Quality Management

Quality PMI’s quality philosophy summarized by Definition of quality No gold-plating Prevention over inspection

PMI’s quality philosophy summarized by

Definition of quality

No gold-plating

Prevention over inspection

PMI Quality Definition QUALITY IS CONFORMANCE TO REQUIREMENTS AND FITNESS OF USE

QUALITY IS CONFORMANCE TO REQUIREMENTS AND FITNESS OF USE

No Gold-plating Don’t give the customer extras Adds no-value to the project because it is beyond the scope Could cost more May be based on impressions not requests

Don’t give the customer extras

Adds no-value to the project because

it is beyond the scope

Could cost more

May be based on impressions not requests

Prevention over inspection Quality must be planned NOT inspected

Quality must be planned NOT inspected

Six Sigma Originally developed by Motorola, Six Sigma refers to an extremely high measure of process capability A Six Sigma capable process will return no more than 3.4 defects per million operations (DPMO) Highly structured approach to process improvement

Originally developed by Motorola, Six Sigma refers to an extremely high measure of process capability

A Six Sigma capable process will return no more than 3.4 defects per million operations (DPMO)

Highly structured approach to process improvement

Six sigma

Six Sigma DMAIC Approach Define critical outputs and identify gaps for improvement Measure the work and collect process data Analyze the data Improve the process Control the new process to make sure new performance is maintained

Define critical outputs and identify gaps for improvement

Measure the work and collect process data

Analyze the data

Improve the process

Control the new process to make sure new performance is maintained

Tools Of TQM Check Sheets Scatter Diagrams Cause-and-Effect Diagram Pareto Charts Flow Charts Histograms Statistical Process Control (SPC)

Tools Of TQM

Check Sheets

Scatter Diagrams

Cause-and-Effect Diagram

Pareto Charts

Flow Charts

Histograms

Statistical Process Control (SPC)

Seven Tools for TQM / / / / /// / // /// // //// /// // / Hour Defect 1 2 3 4 5 6 7 8 A B C / / // (a) Check Sheet: An organized method of recording data Figure 6.5

Seven Tools for TQM (b) Scatter Diagram: A graph of the value of one variable vs. another variable Figure 6.5 Absenteeism Productivity

Seven Tools for TQM (c) Cause and Effect Diagram: A tool that identifies process elements (causes) that might effect an outcome Figure 6.5 Cause Materials Methods Manpower Machinery Effect

Seven Tools for TQM (d) Pareto Charts: A graph to identify and plot problems or defects in descending order of frequency Figure 6.5 Frequency Percent A B C D E

Pareto Chart

Seven Tools for TQM (e) Flow Charts (Process Diagrams): A chart that describes the steps in a process Figure 6.5

Flow Charts Operator takes phone order. Orders wait to be picked up. Supervisor inspects orders. Order is fulfilled. Order waits for sales rep. Is order complete? Yes No Orders are moved to supervisor’s in-box. Orders wait for supervisor.

Seven Tools for TQM (f) Histogram: A distribution showing the frequency of occurrence of a variable Figure 6.5 Distribution Repair time (minutes) Frequency

Seven Tools for TQM (g) Statistical Process Control Chart: A chart with time on the horizontal axis to plot values of a statistic Figure 6.5 Upper control limit Target value Lower control limit Time

Variability is inherent in every process Natural or common causes Special or assignable causes Provides a statistical signal when assignable causes are present Detect and eliminate assignable causes of variation Statistical Process Control (SPC)

Variability is inherent in every process

Natural or common causes

Special or assignable causes

Provides a statistical signal when assignable causes are present

Detect and eliminate assignable causes of variation

Natural Variations Also called common causes Affect virtually all production processes Expected amount of variation Output measures follow a probability distribution For any distribution there is a measure of central tendency and dispersion If the distribution of outputs falls within acceptable limits, the process is said to be “in control”

Also called common causes

Affect virtually all production processes

Expected amount of variation

Output measures follow a probability distribution

For any distribution there is a measure of central tendency and dispersion

If the distribution of outputs falls within acceptable limits, the process is said to be “in control”

Assignable Variations Also called special causes of variation Generally there is some change in the process Variations that can be traced to a specific reason The objective is to discover when assignable causes are present Eliminate the bad causes Incorporate the good causes

Also called special causes of variation

Generally there is some change in the process

Variations that can be traced to a specific reason

The objective is to discover when assignable causes are present

Eliminate the bad causes

Incorporate the good causes

Samples To measure the process, we take samples and analyze the sample statistics following these steps (a) Samples of the product, say five boxes of cereal taken off the filling machine line, vary from each other in weight Figure S6.1 Frequency Weight # # # # # # # # # # # # # # # # # # # # # # # # # # Each of these represents one sample of five boxes of cereal

Samples (b) After enough samples are taken from a stable process, they form a pattern called a distribution Figure S6.1 The solid line represents the distribution Frequency Weight

Samples (c) There are many types of distributions, including the normal (bell-shaped) distribution, but distributions do differ in terms of central tendency (mean), standard deviation or variance, and shape Figure S6.1 Weight Central tendency Weight Variation Weight Shape Frequency

Samples (d) If only natural causes of variation are present, the output of a process forms a distribution that is stable over time and is predictable Figure S6.1 Weight Time Frequency Prediction

Samples (e) If assignable causes are present, the process output is not stable over time and is not predicable Figure S6.1 Weight Time Frequency Prediction ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?

Control Charts Constructed from historical data, the purpose of control charts is to help distinguish between natural variations and variations due to assignable causes

Types of Data Characteristics that can take any real value May be in whole or in fractional numbers Continuous random variables Variables Attributes Defect-related characteristics Classify products as either good or bad or count defects Categorical or discrete random variables

Characteristics that can take any real value

May be in whole or in fractional numbers

Continuous random variables

Defect-related characteristics

Classify products as either good or bad or count defects

Categorical or discrete random variables

Control Charts for Variables For variables that have continuous dimensions Weight, speed, length, strength, etc. x-charts are to control the central tendency of the process R-charts are to control the dispersion of the process These two charts must be used together

For variables that have continuous dimensions

Weight, speed, length, strength, etc.

x-charts are to control the central tendency of the process

R-charts are to control the dispersion of the process

These two charts must be used together

Control Chart

Patterns in Control Charts Normal behavior. Process is “in control.” Figure S6.7 Upper control limit Target Lower control limit

Patterns in Control Charts One plot out above (or below). Investigate for cause. Process is “out of control.” Figure S6.7 Upper control limit Target Lower control limit

Patterns in Control Charts Trends in either direction, 5 plots. Investigate for cause of progressive change. Figure S6.7 Upper control limit Target Lower control limit

Patterns in Control Charts Two plots very near lower (or upper) control. Investigate for cause. Figure S6.7 Upper control limit Target Lower control limit

Patterns in Control Charts Run of 5 above (or below) central line. Investigate for cause. Figure S6.7 Upper control limit Target Lower control limit

Patterns in Control Charts Erratic behavior. Investigate. Figure S6.7 Upper control limit Target Lower control limit