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Statistical Process Control for Short-Runs Department of Industrial & Manufacturing Engineering Tyler Mangin Canan Bilen 5-22-02.

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Presentation on theme: "Statistical Process Control for Short-Runs Department of Industrial & Manufacturing Engineering Tyler Mangin Canan Bilen 5-22-02."— Presentation transcript:

1 Statistical Process Control for Short-Runs Department of Industrial & Manufacturing Engineering Tyler Mangin Canan Bilen

2 Background B.S. Industrial Engineering from North Dakota State University B.S. Industrial Engineering from North Dakota State University Emphasis on Statistical Quality Control Emphasis on Statistical Quality Control Experience: Experience: Quality control internship Quality control internship Consortium of contract manufacturers in North Dakota Consortium of contract manufacturers in North Dakota Center for Nanoscale Science and Engineering Center for Nanoscale Science and Engineering

3 Introduction Introduction to SPC Introduction to SPC Manufacturing environment in North Dakota Manufacturing environment in North Dakota Short-run manufacturing Short-run manufacturing Short-run SPC techniques Short-run SPC techniques Strengths and weaknesses of these techniques Strengths and weaknesses of these techniques Future work Future work

4 Statistical Thinking All work occurs in a system of interconnected processes All work occurs in a system of interconnected processes Variation exists in all processes Variation exists in all processes Understanding & reducing variation are keys to successes Understanding & reducing variation are keys to successes

5 Statistical Process Control Purpose Methodology for monitoring a process Methodology for monitoring a process Proven technique for improving quality and productivity Proven technique for improving quality and productivity Identifies special causes of variation Identifies special causes of variation Signals the need to take corrective action Signals the need to take corrective action Should be usable (with minimal or no math background) Should be usable (with minimal or no math background)

6 Manufacturing in North Dakota Small to medium job shops and contract manufacturers are common Small to medium job shops and contract manufacturers are common Metal fabrication and electronics manufacturing facilities will be most accessible Metal fabrication and electronics manufacturing facilities will be most accessible Operators have minimal mathematics and SPC training Operators have minimal mathematics and SPC training Limited resources available to implement SPC Limited resources available to implement SPC

7 Statistical Quality Needs in ND Should address short-run production Should address short-run production The techniques should be kept as simple as possible The techniques should be kept as simple as possible Keep computation needs to a minimum Keep computation needs to a minimum SPC should demonstrate significant cost reduction (in short duration) SPC should demonstrate significant cost reduction (in short duration)

8 Short-Run Manufacturing Standard for job shops Standard for job shops Common in advanced manufacturing Common in advanced manufacturing Driven by: Driven by: Demand for mass customization Demand for mass customization Availability of flexible production equipment Availability of flexible production equipment Use of just in time techniques Use of just in time techniques Short-runs result in: Short-runs result in: Smaller lot sizes Smaller lot sizes Shorter lead times Shorter lead times Less available process data Less available process data A production run that is not long enough to provide adequate data to construct a control chart.

9 Barriers to SPC in Short-Run Manufacturing Multiple part types Multiple part types Setups and changeovers Setups and changeovers Data scarcity Data scarcity Cost minimization Cost minimization Need for simplicity Need for simplicity

10 Multiple Part Types Each part is likely to have a different average and standard deviation Each part is likely to have a different average and standard deviation Unique control charts required for each chart Unique control charts required for each chart Difficult to detect time-related changes Difficult to detect time-related changes Adds cost to the product Creates excessive paperwork Decreases operator efficiency

11 Setups and Changeovers Setup is a frequently occurring part of process operation Setup is a frequently occurring part of process operation Introduce special causes of variation into the process Introduce special causes of variation into the process Importance of knowing whether the first part is on- target Importance of knowing whether the first part is on- target Two types of process capability: Two types of process capability: 1)Capability after process has been brought into control 2)Capability across runs if the process were run without 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

