Control the process to maintain the gains

Translation of Results Control SPC/ Control charts Capability Analysis Lean Tools & Control Control Plans Translation of Results Building a PI Culture

Translation of Results Control SPC/ Control charts Capability Analysis Lean Tools & Control Control Plans Translation of Results Building a PI Culture

Statistical process control (SPC) SPC is used to monitor, control, and improve process performance over time by studying variation and its source The most common SPC tool is the control charts 1 5 Subgroup . 6 4 3 2 Upper Control Limit X Setting The application of SPC involves three main sets of activities: 1. The first is understanding of the process. This is achieved by business process mapping. 2. The second is measuring the sources of variation assisted by the use of control charts and 3. The third is eliminating assignable (special) sources of variation. Lower Control Limit Date 5/15/06 5/20/06 5/25/06 5/30/06

SPC distinguishes between special-cause and common-cause variability Why use SPC? Prevents unnecessary process adjustments by understanding the natural random variation of the process When the process demonstrates good capability, SPC is effective in defect prevention The following scenario describes why we might make unnecessary adjustments. Suppose we were to stand over a bull’s eye target that was lying on the floor and to the best of our ability we were to drop darts on the center of the bull’s eye. Also, let’s assume that after each drop, we evaluated how far that drop was from the center of the target and we made minor adjustments to our process of dropping to improve the likely hood of hitting the target. If we were to drop 50 darts, we could measure the distance these darts were from the center and calculate a standard deviation. Now let’s review this process. What if the only variability acting on this system was that of common cause. This implies that the minor adjustments we were making after each drop were for naught. Although we thought our adjustments were making a difference, in actuality they were not!!! In fact, by making minor adjustments we actually added more variability to the process than if we stood still and dropped from one spot. If we compared the 50 drops in which we moved, to the 50 drops in which we stood in one spot, we would observe significantly less variability when we stood still. In fact, the standard deviation we measure when we move is the standard deviation of our tweaks (minor adjustments), vs the real standard deviation of the process in which we stood still. SPC distinguishes between special-cause and common-cause variability

Components of a control chart Control charts contain Data arranged in time order Overall mean of the data Upper and lower control limits Uses short-term standard deviation within subgroups to calculate control limits from the overall mean Signals if the data are out of control In this X-bar control chart each data point represents the subgroup average. The red lines represent the Upper and Lower control limits. These control limits are based on the average within subgroup standard deviation. The average within subgroup standard deviation is used to determine the control limits rather than the overall standard deviation. In theory the within subgroup standard deviation should represent the common cause variability. We do not want the variability between the subgroup averages to be greater than the average within group standard deviation. If this occurred we would observe a data point outside the upper or lower control limits. This would indicate an out-of-control process. If any one or a number of the subgroup averages were outside of the control limits this would imply that these subgroup averages may possibly come from a population different from what we expected. The 3-sigma control limits provide a rational and economic guide to minimum economic loss from the two errors: Ascribe a variation or a mistake to a special cause when in fact the cause belongs to the system (common cause) Ascribe a variation or a mistake to the system (common cause) when in fact the cause was special Control limits are independent of specification limits – never put spec limits on control charts

SPC distinguishes between special-cause and common-cause variability Why use Control Charts? All processes have: Natural variability- due to common causes Unnatural variability- due to special causes SPC Control charts help differentiate between natural variability and special-cause variation in a data set These signals do not tell why the process is out of control – that is the metric owner’s responsibility Control charts have upper and lower control limits to reflect the limits of natural variability in the process These control limits are usually set to +/- 3 sigma All processes have a certain amount of inherent variability due to common causes. Once we have identified those factors and settings that allow us to achieve our project goals, we can establish control limits around both our output Y’s and input x’s that reflect the natural limits of the variability in our process. The control limits represent +/- 3 short term standard deviations of our process (control limits are not specification limits). The process through which we establish our +/-3 sigma control limits and subsequently control the process to these limits is known as Statistical Process Control. Out-of-control signals may mean: “the process is not behaving as usual” or “the process is disturbed” This is not the same as “the process produces defective products”! Hopefully the capability of the process is so good that there is room for some disturbances. SPC distinguishes between special-cause and common-cause variability

Natural variation in commuting 22 Minutes Data Collection Day Time (in minutes) Notes Monday 22 Tuesday 20 Wednesday 19 Thursday 35 Accident Friday 18 15 Holiday 29 Left house late 21 2 min Trainer Notes:Without control limits, we often make decisions from data charts, which are inaccurate. Make the point and move on.

