Operations Management

Operations Management Supplement 6 – Statistical Process Control PowerPoint presentation to accompany Heizer/Render Principles of Operations Management, 6e Operations Management, 8e © 2006 Prentice Hall, Inc.

Statistical Process Control (SPC) • Control Charts for Variables • Setting Mean Chart Limits (x-Charts) • Setting Range Chart Limits (R-Charts) • Process Capability • Process Capability Ratio (Cp) • Process Capability Index (Cpk ) Outline

Statistical Process Control (SPC) • Variability is inherent in every process • Natural or common causes • Special or assignable causes • SPC charts provide statistical signals when assignable causes are present • SPC approach supports the detection and elimination of assignable causes of variation

Inspection • Inspection is the activity that is done to ensure that an operation is producing the results expected (post production activity). • Where to inspect • At point of product design • At point of product production (source) • At point of product assembly • At point of product dispatch to customer • At point of product reception by customer

What to Inspect • Variables of an entity • Degree of deviation from a target (continuum scale) • Lifespan of device • Reliability or accuracy of device • Attributes of an entity • Classifies attributes into discrete classes such as good versus bad, or pass or fail • Maximum weight of bag at airport • Minimum height for exit seat

Data Used for Quality Judgments Variables Attributes • Characteristics that can take any real value • May be in whole or in fractional numbers • Continuous random variables, e.g. weight, length, duration, etc. • Defect-related characteristics • Classify products as either good or bad or count defects • Categorical or discrete random variables

How to Inspect: Methods • Visual inspection • Manual inspection (weigh, count ) • Mechanical inspection (machine-based) • Testing of device

Disadvantages of Inspection • Most inspections are not done at the source • Errors are discovered after its too late • No link between error and the cause of errors • Errors are often too costly to correct • As products grow in number more staff are needed for inspection • As products grow in number, more time is needed for inspection • Most inspections involve the inspection of good parts as well as bad ones • CHALLENGE: How could one achieve high product quality without having to inspect all goods produced?

Statistical Process Control • A statistics-based approach for monitoring and inspecting results of a process, through the gathering, structuring, and analyses of product variables/attributes, as well as the taking of corrective actionat the source, during the production process.

Class Example • What can we learn from the results of the bodyguards?

Common Cause Variations • Also called natural causes • Affects virtually all production processes • Generally this requires some change at the systemic level of an organization • Managerial action is often necessary • The objective is to discover avoidable common causes present in processes • Eliminate (when possible) the root causes of the common variations, e.g. different arrival times of suppliers, different arrival times of customers, weight of products poured in box by machine

Assignable Variations • Also called special causes of variation • Generally this is caused by some change in the local activity or process • Variations that can be traced to a specific reason at a localized activity • The objective is to discover special causes that are present • Eliminate the root causes of the special variations, e.g. machine wear, material quality, fatigued workers, misadjusted equipment • Incorporate the good process control

Each of these represents one sample of five boxes of cereal # # # # # Frequency # # # # # # # # # # # # # # # # # # # # # Weight SPC and Statistical Samples To conduct inspection using the SPC approach, one has to compute averages for several small samples instead of using data from individual items: 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

The solid line represents the distribution Frequency Weight SPC Sampling To measure the process, we take samples of same size at different times. We plot the mean of each sample for each point in time (b) After enough samples are taken from a stable process, they form a pattern called a distribution Figure S6.1

Central tendency Variation Shape Frequency Weight Weight Weight Attributes of Distributions (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

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

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

The mean of the sampling distribution (x) will be the same as the population mean m x = m s n The standard deviation of the sampling distribution (sx) will equal the population standard deviation (s) divided by the square root of the sample size, n sx = Central Limit Theorem Regardless of the distribution of the population, the distribution of sample means drawn from the population will tend to follow a normal curve

(a) In statistical control and capable of producing within control limits Frequency Upper Control Limit Lower Control Limit (b) In statistical control but not capable of producing within control limits (c) Out of statistical control and incapable of producing within limits Size (weight, length, speed, etc.) Interpreting SPC Charts Figure S6.2

Three population distributions Mean of sample means = x Beta Standard deviation of the sample means Normal = sx = Uniform s n | | | | | | | -3sx -2sx -1sx x +1sx +2sx +3sx 95.45% fall within ± 2sx 99.73% of all x fall within ± 3sx Population and Sampling Distributions Distribution of sample means Figure S6.3

Sampling distribution of means Process distribution of means x = m (mean) Sampling Distribution Figure S6.4

Steps In Creating Control Charts Take representative sample from output of a process over a long period of time, e.g. 10 units every hour for 24 hours. Compute means and ranges for the variables and calculate the control limits Draw control limits on the control chart Plot a chart for the means and another for the mean of ranges on the control chart Determine state of process (in or out of control) Investigate possible reasons for out of control events and take corrective action Continue sampling of process output and reset the control limits when necessary

In-Class Exercise : Control Charts 6/15 6/16 Calculate X bar and R’s for new data Calculate X double bar and R bar figures for new data Draw X bar chartCalculate LCL and UCL for X bar chartDraw lines for LCL and UCL and for X double bar in chart

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 Charts for Variables

For x-Charts when we know s Upper control limit (UCL) = x + zsx Lower control limit (LCL) = x - zsx where x = mean of the sample means or a target value set for the process z = number of normal standard deviations sx = standard deviation of the sample means = s/ n s = population standard deviation n = sample size Setting Chart Limits

