2Control is the heart of Six Sigma Customers are demanding higher levels of product quality at a lower cost, improved responsiveness, and added value. + Producers must struggle to satisfy technical, performance, schedule, and cost expectations of the customer. = Drive the need for control methods used in Six Sigma & TQM Do it right the first time Eliminate product variation ↓ Delivery of offerings, which are defect free at a min. cycle time
3Six Sigma initiatives to reduce variation In the past, the pursuit of quality was more a philosophy than an art or science… these tools can change that.DesignDesign to standard partsDesign to standard materialsRobust designDesign for assemblyDesign for reliabilityDesign for simplicityProcessShort-cycle manufacturingProcess characterizationProcess standardizationStatistical process controlMaterial and ComponentsPart standardizationTransaction(s) standardizationSupplier statistical process control (SPC)Supplier certificationMaterial requirements planning
4Poka-Yoke Japanese for mistake proofing Poka (inadvertent error)Yokeru (avoidance)Design and implementation of actions to prevent errors, mistakes, or defects in our everyday activities and processes.Errors should not be considered inevitable. Any error type can be reduced considerably, if not eliminated altogether.
5Common types of mistakes Incorrect processingWork pieces placed incorrectlyMissing partsWrong partsWrong blue print or instructionsWrong piece processedOperation skipped or omittedImproper adjustmentEquipment not set up properlyProcess improperly supersizedUse of the wrong tool
6How are these examples of daily Poka-Yoke? Parking garages have low clearance. To insure that cars entering the garage will fit, garages are fitted with a go/no-go gauge at the entrance. Hitting the swinging sign or pipe will not damage the vehicle as much as driving into a concrete beam.Filling pipe insert keeps larger, leaded-fuel nozzle from being insertedGas cap tether does not allow the motorist to drive off without the capGas cap is fitted with ratchet to signal proper tightness and prevent over-tighteningThis iron turns off automatically when it is left unattended or when it is returned to its holderExamples from:
7Keys to implementing Poka-Yoke Utilize Failure Mode-Effects Analysis (FMEA) to identify opportunities.Use the highest principle possible.EliminationReplacementFacilitationDetectionMitigation
8Statistical Process Control (SPC) SPC is a method of analyzing data over time and using the result of the analysis to solve manufacturing and processing problemsCan be applied to almost anything that can be expressed with numbers of data.Control = to keep something within boundariesProcess = any set of conditions or causes, which work together to produce an output or result.Process is a sequence of activities characterized by:Measureable inputsValue-added (VA) activitiesMeasureable OutputsRepeatability
9Statistical ControlA process is within statistical control when the process contains only natural, chance variation.Only when a process is statistically stable can it be treated as a population with constant mean, standard deviation, and distribution.A process control system is a feedback four element system:The ProcessInformation about PerformanceAction on the ProcessActions on the Output
10Prevention vs. Detection Every process contains several sources of variationTwo product characteristics are not equalDifferences among products, transactions, or services may range from very large to very small.No matter how small, variation is always presentTime period and conditions under which measurements are made affect the total process variation visible to the userStrategy of Prevention - It is always more effective to avoid “waste” by not producing it (vs. trying to detect).Minimum Requirements – If specification limits can be determined then anything within those limits is acceptable and everything outside them is unacceptable.
11Causes of Variation Common Causes Special Causes Assignable causes Only natural variation (no patterns, cycles or unusual points.)Process in statistical control when the only source of variation is common cause.Values will tend to forma pattern that can be described by a probability distribution.Assignable causesUnnatural patternsOut of control processCan be detected by simple statistical techniques such as Control Charts.=012395%99.73%-1-2-3
12Continuous Statistical Process Control (SPC) Tools Purposes of Control ChartsControl a CTP characteristic (statistical process control - SPC)Used to monitor a CTQ,CTC or CTD characteristic (Statistical process monitoring-SPM)Used as diagnostic tools for any CT Characteristic.
13Development of Control Charts Based on in-control dataIf non-random causes present, discard dataCorrect control chart limitsCombine location and variation chartsCharts must be reviewed and adjusted throughout usage and after acting on information.Define the problemEstablish the measurement systemDetermine the control chartsPrepare data collectionImplement and use control chartsContinuous Improvement
14Control Charts Commonly based on 3 Sample mean: x-bar-charts x Sample range: R-chartsSample std. deviation: s-chartsFraction defective: p-chartsNumber of defects: c-chartsConsider sample size, desired sensitivity, allowable complexity level of charts and attribute vs. variable data.
15What type of Control Chart depends on what kind of data you have… Attribute dataProduct characteristic evaluated with a discrete choiceGood/bad, yes/noVariable dataProduct characteristic that can be measuredLength, size, weight, height, time, velocity
16Z Values in Control Charts Smaller Z values make more sensitive charts (Type I error)Z = 3.00 is standardCompromise between sensitivity and Type II errors
17Process Control Chart Upper control limit Central Line Lower control =012395%99.73%-1-2-3CentralLineLowercontrollimit12345678910Sample number
18Is your process in Control? No evidence of out-of-control, if :No sample points outside limitsMost points near process averageAbout equal number of points above & below centerlinePoints appear randomly distributed
19Is your process out-of-control? Sample data fall outside control limitsTheory of runs2 out of 3 beyond the warning limits4 out of 5 beyond the 1 limits8 consecutive on one sidePatterns
20Zones For Pattern Tests UCLLCLCLZone AZone BZone C
21Control Chart Patterns 8 consecutive points on one side of the center line.8 consecutive points up or down across zones.14 points alternating up or down.2 out of 3 consecutive points in zone A but still inside the control limits.4 out of 5 consecutive points in zone A or B.
24Control Charts For Variables Each measures process differentlyProcess average and variability must be in controlX Bar (Mean chart )– Measure the central tendency of a process over timeDispersion chartsR (Range) – Measure the gain or loss of uniformity or variability of a process across timeS ChartX-bar and R Charts often used together and jointly interpreted.
293 Control Chart Factors D3D4B3B421.8803.26731.0232.5752.56840.7292.2822.26650.5772.1152.08960.4832.0040.031.97070.4190.0761.9240.1181.88280.3730.1361.8640.1851.81590.3370.1841.8160.2391.761100.3080.2231.7770.2841.716110.2850.2561.7440.3211.679120.2660.3541.646130.2491.6920.3821.618140.2350.3291.6710.4061.594150.3481.6520.4281.572200.1800.4141.5860.5101.490250.1530.4591.5410.5651.435
43Example c-Chart 3 6 9 12 15 18 21 24 Number of defects 2 4 6 8 10 12 2468101214Sample number
44U Chart Variation of the c chart. Each point is the average number of defects per unit in a sample of k unitsThe number of units at are averaged need not be the same for all samples.
45Pre-Control Not waiting for failure to adjust the process Uppercontrollimit=012395%99.73%-1-2-3CentralLineLowercontrollimit12345678910Sample numberEstablish Green zone 1.5 s, and yellow zone 3 s. Everything outside of 3s would be red.
46Using Pre-ControlQualify the process by taking 5 consecutive samples in green zone.The probability of 2 units falling outside of green should prompt a process adjustment or stop.After adjustment or stop, it will need to requalify processState of 2 successive samplesActionBoth A & B inside GreenNo actionA is Green, B is yellowNo ActionA is Yellow, B is GreenBoth A & B are yellow on same side (high or low)Adjust ProcessBoth A & B are yellow on opposite sides (high and low)Stop Process