"Maximizing the Impact of Transactional Six Sigma"

Maximizing the Impact of Transactional Maximizing the Impact of Transactional Six SigmaSix Sigma

Maximizing the Impact of Transactional Maximizing the Impact of Transactional Six SigmaSix Sigma

Presented to the Quality and Productivity Presented to the Quality and Productivity Research ConferenceResearch Conference

June 5, 2002June 5, 2002

Frederick W. FaltinFrederick W. Faltin

Quantitative Management ConsultingQuantitative Management Consulting

Presented to the Quality and Productivity Presented to the Quality and Productivity Research ConferenceResearch Conference

June 5, 2002June 5, 2002

Frederick W. FaltinFrederick W. Faltin

Quantitative Management ConsultingQuantitative Management Consulting

Quantitative Management Consulting

Copyright F. W. Faltin, 2002


As Six Sigma has evolved . . .As Six Sigma has evolved . . .

DMAICDMAIC

DFSSDFSS

Transactional SSTransactional SS



. . . the Toolset Has Grown. . . the Toolset Has Grown

We’ve added/changed presentation of:We’ve added/changed presentation of:

– QFDQFD– Process mappingProcess mapping– DOEDOE– Regression/RSMRegression/RSM– FMEAFMEA

. . . and probably several others I’ve overlooked. . . and probably several others I’ve overlooked



From a Transactional PerspectiveFrom a Transactional Perspective

The Good News is . . . The Good News is . . .

. . . that these changes reflect appropriate . . . that these changes reflect appropriate adaptation of Six Sigma’s statistical and adaptation of Six Sigma’s statistical and engineering contentengineering content



From a Transactional Perspective . . .From a Transactional Perspective . . .

The Good News is . . .The Good News is . . .


The Bad News is . . .The Bad News is . . .




. . . But We’ve Underutilized Valuable . . . But We’ve Underutilized Valuable Techniques from Other DisciplinesTechniques from Other Disciplines



. . . But We’ve Underutilized Valuable . . . But We’ve Underutilized Valuable Techniques from Other DisciplinesTechniques from Other Disciplines

Discrete Event Simulation (Operations Research)Discrete Event Simulation (Operations Research)

Conjoint Analysis (Marketing)Conjoint Analysis (Marketing)

Their roots are in statistical concepts already routinely taught in Transactional Six Sigma, so should be a “slam dunk” if deployed in conjunction with TSS



DFSS/TSS Face a Recurring ChallengeDFSS/TSS Face a Recurring ChallengeDFSS/TSS Face a Recurring ChallengeDFSS/TSS Face a Recurring Challenge

One of the most difficult tasks in DFSS/TSS is reliable One of the most difficult tasks in DFSS/TSS is reliable prediction of how a new design will performprediction of how a new design will perform

There are various potential approaches to a solution, There are various potential approaches to a solution, depending on the nature of the projectdepending on the nature of the project

In In business processbusiness process applications (e.g., Transactional Six applications (e.g., Transactional Six Sigma), there is a clear method of choice which is almost Sigma), there is a clear method of choice which is almost universally underutilized . . .universally underutilized . . .

One of the most difficult tasks in DFSS/TSS is reliable One of the most difficult tasks in DFSS/TSS is reliable prediction of how a new design will performprediction of how a new design will perform

There are various potential approaches to a solution, There are various potential approaches to a solution, depending on the nature of the projectdepending on the nature of the project

In In business processbusiness process applications (e.g., Transactional Six applications (e.g., Transactional Six Sigma), there is a clear method of choice which is almost Sigma), there is a clear method of choice which is almost universally underutilized . . .universally underutilized . . .

Discrete Event Simulation (DES)Discrete Event Simulation (DES)



Discrete Event Simulation: What and Why?Discrete Event Simulation: What and Why?Discrete Event Simulation: What and Why?Discrete Event Simulation: What and Why?

DES starts with an ordinary flowchart, map, schematic, DES starts with an ordinary flowchart, map, schematic, or Process Flow Diagramor Process Flow Diagram

SoftwareSoftware implementation then implementation then adds eventsadds events (orders, (orders, customer arrivals, transactions, . . .) customer arrivals, transactions, . . .) and tracks flowand tracks flow (of (of people, invoices, WIP, vehicles . . .) through the people, invoices, WIP, vehicles . . .) through the system system over timeover time

Automated features gather data on transaction Automated features gather data on transaction volumes, asset utilization, inventory levels, volumes, asset utilization, inventory levels, revenues/costs, waiting times, productivity, . . .revenues/costs, waiting times, productivity, . . .

