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Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
Quality and JIT
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Quality and JIT

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  • 1. Topic 9: Quality and the Toyota System <ul><li>Quality Costs </li></ul><ul><li>Statistical Process Control </li></ul><ul><li>Six Sigma </li></ul><ul><li>Just in Time Production </li></ul>
  • 2. Philip Crosby <ul><li>Former VP of quality control at ITT corp. </li></ul><ul><li>Wrote “Quality is Free: The Art of Making Quality Certain” </li></ul><ul><li>Proposed: “Zero Defects” as the goal for quality </li></ul><ul><ul><li>“ Consider the AQL you would establish on the product you buy. Would you accept an automobile that you knew in advance was 15% defective? %5? 1%? 1/2%? How about nurses that care for newborn babies? Would an AQL of 3% on mishandling be too rigid?” </li></ul></ul><ul><ul><li>“ Mistakes are caused by lack of knowledge and lack of attention ” </li></ul></ul>
  • 3. Crosby’s Quality Postures <ul><li>Uncertainty </li></ul><ul><ul><li>We don’t know why we have problems with quality </li></ul></ul><ul><li>Awakening </li></ul><ul><ul><li>It is absolutely necessary to always have problems with quality </li></ul></ul><ul><li>Enlightenment </li></ul><ul><ul><li>Through management commitment and quality improvement we are identifying and resolving our problems </li></ul></ul><ul><li>Wisdom </li></ul><ul><ul><li>Defect prevention is a routine part of our operation </li></ul></ul><ul><li>Certainty </li></ul><ul><ul><li>We know why we don’t have problems with quality </li></ul></ul>Cost of Quality as a % of sales
  • 4. Categories of Quality Costs <ul><li>Prevention costs </li></ul><ul><ul><li>Costs associated with preventing defects </li></ul></ul><ul><li>Appraisal costs </li></ul><ul><ul><li>Costs associated with assessing quality within a productive system </li></ul></ul><ul><li>Internal failure costs </li></ul><ul><ul><li>Costs associated with losses from disposal of or fixing quality problems </li></ul></ul><ul><li>External failure costs </li></ul><ul><ul><li>Costs associated with releasing poor quality into the demand stream </li></ul></ul><ul><li>Cost of yield loss </li></ul><ul><li>cost to send your employees to quality training </li></ul><ul><li>warranty costs associated with unplanned product repair </li></ul><ul><li>cost of a new automated quality testing device </li></ul><ul><li>cost of rework </li></ul><ul><li>loss of market share due to a national product purity scandal </li></ul><ul><li>litigation cost due to product defect </li></ul>
  • 5. Rework / Elimination of Flow Units Rework: Defects can be corrected by same or other resource Leads to variability Loss of Flow units: Defects can NOT be corrected Leads to variability To get X units, we have to start X/y units Step 1 Test 1 Step 2 Test 2 Step 3 Test 3 Rework Step 1 Test 1 Step 2 Test 2 Step 3 Test 3 Step 1 Test 1 Step 2 Test 2 Step 3 Test 3
  • 6. Calculation of Yield Loss <ul><li>B(1-d 1 )(1-d 2 )(1-d 3 )…(1-d n ) = m </li></ul><ul><li>Thus: B=m/(1-d 1 )(1-d 2 )(1-d 3 )…(1-d n ) </li></ul><ul><li>Where: </li></ul><ul><ul><li>d i = proportion of defectives generated by operation i </li></ul></ul><ul><ul><li>n = number of operations </li></ul></ul><ul><ul><li>m = number of finished products </li></ul></ul><ul><ul><li>B = raw material started in process </li></ul></ul>Example: 1000 finished product needed from a flow cell 4 operations generating 2%,3%,5%,3% proportion defective respectively. How many units must be started in the process?