12 Data Scarcity Traditional charts require a large amount of data Traditional charts require a large amount of data Recommended: at least 25 subgroups of size 5 Recommended: at least 25 subgroups of size 5 Short-runs do not generate enough data Short-runs do not generate enough data If control limits are calculated, they will be unreliable If control limits are calculated, they will be unreliable Historical data may not be available Historical data may not be available The data for short-runs is likely to be auto-correlated The data for short-runs is likely to be auto-correlated

13 Minimizing Cost Maximize revenue by reducing quality-related costs Maximize revenue by reducing quality-related costs Sampling and inspection costs Sampling and inspection costs Process repair costs Process repair costs Cost of false alarms Cost of false alarms Cost of poor quality Cost of poor quality Based on the lifetime of the production run Based on the lifetime of the production run Economic control chart design Economic control chart design

14 Need for Simplicity Regional companies lack resources and experience with SPC Regional companies lack resources and experience with SPC Operator must be able to manage the control charts Operator must be able to manage the control charts If it is not easy to use, it will not be used If it is not easy to use, it will not be used True benefits of SPC come from interaction with the process True benefits of SPC come from interaction with the process

15 Approaches to Short-Run SPC DNOM charts DNOM charts Standardized charts Standardized charts Q-charts Q-charts Bayesian quality control Bayesian quality control Monitoring run-to-run variation Monitoring run-to-run variation

16 DNOM Charts: Deviation from Nominal Principles Different parts will have different target values Different parts will have different target values Calculate the deviation from nominal value Calculate the deviation from nominal value Plot deviation as the quality characteristic Plot deviation as the quality characteristic

17 Infinity Windows Sample Data Three part types: Three part types: Header Header Right jamb Right jamb Left jamb Left jamb Nominal length varies from part to part Nominal length varies from part to part Continuous runs; no batches Continuous runs; no batches

18 DNOM Chart UCL = CL = LCL =

19 DNOM Charts Strengths Groups multiple parts and their data sets on a single chart Groups multiple parts and their data sets on a single chart Provides a continuous view of the process Provides a continuous view of the process Fairly simple to construct and understand Fairly simple to construct and understandShortcomings Assumes variation is equal for all parts Assumes variation is equal for all parts Requires some historical data to calculate control limits Requires some historical data to calculate control limits Does not address quality costs Does not address quality costs Only tracks within-run variation Only tracks within-run variation

20 Principles Multiple part-types flow through a single machine Multiple part-types flow through a single machine Different parts may have different target values Different parts may have different target values Control limits and plot points are standardized to allow charting of multiple part-types Control limits and plot points are standardized to allow charting of multiple part-types Standardized Control Charts

21 Strengths Groups multiple parts and their data sets on a single chart Groups multiple parts and their data sets on a single chart Provides a continuous view of the process Provides a continuous view of the process Fairly simple to construct and understand Fairly simple to construct and understand Does not assume all parts have equal variation Does not assume all parts have equal variationShortcomings Requires some historical data to calculate control limits Requires some historical data to calculate control limits Does not address quality costs Does not address quality costs Only tracks within-run variation Only tracks within-run variation

22 Sample Standardized Chart UCL = CL = 0 LCL = Part A Part B Part C

23 Q-Charts: Self-updating, standardized charts Principles Standardize the quality characteristic of interest Standardize 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 chart Plots multiple part types on a standardized chart Can begin charting with no historical data Can begin charting with no historical data Uses all available information to estimate the parameters (updating control limits) Uses all available information to estimate the parameters (updating control limits)

24 Q-Charts Strengths Charts can be made in real time beginning with the first production unit Charts can be made in real time beginning with the first production unit Does not assume process mean or variation are known in advance Does not assume process mean or variation are known in advance Does not assume all parts have the same variation Does not assume all parts have the same variation Multiple part types can be plotted on a single chart Multiple part types can be plotted on a single chart Uses all available data to update control limits Uses all available data to update control limitsShortcomings Does not address quality costs Does not address quality costs May not be clear to the operator May not be clear to the operator Strictly monitors within-run variation Strictly monitors within-run variation Lacks simplicity requires a PC Lacks simplicity requires a PC

