Report

Quality Management “It costs a lot to produce a bad product.” Norman Augustine Cost of quality 1. Prevention costs 2. Appraisal costs 3. Internal failure costs 4. External failure costs 5. Opportunity costs What is quality management all about? Try to manage all aspects of the organization in order to excel in all dimensions that are important to “customers” Two aspects of quality: features: more features that meet customer needs = higher quality freedom from trouble: fewer defects = higher quality The Quality Gurus – Edward Deming Quality is “uniformity and dependability” Focus on SPC and statistical tools “14 Points” for management 1986 PDCA method 1900-1993 The Quality Gurus – Joseph Juran Quality is “fitness for use” Pareto Principle Cost of Quality General management approach as well as statistics 1951 1904 - 2008 History: how did we get here… • Deming and Juran outlined the principles of Quality Management. • Tai-ichi Ohno applies them in Toyota Motors Corp. • Japan has its National Quality Award (1951). • U.S. and European firms begin to implement Quality Management programs (1980’s). • U.S. establishes the Malcolm Baldridge National Quality Award (1987). • Today, quality is an imperative for any business. What does Total Quality Management encompass? TQM is a management philosophy: • continuous improvement • leadership development • partnership development Cultural Alignment Customer Technical Tools (Process Analysis, SPC, QFD) Developing quality specifications Input Design Design quality Process Output Dimensions of quality Conformance quality Six Sigma Quality 6 • 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 Six Sigma Quality Six Sigma Roadmap (DMAIC) Next Project Celebrate Project $ Define Customers, Value, Problem Statement Scope, Timeline, Team Primary/Secondary & OpEx Metrics Current Value Stream Map Voice Of Customer (QFD) Control Validate Project $ Measure Assess specification / Demand Measurement Capability (Gage R&R) Correct the measurement system Process map, Spaghetti, Time obs. Measure OVs & IVs / Queues Document process (WIs, Std Work) Mistake proof, TT sheet, CI List Analyze change in metrics Value Stream Review Prepare final report Validate Project $ Validate Project $ Improve Analyze (and fix the obvious) Optimize KPOVs & test the KPIVs Redesign process, set pacemaker 5S, Cell design, MRS Visual controls Value Stream Plan Root Cause (Pareto, C&E, brainstorm) Validate Project $ Find all KPOVs & KPIVs FMEA, DOE, critical Xs, VA/NVA Graphical Analysis, ANOVA Future Value Stream Map Six Sigma Organization Quality Quality Improvement Traditional Time Continuous improvement philosophy 1. Kaizen: Japanese term for continuous improvement. A step-by-step improvement of business processes. 2. PDCA: Plan-do-check-act as defined by Deming. Plan Do Act Check 3. Benchmarking : what do top performers do? Tools used for continuous improvement 1. Process flowchart Tools used for continuous improvement 2. Run Chart Performance Time Tools used for continuous improvement 3. Control Charts Performance Metric Time Tools used for continuous improvement 4. Cause and effect diagram (fishbone) Machine Man Environment Method Material Tools used for continuous improvement 5. Check sheet Item ------------------- A B C √√ D E √ √√√ √√√ √√ √ √√ F √ √ √ G √ √√ Tools used for continuous improvement 6. Histogram Frequency Tools used for continuous improvement 7. Pareto Analysis 100% 75% 50 40 50% 30 20 10 25% 0% A B C D E F Percentage Frequency 60 Summary of Tools 1. Process flow chart 2. Run diagram 3. Control charts 4. Fishbone 5. Check sheet 6. Histogram 7. Pareto analysis Case: shortening telephone waiting time… • A bank is employing a call answering service • The main goal in terms of quality is “zero waiting time” - customers get a bad impression - company vision to be friendly and easy access • The question is how to analyze the situation and improve quality