Chapter 6: Quality Management © Holmes Miller 1999 Quality Specifications Design quality: Inherent value of the product in the marketplace Dimensions include: Performance Features Reliability/Durability Serviceability Aesthetics Perceived Quality. Conformance quality: Degree to which the product or service design specifications are met Importance of Quality Costs & market share Market Gains Reputation Volume Price Improved Quality Increased Profits Lower Costs Productivity Rework/Scrap Warranty 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 Funnel Experiment (Deming) Tampering with a stable system only increases the production of faulty items and mistakes. Tampering is taking action based on the belief that a common cause is a special cause. Improvement of a stable system nearly always means reduction of variation. One necessary qualification of anyone in management is -- -- stop asking people to explain ups and downs that come from random variation. Basic Forms of Variation Assignable variation is caused by factors that can be clearly identified and possibly managed Common variation is inherent in the production process Example: A poorly trained employee that creates variation in finished product output. Example: A molding process that always leaves “burrs” or flaws on a molded item. Two Types of Causes for Variation Common Cause Variation (low level) Common Cause Variation (high level) Assignable Cause Variation • Need to measure and reduce common cause variation • Identify assignable cause variation as soon as possible Taguchi’s View of Variation Traditional view is that quality within the LS and US is good and that the cost of quality outside this range is constant, where Taguchi views costs as increasing as variability increases, so seek to achieve zero defects and that will truly minimize quality costs. High High Incremental Cost of Variability Incremental Cost of Variability Zero Zero Lower Target Upper Spec Spec Spec Traditional View Lower Target Upper Spec Spec Spec Taguchi’s View Costs of Quality Appraisal Costs External Failure Costs Costs of Quality Internal Failure Costs Prevention Costs Six Sigma Quality A philosophy and set of methods companies use to eliminate defects in their products and processes Seeks to reduce variation in the processes that lead to product defects The name, “six sigma” refers to the variation that exists within plus or minus six standard deviations of the process outputs The Statistical Meaning of Six Sigma Upper Specification Limit (USL) Lower Specification Limit (LSL) Process A (with st. dev sA) X-3sA X-2sA X-1sA X X+1sA X+2s X+3sA 3 Process B (with st. dev sB) X-6sB X Process capability measure Cp USL LSL 6ˆ x Cp 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 X+6sB •Don’t confuse control limits with specification limits: a process can be in control, yet be incapable of meeting customer specs Six Sigma Quality (Continued) Six Sigma allows managers to readily describe process performance using a common metric: Defects Per Million Opportunities (DPMO) DPMO Number of defects Number of opportunit ies for error per x No. of units unit x 1,000,000 Six Sigma Quality (Continued) Example of Defects Per Million Opportunities (DPMO) calculation. Suppose we observe 200 letters delivered incorrectly to the wrong addresses in a small city during a single day when a total of 200,000 letters were delivered. What is the DPMO in this situation? DPMO 200 1 x 200,000 So, for every one million letters delivered this city’s postal managers can expect to have 1,000 letters incorrectly sent to the wrong address. x 1,000,000 1, 000 Cost of Quality: What might that DPMO mean in terms of overtime employment to correct the errors? Other costs? Six Sigma Quality: DMAIC Cycle Define, Measure, Analyze, Improve, and Control (DMAIC) Developed by General Electric as a means of focusing effort on quality using a methodological approach Overall focus of the methodology is to understand and achieve what the customer wants A 6-sigma program seeks to reduce the variation in the processes that lead to these defects DMAIC consists of five steps…. Six Sigma Quality: DMAIC Cycle (Continued) 1. Define (D) Customers and their priorities 2. Measure (M) Process and its performance 3. Analyze (A) Causes of defects 4. Improve (I) Remove causes of defects 5. Control (C) Maintain quality Example to illustrate the process… We are the maker of this cereal. Consumer Reports has just published an article that shows that we frequently have less than 15 ounces of cereal in a box. Exercise: What should we do? Define -- What is the critical-to-quality characteristic? Measure -- How would we measure to evaluate the