Quality at SourceQuality at Source

Manufacturing Systems AnalysisManufacturing Systems Analysis

Professor: Nour El Professor: Nour El KadriKadri

e-mail: nelkadri@ site.uottawa.cae-mail: nelkadri@ site.uottawa.ca

Quality at SourceQuality at Source

Manufacturing Systems AnalysisManufacturing Systems Analysis

Professor: Nour El Professor: Nour El KadriKadri

e-mail: nelkadri@ site.uottawa.cae-mail: nelkadri@ site.uottawa.ca

What is Quality?What is Quality?

QualityQuality: The ability of a product or : The ability of a product or service to consistently meet or service to consistently meet or exceed customer expectations.exceed customer expectations.– Not something tacked on, but an Not something tacked on, but an

integral part of the product/service.integral part of the product/service.– Comes from the fundamental process, Comes from the fundamental process,

not from material or from inspectionnot from material or from inspection

What does a customer What does a customer perceive as qualityperceive as quality

– PerformancePerformance– AestheticsAesthetics– FeaturesFeatures– ConformanceConformance– ReliabilityReliability– DurabilityDurability– Perceived quality (eg: reputation)Perceived quality (eg: reputation)– ServiceabilityServiceability

ExpectationsExpectations

– Customer’s perceptions and Customer’s perceptions and expectations shift and evolve:expectations shift and evolve: With product life cycle:With product life cycle: Features & functionality are critical in a Features & functionality are critical in a

leading-edge hi-tech productleading-edge hi-tech product Reliability, durability, serviceability are Reliability, durability, serviceability are

critical in a mature productcritical in a mature product With an evolving industry, market or With an evolving industry, market or

technology (cf: “Quality & the Ford Model technology (cf: “Quality & the Ford Model T”)T”)

Why is Quality Why is Quality Important?Important?

Quality is:Quality is:– A critical basis of competition (ie: A A critical basis of competition (ie: A

critical differentiator)critical differentiator)– Critical to SC effectiveness (Partners Critical to SC effectiveness (Partners

demand objective evidence of quality demand objective evidence of quality measures, programs)measures, programs)

– A measure of efficiency & cost saving A measure of efficiency & cost saving (“Quality does not cost anything”)(“Quality does not cost anything”)

– NB: Quality as one key identifier of a NB: Quality as one key identifier of a High-Performance companyHigh-Performance company

Cost of QualityCost of Quality– Prevention Costs: All training, planning, Prevention Costs: All training, planning,

customer assessment, process control, and customer assessment, process control, and quality improvement costs required to quality improvement costs required to prevent defects from occurringprevent defects from occurring

– Appraisal Costs: Costs of activities designed Appraisal Costs: Costs of activities designed to ensure quality or uncover defectsto ensure quality or uncover defects

– Failure Costs: Costs incurred by defective Failure Costs: Costs incurred by defective parts/products or faulty services.parts/products or faulty services. Internal Failure Costs: Costs incurred to fix Internal Failure Costs: Costs incurred to fix

problems that are detected before the problems that are detected before the product/service is delivered to the customer.product/service is delivered to the customer.

External Failure Costs: Costs incurred to fix External Failure Costs: Costs incurred to fix problems that are detected after the problems that are detected after the product/service is delivered to the customer.product/service is delivered to the customer.

Consequences of Poor Consequences of Poor QualityQuality

– LiabilityLiability– Loss of productivityLoss of productivity– Loss of business:Loss of business:

Dissatisfied customers will switchDissatisfied customers will switch You usually won’t know why (<5% of You usually won’t know why (<5% of

dissatisfied customers complain)dissatisfied customers complain) He will cost you add’l business (Average He will cost you add’l business (Average

dissatisfied customer will complain to 19 dissatisfied customer will complain to 19 others)others)

The Evolution of Quality The Evolution of Quality ManagementManagement

Craft production: Strict craftsman Craft production: Strict craftsman concern for qualityconcern for quality

Industrial revolution: Specialization, Industrial revolution: Specialization, division of labour. Little control of or division of labour. Little control of or identification with overall product identification with overall product qualityquality

SPC (Statistical Process SPC (Statistical Process Control)Control)

