Quality Management“It costs a lot to produce a bad product”Norman.ppt

quality management “it costs a lot to produce a bad product.” norman augustine,cost of quality,prevention costs appraisal costs internal failure costs external failure costs 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,1900-1993,1986,quality is “uniformity and dependability” focus on spc and statistical tools “14 points” for management pdca method,the quality gurus joseph juran,1904 - 2008,1951,quality is “fitness for use” pareto principle cost of quality general management approach as well as statistics,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 (1980s). 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,developing quality specifications,input,process,output,design,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,six sigma quality,six sigma roadmap (dmaic),next project,define customers, value, problem statement scope, timeline, team primary/secondary & opex metrics current value stream map voice of customer (qfd),measure assess specification / demand measurement capability (gage r&r) correct the measurement system process map, spaghetti, time obs. measure ovs & ivs / queues,analyze (and fix the obvious) root cause (pareto, c&e, brainstorm) find all kpovs & kpivs fmea, doe, critical xs, va/nva graphical analysis, anova future value stream map,improve optimize kpovs & test the kpivs redesign process, set pacemaker 5s, cell design, mrs visual controls value stream plan,control document process (wis, std work) mistake proof, tt sheet, ci list analyze change in metrics value stream review prepare final report,celebrate project $,six sigma organization,quality improvement,traditional,continuous improvement,time,quality,continuous improvement philosophy,kaizen: japanese term for continuous improvement. a step-by-step improvement of business processes. pdca: plan-do-check-act as defined by deming.,benchmarking : what do top performers do?,tools used for continuous improvement,1. process flowchart,tools used for continuous improvement,2. run chart,tools used for continuous improvement,3. control charts,performance metric,time,tools used for continuous improvement,4. cause and effect diagram (fishbone),tools used for continuous improvement,5. check sheet,tools used for continuous improvement,6. histogram,tools used for continuous improvement,7. pareto analysis,a,b,c,d,e,f,frequency,percentage,50%,100%,0%,75%,25%,10,20,30,40,50,60,summary of tools,process flow chart run diagram control charts fishbone check sheet histogram 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,operator,customer a,receiving party,how can we reduce waiting time?,fishbone diagram analysis,reasons why customers have to wait (12-day analysis with check sheet),pareto analysis: reasons why customers have to wait,ideas for improvement,taking lunches on three different shifts ask all employees to leave messages when leaving desks compiling a directory where next to personnels name appears her/his title,results of implementing the recommendations,before,after,in general, how can we monitor quality?,assignable variation: we can assess the cause common variation: variation that may not be possible to correct (random variation, random noise),by observing variation in output measures!,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:,the standard deviation of the observations:,average & variation example,number of pepperonis per pizza: 25, 25, 26, 25, 23, 24, 25, 27 average: standard deviation:,number of pepperonis 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?,the “goalpost” mentality,a.k.a upper/lower design limits (udl, ldl) upper/lower spec limits (usl, lsl) upper/lower tolerance limits (utl, ltl),but are all good products equal?,taguchis view “quality loss function” (qlf),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 ks capable if,capability index (cpk),cpk = 1 means process is capable at the ks level,another way of writing this is to calculate the capability index:,accuracy and consistency,we say that a process is accurate if its mean 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 = 0.5 ltl = 9 utl = 11,example 2: capability index (cpk),x = 9.5 and = 0.5 ltl = 9 utl = 11,utl,ltl,x,example 3: capability index (cpk),x = 10 and = 2 ltl = 9 utl = 11,utl,ltl,x,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. is the process capable at the 3s level? what is the probability of producing a defect?,solution,ltl = 55 utl = 65 s = 2,no, the process is not capable at the 3s level.,solution,p(defect) = p(x65) =p(x55) + 1 p(x65) =p(z(55-61)/2) + 1 p(z(65-61)/2) =p(z-3) + 1 p(z2) =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,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),when should we use p charts?,when decisions are simple “yes” or “no” by inspection when the sample sizes are large enough (50),statistical process control with p charts,statistical process control with p charts,lets assume that we take t samples of size n ,statistical process control with p charts,