extent of the problem? Analyze -- How can we improve the capability of our cereal box filling process? Improve -- How good is good enough? Motorola’s “Six Sigma” Control -- Statistical Process Control (SPC) Analytical Tools for Six Sigma and Continuous Improvement: Flow Chart Material Received from Supplier No, Continue… Inspect Material for Defects Defects found? Yes Can be used to find quality problems Return to Supplier for Credit Diameter Analytical Tools for Six Sigma and Continuous Improvement: Run Chart Can be used to identify when equipment or processes are not behaving according to specifications 0.58 0.56 0.54 0.52 0.5 0.48 0.46 0.44 1 2 3 4 5 6 7 8 Time (Hours) 9 10 11 12 Analytical Tools for Six Sigma and Continuous Improvement: Pareto Analysis 80% Frequency Can be used to find when 80% of the problems may be attributed to 20% of the causes Design Assy. Instruct. Purch. Training Analytical Tools for Six Sigma and Continuous Improvement: Checksheet Monday Billing Errors Wrong Account Wrong Amount A/R Errors Wrong Account Wrong Amount Can be used to keep track of defects or used to make sure people collect data in a correct manner Number of Lots Analytical Tools for Six Sigma and Continuous Improvement: Histogram Can be used to identify the frequency of quality defect occurrence and display quality performance 0 1 2 Data Ranges 3 4 Defects in lot Analytical Tools: Cause and Effect Diagrams (Fishbone Diagrams) Specifications / information Machines Cutting tool worn Dimensions incorrectly specified in drawing Vise position set incorrectly Clamping force too high or too low Machine tool coordinates set incorrectly Part incorrectly positioned in clamp Dimension incorrectly coded In machine tool program Vice position shifted during production Part clamping surfaces corrupted Steer support height deviates from specification Extrusion temperature too high Error in measuring height Extrusion stock undersized Extrusion die undersized People Extrusion rate too high Material too soft Materials Can be used to systematically track backwards to find a possible cause of a quality problem (or effect) Types of Statistical Sampling Attribute (Go or no-go information) Defectives refers to the acceptability of product across a range of characteristics. Defects refers to the number of defects per unit which may be higher than the number of defectives. p-chart application Variable (Continuous) Usually measured by the mean and the standard deviation. X-bar and R chart applications Analytical Tools for Six Sigma and Continuous Improvement: Control Charts Can be used to monitor ongoing production process quality and quality conformance to stated standards of quality 1020 UCL 1010 1000 990 LCL 980 970 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Statistical Process Normal Behavior Control (SPC) Charts UCL LCL 1 2 3 4 5 6 Samples over time UCL Possible problem, investigate LCL 1 2 3 4 5 6 Samples over time UCL Possible problem, investigate LCL 1 2 3 4 5 6 Samples over time Process Capability Process limits Specification limits How do the limits relate to one another? Process Capability Index, Cpk Capability Index shows how well parts being produced fit into design limit specifications. X LTL UTL - X C pk = min or 3 3 As a production process produces items small shifts in equipment or systems can cause differences in production performance from differing samples. Shifts in Process Mean The Cereal Box Example We are the maker of this cereal. Consumer reports has just published an article that shows that we frequently have less than 15 ounces of cereal in a box. We would like to have 16 ounces in each box. Let’s assume that the government says that we must be within ± 5 percent of the weight advertised on the box. Upper Tolerance Limit = 16 + .05(16) = 16.8 ounces Lower Tolerance Limit = 16 – .05(16) = 15.2 ounces We go out and buy 1,000 boxes of cereal and find that they weight an average of 15.875 ounces with a standard deviation of .529 ounces. Cereal Box Process Capability Specification or Tolerance Limits X LTL UTL X C pk Min ; 3 3 Upper Spec = 16.8 oz Lower Spec = 15.2 oz Observed Weight Mean = 15.875 oz Std Dev = .529 oz 15.875 15.2 16.8 15.875 C pk Min ; 3 (. 529 ) 3(.529) C pk Min.4253; .5829 C pk .4253 What does a Cpk of .4253 mean? This is a process that will produce a relatively high number of defects. Many companies look for a Cpk of 1.3 or better… 6-Sigma company wants 2.0! Basic Forms of Statistical Sampling for Quality Control Acceptance Sampling is sampling to accept or reject the immediate lot of product at hand Statistical Process Control is sampling to determine if the process is within acceptable limits