SPC for Right-Brain Thinkers

Transcription

1 SPC for Right-Brain Thinkers Presented by Lon Roberts, Ph.D. (972) Genesis of Statistical Process Control Pioneer in using 2-D plots to display a time series was William Playfair, a Scottish engineer and the founder of graphical methods of statistics. Playfair was born in 1759 and died in (972) (972)

2 # Christenings (x1000) Christenings in London, Year Ref: Howard Wainer, Graphic Discovery, (972) # Christenings (x1000) Christenings in London, Beginning of English Civil War Smallpox Epidemic 1670 Year 1675 Same Data as 1674 Great Plague of 1665 and Great Fire of Ref: Howard Wainer, Graphic Discovery, (972) (972)

3 Genesis of Statistical Process Control Walter Shewhart Western Electric (WECO) Hawthorne Works Plant Post WWII WWII Pre- WWII Identified process variance as a key precursor of product quality W. Edwards Deming Assignable Causes Chance Causes (972) % 90% 80% 70% 60% 50% 40% 30% 20% 10% Changing Profile of American Workplace Farming Blue Collar Other Services White Collar 0% In 1920, 38% of jobs were Blue Collar This held steady until around 1968 By contrast In 1920, 25% of jobs were White Collar White Collar jobs continued to rise until approx In % of jobs were Blue Collar 61% were White Collar (972) (972)

4 What is Statistical Process Control? 42 Second Tutorial A set of tools for monitoring process variance Typical tools in the SPC toolkit Fishbone diagram Pareto chart Process map Histogram Scatter diagram Run chart Control chart SPC is most often associated with control charts A control chart plots the changes in a variable over time Uses statistically-determined control limits to distinguish random variation from special causes of variation (972) SPC in the White Collar Environment Technical Domain Human Domain (972) (972)

5 Panorama of SPC Applied to Projects Project ID SPC Opportunities Detect/Correct Problems Collect/Analyze Data Construct SPC Charts (972) Project Metrics Progress Metrics... Reveal something about where you are versus where you should be Work-Product Metrics... Reveal something about the quality of the work products... e.g., errors, faults, spec-compliance, test results Process Metrics... Reveal something about the inner workings of the process that leads to the work products... e.g., rework, delays, resource utilization, change requests, buffer consumption Productivity Metrics... Reveal something about human performance/productivity (972) (972)

6 SPC Credo 1. It is better to anticipate and prevent problems rather than wait until they surface and then try to fix them. 2. The sooner a potential problem can be identified the better. 3. It is better to base decisions on facts and data rather than guesses, assumptions, or biases. 4. Minimizing variation is an important criterion for processes that are intended to deliver predictable results. 5. It is easier to spot trends and patterns when quantitative data are plotted in graphs rather than placed in tables. 6. Adjustments in a process that are a reaction to normal variation will result in even greater variation. 7. The benefit-to-cost ratio of utilizing SPC should be > 1. (972) Pinpointing SPC Opportunities Identify the process of interest or desired repeatable outcome Something your agency does on a recurring basis (such as generating SOWs) or some project outcome you would like to sustain (such as on-time completion) Identify the point (or points) in the process that are critical to achieving the desired outcome Decompose the process or project into its components Create a map that shows how the components interrelate ID the critical components that would have the greatest impact on the outcome if something were to go wrong at that stage Establish metrics based on the best available indicators of how the process is performing at that point (972) (972)

7 Dissecting the Process Every process consists of an interconnected set of steps, stages, or sub-processes that can be depicted as a map. Mapping the process allows you to 1) Clarify the boundaries, 2) Ensure the actual and the nominal process are one and the same, and 3) ID possible SPC measurement/control points. Order is Placed by Sales Dept. Promised Delivery Date Order Priority Order is Internally Routed 2-3 Days 3-4 Layers Misrouting Special Conditions Order is Received by Shipping Order Backlog Manual Inventory System Shipping Notifies Billing Paper Correspondence Keying Errors Billing Invoices Customer Shipping & Handling Not Added (972) Anatomy of a Control Chart A Control Chart is a plot that shows how data points vary in relation to what we expect them to be over a period of time This requires 1. That we are able to determine the expected value of the data points if the process was completely stable we call this the mean, or average 2. That we understand how the data points will be distributed on either side of the mean, given the fact that every process has some natural variation 3. That we can quantify this variation (or deviation from the mean) and give it a value and a name: sigma ( ) 4. That we can identify limits and patterns that tell us if the variation of the data is unusual (972) (972)

8 Anatomy of a Control Chart 99.7% UCL 95% 68% LCL Time = χ (972) Anatomy of a Control Chart = χ Zone A Zone B Zone C Zone C Zone B Zone A Time UCL LCL (972) (972)

9 Control Limits vs Specification Limits Control Limits Are calculated using data from the process you are interested in Are plotted on charts that monitor a process ability to be in statistical control Charts with control limits that are particularly relevant to White Collar processes: XMR charts and attribute charts Specification Limits Are assigned perhaps by a customer, competitive standard, or internal policy Are plotted on charts that monitor a process s ability to be in engineering control Charts with specification limits that are particularly relevant to White Collar processes: pre-control charts and run charts (972) Two Classifications of Variance In his 1925 white paper, Walter Shewhart identified two categories of process variance: Variance that is attributable to chance causes Variation that is attributable to assignable causes Today we typically referred to these as common causes and special causes where Common Causes are due to the inherent characteristics of the process by virtue of how it is designed. Variation from common causes is random, but predictable in the sense that we can predict the pattern of variation over the long run. Special Causes are due to a problem condition i.e., something out of the ordinary. (972) (972)