Commuting Control Chart SPECIAL CAUSES Out of Control NATURAL VARIABILITY COMMON CAUSE SPECIAL CAUSES

m = Mean s = Standard Deviation The Out-of-Contol Signal The control limits define the probability level of an extreme reading occurrence UCL = m + 3s a/2 = 0.00135 CL = m LCL = m - 3s Out of Control Point 1s 2s m = Mean s = Standard Deviation

Setting the Control Limits Chart A Chart B

Control Limits vs. Specification Limits Based on natural variation in process Voice of Process Based on past data Specification Limits Based on customer requirements Voice of Customer Targets goals Acceptable targets

Commuting Control Chart Specification Limit Control Limits Goal

Out-of-control signals Data points that fall in the red zones are signals that the process may have shifted There are other signals based on trend analysis that may also indicate an out-of-control condition This graph is demonstrating that if a subgroup average falls outside the control limits, this is an indication or a signal that the process may have shifted. This holds true for all control charts -- both attribute and continuous. There are other signals that can indicate the process is potentially out of control - these are specific to the type of control chart and will be discussed at that time. When a point falls outside of the limits established for a given control chart, those responsible for the underlying process are expected to determine whether a special cause has occurred. If one has, then that cause should be eliminated if possible. It is known that even when a process is in control (that is, no special causes are present in the system), there is approximately a 0.27% probability of a point exceeding 3-sigma control limits. Since the control limits are evaluated each time a point is added to the chart, it readily follows that every control chart will eventually signal the possible presence of a special cause, even though one may not have actually occurred. For a Shewhart control chart using 3-sigma limits, this false alarm occurs on average once every 1/0.0027 or 370.4 observations. Therefore, the in-control average run length (or in-control ARL) of a Shewhart chart is 370.4.1 Meanwhile, if a special cause does occur, it may not be of sufficient magnitude for the chart to produce an immediate alarm condition. If a special cause occurs, one can describe that cause by measuring the change in the mean and/or variance of the process in question. When those changes are quantified, it is possible to determine the out-of-control ARL for the chart.1 It turns out that Shewhart charts are quite good at detecting large changes in the process mean or variance, as their out-of-control ARLs are fairly short in these cases. However, for smaller changes (such as a 1- or 2-sigma change in the mean), the Shewhart chart does not detect these changes efficiently. Other types of control charts have been developed, such as the EWMA chart and the CUSUM chart, which detect smaller changes more efficiently by making use of information from observations collected prior to the most recent data point.1 1 Adapted from Wikipedia.

Identifying Out-of-Control Processes Run- 7 or more consecutive points on either side of the center line Points outside control limit 3 min Trainer Notes:the most famous set of rules are the Westinghouse Rules, that some may know of. The key is to look for Outliers, trends, slow changes in variance, shifts of the mean (sudden), etc…..but never too many rules on one chart, use the ones that detect the behavior your process tends to have (5 rule limit). Trend- 7 or more consecutive points increasing or decreasing Cyclical- Repeating Up and down patterns Minitab can help identify these points. See appendix for more details

Roadmap to implement control charts Identify what to measure to control the process Define the sample size and sample frequency Determine whether your data is continuous or attribute Determine whether you need to collect data in subgroups or as individuals Select the appropriate control chart Find the control limits by Taking 20-25 samples from the process while it runs as usual Recording any events during this period Determining the control limits Verifying the stability of the process during the sampling period Lock the control limits in place Develop an out-of-control action plan (OCAP) and train the people involved An 8-step roadmap to implement SPC. The hardest parts of implementing this roadmap are Ensuring that all people are trained Collecting the data to create the control chart The people know what to do if the process goes out of control The Process Owner knows how to read and interpret the control chart and allows the operators and administrators to act accordingly

Types of Control Charts Call the Process Improvement Team if you want to create one of these control charts

Reacting to out-of-control signals For control charts to be an effective form of control, people must be properly trained Important: When an out-of-control signal is identified, it must be investigated to determine the root cause and implement a corrective action Out-of-control action plans must be in place This is the key to successfully using control charts: to have people and operators that are trained, engaged, and know what to do when an out-of-control signal has been identified. The worst thing that can happen in using control charts is to not allow people to react to out-of-control conditions. Then the control charts just become wall paper and DMAIC becomes another flavor of the month.

OCAP example Flow chart from problem to action no no no no yes yes yes Call mechanic Too cold Switch heater on no no Heater working? no Door open? Window open? no Succeeded? A simple OCAP. yes yes yes yes Close door Close window You are ill, go home Wait

Importance of Acting on Data Allowing individuals involved with the process to not act on out-of-control signals sends the wrong message Operators and Administrators do not feel the business is committed to process improvement The control charts become wallpaper Any control charts that currently exist in which action is not taken should either be removed or be implemented correctly People forget the importance of data JFK Values Behaviors Measures “People Behave based on how they are Measured”

Summary Statistical Process Control (SPC) is used to monitor, control, and improve process performance over time by studying variation and its source Control Charts help identify natural variation versus special cause variation It is vital to act on points outside of the control limits. Out of Control Plans should be implemented when a data point is found out of control.