  • 7. Quality Costs 2% 5% 3% 3% 1000 1142 1119 1086 1031 31 55 33 23
  • 8. The Concept of Consistency: Who is the Better Target Shooter? Not just the mean is important, but also the variance Need to look at the distribution function
  • 9. Two Types of Causes for Variation Common Cause Variation (low level) Common Cause Variation (high level) Assignable Cause Variation <ul><li>Need to measure and reduce common cause variation </li></ul><ul><li>Identify assignable cause variation as soon as possible </li></ul>
  • 10. W. Edwards Deming <ul><li>Quality is first a management responsibility </li></ul><ul><li>There are two keys to ongoing quality improvement </li></ul><ul><ul><li>Employee training </li></ul></ul><ul><ul><li>Reacting to process data in real time </li></ul></ul><ul><li>Variation is the disease and SPC/SQC tools are the cure </li></ul>
  • 11. SPC Objectives <ul><li>Insure high quality production by reducing and controlling process variation. </li></ul><ul><li>Identify types of process variation. </li></ul><ul><ul><li>Common cause variation: small, random forces that continually act on a process </li></ul></ul><ul><ul><li>Special cause: variation that may be assigned to abnormal, unpredictable forces </li></ul></ul><ul><li>Take action whenever a process is judged to have been influenced by special causes. </li></ul>
  • 12. A General SPC Procedure <ul><li>Periodically select from the process a sample of items, inspect them, and note the result. </li></ul><ul><li>Because of common or special causes, the results of every sample will vary. Determine whether the cause of the variation is common or special. </li></ul><ul><li>Take action depending on what was determined in step 2. </li></ul>This procedure is enacted through the use of control charts
  • 13. Statistical Process Control: Control Charts Time Process Parameter Upper Control Limit (UCL) Lower Control Limit (LCL) Center Line <ul><li>Track process parameter over time - mean - percentage defects </li></ul><ul><li>Distinguish between - common cause variation (within control limits) - assignable cause variation (outside control limits) </li></ul><ul><li>Measure process performance: how much common cause variation is in the process while the process is “in control”? </li></ul>
  • 14. Charting Continuous Variables <ul><li>The Xbar-R Chart: tracks the mean and range of a variable calculated from a fixed sample </li></ul><ul><li>The Xbar-S Chart: tracks the mean and standard deviation of a variable calculated from a large sample </li></ul>
  • 15. The Xbar-R Chart <ul><li>Collect sample data by sub-group (normally containing 2 - 5 data points): record the continuous variable under study. </li></ul><ul><li>Compute the mean and range for each sub-group: </li></ul><ul><li>Calculate average mean and average range </li></ul><ul><li>Compute and draw control limits: </li></ul><ul><li>Plot mean and range for each subgroup. </li></ul>
  • 16. Parameters for Creating X-bar Charts
  • 17. Example of an Xbar-R Chart Each data point is the pulling force applied to a glass strand before breaking For 5 obs. D 3 =0 D 4 =2.114 A 2 =0.577
  • 18. Example (cont) For this example, the control limits reduce to: 1 Sub-group 2 3 Range 12 Sub-group 13 14 Mean
  • 19. The Xbar-s Chart <ul><li>Similar to Xbar-r chart except that a larger sample is taken. </li></ul><ul><li>The calculation of control limits may include a sample standard deviation as an estimate of the population standard deviation. </li></ul><ul><li>Control limits are calculated : </li></ul>
  • 20. The Statistical Meaning of Six Sigma Process capability measure 3  Upper Specification Limit (USL) Lower Specification Limit (LSL) Process A (with st. dev  A ) Process B (with st. dev  B ) x  C p P{defect} ppm 1  0.33 0.317 317,000 2  0.67 0.0455 45,500 3  1.00 0.0027 2,700 4  1.33 0.0001 63 5  1.67 0.0000006 0,6 6  2.00 2x10 -9 0,00 <ul><li>Estimate standard deviation: </li></ul><ul><li>Look at standard deviation relative to specification limits </li></ul><ul><li>Don’t confuse control limits with specification limits: a process can be out of control, yet be incapable </li></ul> ˆ = R / d 2 X-3  A X-2  A X-1  A X X+1  A X+2  X+3  A X-6  B X X+6  B
