sequence of interdependent and linked procedures which, at every stage , consume one or more

Sequence of interdependent and linked procedures which, at every stage, consume one or more resources

•employee time, •energy, •machines, •money

to convert

inputs •data,•material,•parts,

into outputs.

These outputs then serve as inputs for the next stage until a known

goal or end result is reached.

The Process

http://www.businessdictionary.com/definition/procedure.html

http://www.businessdictionary.com/definition/stage.html

http://www.businessdictionary.com/definition/resource.html

http://www.businessdictionary.com/definition/employee.html

http://www.businessdictionary.com/definition/energy.html

http://www.businessdictionary.com/definition/machine.html

http://www.businessdictionary.com/definition/money.html

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http://www.businessdictionary.com/definition/data.html

http://www.businessdictionary.com/definition/material.html

http://www.businessdictionary.com/definition/part.html

http://www.businessdictionary.com/definition/output.html

http://www.businessdictionary.com/definition/goal.html

http://www.businessdictionary.com/definition/end-result.html

The ExpertiseManufacturing and Auto

BHELHM-MitsubishiHundaiMULLUCAS-TVS

Services

MarriotTVS S&S

Transport

Etihad AirlinesFly DubaiGoIndigoIRSVijay TanksAir IndiaQantas

Govt

KSEBAir IndiaNPCLPower Projects

Oil & Gas

ARIES Marine, UAENITC, IranMactaggart Scott, UKONGCPEMCO, Qatar

Education

NITBIM, TrichyThought Works

The Expertise

• M.Tech – Quality, Reliability, OR

• Certified Quality Engineer / MBB– ASQ

• More than 3000 Improvement Projects

• Last 1 year revenue impact above 2 Billion USD

• Application in 30 domains with support consultants

• Lead consultant for Qualimations in Gulf/India/Africa

• 20 years application and support



to convert


into outputs.



The Process








http://www.investorwords.com/9305/convert.html












The Process

outputs.into



















The Process

outputs.into

f

















The Process

outputs.















The Process

outputs.

VibrationClimateHolidaysBreakdown














The Process

OutputsCharacteristics


Controllable Factors

Non Controllable Factors








The Process




inputs data,material,parts,

Employee time energy MachinesMoney

OutputsCharacteristics




The ProcessOutputs




inputs data,material,parts,

3.75

mm

Target

0.03 mm

USL:3.78 LSL:3.72

Employee time energy MachinesMoney




The ProcessOutputs

Vibration ( DEPENDEND ON TIME)

Climate (DEPENDED ON SEASON)

Holidays (DIFFERS)

Breakdown (RESOURSE ON MAINTANENCE)



Employee time (LESS,HIGH)energy (LOW,MEDIUM,HIGH)Machines ( M1,M2) Money (LOW,HIGH)

inputs Data (TABULATED,NONTABULATED)

Material (TYPE1,TYPE2),

Parts( PARTA, PARTB),

3.75

mm

Target

0.03 mm

USL:3.78 LSL:3.72

Cost

Process characteristics

Productcharacteristics

Incoming source of variation

A product characteristic is a feature of the product that must be controlled, such as a dimension, a color, or contamination. Each product characteristic should have at least one process characteristic that is associated with it. The process characteristic is the feature or element of the process that affects or controls the product characteristic in question. For example, a product characteristic might be a drilled/bored hole of a certain size and location, and the associated process characteristics might be proper drill size, drill sharpness, and the method used to locate the hole in the machine.

The goal should be to identify, optimize and control the process characteristics so as to avoid having to inspect product characteristics. That's what process control is all about.