Sample mean value

Sample number

99.74%

0.13%

0.13%

Upper control limit

Lower control limit

Process mean

Normaltolerance

ofprocess

0 1 2 3 4 5 6 7 8

Quality Control ChartsQuality Control Charts

DefinitionsDefinitions VariablesVariables Measurements on a continuous scale, Measurements on a continuous scale,

such as such as length or weightlength or weight AttributesAttributes Integer counts of quality Integer counts of quality

characteristics, such characteristics, such as # of good or badas # of good or bad DefectDefect A single non-conforming quality A single non-conforming quality

characteristic, characteristic, such as a blemishsuch as a blemish DefectiveDefective A physical unit that contains one or A physical unit that contains one or

more more defectsdefects

Types of Control ChartsTypes of Control Charts

Data monitoredData monitored Chart name Chart name Sample sizeSample size

Mean, range of sample variablesMean, range of sample variables MR-CHART MR-CHART 2 to 5 2 to 5 unitsunits

Individual variablesIndividual variables I-CHART I-CHART 1 unit 1 unit % of defective units in a sample% of defective units in a sample P-CHART P-CHART at least 100 at least 100

unitsunits Number of defects per unitNumber of defects per unit C/U-CHART C/U-CHART 1 or 1 or

more unitsmore units

Control FactorsControl Factorsnn A A A A22 D D33 D D44 d d22 d d33

22 2.121 1.880 0 3.267 1.128 0.853 2.121 1.880 0 3.267 1.128 0.8533 1.732 1.0233 1.732 1.023 0 2.574 1.693 0.888 0 2.574 1.693 0.8884 1.500 0.7294 1.500 0.729 0 2.282 2.059 0.880 0 2.282 2.059 0.8805 1.342 0.577 0 2.114 2.316 0.8645 1.342 0.577 0 2.114 2.316 0.864

Control factors are used to convert the mean of sample Control factors are used to convert the mean of sample ranges ranges ( R ) to:( R ) to:

(1) standard deviation estimates for individual (1) standard deviation estimates for individual observations, observations, and and(2) standard error estimates for means and ranges of (2) standard error estimates for means and ranges of samplessamples

For example, an estimate of the population standard For example, an estimate of the population standard deviation of individual observations (deviation of individual observations (σσxx) is:) is:

σσxx = R / d = R / d22

Control Factors (cont.)Control Factors (cont.)

Note that control factors depend on the Note that control factors depend on the sample size nsample size n..

Relationships amongst control factors:Relationships amongst control factors:

AA22 = 3 / (d = 3 / (d22 x n x n1/21/2))

DD44 = 1 + 3 x d = 1 + 3 x d33/d/d22

DD33 = 1 – 3 x d = 1 – 3 x d33/d/d22, unless the result is negative, then D, unless the result is negative, then D33 = = 00

A = 3 / nA = 3 / n1/21/2

DD22 = d = d2 2 + 3d+ 3d33

DD11 = d = d2 2 – 3d– 3d33, unless the result is negative, then D, unless the result is negative, then D11 = 0 = 0

Mean-Range control chartMean-Range control chartMR-CHARTMR-CHART

1. Compute the mean of sample means ( X ).1. Compute the mean of sample means ( X ).

2. Compute the mean of sample ranges ( R ).2. Compute the mean of sample ranges ( R ).

3. Set 3-std.-dev. control limits for the sample means:3. Set 3-std.-dev. control limits for the sample means:

UCL = X + AUCL = X + A22RR

LCL = X – ALCL = X – A22RR

4. Set 3-std.-dev. control limits for the sample ranges:4. Set 3-std.-dev. control limits for the sample ranges:

UCL = DUCL = D44RR

LCL = DLCL = D33RR

Control chart for percentage Control chart for percentage defective in a sample — P-CHARTdefective in a sample — P-CHART

1. Compute the mean percentage defective ( P ) 1. Compute the mean percentage defective ( P ) for all samplesfor all samples::

P = Total nbr. of units defective / Total nbr. of units P = Total nbr. of units defective / Total nbr. of units sampledsampled

2. Compute an individual standard error (S2. Compute an individual standard error (SPP ) ) for each samplefor each sample::

SSPP = [( P (1-P ))/n] = [( P (1-P ))/n]1/21/2

Note: n is the sample size, Note: n is the sample size, notnot the total units sampled. the total units sampled.