The current process Custome rA Custome rB Operator Receiving Party How can we reduce waiting time? Fishbone diagram analysis Absent receiving party Working system of operators Absent Too many phone calls Lunchtime Out of office Not at desk Not giving receiving party’s coordinates Complaining Absent Lengthy talk Does not know organization well Takes too much time to explain Leaving a message Customer Does not understand customer Operator Makes custome r wait Reasons why customers have to wait (12-day analysis with check sheet) Daily average Total number A One operator (partner out of office) 14.3 172 B Receiving party not present 6.1 73 C No one present in the section receiving call 5.1 61 D Section and name of the party not given 1.6 19 E Inquiry about branch office locations 1.3 16 F Other reasons 0.8 10 29.2 351 Pareto Analysis: reasons why customers have to wait Frequency Percentage 300 87.1% 250 71.2% 200 49% 150 100 0% A B C D E F Ideas for improvement 1. Taking lunches on three different shifts 2. Ask all employees to leave messages when leaving desks 3. Compiling a directory where next to personnel’s name appears her/his title Results of implementing the recommendations …After Before… Percentage Frequency Percentage Frequency 100% 87.1% 300 300 71.2% 200 Improvement 200 49% 100 100 100% 0% A B C D E F 0% B C A D E F In general, how can we monitor quality…? By observing variation in output measures! 1. Assignable variation: we can assess the cause 2. Common variation: variation that may not be possible to correct (random variation, random noise) Statistical Process Control (SPC) Every output measure has a target value and a level of “acceptable” variation (upper and lower tolerance limits) SPC uses samples from output measures to estimate the mean and the variation (standard deviation) Example We want beer bottles to be filled with 12 FL OZ ± 0.05 FL OZ Question: How do we define the output measures? In order to measure variation we need… The average (mean) of the observations: 1 X N N x i 1 i The standard deviation of the observations: N 2 ( x X ) i i 1 N Average & Variation example Number of pepperoni’s per pizza: 25, 25, 26, 25, 23, 24, 25, 27 Average: Standard Deviation: Number of pepperoni’s per pizza: 25, 22, 28, 30, 27, 20, 25, 23 Average: Standard Deviation: Which pizza would you rather have? When is a product good enough? High a.k.a Upper/Lower Design Limits (UDL, LDL) Upper/Lower Spec Limits (USL, LSL) Upper/Lower Tolerance Limits (UTL, LTL) Incremental Cost of Variability Zero Lower Target Tolerance Spec Upper Tolerance Traditional View The “Goalpost” Mentality But are all ‘good’ products equal? High Incremental Cost of Variability Taguchi’s View “Quality Loss Function” (QLF) Zero Lower Spec Target Spec Upper Spec LESS VARIABILITY implies BETTER PERFORMANCE ! Capability Index (Cpk) It shows how well the performance measure fits the design specification based on a given tolerance level A process is k capable if X k UTL and X k LTL UTL X 1 k and X LTL 1 k Capability Index (Cpk) Another way of writing this is to calculate the capability index: X LTL UTL X C pk min , k k Cpk < 1 means process is not capable at the k level Cpk >= 1 means process is capable at the k level Accuracy and Consistency We say that a process is accurate if its mean X is close to the target T. We say that a process is consistent if its standard deviation is low. Example 1: Capability Index (Cpk) X = 10 and σ = 0.5 LTL = 9 UTL = 11 LTL X UTL 1110 10 9 C pk min or 0.667 3 0.5 3 0.5 Example 2: Capability Index (Cpk) X = 9.5 and σ = 0.5 LTL = 9 UTL = 11 LTL X UTL Example 3: Capability Index (Cpk) X = 10 and σ = 2 LTL = 9 UTL = 11 LTL X UTL Example Consider the capability of a process that puts pressurized grease in an aerosol can. The design specs call for an average of 60 pounds per square inch (psi) of pressure in each can with an upper tolerance limit of 65psi and a lower tolerance limit of 55psi. A sample is taken from production and it is found that the cans average 61psi with a standard deviation of 2psi. 1. Is the process capable at the 3 level? 