Control PlanLinda : This is the last Step in Control, and therefore, the last step in Six Sigma. Now is the time to make the final decisions on how to control the process in its full implementation and complete the Rockledge Case!
Linda : You were introduced to the components of a Process Control System back in Step 1, but we weren't ready to decide on the control strategies for Rockledge yet, because we hadn't validated the measurement system or confirmed the improvement in the pilot site. Now that we can be sure that the improvements are sound in a pilot environment, we're ready to set up the control system for the full implementation.
Linda : In this lesson you'll learn to describe the options for controlling the process, starting with Risk Management.
Linda : The second strategy to be considered is Mistake Proofing.
Linda : And finally, there are statistical process control charts.
Linda : The latter is perhaps the most complex of the three mechanisms, so you'll be guided through the different types of charts available
Linda : And the creation and evaluation of one commonly used chart. We've got a lot of ground to cover before we're done with this case.
Linda : Before you get into the various control options, here is a reminder of the X's that we need to control at Rockledge. Keep them in mind as you are learning about the strategies, and we'll come back to the case towards the end of this lesson.
Linda : Here are the three mechanisms you can choose from to maintain your process improvement. They are not exactly sequential, meaning that you don't try risk management first, then mistake proofing, and then SPC. However, there is somewhat of a hierarchy.
Linda : Risk Management and Mistake Proofing should be considered before resorting to SPC because they are strategies for avoiding potential problems altogether, whereas SPC can only monitor for potential problems. This become more apparent as we look at each one in more detail.
Linda : The risk management process is similar to the failure modes and effects analysis covered in the measure phase; however, now the focus is on identifying risks to the "X's" -- the sub-processes or product characteristics that were improved.
Linda : Both tools ask you to attach a score to the risk. With the F-M-E-A, the risk priority number is calculated by multiplying Severity times Frequency of Occurrence times Detectability; while the Risk Management score is Impact times Probability. The two formulas use different terms, but really Serverity and Impact are synonymous, and so are Frequency of occurrence and Probability. The key difference is that Dectabality is also included in the FMEA score.
Linda : Risk Management provides a systematic method for identifying risk elements that might interfere with improvement and control, quantifying those elements, establishing a risk abatement plan, and continuously monitoring the progress of the plan. It is a clear method of communicating risks to management and drives clear decisions on risks.
Linda : This is a sample Risk Management document. Select each numbered area to hear how it relates to the steps in the process.
Linda : In this area, potential risks to your improved processes or product characteristics are identified through a description and a type classification
Linda : In this area, the risks are rated according to probability of occurrence and impact on the process. Charts are available in the Resources section of this course to determine those ratings. Those two factors are multiplied to give the issue an overall score.
Linda : Once the risk is identified and rated, a plan is established for abatement, responsibility is assigned, and a measure of success is established.
Linda : Finally the abatement plan is monitored for completion. When the plan is complete, the probability and occurrence should be revised, the risk score recalculated, and that number entered as residual risk.
Linda : The second, and really the most ideal, approach to process control is mistake proofing. As I mentioned before, Mistake Proofing is a method for avoiding errors in a process, so it should be considered prior to utilizing Statistical Process Control.
Linda : The simplest definition I can give is "Mistake Proofing is a technique for eliminating errors by making it impossible to make mistakes in the process.
Linda : The traditional approach to controlling a process was inspection.
Linda : The problem with inspection is it allowed errors to continue,
Linda : caused wasted time and money, as well as customer dissatisfaction due to defects,
Linda : and relied on the abilities of the inspector to catch the defects.
Linda : The mistake-proofing process instead takes action on the original cause of errors,
Linda : By brainstorming sources of errors using tools such as the fishbone chart,
Linda : taking action on the errors
Linda : And thus preventing defects from occurring. It is this attention to process improvement that will take us to Six Sigma performance.
Linda : How you mistake proof a process is different with every situation. As a greenbelt it will be your responsibility to brainstorm mistake-proofing solutions for processes in your work, so I want to give you an opportunity to practice thinking in this manner. Here are a few processes to consider. How could they be mistake-proofed? Type some ideas in each field.
Linda : Unfortunately, mistake proofing is not always possible or economically practical. When that is the case, you must use statistical process control, or SPC, to keep your improvements in control.