  • 21. Control Limits and Specification Limits <ul><li>Control limits of a quality characteristic represent natural variation in a process </li></ul><ul><li>Specification limits indicate acceptable variation set by the customer </li></ul><ul><li>The process capability index is useful in comparison: </li></ul><ul><li>The capability index may be adjusted to to consider how well the process is “centered” within the limits </li></ul>K=2 |design target - process average | / specification range
  • 22. Process Capability Example USL=10 LSL=9.5  = .02 9.5 10.0 K=2 |9.75 - 9.95| / .5 = .8
  • 23. PC Example (cont) USL=10 LSL=9.5  = .02 9.5 10.0 K=2 |9.75 - 9.79| / .5 = .16
  • 24. Charting Discrete Attributes <ul><li>Charts that track the number of units defective </li></ul><ul><ul><li>P Chart: fraction of a sample that is defective given different sample sizes </li></ul></ul><ul><ul><li>NP Chart: fraction of a sample that is defective given constant sample sizes </li></ul></ul>
  • 25. Attribute Based Control Charts: The p-chart <ul><li>Estimate average defect percentage </li></ul><ul><li>Estimate Standard Deviation </li></ul><ul><li>Define control limits </li></ul><ul><li>Divide time into: - calibration period (capability analysis) - conformance analysis </li></ul>=0.013 =0.091 =0.014 Period n defects p UCL= + 3  ˆ LCL= - 3  ˆ  ˆ = =0.052
  • 26. Attribute Based Control Charts: The p-chart
  • 27. Example of a P Chart Note: control limits calculated assuming z=3 Quantities of light bulbs are tested to see if they function
  • 28. Example of a P Chart (cont) For this example, the control limits reduce to: 5 10 15 20 25 Percent Defective UCL LCL p Sub-group
  • 29. The NP Chart <ul><li>Similar to the P Chart except assumes constant sample size </li></ul><ul><li>Calculation of the control limits must be performed only once </li></ul>
  • 30. Discrete Attributes (cont) <ul><li>Charts that track the number of defects in one or more units </li></ul><ul><ul><li>U Chart: defects in a variable sized sample volume </li></ul></ul><ul><ul><li>C Chart: defects in a fixed sized sample </li></ul></ul>
  • 31. The U Chart <ul><li>Collect sample data: for each sample record the number of units sampled (n) and the number of defects (c) </li></ul><ul><li>Compute the number of defects per unit for each sample sub-group: (u = c/n) </li></ul><ul><li>Calculate the mean defects per unit: </li></ul><ul><li>Compute and draw control limits </li></ul><ul><li>Plot u </li></ul>
  • 32. The C Chart <ul><li>Similar to the U Chart except assumes constant sample size </li></ul><ul><li>Calculation of the control limits must be performed only once </li></ul>
  • 33. Example of a C Chart Note: control limits calculated assuming z=3 In this example, a data point represents the number of rips found in 5 yards of nylon fabric
  • 34. Example of a C Chart For this example, the control limits reduce to: 5 10 Defectives UCL C Sub-group
  • 35. We assume the process is in an “in control” state when: <ul><li>Points are within the control limits </li></ul><ul><li>Consecutive groups of points do not take a particular form. </li></ul><ul><ul><li>Runs on one side of the central line (7 out of 7, 10 out of 11, or 12 out of 14) </li></ul></ul><ul><ul><li>Trends of a continued rise or fall of points (7 out of 7) </li></ul></ul><ul><ul><li>Periodicity or same pattern repeated over equal interval </li></ul></ul><ul><ul><li>Hugging the central line (most points within the center half of the control zone) </li></ul></ul><ul><ul><li>Hugging the control limits (2 out of 3, 3 out of 7, or 4 out of 10 points within the outer 1/3 zone) </li></ul></ul>
  • 36. Statistical Process Control Capability Analysis Conformance Analysis Investigate for Assignable Cause Eliminate Assignable Cause <ul><li>Capability analysis </li></ul><ul><li>What is the currently &quot;inherent&quot; capability of my process when it is &quot;in control&quot;? </li></ul><ul><li>Conformance analysis </li></ul><ul><li>SPC charts identif y when control has likely been lost and assignable cause variation has occurred </li></ul><ul><li>Investigate for assignable cause </li></ul><ul><li>Find “Root Cause(s)” of Potential Loss of Statistical Control </li></ul><ul><li>Eliminate or replicate assignable cause </li></ul><ul><li>Need Corrective Action To Move Forward </li></ul>