The ProcessOutputs



Holidays (DIFFERS)







3.75

mm

Target

0.03 mm

USL:3.78 LSL:3.72





The Process

Outputs

Target

0.03 mm

USL:3.78 LSL:3.72

Outputs

Target

0.03 mm

USL:3.78 LSL:3.72

Outputs

Target

0.03 mm

USL:3.78 LSL:3.72

Sigma=3.25 Sigma=3.03 Sigma=2.56

The ProcessOutputs



Holidays (DIFFERS)







3.75

mm

Target

0.03 mm

USL:3.78 LSL:3.72





The ProcessOutputs



Holidays (DIFFERS)







3.75

mm

Target

0.03 mm

USL:3.78 LSL:3.72


Process variation

Pro

cess

A

vera

ge




Pro

cess

A

vera

ge

The ProcessOutputs



Holidays (DIFFERS)







3.75

mm

Target

0.03 mm

USL:3.78 LSL:3.72


Process variation




Pro

cess

A

vera

ge

The Process Problem

Outputs

3.75

mm

Target

0.03 mm

USL:3.78 LSL:3.72

Process variation

Pro

cess

A

vera

ge

The Process Problem

Outputs Current

3.75

mm

Target

0.03 mm

USL:3.78 LSL:3.72

Process variation

Pro

cess

A

vera

ge

Outputs Expected

3.75

mm

Target

0.03 mm

USL:3.78 LSL:3.72

Process variation

The Process



Holidays (DIFFERS)








Pro

cess

A

vera

ge

Outputs Current

3.75

mm

Target

0.03 mm

USL:3.78 LSL:3.72

Process variation



The Process



Holidays (DIFFERS)








Outputs Objective

Process average (center)

Process Variation (Minimal)

Supplier

Process Control



Holidays (DIFFERS)








Outputs Objective



Supplier

Process Control

R chart control X chart control

Process Control



Holidays (DIFFERS)








Outputs Objective



Supplier

Y = f(x): Process Outcome a Result of Process Inputs

Process Control


Input Output



3.06 mm +/- .03mm

2.04mm +/-0.3mm

Quality of Slot width

Slot widthSupplier Sheet Metal

Gage

Measure of quality

x1x2

x3

Output Quality (Y) = F ( X1,X2,X3)

X2: [ F1,F2,F3,F4….]

DMAIC


The mathematical term Y = f(x), which translates as simply Y is a function of x, illustrates the idea that the important process outcomes (Ys) are a result of the drivers (x‘s) within processes.

The goal of DMAIC is to identify which few process and input variables mainly influence the process output measures. Each DMAIC phase can therefore be described by how it contributes to this goal:

Define: Understand the project Y and how to measure it.Measure: Prioritize potential x‘s and measure x‘s and Y.Analyze: Test x-Y relationships and verify/quantify important x‘s.Improve: Implement solutions to improve Y and address important x‘s.Control: Monitor important x‘s and the Y over time.

DMAICY = f(x1,x2,x3,x4,x5……………….xn)

Define: Understand the project Y and how to measure it.

Y = f(x1,x2,x3,x4,x5……………….xn)

The project charter delivers the Y by clearly stating what the business or process problem is.

It also expresses the Y as a measurable process metric that tells how well the process is performing today (the baseline) and how performance should be after process improvement (the goal).

Voice of Customer SIPOC

VOC is used to verify the importance of the Y metric and to set specifications for the Y . Since the output column usually shows multiple Ys, VOC is again needed to determine which Y should be included in the project.

SIPOC (suppliers, inputs, process, outputs, customers) diagram clearly links the project Y to the process output. The output column of the SIPOC shows which Y is a result of the process. In the input column, the SIPOC provides a list of potential Xs.



The business case finally links the project Y to the so called “Big Y.” This means it shows how achieving the project Y contributes to higher-level business objectives like financial targets, customer satisfaction or strategically relevant goals (on time to market, on-time delivery, inventory level, etc.).

The project team can close the Define phase when it has a measurable, clearly defined Y with set specifications that help to distinguish between desired and not desired process performance.

Y = f(x1,x2,x3,x4,x5……………….xn)



DMAIC Phase Steps Tools Used

D – Define Phase: Define the project goals and customer (internal and external) deliverables.

Define Customers and Requirements (CTQs) Project CharterDevelop Problem Statement, Goals and Benefits Process FlowchartIdentify Champion, Process Owner and Team SIPOC DiagramDefine Resources Stakeholder AnalysisEvaluate Key Organizational Support DMAIC Work Breakdown Structure

Develop Project Plan and Milestones CTQ DefinitionsDevelop High Level Process Map Voice of the Customer Gathering

Y = f(x1,x2,x3,x4,x5……………….xn)

DMAICY = f(x): Process Outcome a Result of Process Inputs

Measure: Prioritize potential x‘s and measure x‘s and Y.