Linda : An SPC chart is essentially a feedback system. It's a time-ordered plot of the data that provides a statistical signal when variation is present in a process so that you can take actions to eliminate it. Generating such charts requires ongoing data collection on a process, so you can see why it would be a second choice to mistake proofing.
Linda : Think of a process mapped on an SPC chart like a car driving along a highway, the yellow lines defining a lane represent the expected variation for the process.
Linda : The car might wiggle back and forth a little in its lane - that is inherent to the driving process.
Linda : But when the car heads outside of the lane, there is some special cause interfering, like the driver talking on his cell phone! When the police see that driver getting out of control, they can intervene.
Linda : However, for most processes it is not so easy to see when they begin to move out of control. That is why you collect measurements and create SPC charts and data, because they give you the ability to detect and act upon special causes that are impacting the process performance. That is the basic idea behind statistical process control, but it is not quite that simple. There are several different types of charts and many data patterns you should be aware of. Linda : There are three categories of control charts: Variable control charts; Attribute control charts; and Process focused charts.
Linda : A variable control chart tracks continuous data such as cycle time, length, or diameter, so it provides the most thorough information of all the charting options. And while collecting one piece of continuous data is more costly, fewer pieces are required to get sufficient information on a process.
Linda : An attribute chart tracks discrete data such as pass/fail, good/bad or yes/no. There can be many characteristics tracked in one chart and it is less expensive to maintain, but provides far less information about the process than variable control charts.
Linda : A Process focused chart monitors several characteristics of the same process. It measures only deviation from an established target.
Linda : When selecting a control chart, you choose between an Attribute or Variable charts, based on the data you have. I'll give you a brief overview of the selection process.
Linda : Let's start with Variable control charts, which are used for continuous data. You must first determine the volume of the data.
Linda : Low volume typically occurs because of expense of data collection or when you have a fairly homogenous process output, such as a batch of chemicals. In this case you use Individuals and Moving Range Charts.
Linda : High volume refers to data that is conducive to subgroup sampling. This is the data type we have been primarily focusing on in this training. The X-bar and Range, charts are the most common option for this data type.
Linda : Attribute charts are used for discrete data, such as yes or no.
Linda : The first question to ask is if your lot, or sample, size is constant.
Linda : If the answer is No, you can monitor for the number of defects per lot with the u chart or the percentage of nonconforming units with the p chart
Linda : If the answer is Yes, you can monitor the number of defects per inspection lot with the c chart or the number of non-conforming units with the np chart.
Linda : In this training we will focus on variable control charts because they provide the most valuable information for control, and specifically on a very commonly used chart set, the Xbar and R charts. Some common types of variable control charts are
Linda : the X-bar chart which is a plot of sample means collected over time
Linda : The R chart, which is a plot of a sample range over time
Linda : The S chart, which is a plot of sample standard deviation over time
Linda : And, the individuals chart which is a plot of the individual measurements overtime.
Linda : Like any other statistical process in this training, the SPC chart's usefulness begins with deciding on rational data subgroups that will provide the most information about the process. An example from the production of plastic resin will help to illustrate proper use of the Xbar and R charts.
Linda : In resin production, raw materials go into a device called an extruder that mixes the materials
Linda : and it generates resin pellets.
Linda : Our chart will focus on how to control one of the variables in this process, which is the temperature in the extruder.
Linda : Data subgroups should consider size, frequency and number of samples.
Linda : The sample size for each subgroup should be anywhere from 3 to 5 data points collected in quick successsion. That number will represent normal piece-to-piece variability in a short time period, so that unusual variation between subgroups would signal a process shift that should be investigated.
Linda : In terms of frequency, subgroups should be collected often enough to reflect potential opportunities for change. That might be hourly, daily, monthly, at every shift change, etcetera.
Linda : And, the number of samples should be enough to assure that sources of variation have had time to appear.
Linda : For the extruder case, each sample includes five data points taken in quick succession. The samples are collected every 30 minutes during an eight-hour shift.
Linda : In Minitab, the control charts are generated by choosing Control Charts from the Stat menu. As I mentioned before, we'll look at the X-bar R charts,
Linda : but Minitab will also create several others.
