITY

Statistical Solution:

Improve Phase

Improve phase is all about improvement. All the previous steps are used to narrow down the scope of actual cause causing costly pain. Let‟s say that we have run the six sigma method to improve the product quality. Later we came to know that the profile cutting machine has some fault. All the cut parts have slight variation. Now, what we do next? Change the machine or further drill on to the subject. Obviously, the later option is economically viable. Further, we sorted out some key factors of oxy machine that affects dimension: • Cutting Speed. • Kerf value. • Nozzle quality. • Plate Thickness. • Program fault. But, still its quite difficult to narrow the scope of improvement. Lots of question still arises – How cutting speed affects product and what damage it causes alone. What is the net impact of kerf value. Net effect cutting speed and kerf value. Do we need to change the nozzle and what will be the frequency. Do we need to reboot the program. All these answers are best addressed by Design of Experiment.

6 Sigma

Improve Phase

QUALITY

Design of Experiment (DOE): The process is always behaves in a manner of how or what the input is given and what are the external affecting parameters. To model the behavior of each individual factor and the behavior in interactions with other factors, for the desired output is best done by DOE. In the previous example, suppose we want to figure out, how beneficial it would be if we would replace the old nozzles. After all no one wants useless expenses in the name of solution.

Example: Let‟s say that the company wants to know the effect of cutting speed and kerf value to avoid the costly expenses. They want to analyze the defects corresponding these factors to establish the standard parameters for quality product. Company conducted a test run for 10mm thick plate for which they qualified the kerf value and cutting speed as “High” or “Low”. After conducting practical experiment, company tabulated the defect quantity as given: Kerf Value (mm) Cutting Speed (mm/m)

High

Low

High

5

10

Low

9

14

6 Sigma

Improve Phase

QUALITY

Design of Experiment (DOE): Solution:

However, for validating the experiment they repeated the experiment. Total factors are 3 and level is 2, so total runs = 2³. DOE calls for codification of High as (+) and Low as (-). The combination “ Cutting Speed x Kerf Value” is coded as “ +, -” means first factor is high while other is low.

:

Cutting Speed

Kerf Value

Cutting Speed x Kerf Value

+

+

+

5

6

+

-

+, -

10

11

-

+

-, +

9

8

-

-

+

14

12

Response

Main Effect : We can clearly see the impact of cutting speed and kerf value over the response. The impact is significant and this is called as main effect. However, the statistical model will give the relation of factors and response more appropriately.

6 Sigma QUALITY

Improve Phase

Design of Experiment (DOE): Statistical Solution:

When DOE test is run for the given problem, the solution is as follows: Analysis of Variance for Response, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P Cutting Speed 1 15.125 15.125 15.125 17.29 0.014 Kerf Value 1 45.125 45.125 45.125 51.57 0.002 Cutting Speed*Kerf Value 1 0.125 0.125 0.125 0.14 0.725 Error 4 3.500 3.500 0.875 Total 7 63.875 S = 0.935414 R-Sq = 94.52% R-Sq(adj) = 90.41% Normal Probability Plot (response is Response)

Interpretation: The p-value is 0.725 for effect

Response = 9.3 + 1.3(Cutting Speed) – 2.3(Kerf)

95 90 80

Percent

of interaction, hence it is least significant for consideration. But p-value for cutting speed and kerf value is less than 0.05 so it‟s significant. If we see the main effects plot for responses , we get more relation that is : Increase in cutting speed results decrease in rejections and increase in kerf value also results in decrease in rejections. Further, to design the parameter we require following regression model:

99

70 60 50 40 30 20 10 5

1

-3

-2

-1

0 1 Standardized Residual

2

3

6 Sigma QUALITY

Improve Phase

Design of Experiment (DOE): “Insight” DOE is not so easy as it seems to be in earlier example of cutting machine. DOE is not only crucial part of six sigma project but also serves as validating the changes for Control Phase. There are many ways to conduct DOE, for the sake of understanding we followed the simplest path. Other complex tests are used when the factors are more or it is related to the physiological traits for instance “predicting customer‟s buying behavior”, “determining competitive pricing”, “predict and optimize response rates to advertisements”. Concept of DOE was laid by Ronal Fisher and is now the most vital tool in today‟s world. Many key figures of DOE test reveals the vital information about how the process is affected by several factors. Which is why, the tool is so vital in business management. Robust design method, also called as Taguchi method is quite vital in manufacturing industries. Taguchi method mainly deals with the factors on which we do not possess control like wear and tear of tool etc. This the reason why some of the manufacturing industry exceeds in process excellence over other.

