Measure : SPC Dedy Sugiarto

Statistical Process Control ≈ Variation or Variability

No two units of product produced by a manufacturing process are identical. Some variation is inevitable. Simple case: make signature two times! Statistics is the science of analyzing data and drawing conclusions, taking variation in the data into account.

Less Variation = Higher Quality What is Variation? Major Point Quality improvement relies on reducing variation rather than inspect-rework-scrap based on spec limits. This theme forms the basis for the remainder of the module. Discussion Open the class for any discussion, or question and answer that has not taken place yet. 32

http://www.asq.org SPC = The application of statistical techniques to control a process, reducing variation so that performance remains within boundaries, or specification limits. SQC = The application of statistical techniques to control quality; includes acceptance sampling (inspection of a sample from a lot to decide whether to accept that lot) as well as SPC.

The seven major tools are Histogram Control Chart Flow Chart (With Microsoft Visio) Check Sheet (With Microsoft Excel) Scatter Diagram Pareto Diagram Cause-and-Effect Diagram

Seven Tools of Quality Improvement Histogram - A graphic summary of variation in a set of data. The pictorial nature of the histogram lets people see patterns that are difficult to detect in a simple table of numbers. Control chart - A chart with upper and lower control limits on which values of some statistical measure for a series of samples or subgroups are plotted. The chart frequently shows a central line to help detect a trend of plotted values toward either control limit. Flowchart/process map - Graphical tools for process understanding. A flowchart creates a graphical representation of the steps in a process. A process map adds lists of inputs and outputs for each step. Check sheet - A simple data-recording device. The check sheet is custom-designed by the user, which allows him or her to interpret the results easily.

Seven Tools of Quality Improvement Scatter diagrams - A graphical technique to analyze the relationship between two variables. Two sets of data are plotted on a graph, with the y-axis being used for the variable to be predicted and the x-axis being used for the variable to make the prediction. The graph will show possible relationships among variables: those who know most about the variables must evaluate whether they are actually related or only appear to be related. Pareto chart - A graphical tool for ranking causes from most significant to least significant. It is based on the Pareto principle, which was first defined by J. M. Juran in 1950. The principle, named after nineteenth-century economist Vilfredo Pareto, suggests that most effects come from relatively few causes; that is, 80% of the effects come from 20% of the possible causes Cause-effect diagram - A tool for analyzing process dispersion. It is also referred to as the "Ishikawa diagram," because Kaoru Ishikawa developed it, and the "fishbone diagram," because the complete diagram resembles a fish skeleton. The diagram illustrates the main causes and subcauses leading to an effect (symptom). .

Types of variation Common Causes of variaton Special Causes of variaton

Special Causes Common Causes * Change in raw material * Badly maintained machines * Change in machine setting * Poor lighting * Broken tool or die or pattern * Poor workstation layout * Failure to clean equipment * Poor instructions * Equipment malfunction * Poor supervision * Keying in incorrect data * Materials and equipment no suited to the requirements

Examining Variation Common Causes The cause of variations in a stable process is called a Common Cause. A common cause is a natural cause of variation in the system. Major Point To introduce the definition of common causes Example Make sure the funnel has been reset and the funnel is centered. Drop a couple of balls through the funnel. Ask What causes the variation in this process? The pins. Do the pins always cause variation? Yes Explain The cause of variation that always acts the same way in a process is called a common cause variation. Variations in a stable process are always caused by common causes. Define Common causes of variation are ever-present sources of variation in a stable process. Exercise Use the following idea to get people thinking about common cause variation Process = Driving from home to work Measurement = time to get from home to work 44

Normal distribution at all times Examining Variation Stable Process Normal distribution at all times Prediction Major Point A stable process has the same center and spread at all times Explain For a stable process to have a normal distribution it must have the same bell-shaped curve (i.e., the same spread, and same center) at all times. Discuss The general definition of stability just says the same distribution at all times. Some distributions, e.g., defects, will NOT be normal. For simplicity, in this class we will assume a stable process has the same normal distribution at all times. Poster Keep the poster of the definition of stability in front of the class throughout the remainder of this session. Time 43

Examining Variation Activity: Comparing stable processes Which process has better quality? 150 150 140 140 130 130 120 120 110 110 Thickness 100 Thickness 100 90 90 80 80 70 70 60 60 50 Major Point The less the variation from the target, the better the quality Ask For each A-B pair which is better? Why? What is meant by on-target? For pair #1 which is on target? Trick question. Both are on target For pair #2 which has less variation from its mean? Vocabulary On target refers to the average, long term behavior 50 Sequence 5 10 15 20 25 Sequence 5 10 15 20 25 B A 49

Reducing Variation Improving a Stable Process Two strategies for improving a stable process Centering at Target Reducing Common Cause Variation Major Point There are two strategies for improving a stable process: centering at target, reducing common cause variation. Ask How can you tell which is most appropriate? Examine trend charts 79