If n is constant, each sample has the same standard If n is constant, each sample has the same standard error.error.

3. Set 3-std.-dev. control limits:3. Set 3-std.-dev. control limits:

UCL = P + 3SUCL = P + 3SPP

LCL = P – 3SLCL = P – 3SPP

Control chart for individual Control chart for individual observations — I-CHARTobservations — I-CHART

1. Compute the mean observation value ( X )1. Compute the mean observation value ( X )

X = Sum of observation values / NX = Sum of observation values / N

where N is the number of observationswhere N is the number of observations

2. Compute 2. Compute moving range absolute valuesmoving range absolute values, starting at obs. , starting at obs. nbr. 2:nbr. 2:

Moving range for obs. 2 = obs. 2 – obs. 1Moving range for obs. 2 = obs. 2 – obs. 1

Moving range for obs. 3 = obs. 3 – obs. 2Moving range for obs. 3 = obs. 3 – obs. 2

……

Moving range for obs. N = obs. N – obs. N – 1 Moving range for obs. N = obs. N – obs. N – 1

3. Compute the mean of the moving ranges ( R ):3. Compute the mean of the moving ranges ( R ):

R = Sum of the moving ranges / N – 1 R = Sum of the moving ranges / N – 1

Control chart for individual Control chart for individual observations — I-CHART (cont.)observations — I-CHART (cont.)

4. Estimate the population standard deviation (4. Estimate the population standard deviation (σσXX):):

σσXX = R / d = R / d22

Note: Sample size is always 2, so dNote: Sample size is always 2, so d22 = 1.128. = 1.128.

5. Set 3-std.-dev. control limits:5. Set 3-std.-dev. control limits:

UCL = X + 3UCL = X + 3σσXX

LCL = X – 3LCL = X – 3σσXX

Control chart for number of Control chart for number of defects per unit — C/U-CHARTdefects per unit — C/U-CHART

1. Compute the mean nbr. of defects per unit ( C ) 1. Compute the mean nbr. of defects per unit ( C ) for all samplesfor all samples::C = Total nbr. of defects observed / Total nbr. of units C = Total nbr. of defects observed / Total nbr. of units

sampledsampled

2. Compute an individual standard error 2. Compute an individual standard error for each samplefor each sample::

SSCC = ( C / n) = ( C / n)1/21/2

Note: n is the sample size, Note: n is the sample size, notnot the total units sampled. the total units sampled.If n is constant, each sample has the same standard error.If n is constant, each sample has the same standard error.

3. Set 3-std.-dev. control limits:3. Set 3-std.-dev. control limits:

UCL = C + 3SUCL = C + 3SCC

LCL = C – 3SLCL = C – 3SCC

Notes:Notes:● ● If the sample size is constant, the chart is a C-CHART.If the sample size is constant, the chart is a C-CHART.● ● If the sample size varies, the chart is a U-CHART.If the sample size varies, the chart is a U-CHART.● ● Computations are the same in either case.Computations are the same in either case.

SPC & Cost of QualitySPC & Cost of Quality

– Deming (Promoted SPC in Japan):Deming (Promoted SPC in Japan): The cause of poor quality is the system, not The cause of poor quality is the system, not

the employeethe employee Mgmt is responsible to correct poor qualityMgmt is responsible to correct poor quality

– Juran (“Cost of Quality”: Emphasized Juran (“Cost of Quality”: Emphasized need for accurate and complete need for accurate and complete identification of the costs of quality) :identification of the costs of quality) : Quality means fitness for useQuality means fitness for use Quality begins in knowing what customers Quality begins in knowing what customers

want, planning processes which are capable want, planning processes which are capable of producing the required level of qualityof producing the required level of quality

From Quality to Quality From Quality to Quality AssuranceAssurance

Changing emphasis from “Quality” to “Quality Changing emphasis from “Quality” to “Quality Assurance”(Prevent defects rather than finding Assurance”(Prevent defects rather than finding them after they occur)them after they occur)

New techniques for Quality Improvement New techniques for Quality Improvement (eg: TQM, Six Sigma):(eg: TQM, Six Sigma):

New quality programs (Provide objective New quality programs (Provide objective measures of quality for use of customers, SC measures of quality for use of customers, SC partners, etc.)partners, etc.)– Baldridge AwardBaldridge Award– ISO 9000/14000 CertificationISO 9000/14000 Certification– Industry-specific programs (eg: TL9000(Telecom))Industry-specific programs (eg: TL9000(Telecom))

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Importance to customer

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Doesn’t leak in rainNo road noise

Importance weighting

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Source: Based on John R. Source: Based on John R. Hauser Hauser and Don Clausing, “The and Don Clausing, “The House of House of Quality,” Quality,” Harvard Business Harvard Business ReviewReview, , May-June 1988. May-June 1988.