  • 37. How do you get a Six Sigma Process? Step 1: Do Things Consistently ISO 9000 can be very helpful Step 2: Reduce Variability in the Process Taguchi: Even small deviations are quality losses. It is not enough to look at “Good” vs “Bad” Outcomes. Only looking at good vs bad wastes opportunities for learning; especially as failures become rare (closer to six sigma) you need to learn from the “near misses” Step 3: Accommodate Residual Variability Through Robust Design Double-checking and Fool-proofing
  • 38. CUSTOMER FOCUS CONTINUOUS IMPROVEMENT MANAGEMENT COMMITMENT & LEADERSHIP EMPLOYEE INVOLVEMENT ANALYTICAL PROCESS THINKING MGT BY FACT EMPOWERMENT PLANNING TRAINING A Systems View of Total Quality Management
  • 39. Toyota Production System <ul><li>Pillars: </li></ul><ul><ul><li>1. just-in-time , and </li></ul></ul><ul><ul><li>2. autonomation , or automation with a human touch </li></ul></ul><ul><li>Practices: </li></ul><ul><ul><li>setup reduction (SMED) </li></ul></ul><ul><ul><li>worker training </li></ul></ul><ul><ul><li>vendor relations </li></ul></ul><ul><ul><li>quality control </li></ul></ul><ul><ul><li>foolproofing (baka-yoke) </li></ul></ul><ul><ul><li>many others </li></ul></ul>
  • 40. JIT Implementation <ul><li>Adopt goal to eliminate all forms of waste </li></ul><ul><li>Improve workplace cleanliness and order </li></ul><ul><li>Promote flow manufacturing </li></ul><ul><li>Level production requirements </li></ul><ul><li>Improve and standardize all process steps </li></ul>
  • 41. The Seven Zeros <ul><li>Zero Defects: To avoid delays due to defects. (Quality at the source) </li></ul><ul><li>Zero (Excess) Lot Size: To avoid “waiting inventory” delays. (Usually stated as a lot size of one .) </li></ul><ul><li>Zero Setups: To minimize setup delay and facilitate small lot sizes. </li></ul><ul><li>Zero Breakdowns: To avoid stopping tightly coupled line. </li></ul><ul><li>Zero (Excess) Handling: To promote flow of parts. </li></ul><ul><li>Zero Lead Time: To ensure rapid replenishment of parts (very close to the core of the zero inventories objective). </li></ul><ul><li>Zero Surging: Necessary in system without WIP buffers. </li></ul>
  • 42. Cross Training and Plant Layout <ul><li>Cross Training: </li></ul><ul><ul><li>Adds flexibility to inherently inflexible system </li></ul></ul><ul><ul><li>Allows capacity to float to smooth flow </li></ul></ul><ul><ul><li>Reduces boredom </li></ul></ul><ul><ul><li>Fosters appreciation for overall picture </li></ul></ul><ul><ul><li>Increase potential for idea generation </li></ul></ul>
  • 43. <ul><li>Plant Layout: </li></ul><ul><ul><li>Promote flow with little WIP </li></ul></ul><ul><ul><li>Facilitate workers staffing multiple machines </li></ul></ul><ul><ul><li>U-shaped cells </li></ul></ul><ul><ul><ul><li>Maximum visibility </li></ul></ul></ul><ul><ul><ul><li>Minimum walking </li></ul></ul></ul><ul><ul><ul><li>Flexible in number of workers </li></ul></ul></ul><ul><ul><ul><li>Facilitates monitoring of work entering and leaving cell </li></ul></ul></ul><ul><ul><ul><li>Workers can conveniently cooperate to smooth flow and address problems </li></ul></ul></ul>
  • 44. U-Shaped Manufacturing Cell Inbound Stock Outbound Stock
  • 45. Kanban <ul><li>Definition: A “kanban” is a sign-board or card in Japanese and is the name of the flow control system developed by Toyota. </li></ul><ul><li>Role: </li></ul><ul><ul><li>Kanban is a tool for realizing just-in-time. For this tool to work fairly well, the production process must be managed to flow as much as possible. This is really the basic condition. Other important conditions are leveling production as much as possible and always working in accordance with standard work methods. </li></ul></ul><ul><li>– Ohno 1988 </li></ul><ul><li>Push vs. Pull: Kanban is a “pull system” </li></ul><ul><ul><li>Push systems schedule releases </li></ul></ul><ul><ul><li>Pull systems authorize releases </li></ul></ul>
  • 46. One-Card Kanban Outbound stockpoint Outbound stockpoint Production cards Completed parts with cards enter outbound stockpoint. When stock is removed, place production card in hold box. Production card authorizes start of work.
  • 47. The Lessons of JIT <ul><ul><li>The production environment itself is a control </li></ul></ul><ul><ul><li>Operational details matter strategically </li></ul></ul><ul><ul><li>Controlling WIP is important </li></ul></ul><ul><ul><li>Speed and flexibility are important assets </li></ul></ul><ul><ul><li>Quality can come first </li></ul></ul><ul><ul><li>Continual improvement is a condition for survival </li></ul></ul>

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