Reducing Variation Centering at Target Time Thickness 200 200 180 180 160 160 140 140 120 120 Thickness 100 100 80 80 60 Major Point If the process is off-target and stable, then you must make the necessary adjustments to move toward the target. 60 40 40 20 20 Time 80

Reducing Common Cause Variation Reducing Variation Reducing Common Cause Variation 200 200 180 180 160 160 140 140 120 120 Thickness 100 100 80 80 60 60 40 40 Major Point If the process is on-target and stable, but has large variation, then you must identify and work to reduce common cause variation. 20 20 Time 81

Reducing Variation Reducing Variation in a Stable Process Make Permanent Changes Changes are based on the scientific approach Structured problem solving Planned experiments Major Point To improve a stable process, the process must be permanently changed Discuss There are two key concepts on this slide It is important that changes be permanent rather than temporary “let’s see if this will work” changes. Changes must be scientific rather than “seat-of-the-pants” or intuitive Examples New Equipment Upgraded Equipment New Procedure New Machine Settings Higher Quality Materials Examples: new equipment, equipment upgrade, new procedure, new machine settings, better raw material 82

Examining Variation Special Causes Anything that causes variations that are not part of the stable process is called a special cause, assignable cause, or unnatural cause. Major Point To introduce the definition of special cause. Do Display the results of the driving to work example on the flipchart. Ask What are the special causes of variation? accidents, breakdown, road repair, etc. 58

Examining Variation Unstable Process Any process that is not stable is called an unstable or out-of-control process. ? ? Prediction Major Point To introduce the definition of an unstable process. Ask What is an unstable process? this one is just stupid enough to be fun Any process that is NOT stable is unstable! Time 51

Reducing Variation in an Unstable Process Do not ignore special causes. Do quickly detect special cause variations. Do stop production until the process is fixed. (Reactive) Do identify and permanently eliminate special causes. (Preventive) Major Point Since unstable processes are characterized by special causes of variation, they can be improved by removing these special causes. Ask How can we avoid tampering? Never make adjustments Discuss Tampering is adjusting a stable process, reacting to a common cause as if it were a special cause. The opposite of this is ignoring special cause variation in unstable processes. This is equally bad. There are two modes of dealing with special cause variation: Reactive (local): Quick detection and response to special cause variation. Preventive (systemic): Identification and permanent elimination of special cause. 85

Improving an Unstable Process Reducing Variation Improving an Unstable Process Four Step Process Detect the special cause variation. Identify the special cause. Fix the process Remove the special cause, or Compensate for the special cause. Prevent the special cause from occurring again Major Point We must react carefully and appropriately to special cause variation. The four step model is one way to apply a systematic approach. Discuss Point out that fixing is reactive, while preventing is preventive. Both are appropriate. Note the options under fix Remove Compensate Two ways to deal with idiots: idiot proof the process, or fire the idiots. 86

Improving an Unstable Process Reducing Variation Improving an Unstable Process Reactive 200 180 160 140 120 100 80 60 40 20 Thickness Time Not Here Detect Here Detect Here Major Point Both local and system efforts can improve an unstable process. Discuss The term reactive is not intended to carry a negative connotation. Rather, it denotes reaction (response) to special causes that occur locally. A trend chart makes timely response possible. 87

Detecting Variation How can we decide if variation is the result of common or special cause? By using control chart Major Point To motivate the need for a control chart Ask How can we avoid tampering? Never adjust What is wrong with this strategy? It ignores special cause variation. What is the key to taking correct action (not tampering vs. corrective action) Recognize the type of variation If stable, do not adjust; work on reducing common cause variation If unstable, respond to special cause variation How can we tell if a process is stable or unstable? There is subjective judgment involved 95

Control Chart (The Concept) Process that not in a state of statistical control will show excessive variations and exhibit variations that change with time. Process in a state of statistical control is called statistically stable. Control chart is used to detect whether a process is statistically stable.

Control Chart (Assumption on the Statistic) It is independent, i.e. value is not influenced by its past value and will not affect future values. It is normally distributed, i.e. the data has a normal probability density function.

Control Chart (Types of Chart) Different charts is used depending on the nature of the charted data. For continuous (variables) data: Shewhart sample mean and range charts. Shewhart sample mean and standard deviation charts. Shewhart sample and moving range charts.

Control Chart (Types of Chart) For discrete (attributes) data: Sample proportion defective chart. Sample number of defectives. Sample number of defects. Sample number of defects per unit.

Control Chart Selection Quality Characteristic variable attribute defective defect no n>1? x and MR constant sampling unit? yes constant sample size? yes p or np no n>=10 or computer? x and R yes no no yes p-chart with variable sample size c u x and s

Interpreting x-bar charts : one point outside control limit

Interpreting R charts : one point outside control limit

Interpreting x-bar and R charts : one point outside control limit