Result: a “virtual history” of system performanceResult: a “virtual history” of system performance

DES starts with an ordinary flowchart, map, schematic, DES starts with an ordinary flowchart, map, schematic, or Process Flow Diagramor Process Flow Diagram

SoftwareSoftware implementation then implementation then adds eventsadds events (orders, (orders, customer arrivals, transactions, . . .) customer arrivals, transactions, . . .) and tracks flowand tracks flow (of (of people, invoices, WIP, vehicles . . .) through the people, invoices, WIP, vehicles . . .) through the system system over timeover time

Automated features gather data on transaction Automated features gather data on transaction volumes, asset utilization, inventory levels, volumes, asset utilization, inventory levels, revenues/costs, waiting times, productivity, . . .revenues/costs, waiting times, productivity, . . .

Result: a “virtual history” of system performanceResult: a “virtual history” of system performance



For example, a schematic-based simulation might start like this . . .For example, a schematic-based simulation might start like this . . .



. . . or a map-based simulation, something like this . . .. . . or a map-based simulation, something like this . . .



Discrete Event Simulation: What and Why?Discrete Event Simulation: What and Why?Discrete Event Simulation: What and Why?Discrete Event Simulation: What and Why?

DES can evaluate at modest cost even systems DES can evaluate at modest cost even systems which do not yet exist, and would be prohibitively which do not yet exist, and would be prohibitively expensive to buildexpensive to build

Answering “what if” questions is as simple as Answering “what if” questions is as simple as changing resource levels or redefining process flowchanging resource levels or redefining process flow

DES can model even complex processes with DES can model even complex processes with comparative easecomparative ease

Excellent tools now exist which make simulation Excellent tools now exist which make simulation much simpler and quicker to use, much simpler and quicker to use, and which are and which are relatively easy to learnrelatively easy to learn

DES can evaluate at modest cost even systems DES can evaluate at modest cost even systems which do not yet exist, and would be prohibitively which do not yet exist, and would be prohibitively expensive to buildexpensive to build

Answering “what if” questions is as simple as Answering “what if” questions is as simple as changing resource levels or redefining process flowchanging resource levels or redefining process flow

DES can model even complex processes with DES can model even complex processes with comparative easecomparative ease

Excellent tools now exist which make simulation Excellent tools now exist which make simulation much simpler and quicker to use, much simpler and quicker to use, and which are and which are relatively easy to learnrelatively easy to learn



DES Has Astonishing Versatility . . .DES Has Astonishing Versatility . . .DES Has Astonishing Versatility . . .DES Has Astonishing Versatility . . . Asset mix optimization for an equipment leasing businessAsset mix optimization for an equipment leasing business Telecommunications systems designTelecommunications systems design Service contract net income projectionsService contract net income projections GE Medical Systems’ industry-leading tool for designing medical GE Medical Systems’ industry-leading tool for designing medical

imaging facilitiesimaging facilities Optimal inventory positioningOptimal inventory positioning Streamlining NBC’s advertising sales processStreamlining NBC’s advertising sales process Equipment flow modeling for a truck leasing companyEquipment flow modeling for a truck leasing company Financial services process design/optimizationFinancial services process design/optimization Hospital constructionHospital construction Management of replacement parts having low volume demandManagement of replacement parts having low volume demand Virtually all aspects of Supply Chain ManagementVirtually all aspects of Supply Chain Management

Asset mix optimization for an equipment leasing businessAsset mix optimization for an equipment leasing business Telecommunications systems designTelecommunications systems design Service contract net income projectionsService contract net income projections GE Medical Systems’ industry-leading tool for designing medical GE Medical Systems’ industry-leading tool for designing medical

imaging facilitiesimaging facilities Optimal inventory positioningOptimal inventory positioning Streamlining NBC’s advertising sales processStreamlining NBC’s advertising sales process Equipment flow modeling for a truck leasing companyEquipment flow modeling for a truck leasing company Financial services process design/optimizationFinancial services process design/optimization Hospital constructionHospital construction Management of replacement parts having low volume demandManagement of replacement parts having low volume demand Virtually all aspects of Supply Chain ManagementVirtually all aspects of Supply Chain Management

and on, and on, and on . . .and on, and on, and on . . .



