MIM 558 Comparative Operations Management Dr. Alan Raedels, C.P.M.

Dimensions of Quality Performance Features Reliability Durability Conformance Serviceability Aesthetics Perceived quality Procureability Responsiveness

Cost of Quality Costs of Conformance –Prevention –Appraisal Costs of Nonconformance –Internal failure –External failure

Six Steps to Improving Quality 1. Plan improvement a. Measure and track quality status b. Set targets for improvement 2. Systematically search for problems –Are we working on the right problem? –Location, time, operation 3. Systematically search for causes –Ask why 5 times –Cause & effect diagrams –Pareto diagrams –Experimentation –SQC 4. Propose corrective action 5. Verify that corrective action is effective 6. Standardize into permanent practice

Disadvantages of Inspection Wasteful –Sampling and inspection add cost and decrease value Inaccurate –Even 100% inspection is only 80% effective because of the possibility of human errors Impractical –Inspection may involve destructive testing Wrong message –Inspection communicates to people and suppliers that bad parts will still be tolerated. High risks –In sampling and inspection there is a risk of accepting bad lots and rejecting good lots No continuous improvement –Sampling is still inspection, not prevention, so that quality cannot be continuously improved.

Statistical Quality Control Process Control Acceptance Sampling Variables Charts Attributes Charts VariablesAttributes Types of Statistical Quality Control

Characteristics for which you focus on defects Classify products as either ‘good’ or ‘bad’, or count # defects –e.g., radio works or not Categorical or discrete random variables AttributesVariables Quality Characteristics ¨Characteristics that you measure, e.g., weight, length ¨May be in whole or in fractional numbers ¨Continuous random variables

Process Control Charts

Show changes in data pattern –e.g., trends Make corrections before process is out of control Show causes of changes in data –Assignable causes Data outside control limits or trend in data –Natural causes Random variations around average Control Chart Purposes

Control Charts R Chart Variables Charts Attributes Charts X Chart P C Continuous Numerical Data Categorical or Discrete Numerical Data Control Chart Types

1. Take random samples of size n where n depends on type of control chart. X-bar, n=3-10; p, n=30-100; c, n=1 2. For each sample calculate sample statistic such as X-bar, R or p. 3. Plot the sample statistics sequentially 4. Calculate grand means and control limits 5. Evaluate results and recalculate control limits if necessary. Developing a Control Chart

Type of variables control chart –Interval or ratio scaled numerical data Shows sample means over time Monitors process average Example: Weigh samples of coffee & compute means of samples; Plot  X Chart

 X Chart Control Limits Sample Range at Time i # Samples Sample Mean at Time i From page 91, Tague

Factors for Computing Control Chart Limits

Type of variables control chart –Interval or ratio scaled numerical data Shows sample ranges over time –Difference between smallest & largest values in inspection sample Monitors variability in process Example: Weigh samples of coffee & compute ranges of samples; Plot R Chart

Sample Range at Time i # Samples From page 91, Tague R Chart Control Limits

Type of attributes control chart –Nominally scaled categorical data e.g., good-bad Shows % of nonconforming items Example: Count # defective chairs & divide by total chairs inspected; Plot –Chair is either defective or not defective p Chart

p Chart Control Limits # Defective Items in Sample i Size of sample i z = 2 for 95.5% limits; z = 3 for 99.7% limits

Type of attributes control chart –Discrete quantitative data Shows number of nonconformities (defects) in a unit –Unit may be chair, steel sheet, car etc. –Size of unit must be constant Example: Count # defects (scratches, chips etc.) in each chair of a sample of 100 chairs; Plot c Chart

c Chart Control Limits # Defects in Unit i # Units Sampled Use 3 for 99.7% limits

Process Capability C pk Assumes that the process is: under control normally distributed

Meanings of C pk Measures C pk = negative number C pk = zero C pk = between 0 and 1 C pk = 1 C pk > 1