Hour 1 Sample Weight of Number Oat Flakes 1 17 2 13 3 16 4 18 5 17 6 16 7 15 8 17 9 16 Mean 16.1 s = 1 Hour Mean Hour Mean 1 16.1 7 15.2 2 16.8 8 16.4 3 15.5 9 16.3 4 16.5 10 14.8 5 16.5 11 14.2 6 16.4 12 17.3 n = 9 UCLx = x + zsx= 16 + 3(1/3) = 17 ozs LCLx = x - zsx = 16 - 3(1/3) = 15 ozs Setting Control Limits For 99.73% control limits, z = 3

Variation due to assignable causes Out of control 17 = UCL Variation due to natural causes 16 = Mean 15 = LCL Variation due to assignable causes | | | | | | | | | | | | 1 2 3 4 5 6 7 8 9 10 11 12 Out of control Sample number Setting Control Limits Control Chart for sample of 9 boxes

For x-Charts when we don’t know s Upper control limit (UCL) = x + A2R Lower control limit (LCL) = x - A2R where R = average range of the samples A2 = control chart factor found in Table S6.1 x = mean of the sample means Setting Chart Limits

Sample Size Mean Factor Upper Range Lower Range n A2D4D3 2 1.880 3.268 0 3 1.023 2.574 0 4 .729 2.282 0 5 .577 2.115 0 6 .483 2.004 0 7 .419 1.924 0.076 8 .373 1.864 0.136 9 .337 1.816 0.184 10 .308 1.777 0.223 12 .266 1.716 0.284 Control Chart Factors Table S6.1

Process average x = 16.01 ounces Average range R = .25 Sample size n = 5 Setting Control Limits

Process average x = 16.01 ounces Average range R = .25 Sample size n = 5 UCLx = x + A2R = 16.01 + (.577)(.25) = 16.01 + .144 = 16.154 ounces From Table S6.1 Setting Control Limits

Process average x = 16.01 ounces Average range R = .25 Sample size n = 5 UCLx = x + A2R = 16.01 + (.577)(.25) = 16.01 + .144 = 16.154 ounces UCL = 16.154 Mean = 16.01 LCLx = x - A2R = 16.01 - .144 = 15.866 ounces LCL = 15.866 Setting Control Limits

R – Chart • Type of variables control chart • Shows sample ranges over time • Difference between smallest and largest values in sample • Monitors process variability • Independent from process mean

Upper control limit (UCLR) = D4R Lower control limit (LCLR) = D3R where R = average range of the samples D3 and D4 = control chart factors from Table S6.1 Setting Chart Limits For R-Charts

Average range R = 5.3 pounds Sample size n = 5 From Table S6.1 D4= 2.115, D3 = 0 UCLR = D4R = (2.115)(5.3) = 11.2 pounds UCL = 11.2 Mean = 5.3 LCLR = D3R = (0)(5.3) = 0 pounds LCL = 0 Setting Control Limits

(a) These sampling distributions result in the charts below (Sampling mean is shifting upward but range is consistent) UCL (x-chart detects shift in central tendency) x-chart LCL UCL (R-chart does not detect change in spread) R-chart LCL Mean and Range Charts Figure S6.5

(b) These sampling distributions result in the charts below (Sampling mean is constant but dispersion is increasing) UCL (x-chart does not detect the increase in mean) x-chart LCL UCL (R-chart detects increase in dispersion) R-chart LCL Mean and Range Charts Figure S6.5

Automated Control Charts

Control Charts for Attributes • For variables that are categorical • Good/bad, yes/no, acceptable/unacceptable • Measurement is typically counting defectives • Charts may measure • Percent defective (p-chart) • Number of defects (c-chart)

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

Process Capability • The natural variation of a process should be small enough to produce products that meet the standards required • A process in statistical control does not necessarily meet the design specifications • Process capability is a measure of the relationship between the natural variation of the process and the design specifications

Upper Specification - Lower Specification 6s Cp = Process Capability Ratio • A capable process must have a Cp of at least 1.0 • Does not look at how well the process is centered in the specification range • Often a target value of Cp = 1.33 is used to allow for off-center processes • Six Sigma quality requires a Cp = 2.0

Process mean x = 210.0 minutes Process standard deviation s = .516 minutes Design specification = 210 ± 3 minutes Upper Specification - Lower Specification 6s Cp = Process Capability Ratio Insurance claims process

Process mean x = 210.0 minutes Process standard deviation s = .516 minutes Design specification = 210 ± 3 minutes Upper Specification - Lower Specification 6s Cp = 213 - 207 6(.516) = = 1.938 Process Capability Ratio Insurance claims process

Process mean x = 210.0 minutes Process standard deviation s = .516 minutes Design specification = 210 ± 3 minutes Upper Specification - Lower Specification 6s Cp = 213 - 207 6(.516) = = 1.938 Process Capability Ratio Insurance claims process Process is capable

UpperSpecification - xLimit 3s Lowerx - Specification Limit 3s Cpk = minimum of , Process Capability Index • A capable process must have a Cpk of at least 1.0 • A capable process is not necessarily in the center of the specification, but it falls within the specification limit at both extremes

New process mean x = .250 inches Process standard deviation s = .0005 inches Upper Specification Limit = .251 inches Lower Specification Limit = .249 inches Process Capability Index New Cutting Machine

Operations Management