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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?.
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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 • Quality is “uniformity and dependability” • Focus on SPC and statistical tools • “14 Points” for management • PDCA method 1900-1993 1986
The Quality Gurus – Joseph Juran • Quality is “fitness for use” • Pareto Principle • Cost of Quality • General management approach as well as statistics 1904 - 2008 1951
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.
Technical Tools (Process Analysis, SPC, QFD) Customer Cultural Alignment What does Total Quality Management encompass? • TQM is a management philosophy: • continuous improvement • leadership development • partnership development
Design quality Dimensions of quality Conformance quality Developing quality specifications Design Input Process Output
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
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 (andfix 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 Validate Project $ Validate Project $ Validate Project $ Validate Project $ Six Sigma Roadmap (DMAIC) Next Project Celebrate Project $
Quality Improvement Continuous Improvement Quality Traditional Time
Plan Do Act Check 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
Performance Time Tools used for continuous improvement 2. Run Chart
Tools used for continuous improvement 3. Control Charts Performance Metric Time
Machine Man Environment Method Material Tools used for continuous improvement 4. Cause and effect diagram (fishbone)
Tools used for continuous improvement 5. Check sheet
Frequency Tools used for continuous improvement 6. Histogram
Tools used for continuous improvement 7. Pareto Analysis 100% 60 75% 50 40 Frequency 50% Percentage 30 20 25% 10 0% A B C D E F
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
Customer B The current process Operator Receiving Party Customer A How can we reduce waiting time?
Absent receiving party Working system of operators Absent Too many phone calls Lunchtime Out of office Makes customer wait Not at desk Absent Not giving receiving party’s coordinates Does not understand customer Lengthy talk Does not know organization well Complaining Takes too much time to explain Leaving a message Customer Operator Fishbone diagram analysis
Reasons why customers have to wait (12-day analysis with check sheet)
Frequency Percentage 87.1% 300 250 71.2% 200 49% 150 100 0% A B C D E F 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 personnel’s name appears her/his title
Percentage Percentage Frequency Frequency 100% 87.1% 300 300 71.2% Improvement 200 200 49% 100 100 100% 0% 0% A B C D E F B C A D E F Results of implementing the recommendations …After Before…
In general, how can we monitor quality…? By observing variation in output measures! • Assignable variation: we can assess the cause • 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: The standard deviation of the observations:
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?
High Incremental Cost of Variability Zero Lower Tolerance Target Spec Upper Tolerance Traditional View When is a product good enough? a.k.a Upper/Lower Design Limits (UDL, LDL) Upper/Lower Spec Limits (USL, LSL) Upper/Lower Tolerance Limits (UTL, LTL) The “Goalpost” Mentality
High Incremental Cost of Variability Zero Lower Spec Target Spec Upper Spec But are all ‘good’ products equal? Taguchi’s 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) Another way of writing this is to calculate the capability index: Cpk < 1 means process is not capable at the ks level Cpk >= 1 means process is capable at the ks level
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.
LTL UTL X Example 1: Capability Index (Cpk) X = 10 and σ = 0.5 LTL = 9 UTL = 11
Example 2: Capability Index (Cpk) X = 9.5 and σ = 0.5 LTL = 9 UTL = 11 LTL UTL X
Example 3: Capability Index (Cpk) X = 10 and σ = 2 LTL = 9 UTL = 11 LTL UTL 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(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.
Upper Control Limit Central Line Lower Control Limit 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)
Statistical Process Control with p Charts 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 Let’s assume that we take t samples of size n …