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Taguchi analysisTaguchi analysisLoss functionLoss functionL(x) = k(x-T)L(x) = k(x-T)22

wherewherex = any individual value of the quality characteristicx = any individual value of the quality characteristicT = target quality valueT = target quality valuek = constant = L(x) / (x-T)k = constant = L(x) / (x-T)22

Average or expected loss, variance Average or expected loss, variance knownknownE[L(x)] = k(E[L(x)] = k(σσ22 + D + D22))wherewhere

σσ22 = Variance of quality characteristic = Variance of quality characteristic DD22 = ( x – T) = ( x – T)22

Note: x is the mean quality characteristic. DNote: x is the mean quality characteristic. D22 is zero if the is zero if the mean equals the target.mean equals the target.

Taguchi analysis (cont.)Taguchi analysis (cont.)

Average or expected loss, variance Average or expected loss, variance unkownunkown

E[L(x)] = k[E[L(x)] = k[ΣΣ ( x – T) ( x – T)22 / n] / n]

When smaller is betterWhen smaller is better (e.g., percent of impurities) (e.g., percent of impurities)

L(x) = kxL(x) = kx22

When larger is betterWhen larger is better (e.g., product life) (e.g., product life)

L(x) = k (1/xL(x) = k (1/x22))

TQMTQM Total Quality Management: A philosophy Total Quality Management: A philosophy

that involves everyone in an organization that involves everyone in an organization in a continual effort to improve quality in a continual effort to improve quality and achieve customer satisfaction.and achieve customer satisfaction.– The TQM Approach:The TQM Approach:

Find out what the customer wantsFind out what the customer wants Design a product or service that meets or exceeds Design a product or service that meets or exceeds

customer wantscustomer wants Design processes that facilitates doing the job Design processes that facilitates doing the job

right the first timeright the first time Keep track of resultsKeep track of results Extend quality initiatives to include suppliers & Extend quality initiatives to include suppliers &

distributors.distributors.

Elements of TQMElements of TQM

Continual improvementContinual improvement Competitive benchmarkingCompetitive benchmarking Employee empowerment (eg: Quality circles, Employee empowerment (eg: Quality circles,

etc.)etc.) Team approachTeam approach Decisions based on factsDecisions based on facts Knowledge of toolsKnowledge of tools Supplier qualitySupplier quality Identify and use quality championIdentify and use quality champion Develop quality at the sourceDevelop quality at the source Include suppliersInclude suppliers

Criticism of TQMCriticism of TQM

– Criticisms of TQM include:Criticisms of TQM include: Blind pursuit of TQM programsBlind pursuit of TQM programs Programs may not be linked to strategiesPrograms may not be linked to strategies Quality-related decisions may not be tied to Quality-related decisions may not be tied to

market performancemarket performance Failure to carefully plan the programFailure to carefully plan the program

Obstacles to Implementing Obstacles to Implementing TQMTQM

Poor inter-organizational communicationPoor inter-organizational communication View of quality as a “quick fix”View of quality as a “quick fix” Emphasis on short-term financial resultsEmphasis on short-term financial results Internal political and “turf” warsInternal political and “turf” wars Lack of:Lack of:

– Company-wide definition of qualityCompany-wide definition of quality– Strategic plan for changeStrategic plan for change– Customer focusCustomer focus– Real employee empowermentReal employee empowerment– Strong motivationStrong motivation– Time to devote to quality initiativesTime to devote to quality initiatives– LeadershipLeadership