10 Two Classifications of Data There are two types of data that processes can yield Data that results from some aspect of the process that can be measured and quantified on a numerical scale, such as wait-time or time-in-process Data that results from some aspect that can be observed as being present or not present, such as errors or defects Data of the first type are called variables and data of the second type are called attributes Why is this distinction in data types important? It dictates the kind of SPC charts we can use It determines how much information we can extract (972) What Insights Can Control Charts Provide? An SPC chart is essentially a visual filtering tool for distinguishing signals from noise... where The signals are problem condition you wish to detect The noise is the random variation of the process More specifically, control charts can reveal Special causes of variance i.e., data values that are due to something other than normal random variation Patterns that may suggest an unnatural trending condition A shift in the expected value (or baseline) for the variable of interest SPC charts can also provide data to support justification for process improvements (972) (972)

11 SPC in White Collar Organizations Special challenges Infrequent data limits sample size to 1, thus losing any benefit that may result from sample averages Attribute data are more typical than variables data less beneficial for early-prevention of problems 99.99% of SPC reference and application literature deals with manufacturing processes little guidance in dealing with the special circumstances of white collar processes Many white collar managers do not see the need for or appreciate the benefits of SPC within their domain SPC charts especially relevant to white collar processes: chart of individuals, pre-control chart, attribute charts (972) Chart of Individuals Plots individual data values, rather than sample averages Often called an XmR chart, where X refers to the variable you are tracking and mr refers to the moving range Customer Satisfaction Rating (972) UCL= s 1s _ X=6.2-1s -2s LCL= (972)

12 Pre-Control Charts Plots individual values Can be for either attributes or variables data Upper and lower limits are based on specifications ¼ (USL LSL) Red Zone Yellow Zone Upper Spec. Limit C/L Green Zone Green Zone ½ (USL LSL) Yellow Zone Red Zone Lower Spec. Limit (972) Identifying Suspicious Patterns Is based on how the data would be distributed under normal conditions in other words Most of the data points will fall in Zone A About a fourth of the data points will fall in Zone B About 5% of the data points will fall in Zone C Typically the data will have a zigzag pattern Here are thumb rules you might use to ID irregular patterns: One point beyond Zone A Out of 2 points in a row: 1 in Zone A, the other in Zone A or B Out of 3 points in a row: 2 in Zone B, the other 1 in Zone C Out of 4 points in a row: 1 point in Zone B, the other 3 in Zone C 3 points in a row trending up or down 7 points in a row alternating up and down (972) (972)

13 SPC Applications in Government Agencies Some opportunities are clear, take for instance this excerpt from a 2013 GAO report on Personnel Security Clearances: Some of the quality indicators the report called out were: Processing time for security clearances Errors and omissions in investigative reports Errors and omissions in adjudicative files Problems with reciprocity of security clearances Security incident response time Achievement of milestone remediation dates Source: GAO T, 31 October (972) Why is SPC Important to My Organization? 1. Saves time by preventing problems (process cycle time ). 2. Saves money by reducing scrap and rework (yield ). 3. Improves outcome-consistency by controlling variation. 4. Provides factual data to impel managers and other decision makers to invest in process improvements. 5. Takes the guesswork out of the actions needed to implement process improvements. 6. Tells you if process improvements were effective. 7. Actively engages the people who collect the data and analyze the charts giving them a greater sense of ownership and control over their area of responsibility. (972) (972)

14 Recap in a Nutshell SPC is a graphical tool for monitoring the behavior of a process for the purpose of 1) ensuring any variation is normal and random, and 2) ensuring the process is not drifting off course. SPC uses a variety of charts to plot data that are taken from within the process over a period of time. The SPC charts have zones and limits that are based on a simple statistical analysis of what the process is capable of delivering. (972) Dos and Don ts 1. Start with a doable application that offers a clear returnon- investment for the time and effort 2. Take into account the rate that data will available for slow data streams consider using XmR and Pre-Control Charts 3. Use common sense when interpreting patterns on control charts avoid overreliance on thumb rules 4. Remember to take human factors into account resistance to change, math aversion, fear of added workload, etc. 5. Don t use the tool to browbeat or expose others 6. Don t mechanize the process get people involved in setting up the SPC initiative allow them collect and plot data encourage them to use SPC to identify problems and to make a data-driven case for needed improvements (972) (972)

15 References SPC for Right-Brain Thinkers: Process Control for Non-Statisticians, Lon Roberts, Quality Press, 2005 Link Gut Feelings: The Intelligence of the Unconscious, Gerd Gigerenzer, Viking, 2007 Link Personal Security Clearances: Full Development and Implementation of Metrics Needed to Measure Quality of Process, GAO T, 2013 Link The Psychology of Judgment and Decision Making, Scott Plous, McGraw-Hill, 1993 Link A Whole New Brain, Daniel H. Pink, Riverhead Books, 2006 Link Statistical Process Control of Project Performance, Walt Lipke, Crosstalk: The Journal of Defense Software Engineering, March 2002 Link A Statistical Project Control Tool for Engineering Managers, Garland T.Bauch and Christopher A. Chung, Project Management Journal, June 2001 Link Improving Traditional Earned Value Management by Incorporating Statistical Process Charts, Sou-Sen Len, et. al., ISARC 2006 Conference Proceedings Link Myths About Process Behavior Charts, Donald J. Wheeler, Quality Digest, 2011 Link Statistical Process Control for Right-Brain Thinkers, Lon Roberts, Six Sigma IQ, Feb Link SPC Resources Website Link (972) Ph: (972) Lon@R2assoc.com Author of... Lon Roberts, Ph.D. Ratchet Up Your Skills With These Workshops! See descriptions at Statistical Process Control for White Collar Processes Gleaning Facts From Figures TM (972) (972)