The Measure phase usually starts with a fishbone diagram and/or a detailed process mapping.

Given the clearly defined Y from the Define phase, the fishbone helps to identify all potential causes (x‘s) of this Y; the detailed process mapping also shows which process x‘s mostly influence the process Y.

At the end of this step, the project team should have a full picture of potential x‘s that it might next have to reduce to a manageable and measurable few.



It is always important to remember that the brainstorming, as well as the reduction of potential x‘s, happens based on process expertise, not yet on facts and data.

As the next step, the team sets up a data collection plan that allows for measuring both x‘s and Y in such a way that the data collected can later be used to identify cause-and-effect (i.e., x-Y) relationships with the help of graphical and statistical tools.

Of course, for all x‘s and Ys to be measured, an operational definition and – if possible – a gage R&R study should be conducted in order to guarantee reliable data.

Having the x‘s and Y data collected, the team would now start identifying patterns in data. Usually control charts, time series plots, and frequency plots are used to separate common from special cause variation. X‘s that influence special cause variation are identified, and if they can be explained and avoided in the future, they are removed from the data set. Additionally, Pareto analyses where the Y is stratified by categories of one x help to further scope the project.



The final step in the Measure phase is to determine the baseline capability of the process Y: Yield, Cpk or process sigma values indicate how well the process Y is performing today. This also sometimes leads to re-setting the initially stated goals in the project charter.

The Measure phase ends with related data for the Y and the most important x‘s, where x‘s of special cause variation have already been removed from the data set.



M – Measure Phase: Measure the process to determine current performance; quantify the problem.

Define Defect, Opportunity, Unit and Metrics Process FlowchartDetailed Process Map of Appropriate Areas Data Collection Plan/ExampleDevelop Data Collection Plan Benchmarking

Validate the Measurement System Measurement System Analysis/Gage R&R

Collect the Data Voice of the Customer GatheringBegin Developing Y=f(x) Relationship Process Sigma CalculationDetermine Process Capability and Sigma Baseline

Y = f(x1,x2,x3,x4,x5……………….xn)


Analyze: Test x-Y relationships and verify/quantify important x‘s.

In terms of x‘s and Ys, the Analyze phase is quite simple: All graphical tools (e.g., stratified frequency plots, pie charts, scatter plots, etc.) and statistical tools (hypothesis tests, regression analysis, design of experiments) that Green Belts and Black Belts learn during training have just one goal: Verifying and quantifying X-Y relationships.



The large number of different tools available is simply because different data types (continuous or discrete) of x and Y require different tools, as illustrated in the figure below. In addition to this data door, the tools of the process door (waste analysis, value-added analysis) supplement the quantitative data analysis with a more qualitative analysis and confirmation of important process x‘s.

At the end of the Analyze phase, the critical few x‘s that contribute most to the problem of the process Y are known.



A – Analyze Phase: Analyze and determine the root cause(s) of the defects.Define Performance Objectives HistogramIdentify Value/Non-Value Added Process Steps Pareto ChartIdentify Sources of Variation Hypothesis Testing Determine Root Cause(s) Scatter PlotDetermine Vital Few x’s, Y=f(x) Relationship Regression Analysis

Cause and Effect/Fishbone Diagram 5 Whys Process Map Review and Analysis Statistical Analysis

Y = f(x1,x2,x3,x4,x5……………….xn)


Improve: Implement solutions to improve Y and address important x‘s.

Analog to the Measure phase, the Improve phase also starts with getting a full picture. This time it is a full picture of potential solutions that – by addressing the critical few x‘s – can help improve the Y.

Brainstorming and creativity techniques help to generate these potential solutions. In order to reduce these solutions to those that should be implemented, each solution is rated against specific criteria.