6 Sigma QUALITY

Control Phase

Statistical Process Control (SPC): Improvement phase had addressed the factors causing costly pain. Control Phase is all about sustaining the improvement. Consider a manufacturing unit produces certain variety of parts for instance – profile, drilled and bent parts. Also, the unit is equipped with profile cutting machine, vertical drilling machine and press brake. If the unit makes its inspection only at final step i.e. before the product is delivered to the customer then three cases arises – parts ok, parts to be reworked or parts to be rejected. hence the cost of production will include cost of ok parts, cost of defective parts and cost of rework leading to high operational cost. If we consider a system in which only the process producing parts are monitored and controlled, then the cost of rework, cost of rejection and cost of inspection can be drastically reduced. The technique is called as Statistical Process Control or SPC. SPC is all about managing the production process. Dr. Walter Shewhart and Dr. Edward Deming developed the SPC technique. SPC helps in :  keeping production processes stable.  predicting cause of variation before defective production.  fixing maintenance frequency to avoid production loss.  prevent defects.

6 Sigma QUALITY

Control Phase

Statistical Process Control (SPC): “variation causes defects” Assignable cause of variation: When the cause of variation causes differences in the output which is significant as per customer‟s specification, it is called as assignable causes. Example – Poor quality of RM, improper machine calibrations etc.

Common cause of variation: When the variations are uncontrollable like weather conditions and causes insignificant damages to output of product, then it is called as common causes. They are random in nature and are predictable. However, if these causes are unpredictable and process is sensitive then it can affect the quality of output.

Process monitoring tool: Control Charts. Histograms. Scatter Plots. Process Checklist. C & E Diagram. A3 Reports etc. These are the commonly used tools in process monitoring. But, for the sake of understanding we will se the Control charts only. As the others are bit complex at present.

6 Sigma QUALITY

Control Phase

Control Chart: Control Chart is used to capture the behavior of products over a span of time. Samples are randomly picked from the production line and checked for the conformance. When the process fails it will have an impact on the parts, which will be captured in the control chart. Then the variation is analyzed for immediate rectification.

Elements of Control Chart: CTQ or “X” – The product characteristic that we check in random inspection. Process Mean or “μ” – The process mean of observed sample. n – Sample quantity in random pick. σ – Standard Deviation (SD) of observed “X”. UCL – Upper Control Limit. They are 3 SD above μ. LCL – Lower Control Limit. They are 3 SD below μ.

Relation: • μ = (Σxi) / n. • σ = √(Xi – μ)² / n • UCL = μ + 3σ • LCL = μ - 3σ and UCL-LCL = 6σ (Six Sigma Controlled)

6 Sigma QUALITY

Control Phase

Control Charts:

WECO Rule talks about the sensitivity of variations like when 4 out of 5 dots are between 1and 2 sigma away from mean. The rule is set by Western Electric Company.

Capability Trend of 6σ: “the six sigma way – 99.7% defect free”

6 Sigma QUALITY

-X and R Chart:

Control Phase

The process is about taking small sample from production line and then taking mean of it and plotting against range (R). The key question depending on DOE are • what is the sample size. • time to order the sampling. • how many samples. The sample size depends on rational sub grouping i.e. depends on process cycle time. Further, the time to order should be in such a way that variations does not causes major problems. Similarly the sample size is chosen.

-

• X = Sample Mean = ΣXi / n

• R = X (Highest) – X(Lowest) Center Line: • Center Line for R chart: R = ΣR / t, “t” is the number of sample.

• Center Line for X chart: X = ΣX / t.

6 Sigma QUALITY

-X and R Chart:

Control Phase

The process is about taking small sample from production line and then taking mean of it and plotting against range (R). The key question depending on DOE are • what is the sample size. • time to order the sampling. • how many samples. The sample size depends on rational sub grouping i.e. depends on process cycle time. Further, the time to order should be in such a way that variations does not causes major problems. Similarly the sample size is chosen.

-

• X = Sample Mean = ΣXi / n

• R = X (Highest) – X(Lowest) Center Line: • Center Line for R chart: R = ΣR / t, “t” is the number of sample.

• Center Line for X chart: X = ΣX / t.

6 Sigma QUALITY

Control Phase

Mistake proofing: Mistake-proofing is required to assure the process is self driven and unintentional variations made/done by any people does not affects the end result. Key features are : Mistake-proofing is applied in Improve and Control phase with respect to DOE. It is not only applied to human error but also to software or other variations. Standard Operating Procedure (SOP), Checklists, maintenance logs are means of control. FMEA enlists the possible cause of failure and is controlled on day to day basis.

Awareness: All the steps discussed so far are ineffective if the quality awareness does not translates well down the line. Six sigma way results not only defect free processes but is also the source of financial savings. Majority of managers find difficulties in statistical approach because they trust their experience. But, six sigma digs were we fail to perceive the problem. Most of the business leaders believes in increasing business, but what about sustaining the business. Endangered businesses situation is a result of either tough market or failure in quality as per customer. Six sigma process requires awareness to all the people linked to the end product.

6 Sigma QUALITY

“With increasing globalization, every steel company must innovate to prosper and compete in this new environment. POSCO was in difficult situation you might almost say a crisis – a few years ago as we faced this new global competitive threat. As a management team, we felt that Six Sigma was a good vehicle to change all employees’ way of thinking, current working styles and mind-sets” - Ku-taek Lee, Chairman and CEO, POSCO.

Thank You By Rahil, Kaina India