Six SigmaSix Sigma

Six Sigma Six Sigma (eg: Jack Welch @ GE):(eg: Jack Welch @ GE):– Statistically: Having no more than 3.4 Statistically: Having no more than 3.4

defects per milliondefects per million– Conceptually: A program designed to Conceptually: A program designed to

reduce defectsreduce defects

Six Sigma programs Six Sigma programs Improve quality, save time & cut costsImprove quality, save time & cut costs Are employed in a wide variety of areas (Design, Are employed in a wide variety of areas (Design,

Production, Service, Inventory , Management, Production, Service, Inventory , Management, Delivery)Delivery)

Focus on management as well as on the Focus on management as well as on the technical componenttechnical component

Requires specific tools & techniquesRequires specific tools & techniques Require commitment & active participation by sr Require commitment & active participation by sr

management to:management to: Provide strong leadershipProvide strong leadership Define performance metrics Define performance metrics Select projects likely to succeedSelect projects likely to succeed

Six Sigma ProgramsSix Sigma Programs

Select and train appropriate peopleSelect and train appropriate people The team includes top management & The team includes top management &

program champions as well as master “black program champions as well as master “black belts”, “Black belts” & “Green belts”belts”, “Black belts” & “Green belts”

A methodical, five- step process: Define, A methodical, five- step process: Define, Measure, Analyze, Improve, Control (DMAIC)Measure, Analyze, Improve, Control (DMAIC)

Process Capability AnalysisProcess Capability Analysis

1. Compute the mean of sample means ( X ).1. Compute the mean of sample means ( X ).

2. Compute the mean of sample ranges ( R ).2. Compute the mean of sample ranges ( R ).

3. Estimate the population standard deviation (3. Estimate the population standard deviation (σσxx):):

σσxx = R / d = R / d22

4. Estimate the natural tolerance of the process:4. Estimate the natural tolerance of the process:

Natural tolerance = 6Natural tolerance = 6σσxx

5. Determine the specification limits:5. Determine the specification limits:

USL = Upper specification limitUSL = Upper specification limit

LSL = Lower specification limitLSL = Lower specification limit

Process capability analysis Process capability analysis (cont.)(cont.)

6. Compute capability indices:6. Compute capability indices:

Process capability potentialProcess capability potential

CCpp = (USL – LSL) / 6 = (USL – LSL) / 6σσxx

Upper capability indexUpper capability index

CCpUpU = (USL – X ) / 3 = (USL – X ) / 3σσxx

Lower capability indexLower capability index

CCpLpL = ( X – LSL) / 3 = ( X – LSL) / 3σσxx

Process capability indexProcess capability index

CCpkpk = Minimum (C = Minimum (CpUpU, C, CpLpL) )

Multiplicative seasonalityMultiplicative seasonality

The seasonal index is the expected The seasonal index is the expected ratioratio of of actual data to the average for the year.actual data to the average for the year.

Actual data / Index = Seasonally adjusted dataActual data / Index = Seasonally adjusted data

Seasonally adjusted data x Index = Actual dataSeasonally adjusted data x Index = Actual data

Multiplicative seasonal Multiplicative seasonal adjustmentadjustment

1.1. Compute moving average based on length of Compute moving average based on length of seasonality (4 quarters or 12 months).seasonality (4 quarters or 12 months).

2.2. Divide actual data by corresponding moving average.Divide actual data by corresponding moving average.

3.3. Average ratios to eliminate randomness.Average ratios to eliminate randomness.

4.4. Compute normalization factor to adjust mean ratios so Compute normalization factor to adjust mean ratios so they sum to 4 (quarterly data) or 12 (monthly data).they sum to 4 (quarterly data) or 12 (monthly data).

5.5. Multiply mean ratios by normalization factor to get Multiply mean ratios by normalization factor to get final seasonal indexes.final seasonal indexes.

6.6. Deseasonalize data by dividing by the seasonal index.Deseasonalize data by dividing by the seasonal index.

7.7. Forecast deseasonalized data.Forecast deseasonalized data.

8.8. Seasonalize forecasts from step 7 to get final Seasonalize forecasts from step 7 to get final forecasts.forecasts.

Additive seasonalityAdditive seasonality

The seasonal index is the expected The seasonal index is the expected differencedifference between actual data and the average for the between actual data and the average for the year.year.