Linda : The resulting charts look something like this. Before talking about the results on this Xbar / R chart, I want to discuss the basic components of the chart.
Linda : The green line down the center represents the process average for the data collected.
Linda : The red lines above and below that are control limits, meaning that if the data is inside these limits, the process is in control - with certain exceptions that I'll talk about in a minute. Control limits can be calculated, based upon a formula that uses the process average and sample size; however, in Minitab the lines appear by default at plus and minus three-sigma around the centerline. The plus and minus 3 sigma has become accepted practice. Be careful not to confuse control limits with specification limits. These lines do not have any association with what the customer wants, they are based purely on the data that has been collected, so keep in mind that a process could be in control and still not be meeting the customer's expectations.
Linda : And, each data point on the chart represents the average or range, as the case may be, of one data sample.
Linda : Knowing that, what can we tell about the temperatures on the extruder? Do you think they are they staying in control?
Linda : This is what a process in control would look like,
Linda : it shows only random variation around the centerline.
Linda : On the other hand, there are several significant patterns that can appear in the data to indicate an out of control state. These patterns fall into the three basic categories shown on screen. Click on each set of images to hear a description of the problem. Keep in mind that these are general concepts for process control; each GE business has its own guidelines for determining adequate process control. Please check with your MBB for guidance.
Linda : In the first examples you see data points that have gone above or below the control limits. Those extreme values indicate a special cause is effecting the process and should be investigated.
Linda : The second set of examples shows several data points in a row on one side of the centerline. This indicates a potential process shift. Analysis should be performed to confirm this possibility, and to identify when the trend began.
Linda : The third set indicates another type of trend, which is 6 or more points in a row that show a steady downward or upward progression. Like the second example, knowing the time trend began can be useful in analysis of the problem.
Linda : Returning to the charts on extruder temperatures, what, if any problems do you see in the data based on the Minitab rules.
Linda : It's been a while since we talked about the Rockledge case, but I hope you didn't forget that we have a job to do! Before we can claim success, we've got to put a control system in place there. Let's take our X's one at a time.
Linda : There is something you need to know that will help us decide the control strategy for this improvement: A torque wrench can be set by the manufacturer to operate only within a certain range of torque. Based on that information, what is the best strategy to use?
Linda : Since Mistake Proofing is the best option, I'd like you to take a minute to think about ways to mistake proof our other X, use of the right nut type. Type your ideas in the text box, then click Done.
Linda : There are a several approaches to mistake proofing the nut type. I think the most thorough is simply to remove the old nut type from the plant so that it cannot be accidentally used. However, there could be other uses for the old nut type that makes it impossible to eliminate, so another option is color coding the nut, placing documentation at each gas turbine indicating that only the blue nuts should be used on this machine, and note this change in existing quality plans. These are a couple of my ideas, you may have thought of some other good solutions.
Linda : Now let's review what you've learned in this step.
Linda : You can now describe the Risk Management role and function in the control phase.
Linda : The concept of Mistake Proofing,
Linda : And finally, the use of statistical process control charts --
Linda : Particularly the X bar and R charts.
Linda : Reviewing the key parts of a process control system, you'll see that having completed step 12, we can now finalize it for the Rockledge case.
Linda : We know our process controls, in both cases we are able to mistake proof the X's
Linda : we have decided on what documentation is required
Linda : and we have already modeled the data collection in the pilot environment, so data on the fully implemented process can be collected in the same fashion. That plan should be documented at this stage.
Linda : Congratulations! You've accomplished your learning goals for this step and have concluded the final step in the Rockledge Case.
Master : Well, kid, just when I was getting to like you, this is our last visit. I've taught you all I can, now its time for you to get out there on your own. But before you go, let's review this last phase you went through, the control phase.
Master: In step one, you started learning about the components of a process control system, including the need for an implementation plan, documentation and training, as well as the requirement that you re-evaluate process capability to confirm the improvements. And you were reminded that the measurement system for the project improvements should be validated before going any further with the implementation.
Master : Then in step 2, you reviewed the process capability analysis. In order to statistically confirm the improvements it should be completed both in the pilot and once the improved process is fully implemented.
Master: Finally in step 3 you completed the process control system by choosing between a risk management system, mistake proofing and statistical process control charting.
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