McGraw-Hill/IrwinCopyright © 2009 by The McGraw-Hill Companies, Inc. All Rights Reserved. Chapter 17 Process Improvement Using Control Charts

17-2 Process Improvement Using Control Charts 17.1Quality: Meaning and Historical Perspective 17.2Statistical Process Control and Causes of Variation 17.3Sampling a Process, Rational Subgrouping and Control Charts 17.4x and R Charts 17.5Pattern Analysis

17-3 Process Improvement Using Control Charts Continued 17.6Comparison of a Process with Specifications: Capability Studies 17.7Charts for Fraction Nonconforming 17.8Cause and Effect, Defect Concentration Diagrams (Optional)

17-4 Quality: Meaning and Perspective Quality –Fitness for use –Extent to which customer expectations are met Types of quality –Quality of design –Quality of conformance –Quality of performance

17-5 History of the Quality Movement 1924Statistical Quality Control/Control Charts, Shewart/Bell Telephone Late ’20’sStatistical Acceptance Sampling, Bell Telephone 1946American Society for Quality Control created 1950W. Edwards Deming introduces statistical quality control in Japan 1951Deming Prize established in Japan 1980’sTotal Quality Management (TQM) 1988Malcolm Baldrige National Quality Awards established 1990’sISO 9000, international quality standards adopted

17-6 ISO 9000 Series of international quality standards Establishes structures and processes for quality control systems at every step of the production process – design, raw materials, in-process monitoring, and so on Imposes quality discipline Broad acceptance internationally

17-7 W. Edwards Deming’s 14 Points (1 of 3) 1.Create constancy of purpose toward improvement of product and service with a plan to become competitive, stay in business, and provide jobs 2.Adopt a new philosophy 3.Cease dependence on mass inspection 4.End the practice of awarding business on the basis of price tag 5.Improve constantly and forever the system of production and service to improve quality and productivity

17-8 W. Edwards Deming’s 14 Points (2 of 3) 6.Institute training 7.Institute leadership 8.Drive out fear, so that everyone may work more effectively for the company 9.Break down organizational barriers 10.Eliminate slogans, exhortations and arbitrary numerical goals and targets for the workforce which urge the workers to achieve new levels of productivity and quality without providing methods

17-9 W. Edwards Deming’s 14 Points (3 of 3) 11.Eliminate work standards and numerical quotas 12.Remove barriers that rob employees of their pride of workmanship 13.Institute vigorous program of education and self-improvement 14.Take action to accomplish the transformation

17-10 Statistical Process Control Historical inspection approach –Inspection of output –Action on output Scrap, rework, downgrade (expensive!) Statistical process control –Monitor and study process variation –Goal: Continuous process improvement –Preventing by quality through process improvement

17-11 Causes of Process Variation Common causes –Typical (random) variation inherent in process design –Process in statistical control Assignable causes –Unusual process variation –Intermittent or permanent process changes –Not common to all process observations –Process not in statistical control

17-12 Sampling a Process and Rational Subgrouping and Control Charts Must decide which process variables to study –Best to study a quantitative variable This means we are employing measurement data We will take a series of samples over time –Usually called subgroups –Usually of size two to six –Usually observed over a short period of time Want to observe often enough to detect important process changes

17-13 Example 17.1: The Hole Location Case A manufacturer produces compressor shells Several holes must be punched Experience suggests that substantial changes can occur

17-14 Example 17.1: 20 Subgroups of 5 Holes (Target Value is 3.00)

17-15 Example: 17.1 The Hole Location Case #2

17-16 Control Charts A control chart employs a center line, upper control limit and lower control limit The center line represents average performance The upper and lower control limits are established so that when in control almost all plot points will be between the limits

17-17 Control Chart Example

17-18 x Chart

17-19 x and R Chart: Control Limits Values of d 2, A 2, D 3 and D 4 depend on subgroup size n and are found in Table 17.3

17-20 Control Chart Constants for x and R Charts (Table 17.3)

17-21 Detecting a Shift in Process Mean

17-22 Detecting an Increase in Process Variation

17-23 Pattern Analysis An observation beyond the control limits indicates the presence of an assignable cause Other types of patterns also indicate the presence of an assignable cause These patterns are more easily described in terms of control chart zones –A, B, C

17-24 Pattern Analysis Continued

17-25 Pattern Analysis for Control Charts Any of the following patterns is evidence of the likely presence of an assignable cause of variation –One point beyond zone A (three standard deviation limits) –Two of three consecutive points in zone A (the two standard deviation warning limits, or beyond) on one side of the center line –Four of five consecutive points in zone B (the one standard deviation limits, or beyond) on one side of the center line –A run of eight consecutive points (runs up, down or on the same side of center line) –Any nonrandom pattern – trend, fanning out, cycle or alternating pattern Otherwise, the process is in statistical control

17-26 One Beyond Limits

17-27 Two of Three in A or Beyond

17-28 Four of Five in B or Beyond

17-29 Other Out-of-Control Patterns

17-30 Process Capability Studies Natural tolerance limits for a normally distributed process in statistical control will contain approximately percent of the process observations and is given by If the natural tolerance limits are inside the process specification limits, we say that the process is capable of meeting specifications

17-31 Example 17.9: The Hole Location Case The natural tolerance limits fall outside the process specification limits. Thus the process is not capable of meeting specifications

17-32 Example 14.9: Calculating the Fraction out of Specification for the Hole Data

17-33 Example: A Capable Process Natural tolerance limits are within specification limits

17-34 Sigma Level Capability and Process Leeway

17-35 Charts for Fraction Nonconforming Control Limits and Center Line for a p Chart

17-36 Example 17.10: Sales Invoice Data #1

17-37 Example 17.10: Sales Invoice Data #2

17-38 Example 17.10: MegaStat Output of a p Chart for the Sales Invoice Data

17-39 Cause-and-Effect Diagram A cause-and-effect diagram for “why tables are not cleared quickly in a restaurant” Also known as Ishikawa diagrams or fishbone charts

17-40 Defect Concentration Diagram A defect concentration diagram showing the locations of enamel chips on kitchen ranges