Relationship With Other DFSS ToolsRelationship With Other DFSS ToolsRelationship With Other DFSS ToolsRelationship With Other DFSS Tools

Full suite of Six Sigma analysis techniques applicable Full suite of Six Sigma analysis techniques applicable to analysis of system performance data from DESto analysis of system performance data from DES

DES evaluates “what if” scenarios on a case by case DES evaluates “what if” scenarios on a case by case basis—and is not an optimization scheme as suchbasis—and is not an optimization scheme as such

Design of Experiments provides a disciplined Design of Experiments provides a disciplined experimental approach, so DOE & regression are experimental approach, so DOE & regression are often real “naturals” for use in conjunction with DESoften real “naturals” for use in conjunction with DES

Full suite of Six Sigma analysis techniques applicable Full suite of Six Sigma analysis techniques applicable to analysis of system performance data from DESto analysis of system performance data from DES

DES evaluates “what if” scenarios on a case by case DES evaluates “what if” scenarios on a case by case basis—and is not an optimization scheme as suchbasis—and is not an optimization scheme as such

Design of Experiments provides a disciplined Design of Experiments provides a disciplined experimental approach, so DOE & regression are experimental approach, so DOE & regression are often real “naturals” for use in conjunction with DESoften real “naturals” for use in conjunction with DES



Discrete Event Simulation: Discrete Event Simulation: Applications IssuesApplications Issues

Accuracy/realism of input distributionsAccuracy/realism of input distributionsRepresentation of decision policiesRepresentation of decision policiesSensitivity/stability analysisSensitivity/stability analysisSimulation run length/sample sizeSimulation run length/sample sizeValidation of model, resultsValidation of model, results



So What’s the Takeaway?So What’s the Takeaway?So What’s the Takeaway?So What’s the Takeaway?

As a Transactional Six Sigma tool, Discrete As a Transactional Six Sigma tool, Discrete Event Simulation offers . . .Event Simulation offers . . .

powerful and flexible modeling capabilitypowerful and flexible modeling capabilityrealistic performance predictionsrealistic performance predictionsease of training and useease of training and usea low-cost means of avoiding major risksa low-cost means of avoiding major risks

. . . in short, it’s the method of choice for . . . in short, it’s the method of choice for Transactional process designTransactional process design

As a Transactional Six Sigma tool, Discrete As a Transactional Six Sigma tool, Discrete Event Simulation offers . . .Event Simulation offers . . .

powerful and flexible modeling capabilitypowerful and flexible modeling capabilityrealistic performance predictionsrealistic performance predictionsease of training and useease of training and usea low-cost means of avoiding major risksa low-cost means of avoiding major risks

. . . in short, it’s the method of choice for . . . in short, it’s the method of choice for Transactional process designTransactional process design



TSS Faces Another Recurring TSS Faces Another Recurring Challenge: Product DesignChallenge: Product Design

Service and financial businesses adopting Service and financial businesses adopting Transactional Six Sigma are often in the Transactional Six Sigma are often in the midst of continuing new product designmidst of continuing new product design

QFD, VOC are a great startQFD, VOC are a great startSix Sigma tools provide analytical capability Six Sigma tools provide analytical capability

to assess customer preference trade-offsto assess customer preference trade-offsBut where does quantitative data on these But where does quantitative data on these

trade-offs come from?trade-offs come from?



Conjoint Analysis: What and How?Conjoint Analysis: What and How?

A product/process design tool for assessing A product/process design tool for assessing customer preferencescustomer preferences

Focuses on quantifying trade-offs between Focuses on quantifying trade-offs between alternative product features/costsalternative product features/costs

In statistical terms, what CA really estimates In statistical terms, what CA really estimates are the are the contrastscontrasts between alternative options, between alternative options, in normalized units of utilityin normalized units of utility

These are then interpreted to quantitate These are then interpreted to quantitate relative importance and value of different relative importance and value of different features, levelsfeatures, levels



Conjoint Analysis: What and How?Conjoint Analysis: What and How?

““Y”: customer rankings or scores of Y”: customer rankings or scores of alternative feature combinationsalternative feature combinations

““X’s”: features/costs which are factors X’s”: features/costs which are factors defining the product/processdefining the product/process

Each analysis of feature/cost contrasts the Each analysis of feature/cost contrasts the preferences of a preferences of a single respondentsingle respondent

Subsequent analysis determines, e.g., Subsequent analysis determines, e.g., variability among preferences, relationship variability among preferences, relationship to customer demographics, etc.to customer demographics, etc.



