Statistical Process Statistical Process Control for Short-Control for Short-

RunsRunsDepartment of Industrial & Manufacturing Department of Industrial & Manufacturing

EngineeringEngineering

Tyler ManginTyler ManginCanan BilenCanan Bilen

5-22-025-22-02

BackgroundBackground

B.S. Industrial Engineering from North B.S. Industrial Engineering from North Dakota State UniversityDakota State University

Emphasis on Statistical Quality ControlEmphasis on Statistical Quality Control

Experience:Experience: Quality control internshipQuality control internship Consortium of contract manufacturers in North DakotaConsortium of contract manufacturers in North Dakota Center for Nanoscale Science and EngineeringCenter for Nanoscale Science and Engineering

IntroductionIntroduction Introduction to SPCIntroduction to SPC

Manufacturing environment in North DakotaManufacturing environment in North Dakota

Short-run manufacturingShort-run manufacturing

Short-run SPC techniquesShort-run SPC techniques Strengths and weaknesses of these techniquesStrengths and weaknesses of these techniques

Future workFuture work

Statistical ThinkingStatistical Thinking

All work occurs in a system of All work occurs in a system of interconnected processesinterconnected processes

Variation exists in all processesVariation exists in all processes

Understanding & reducing variation are Understanding & reducing variation are keys to successeskeys to successes

Statistical Process Statistical Process ControlControl

PurposePurpose Methodology for monitoring a processMethodology for monitoring a process Proven technique for improving quality and Proven technique for improving quality and

productivityproductivity Identifies special causes of variationIdentifies special causes of variation Signals the need to take corrective actionSignals the need to take corrective action Should be usable Should be usable (with minimal or no math (with minimal or no math

background)background)

Manufacturing in North Manufacturing in North DakotaDakota

Small to medium job shops and contract Small to medium job shops and contract manufacturers are commonmanufacturers are common

Metal fabrication and electronics manufacturing Metal fabrication and electronics manufacturing facilities will be most accessiblefacilities will be most accessible

Operators have minimal mathematics and SPC Operators have minimal mathematics and SPC trainingtraining

Limited resources available to implement SPCLimited resources available to implement SPC

Statistical Quality Needs Statistical Quality Needs in NDin ND

Should address short-run productionShould address short-run production

The techniques should be kept as simple as The techniques should be kept as simple as possiblepossible

Keep computation needs to a minimumKeep computation needs to a minimum

SPC should demonstrate significant cost SPC should demonstrate significant cost reduction (in short duration)reduction (in short duration)

Short-Run Short-Run ManufacturingManufacturing

Standard for job shopsStandard for job shops Common in advanced manufacturingCommon in advanced manufacturing Driven by:Driven by:

Demand for mass customizationDemand for mass customization Availability of flexible production equipmentAvailability of flexible production equipment Use of “just in time” techniquesUse of “just in time” techniques

Short-runs result in:Short-runs result in: Smaller lot sizesSmaller lot sizes Shorter lead timesShorter lead times Less available process dataLess available process data

““A production run that is not long enough to A production run that is not long enough to provide adequate data to construct a control chart.”provide adequate data to construct a control chart.”

Barriers to SPC in Short-Barriers to SPC in Short-Run ManufacturingRun Manufacturing

Multiple part typesMultiple part types Setups and changeoversSetups and changeovers Data scarcityData scarcity Cost minimizationCost minimization Need for simplicityNeed for simplicity

Multiple Part TypesMultiple Part Types

Each part is likely to have a different Each part is likely to have a different average and standard deviationaverage and standard deviation

Unique control charts required for each Unique control charts required for each chartchart

Difficult to detect time-related changesDifficult to detect time-related changes Adds cost to the product

Creates excessive paperwork Decreases operator efficiency

Setups and ChangeoversSetups and Changeovers Setup is a frequently occurring part of process Setup is a frequently occurring part of process

operationoperation Introduce special causes of variation into the processIntroduce special causes of variation into the process Importance of knowing whether the first part is “on-Importance of knowing whether the first part is “on-

target”target” Two types of process capability:Two types of process capability:

1)1) Capability after process has been brought into Capability after process has been brought into controlcontrol

2)2) Capability across runs if the process were run Capability across runs if the process were run without adjustment after initial setupwithout adjustment after initial setup

Creates the need to monitor “run-to-run variation”Creates the need to monitor “run-to-run variation” Ensuring quick, consistent setups is critical Ensuring quick, consistent setups is critical

Data ScarcityData Scarcity Traditional charts require a large amount of data Traditional charts require a large amount of data

Recommended: at least 25 subgroups of size 5Recommended: at least 25 subgroups of size 5

Short-runs do not generate enough dataShort-runs do not generate enough data

If control limits are calculated, they will be unreliableIf control limits are calculated, they will be unreliable

Historical data may not be availableHistorical data may not be available

The data for “short-runs” is likely to be auto-correlatedThe data for “short-runs” is likely to be auto-correlated