25 Bayesian Quality Control: Economic charts Principles The system is modeled by partially observable Markov processes The system is modeled by partially observable Markov processes The system is generally assumed to have two states: in-control & out-of-control The system is generally assumed to have two states: in-control & out-of-control The operator is faced with certain action-decisions: The operator is faced with certain action-decisions: Do nothing Do nothing Inspect output Inspect output Inspect machine Inspect machine Repair machine Repair machine The model is a decision-making tool for minimizing quality costs over the length of the production run The model is a decision-making tool for minimizing quality costs over the length of the production run

26 Bayesian Quality Control Strengths Addresses quality costs as a factor in process control Addresses quality costs as a factor in process control Advises operators on which action to take based on probabilistic analysis Advises operators on which action to take based on probabilistic analysis Accounts for finite production horizon Accounts for finite production horizonShortcomings Models require accurate historical data Models require accurate historical data Models must be individualized to the specific production process Models must be individualized to the specific production process Not designed to handle multiple part types Not designed to handle multiple part types

27 Monitoring Run-to-Run Variation: A new concept Setups are: Time between last unit of one run and first good unit of the next run Time between last unit of one run and first good unit of the next run Integral part of process operation Integral part of process operation Occur frequently Occur frequently Reducing setup time implies reduction of: Test runs Test runs Inspections Inspections Process adjustment Process adjustment Scrap & rework Scrap & rework

28 Monitoring Run-to-Run Variation Principles Plot the mean of the first sample taken after setup Plot the mean of the first sample taken after setup Each setup generates one plot point Each setup generates one plot point Plot each setup on one control chart Plot each setup on one control chart Over time setup related variation is detected Over time setup related variation is detected Attempts to detect run-to-run variation Attempts to detect run-to-run variation

29 Monitoring Run-to-Run Variation Strengths Addresses setup induced variation Addresses setup induced variation Becomes more effective as setups become more common Becomes more effective as setups become more common Is a philosophy not a technique Is a philosophy not a techniqueShortcomings Long-term approach Long-term approach Does not address data scarcity Does not address data scarcity Does not address quality costs Does not address quality costs Lacks a well-defined methodology Lacks a well-defined methodology

30 SPC Techniques Summary Multiple Part Types Setup Related Variation Data Scarcity Quality Costs Simplicity & Usability DNOM charts + + Standardized Charts + + Q-Charts + + Bayesian Quality Control + Run-to-run Variation + +

31 Future Work Develop Run-to-Run Variation Charts as the focus of my thesis: Further analysis of the shortcomings of the Monitoring Run-to-Run framework Further analysis of the shortcomings of the Monitoring Run-to-Run framework Determine needs of job-shops and other low- volume manufacturers Determine needs of job-shops and other low- volume manufacturers Modify the Run-to-Run charts to fit the needs of regional companies Modify the Run-to-Run charts to fit the needs of regional companies Develop guidelines to maximize the potential for implementation Develop guidelines to maximize the potential for implementation

32 Review Introduction to SPC Introduction to SPC Manufacturing environment in North Dakota Manufacturing environment in North Dakota Short-run manufacturing Short-run manufacturing Short-run SPC techniques Short-run SPC techniques Strengths and weaknesses of these techniques Strengths and weaknesses of these techniques Future work Future work

33 Thanks to… Dr. Bilen Dr. Bilen Ritesh Saluja Ritesh Saluja Faculty and staff of NDSUs Industrial and Manufacturing Engr. department Faculty and staff of NDSUs Industrial and Manufacturing Engr. department QPR Conference QPR Conference

34 Discussion Comments Questions Suggestions


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