Two important criteria are how much a solution contributes to improving the Yand how much it addresses specific x‘s. (Of course other criteria, like easiness of implementation, costs, also are important.) Before starting the implementation of the improved process, a failure modes and effect analysis helps to identify ways that the process Y can fail and the potential causes (newly or previously identified x‘s) and how to prevent these failures from happening.

At the end of the Improve phase, short-term data (e.g., from a pilot program) demonstrates that the identified solution or solution package has really improved the Y.



I – Improve Phase: Improve the process by eliminating defects.

Perform Design of Experiments BrainstormingDevelop Potential Solutions Mistake ProofingDefine Operating Tolerances of Potential System Design of ExperimentsAssess Failure Modes of Potential Solutions Pugh MatrixValidate Potential Improvement by Pilot Studies QFD/House of Quality

Correct/Re-Evaluate Potential SolutionFailure Modes and Effects Analysis (FMEA)

Simulation Software

Y = f(x1,x2,x3,x4,x5……………….xn)


Control: Monitor important x‘s and the Y over time.

The Control phase ensures that the new performance of the Y is sustained over time. In order to achieve this, a process management chart is developed that shows the new process flow, offers critical check points during the process, and has recommended actions in case the process does not continue on target.

In a process management chart, the previously identified x‘s are called leading indicators (i.e., checkpoints during the process) and the Y is the lagging indicator (i.e., the final checkpoint at the end of a process cycle).



Additionally, a performance measurement and monitoring system or dashboard is established that helps the process owner to measure and control the critical leading (x‘s) and lagging (Y) indicators on a continuous base. Control charts are again the best tool to show the performance of the Y over time.

After the handover of these tools to the process owner, the project is closed by evaluating the achieved results in terms of x‘s that were identified and the improvement in the Y.



C – Control Phase: Control future process performance.Define and Validate Monitoring and Control System Process Sigma Calculation

Develop Standards and ProceduresControl Charts (Variable and Attribute)

Implement Statistical Process Control Cost Savings CalculationsDetermine Process Capability Control PlanDevelop Transfer Plan, Handoff to Process Owner

Verify Benefits, Cost Savings/Avoidance, Profit Growth

Close Project, Finalize Documentation Communicate to Business, Celebrate

Y = f(x1,x2,x3,x4,x5……………….xn)

VOC-Voice of CustomerY = f(x): Process Outcome a Result of Process Inputs


Y = f(x1,x2,x3,x4,x5……………….xn)

Voice of the Customer is a market research technique that produces a detailed set of customer wants and needs, organized into a hierarchical structure, and then prioritized in terms of relative importance and satisfaction with current alternatives.

Voice of the Customer studies typically consist of both qualitative and quantitative research steps. They are generally conducted at the start of any new product, process, or service design initiative in order to better understand the customer's wants and needs, and as the key input for new product definition, Quality Function Deployment (QFD), and the setting of detailed design specifications.

VOC-Voice of CustomerY = f(x): Process Outcome a Result of Process Inputs


Y = f(x1,x2,x3,x4,x5……………….xn)

You are in charge of a process that needs to be optimized. You suspect that two factors contribute to yield ,but are not sure if one or both of them contribute. To save time and money you test factor A at 5 levels and factor B at 7 levels with no repetitions. Find the significance of factors A and B.

1 2 3 4 5

1 15 35 23 17 18

2 28 65 32 28 24

3 41 36 17 28 74

4 54 65 35 65 32

5 67 14 33 52 71

6 80 25 29 33 37

7 93 65 27 25 46

Factor BF

ac

tor

A

Number of manufactured goods..

You are in charge of a process that needs to be optimized. You suspect that two factors contribute to errors ,but are not sure if one or both of them contribute. To save time and money you test factor A at 5 levels and factor B at 7 levels with no repetitions. Find the significance of factors A and B. The level of significance is 95% (alpha = 0.05).


1 2 3 4 5 Total R R Square

1 15 35 23 17 18 108 11664

2 28 65 32 28 24 177 31329

3 41 36 17 28 74 196 38416

4 54 65 35 65 32 251 63001

5 67 14 33 52 71 237 56169

6 80 25 29 33 37 204 41616

7 93 65 27 25 46 256 65536

Total C 378 305 196 248 302

C Square 142884 93025 38416 61504 91204

Factor B

Fa

cto

r A



Fcrit

2,78

2.51



H0 Ha

Hypothesis Both are the same. There is no relation.