Minimizing CostMinimizing Cost Maximize revenue by reducing quality-related Maximize revenue by reducing quality-related

costscostsSampling and inspection costsSampling and inspection costsProcess repair costsProcess repair costsCost of false alarmsCost of false alarmsCost of poor qualityCost of poor quality

Based on the lifetime of the production runBased on the lifetime of the production run

Economic control chart designEconomic control chart design

Need for SimplicityNeed for Simplicity Regional companies lack resources and Regional companies lack resources and

experience with SPCexperience with SPC

Operator must be able to manage the control Operator must be able to manage the control chartscharts

If it is not easy to use, it will not be usedIf it is not easy to use, it will not be used

True benefits of SPC come from interaction with True benefits of SPC come from interaction with the processthe process

Approaches to Short-Run Approaches to Short-Run SPCSPC

DNOM chartsDNOM charts Standardized chartsStandardized charts Q-chartsQ-charts Bayesian quality controlBayesian quality control Monitoring run-to-run variationMonitoring run-to-run variation

DNOM Charts:DNOM Charts:Deviation from NominalDeviation from Nominal

PrinciplesPrinciples Different parts will have different target valuesDifferent parts will have different target values

Calculate the deviation from nominal valueCalculate the deviation from nominal value

Plot deviation as the quality characteristicPlot deviation as the quality characteristic

Infinity Windows Sample Infinity Windows Sample DataData

Part Date TimeNominal Length

Actual Length

Right Jamb 14-Feb 6:51 AM 59.268 59.258Header 14-Feb 6:54 AM 23 22.993Header 14-Feb 6:56 AM 35.875 35.86Right Jamb 14-Feb 7:00 AM 37.518 37.511Left Jamb 14-Feb 7:08 AM 37.518 37.507Header 14-Feb 7:12 AM 43.875 43.869Header 14-Feb 7:14 AM 27.75 27.75Right Jamb 14-Feb 7:15 AM 37.518 37.5169Left Jamb 14-Feb 7:18 AM 37.518 37.5071Header 14-Feb 10:06 AM 39.875 39.8617

Three part types:Three part types: HeaderHeader Right jambRight jamb Left jambLeft jamb

Nominal length varies Nominal length varies from part to partfrom part to part

Continuous runs; no Continuous runs; no batchesbatches

DNOM ChartDNOM Chart

Infinity Windows Data

-0.03

-0.02

-0.01

0

0.01

0.02

1 5 9 13 17 21 25

Sample Number

De

via

tio

n f

rom

No

min

al

UCL = 0.0137

CL = - 0.0046

LCL = - 0.023

DNOM ChartsDNOM Charts

StrengthsStrengths Groups multiple parts and their data sets on a single Groups multiple parts and their data sets on a single

chartchart Provides a continuous view of the processProvides a continuous view of the process Fairly simple to construct and understandFairly simple to construct and understand

ShortcomingsShortcomings Assumes variation is equal for all partsAssumes variation is equal for all parts Requires some historical data to calculate control Requires some historical data to calculate control

limitslimits Does not address quality costsDoes not address quality costs Only tracks within-run variationOnly tracks within-run variation

PrinciplesPrinciples Multiple part-types flow through a single Multiple part-types flow through a single

machinemachine

Different parts may have different target Different parts may have different target valuesvalues

Control limits and plot points are standardized Control limits and plot points are standardized to allow charting of multiple part-typesto allow charting of multiple part-types

Standardized Control Standardized Control ChartsCharts

Standardized Control Standardized Control ChartsCharts

StrengthsStrengths Groups multiple parts and their data sets on a single Groups multiple parts and their data sets on a single

chartchart Provides a continuous view of the processProvides a continuous view of the process Fairly simple to construct and understandFairly simple to construct and understand Does Does notnot assume all parts have equal variation assume all parts have equal variation

ShortcomingsShortcomings Requires some historical data to calculate control Requires some historical data to calculate control

limitslimits Does not address quality costsDoes not address quality costs Only tracks within-run variationOnly tracks within-run variation

Sample Standardized Sample Standardized ChartChart

Sample Standardized Chart

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1 5 9 13 17 21 25 29

Sample Number

Sta

nd

ard

ize

d V

alu

es

UCL = 0.577

CL = 0

LCL = - 0.577

Part A Part B Part C

Q-Charts:Q-Charts:Self-updating, standardized chartsSelf-updating, standardized charts

PrinciplesPrinciples Standardize the quality characteristic of interestStandardize the quality characteristic of interest

The standardized statistic will be i.i.d. N(0,1)The standardized statistic will be i.i.d. N(0,1)

Plots multiple part types on a standardized chartPlots multiple part types on a standardized chart

Can begin charting with no historical dataCan begin charting with no historical data

Uses all available information to estimate the Uses all available information to estimate the parameters (updating control limits)parameters (updating control limits)