2. What is the probability of producing a defect? Solution LTL = 55 UTL = 65 X 61 = 2 C pk C pk X LTL UTL X min( , ) 3 3 61 55 65 61 min( , ) min(1,0.6667) 0.6667 6 6 No, the process is not capable at the 3 level. Solution P(defect) = P(X<55) + P(X>65) =P(X<55) + 1 – P(X<65) =P(Z<(55-61)/2) + 1 – P(Z<(65-61)/2) =P(Z<-3) + 1 – P(Z<2) =G(-3)+1-G(2) =0.00135 + 1 – 0.97725 (from standard normal table) = 0.0241 2.4% of the cans are defective. Example (contd) Suppose another process has a sample mean of 60.5 and a standard deviation of 3. Which process is more accurate? This one. Which process is more consistent? The other one. Control Charts Upper Control Limit Central Line Lower Control Limit Control charts tell you when a process measure is exhibiting abnormal behavior. Two Types of Control Charts • X/R Chart This is a plot of averages and ranges over time (used for performance measures that are variables) • p Chart This is a plot of proportions over time (used for performance measures that are yes/no attributes) Statistical Process Control with p Charts When should we use p charts? 1. When decisions are simple “yes” or “no” by inspection 2. When the sample sizes are large enough (>50) Sample (day) Items Defective Percentage 1 200 10 0.050 2 200 8 0.040 3 200 9 0.045 4 200 13 0.065 5 200 15 0.075 6 200 25 0.125 7 200 16 0.080 Statistical Process Control with p Charts Let’s assume that we take t samples of size n … t ot alnumber of "defect s" p (number of samples) (samplesize) sp p (1 p ) n UCL p zs p LCL p zs p Statistical Process Control with p Charts 80 1 p 0.066 6 200 15 0.066(1 0.066) sp 0.017 200 UCL 0.066 3 0.017 0.117 LCL 0.066 3 0.017 0.015 Statistical Process Control with p Charts UCL = 0.117 p = 0.066 LCL = 0.015 Statistical Process Control with X/R Charts When should we use X/R charts? 1. It is not possible to label “good” or “bad” 2. If we have relatively smaller sample sizes (<20) Statistical Process Control with X/R Charts Take t samples of size n (sample size should be 5 or more) 1 n X xi n i 1 X is the mean for each sample R max {xi } min{xi } R is the range between the highest and the lowest for each sample Statistical Process Control with X/R Charts 1 t X Xj t j 1 X is the average of the averages. 1 t R Rj t j 1 R is the average of the ranges Statistical Process Control with X/R Charts define the upper and lower control limits… UCLX X A2 R LCLX X A2 R UCL R D4 R LCLR D3 R Read A2, D3, D4 from Table TN 8.7 Example: SPC for bottle filling… Sample Observation (xi) Average 1 11.90 11.92 12.09 11.91 12.01 2 12.03 12.03 11.92 11.97 12.07 3 11.92 12.02 11.93 12.01 12.07 4 11.96 12.06 12.00 11.91 11.98 5 11.95 12.10 12.03 12.07 12.00 6 11.99 11.98 11.94 12.06 12.06 7 12.00 12.04 11.92 12.00 12.07 8 12.02 12.06 11.94 12.07 12.00 9 12.01 12.06 11.94 11.91 11.94 10 11.92 12.05 11.92 12.09 12.07 Range (R) Example: SPC for bottle filling… Calculate the average and the range for each sample… Sample Observation (xi) Average Range (R) 1 11.90 11.92 12.09 11.91 12.01 11.97 0.19 2 12.03 12.03 11.92 11.97 12.07 12.00 0.15 3 11.92 12.02 11.93 12.01 12.07 11.99 0.15 4 11.96 12.06 12.00 11.91 11.98 11.98 0.15 5 11.95 12.10 12.03 12.07 12.00 12.03 0.15 6 11.99 11.98 11.94 12.06 12.06 12.01 0.12 7 12.00 12.04 11.92 12.00 12.07 12.01 0.15 8 12.02 12.06 11.94 12.07 12.00 12.02 0.13 9 12.01 12.06 11.94 11.91 11.94 11.97 0.15 10 11.92 12.05 11.92 12.09 12.07 12.01 0.17 Then… X 12.00 is the average of the averages R 0.15 is the average of the ranges Finally… Calculate the upper and lower control limits UCLX 12.00 0.58 0.15 12.09 LCLX 12.00 0.58 0.15 11.91 UCLR 2.11 0.15 1.22 LCLR 0 0.15 0 The X Chart UCL = 12.10 X = 12.00 LCL = 11.90 The R Chart UCL = 0.32 R = 0.15 LCL = 0.00 The X/R Chart UCL X LCL What can you conclude? UCL R LCL