Actual data - Index = Seasonally adjusted Actual data - Index = Seasonally adjusted datadata

Seasonally adjusted data + Index = Actual Seasonally adjusted data + Index = Actual datadata

Additive seasonal adjustmentAdditive seasonal adjustment

1.1. Compute moving average based on length of Compute moving average based on length of seasonality (4 quarters or 12 months).seasonality (4 quarters or 12 months).

2.2. Compute differences: Actual data - moving average.Compute differences: Actual data - moving average.

3.3. Average differences to eliminate randomness.Average differences to eliminate randomness.

4.4. Compute normalization factor to adjust mean Compute normalization factor to adjust mean differences so they sum to zero.differences so they sum to zero.

5.5. Compute final indexes: Mean difference – Compute final indexes: Mean difference – normalization factor.normalization factor.

6.6. Deseasonalize data: Actual data – seasonal index.Deseasonalize data: Actual data – seasonal index.

7.7. Forecast deseasonalized data.Forecast deseasonalized data.

8.8. Seasonalize forecasts from step 7 to get final Seasonalize forecasts from step 7 to get final forecasts.forecasts.

How to start up a control chart How to start up a control chart systemsystem

1.1. Identify quality characteristics. Identify quality characteristics.

2. Choose a quality indicator.2. Choose a quality indicator.

3. Choose the type of chart.3. Choose the type of chart.

4. Decide when to sample.4. Decide when to sample.

5. Choose a sample size.5. Choose a sample size.

6. Collect representative data.6. Collect representative data.

7. If data are seasonal, perform seasonal adjustment.7. If data are seasonal, perform seasonal adjustment.

8. Graph the data and adjust for outliers.8. Graph the data and adjust for outliers.

How to start up a control chart How to start up a control chart system (cont.)system (cont.)

9. Compute control limits9. Compute control limits

10. Investigate and adjust special-cause variation.10. Investigate and adjust special-cause variation.

11. Divide data into two samples and test stability of limits.11. Divide data into two samples and test stability of limits.

12. If data are variables, perform a process capability study:12. If data are variables, perform a process capability study:a. Estimate the population standard deviation.a. Estimate the population standard deviation.b. Estimate natural tolerance.b. Estimate natural tolerance.c. Compute process capability indices.c. Compute process capability indices.d. Check individual observations against d. Check individual observations against

specifications.specifications.

13. Return to step 1.13. Return to step 1.

Quick reference to quality Quick reference to quality formulasformulas

Control factorsControl factors

nn A A A A22 D D33 D D44 d d22 d d33

2 2.121 1.880 0 3.267 1.128 0.8532 2.121 1.880 0 3.267 1.128 0.853

3 1.732 1.0233 1.732 1.023 0 2.574 1.693 0.888 0 2.574 1.693 0.888

4 1.500 0.7294 1.500 0.729 0 2.282 2.059 0.880 0 2.282 2.059 0.880

5 1.342 0.577 0 2.114 2.316 0.8645 1.342 0.577 0 2.114 2.316 0.864

Process capability analysisProcess capability analysis

σσxx = R / d = R / d22

CCpp = (USL – LSL) / 6 = (USL – LSL) / 6σσxx CCpUpU = (USL – X ) / = (USL – X ) / 33σσxx

CCpLpL = ( X – LSL) / 3 = ( X – LSL) / 3σσxx C Cpkpk = Minimum (C = Minimum (CpUpU, , CCpLpL) )

Quick reference to quality Quick reference to quality formulas (cont.)formulas (cont.)

Means and rangesMeans and rangesUCL = X + AUCL = X + A22RR UCL = DUCL = D44RRLCL = X – ALCL = X – A22RR LCL = DLCL = D33RR

Percentage defective in a samplePercentage defective in a sample SSPP = [( P (1-P ))/n] = [( P (1-P ))/n]1/21/2 UCL = P + 3SUCL = P + 3SPP

LCL = P – 3SLCL = P – 3SPP

Individual quality observationsIndividual quality observations σσxx = R / d = R / d22 UCL = X + 3UCL = X + 3σσXX

LCL = X – 3LCL = X – 3σσXX

Number of defects per unitNumber of defects per unitSSCC = ( C / n) = ( C / n)1/21/2 UCL = C + 3SUCL = C + 3SCC

LCL = C – 3SLCL = C – 3SCC