Q-ChartsQ-Charts

StrengthsStrengths Charts can be made in real time beginning with the first Charts can be made in real time beginning with the first

production unitproduction unit Does not assume process mean or variation are known in Does not assume process mean or variation are known in

advanceadvance Does not assume all parts have the same variationDoes not assume all parts have the same variation Multiple part types can be plotted on a single chartMultiple part types can be plotted on a single chart Uses all available data to update control limitsUses all available data to update control limits

ShortcomingsShortcomings Does not address quality costsDoes not address quality costs May not be clear to the operatorMay not be clear to the operator Strictly monitors within-run variation Strictly monitors within-run variation Lacks simplicityLacks simplicity requires a PC requires a PC

Bayesian Quality Control:Bayesian Quality Control:Economic chartsEconomic charts

PrinciplesPrinciples The system is modeled by partially observable Markov The system is modeled by partially observable Markov

processesprocesses The system is generally assumed to have two states: The system is generally assumed to have two states:

in-control & out-of-controlin-control & out-of-control The operator is faced with certain action-decisions:The operator is faced with certain action-decisions:

Do nothingDo nothingInspect outputInspect outputInspect machineInspect machineRepair machineRepair machine

The model is a decision-making tool for minimizing The model is a decision-making tool for minimizing quality costs over the length of the production runquality costs over the length of the production run

Bayesian Quality ControlBayesian Quality Control

StrengthsStrengths Addresses quality costs as a factor in process controlAddresses quality costs as a factor in process control Advises operators on which action to take based on Advises operators on which action to take based on

probabilistic analysisprobabilistic analysis Accounts for finite production horizonAccounts for finite production horizon

ShortcomingsShortcomings Models require accurate historical dataModels require accurate historical data Models must be individualized to the specific Models must be individualized to the specific

production processproduction process Not designed to handle multiple part typesNot designed to handle multiple part types

Monitoring Run-to-Run Monitoring Run-to-Run Variation:Variation:A new conceptA new concept

Setups are:Setups are: Time between last unit of one run and first good unit of Time between last unit of one run and first good unit of

the next runthe next run Integral part of process operationIntegral part of process operation Occur frequentlyOccur frequently

Reducing setup time implies reduction of:Reducing setup time implies reduction of: Test runsTest runs InspectionsInspections Process adjustmentProcess adjustment Scrap & reworkScrap & rework

Monitoring Run-to-Run Monitoring Run-to-Run VariationVariation

PrinciplesPrinciples Plot the mean of the first sample taken after setupPlot the mean of the first sample taken after setup

Each setup generates one plot pointEach setup generates one plot point

Plot each setup on one control chartPlot each setup on one control chart

Over time setup related variation is detectedOver time setup related variation is detected

Attempts to detect “run-to-run” variationAttempts to detect “run-to-run” variation

Monitoring Run-to-Run Monitoring Run-to-Run VariationVariation

StrengthsStrengths Addresses setup induced variationAddresses setup induced variation Becomes more effective as setups become Becomes more effective as setups become

more commonmore common Is a philosophy not a techniqueIs a philosophy not a technique

ShortcomingsShortcomings Long-term approachLong-term approach Does not address data scarcityDoes not address data scarcity Does not address quality costsDoes not address quality costs Lacks a well-defined methodologyLacks a well-defined methodology

SPC Techniques SPC Techniques SummarySummary

MultiplePart

Types

SetupRelated

VariationData

ScarcityQuality Costs

Simplicity &

Usability

DNOM charts + +

Standardized Charts + +

Q-Charts + +

BayesianQuality Control +

Run-to-run Variation + +

Future WorkFuture WorkDevelop “Run-to-Run Variation Charts” Develop “Run-to-Run Variation Charts” as the focus of my thesis:as the focus of my thesis:

Further analysis of the shortcomings of the Further analysis of the shortcomings of the “Monitoring Run-to-Run” framework“Monitoring Run-to-Run” framework

Determine needs of job-shops and other low-Determine needs of job-shops and other low-volume manufacturersvolume manufacturers

Modify the Run-to-Run charts to fit the needs of Modify the Run-to-Run charts to fit the needs of regional companiesregional companies

Develop guidelines to maximize the potential for Develop guidelines to maximize the potential for implementationimplementation

ReviewReview Introduction to SPCIntroduction to SPC

Manufacturing environment in North DakotaManufacturing environment in North Dakota

Short-run manufacturingShort-run manufacturing

Short-run SPC techniquesShort-run SPC techniques Strengths and weaknesses of these techniquesStrengths and weaknesses of these techniques

Future workFuture work

Thanks to…Thanks to…

Dr. BilenDr. Bilen Ritesh SalujaRitesh Saluja Faculty and staff of NDSU’s Industrial and Faculty and staff of NDSU’s Industrial and

Manufacturing Engr. departmentManufacturing Engr. department QPR ConferenceQPR Conference

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