Conjoint Analysis: ExampleConjoint Analysis: Example

PackagePackage Brand Brand PricePrice GHS?GHS? Guarantee?Guarantee? RankingRanking

AA K K 1.191.19 NN NN 13 13AA G G 1.391.39 NN YY 11 11AA B B 1.591.59 YY NN 17 17BB K K 1.391.39 YY YY 2 2BB G G 1.591.59 NN NN 14 14BB B B 1.191.19 NN NN 3 3CC K K 1.591.59 NN YY 12 12CC G G 1.191.19 YY NN 7 7CC B B 1.391.39 NN NN 9 9

etc.etc.

(Spot Remover example, Green and Wind, (Spot Remover example, Green and Wind, Harvard Business ReviewHarvard Business Review))



Conjoint Analysis: ComputationConjoint Analysis: Computation

If scores are used (vs. rankings), ANOVA is the If scores are used (vs. rankings), ANOVA is the method of solutionmethod of solution

In case of rankings, generally no unique solutionIn case of rankings, generally no unique solution– Additional criteria employed to define objective Additional criteria employed to define objective

function to be optimizedfunction to be optimized– MONANOVA (Kruskal, JRSS, 1965) or other math MONANOVA (Kruskal, JRSS, 1965) or other math

programming techniques employed for solutionprogramming techniques employed for solution



Conjoint Analysis: ExampleConjoint Analysis: Example

FeatureFeature UtilityUtility

PackagePackageAA 0.10.1BB 1.01.0CC 0.60.6

Brand Brand KK 0.30.3GG 0.20.2BB 0.50.5

PricePrice1.191.19 1.01.01.391.39 0.70.71.591.59 0.10.1

Good Housekeeping Seal?Good Housekeeping Seal?YY 0.30.3NN 0.20.2

Guarantee?Guarantee?YY 0.70.7NN 0.20.2

RESULTSRESULTS



Conjoint Analysis: DesignConjoint Analysis: Design

Structure is usually that of several variables Structure is usually that of several variables (product features), each at several levels (choices)(product features), each at several levels (choices)

Usual statistical issues of design, analysis (e.g., Usual statistical issues of design, analysis (e.g., presence of interactions) pertainpresence of interactions) pertain

Pairwise presentation of features to respondents Pairwise presentation of features to respondents may constrain design, estimabilitymay constrain design, estimability

Indicator variable formulation common—yields Indicator variable formulation common—yields problem with many 0-1 variablesproblem with many 0-1 variables

Fractional factorials, orthogonal arrays are design Fractional factorials, orthogonal arrays are design methods of choicemethods of choice



Conjoint Analysis: Marketing IssuesConjoint Analysis: Marketing Issues

Identification of candidate product featuresIdentification of candidate product featuresComposition of sampleComposition of sampleWording, administration of surveyWording, administration of surveyInterpretation of utility contrastsInterpretation of utility contrastsSoundness, applicability of conclusions within Soundness, applicability of conclusions within

a real market environmenta real market environment



So What’s the Takeaway?So What’s the Takeaway?So What’s the Takeaway?So What’s the Takeaway?

As a component of Transactional Six Sigma, As a component of Transactional Six Sigma, Conjoint Analysis offers . . .Conjoint Analysis offers . . .

a tool for quantifying customer preferencesa tool for quantifying customer preferencesenhanced product/process design capabilityenhanced product/process design capabilitya natural extension of the existing Six Sigma a natural extension of the existing Six Sigma

toolsettoolset

. . . a long overlooked addition to the TSS . . . a long overlooked addition to the TSS curriculumcurriculum

As a component of Transactional Six Sigma, As a component of Transactional Six Sigma, Conjoint Analysis offers . . .Conjoint Analysis offers . . .

a tool for quantifying customer preferencesa tool for quantifying customer preferencesenhanced product/process design capabilityenhanced product/process design capabilitya natural extension of the existing Six Sigma a natural extension of the existing Six Sigma

toolsettoolset

. . . a long overlooked addition to the TSS . . . a long overlooked addition to the TSS curriculumcurriculum



Where Does That Leave Us?Where Does That Leave Us?

Continuous improvement has a role here as Continuous improvement has a role here as elsewhere!elsewhere!

Discrete Event Simulation and Conjoint Discrete Event Simulation and Conjoint Analysis are examples of techniques from Analysis are examples of techniques from other fields which add value to TSSother fields which add value to TSS

They’re probably not alone in this . . .They’re probably not alone in this . . .

Let’s make these techniques routine elements of Let’s make these techniques routine elements of Transactional Six Sigma training, and look for other Transactional Six Sigma training, and look for other

opportunities, as wellopportunities, as well

"Maximizing the Impact of Transactional Six Sigma"

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