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

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 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

What does Total Quality Management encompass? TQM is a management philosophy: continuous improvement leadership development partnership development Cultural Alignment Technical Tools (Process Analysis, SPC, QFD) Customer

Developing quality specifications InputProcessOutput Design Design quality Dimensions of quality Conformance quality

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. PlanDo ActCheck 3.Benchmarking : what do top performers do?

Tools used for continuous improvement 1. Process flowchart

Tools used for continuous improvement 2. Run Chart PerformanceTime

Tools used for continuous improvement 3. Control Charts Performance Metric Time

Tools used for continuous improvement 4. Cause and effect diagram (fishbone) Environment Machine Man MethodMaterial

Tools used for continuous improvement 5. Check sheet ItemABCDEFG √ √ √ √ √ √ √ √√√√√√ √

Tools used for continuous improvement 6. Histogram Frequency

Tools used for continuous improvement 7. Pareto Analysis ABCDEF Frequency Percentage 50% 100% 0% 75% 25%

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 r B Operator Custome r A Receiving Party How can we reduce waiting time?

Makes custome r wait Absent receiving party Working system of operators CustomerOperator Fishbone diagram analysis Absent Out of office Not at desk Lunchtime Too many phone calls Absent Not giving receiving party’s coordinates Complaining Leaving a message Lengthy talk Does not know organization well Takes too much time to explain Does not understand customer

Daily average Total number AOne operator (partner out of office) BReceiving party not present6.173 CNo one present in the section receiving call5.161 DSection and name of the party not given1.619 EInquiry about branch office locations1.316 FOther reasons Reasons why customers have to wait (12-day analysis with check sheet)

Pareto Analysis: reasons why customers have to wait ABCDEF FrequencyPercentage 0% 49% 71.2% %

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 ABCDEF Frequency Percentage 100% 0% 49% 71.2% % 100% BCADEF Frequency Percentage 0% Before… …After Improvement

In general, how can we monitor quality…? 1.Assignable variation: we can assess the cause 2.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:

What is the key assumption behind SPC? LESS VARIABILITY implies BETTER PERFORMANCE ! TargetLower specUpper spec HighLow Cost Performance Measure

Capability Index (C pk ) It shows how well the performance measure fits the design specification based on a given tolerance level A process is k  capable if

Capability Index (C pk ) C pk < 1 means process is not capable at the k  level C pk >= 1 means process is capable at the k  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: Capability Index (C pk ) X = 10 and σ = 0.5 LTL = 9 UTL = 11 UTLLTL 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. 1.Is the process capable at the 3  level? 2.What is the probability of producing a defect?

Solution LTL = 55 UTL = 65  = 2 No, the process is not capable at the 3  level.

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.

Solution P(defect) = 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) = – (from standard normal table) = % of the cans are defective.

Control Charts Control charts tell you when a process measure is exhibiting abnormal behavior. Upper Control Limit Central Line Lower Control Limit

Two Types of Control Charts p Chart This is a plot of proportions over time (used for performance measures that are yes/no attributes) X/R Chart This is a plot of averages and ranges over time (used for performance measures that are variables)

LCL = UCL = p = 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)ItemsDefectivePercentage Statistical Process Control with p Charts

Let’s assume that we take t samples of size n …

Statistical Process Control with p Charts

LCL = UCL = p = Statistical Process Control with p 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) R is the range between the highest and the lowest for each sample Statistical Process Control with X/R Charts X is the mean for each sample

Statistical Process Control with X/R Charts X is the average of the averages. R is the average of the ranges

define the upper and lower control limits… Statistical Process Control with X/R Charts Read A 2, D 3, D 4 from Table TN 8.7

Example: SPC for bottle filling… SampleObservation (x i )AverageRange (R)

Example: SPC for bottle filling… SampleObservation (x i )AverageRange (R) Calculate the average and the range for each sample…

Then… is the average of the averages is the average of the ranges

Finally… Calculate the upper and lower control limits

LCL = UCL = The X Chart X = 12.00

The R Chart LCL = 0.00 R = 0.15 UCL = 0.32

The X/R Chart LCL UCL X LCL R UCL What can you conclude? The process is in control

Example Samplen DefectsSamplenDefects a. Develop a p chart for 95 percent confidence (z = 1.96) b. Based on the plotted data points, what comments can you make?

Solution Ten defectives were found in 10 samples of size 15. UCL = (.0645) =.194 LCL = (.0645) = zero Defect proportion on Days 1 and 8 is 0.2, so process out of control.