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

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

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

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

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

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

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

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

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

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

Setups and Changeovers Setup is a frequently occurring part of process operation Introduce special causes of variation into the process Importance of knowing whether the first part is “on-target” Two types of process capability: Capability after process has been brought into control Capability across runs if the process were run without adjustment after initial setup Creates the need to monitor “run-to-run variation” Ensuring quick, consistent setups is critical

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

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

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

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

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

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

DNOM Chart UCL = 0.0137 CL = - 0.0046 LCL = - 0.023

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

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

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

Sample Standardized Chart UCL = 0.577 CL = 0 LCL = - 0.577 Part A Part B Part C

Q-Charts: Self-updating, standardized charts Principles Standardize the quality characteristic of interest The standardized statistic will be i.i.d. N(0,1) Plots multiple part types on a standardized chart Can begin charting with no historical data Uses all available information to estimate the parameters (updating control limits)

Q-Charts Strengths Shortcomings 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 all parts have the same variation Multiple part types can be plotted on a single chart Uses all available data to update control limits Shortcomings Does not address quality costs May not be clear to the operator Strictly monitors within-run variation Lacks simplicity requires a PC

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

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

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 Integral part of process operation Occur frequently Reducing setup time implies reduction of: Test runs Inspections Process adjustment Scrap & rework

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

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

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

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 Determine needs of job-shops and other low-volume manufacturers Modify the Run-to-Run charts to fit the needs of regional companies Develop guidelines to maximize the potential for implementation

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

Thanks to… Dr. Bilen Ritesh Saluja Faculty and staff of NDSU’s Industrial and Manufacturing Engr. department QPR Conference

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