One is different are different.

There is a relation.

F-statistic F0 < Fcrit F0 > Fcrit

P-value p > alpha p < alpha

Factor Response

• Temperature• Concentration• Speed• Feed• Operator

• Yield

Factor/Level Response

• Temperature– 30 deg– 50 deg– 75 deg

• Concentration– 8%– 12%– 16%

• Speed• Feed• Operator

• Yield

Factor/Level Response

• Temperature– 30 deg

– 50 deg

– 75 deg

• Yield• Concentration– 8%

– 12%

– 16%

Design of Experiments (DOE) techniques enable designers to determine simultaneously the individual and interactive effects of many factors that could affect the output results in any design.

DOE also provides a full insight of interaction between design elements; therefore, helping turn any standard design into a robust one.

DOE helps to pin point the sensitive parts and sensitive areas in your designs that cause problems in Yield. Designers then are able to fix them and produce robust and higher yield designs prior going into production.

Dr. Taguchi pointed out that

•For long term effect of quality, it must be designed into the products.•All activities of a manufacturing organization have roles to play in building quality into the products.•Return on investment is much higher when quality issues are addressed further up-front in engineering.

• Experiment planning is the necessary first step (with many people/team and use consensus decisions)• Design smallest experiments with key factors• Run experiments in random order• Predict and verify expected results before implementation.

What are key process output variables?

Key process output variables (sometimes referred to as Key Characteristics) are traits or features of a part, piece of material, assembly, subsystem, or system whose variation has a significant influence on fit, performance, reliability, manufacturability, or assembly. In short, they are characteristics that have a big impact on efficiency and/or customer satisfaction. Variation in key process output variables leads to lower levels of quality and reliability and, ultimately, higher costs.

Examples of key process output variables are:

- Slip torque of an actuator

- Gap between car body and rear quarter glass panel

- Cabin wind noise

- Fill volume for a bottled beverage

What are key process input variables?

Key process input variables are process inputs that have a significant impact on the variation found in a key process output variable. That is, if the key process input variables were controlled (e.g. held constant), the process would produce predictable and consistent outputs.

For example, if the flatness of a clutch disk has a significant impact on the slip torque of an actuator, then clutch disk flatness would be a key process input variable.

As another example, my wife likes to experiment with baking the healthiest cookies possible by substituting fat free yogurt for butter. Clearly, the absence of butter has a large impact on the consistency and taste of the cookies. By finding an acceptable butter/yogurt mix and then holding this key process input variable constant, I should expect consistent cookie consistency!

How are key process input variables identified?

Since we have limited resources, the challenge is to determine which variables are important enough to control. While considerable effort is spent on trial-and-error approaches, these are extremely inefficient and almost never result in optimal solutions.

Fortunately, a tool exists that is perfect for this problem. Design of Experiments (DOE) is a tool that allows us to conduct structured experimentation to efficiently model process behavior and understand the cause-and-effect relationships present. Many types of experiments are available and their appropriate use depends on the experiment objectives, the number of factors to be investigated, the types of factors, etc.

Most products and processes must satisfy multiple customer requirements (and so there are multiple key process output variables). For example, a part may need to possess a specific strength and flexibility. Often, optimizing one performance metric will degrade another. DOE can be used to jointly optimize systems where multiple objectives are present--that is, find the sweet spot.

Rigorous use of DOE is invaluable in determining not only which input variables must be controlled to produce consistent outputs but to what extent those variables must be controlled. That is, we may quantify the sensitivity of the output variable to changes in the input variable. And this is exactly the information that is needed to deploy effective control charts.

· Determine what to verify/measure· Determine how to verify/measure· Determine how many to verify/measure, i.e. statistical significance· Determine when to verify/measure· Define acceptance/rejection criteria· Define required documentation

The Six Sigma View of Business

sequence of interdependent and linked procedures which, at every stage , consume one or more

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