In this step of the quality improvement process you analyze the baseline data collected in the previous step (Step 5) in order to ensure process stability.
Two tools which are useful in this analysis are a control chart and a run chart . Both of these tools organize the data and allow the team to make sense of a mass of confusing information.
A Control chart is more powerful and they are better at revealing whether a process is stable and if its future performance will be predictable.
However, even if you decide to begin with the simpler run chart to assess process stability, with careful interpretation it will work for the majority of quality improvement projects.
Later you can convert the run chart to a control chart with just a little extra work.
These two tools are important because they help you identify special cause variation in the process. Whenever an individual or process repeats a sequence of actions, there will be some variation in the process.
Here's an example:
Think about the amount of time it takes you to get up in the morning, get dressed, and leave your house for your daily routine during the past four weeks.
Although the average time might be 48 minutes, no two days were exactly the same. On one occasion it took 36 minutes for you to get out of the house. On another day it took you 52 minutes to leave the house.
This is where a control chart or a run chart can help you analyze the data. Control Charts, and to a lesser extent run charts, display variation and unusual patterns such as runs, trends, and cycles and other process stability issues.
Data that are outside of the computed control limits, or unusual patterns in the graphic display of data, may be signals of the presence of special-cause variation that should be investigated.
But what if, over a period of 10 days, a series of times is recorded that averaged 34 minutes? Maybe your getting out of the door in the morning process now includes skipping breakfast. This is not just variation. This data would indicate that your process has changed.
While this example portrays an obvious change in the process, subtle changes often occur without the knowledge of those operating the process. These minor changes produce enough variation to be evident when control charts or runs charts are used and analyzed.
If special-cause variation is found in the process, you should find the cause before moving on to the next step in the quality improvement process.
Depending on the nature of the special-cause, the team may act to remove it, take note of it but no action, or incorporate it in the process. When special-cause variation reduces the effectiveness and efficiency of the process, the team must investigate the root cause and take action to remove it.
If it is determined that the special-cause was temporary in nature, no action may be required beyond understanding the reason for it. In the example above, the early phone call caused a variation in the data which was easily explained and required no further action.
Occasionally, special-cause variation actually signals an improvement in the process, bringing it closer to the quality improvement objective. When that happens, the team may want to incorporate the change permanently.
If the team fails to investigate a signal of special-cause variation and continues on with their improvement activities, the process may be neither stable nor predictable in the future. This lack of stability and predictability may cause additional problems to occur, preventing the achievement of the quality improvement objective.
Step 1: Select the process to be improved and establish a well-defined process improvement objective. The objective can be established by the team or come from management.
Step 2: Organize a team to improve the process. This involves selecting the “right” people to serve on the team; identifying the resources available for the improvement effort, such as people, time, money, and materials; setting reporting requirements; and determining the team’s level of authority. These elements should be formalized in a written charter.
Step 3: Define the current process using a flow chart. This will generate a step-by-step map of the activities, actions, and decisions which occur between the starting and stopping points of the process.
Step 4: Simplify the process by removing redundant or unnecessary activities. It's likely that people may be seeing the process on paper in its entirety for the first time from Step 3. This can be a real "eye-opener" which will prepare them to take the first steps in improving the process.
Step 5: Develop a plan for collecting data and collect baseline data if it's not already being collected. This baseline data will be used as a "yardstick" later in the quality improvement process. This begins the
evaluation of the process against the process improvement objective established in Step 1. The flowchart in Step 3 is used to help determine who should collect data and where in the process data should be collected.
Step 6: Assess whether the process is stable. Create a control chart or run chart out of the data collected in Step 5 to gain a better understanding of what is happening in the process. Future actions of the team are dictated by whether special cause variation is found in the process.
Step 7: Assess whether the process is capable. Create a histogram to
compare the data collected in Step 5 against the process improvement objective established in Step 1. Usually the process simplification actions in Step 4 are not enough to make the process capable of meeting the objective and the team will have to continue on to Step 8 in search of root causes. Even if the data indicate that the process is meeting the objective, the team should consider whether it is feasible to improve the process further before going on to Step 14.
Step 8: Identify the root causes which prevent the process from meeting the objective. Use a cause-and-effect diagram or brainstorming to generate possible reasons why the process fails to meet the desired objective.
Step 9: Develop a plan for implementing a process change based on the possible reasons for the process’s inability to meet the objective set for it. These root causes were identified in Step 8. The planned quality improvement involves revising the steps in the simplified flowchart created after changes were made in Step 4.
Step 10: Modify the data collection plan developed in Step 5, if necessary.
Step 12: Assess whether the changed process is stable . Same as Step 6, use a control chart or run chart to determine process stability. If the process is stable, the team can move on to Step 13; if not, you should return the process to its former state and plan another change.
Step 13: Assess whether the change improved the process. Using the data collected in Step 11 and a histogram, the team determines whether the process is closer to meeting the process improvement objective established in Step 1. If the objective is met, the team can progress to Step 14; if not, the team must decide whether to keep or discard the change.
Step 14: Determine whether additional process improvements are feasible. The team is faced with this decision following process simplification in Step 7 and again after initiating an improvement in Steps 8 through 13. In Step 14, the team has the choice of embarking on continuous process improvement by reentering the model at Step 9, or simply monitoring the performance of the process until
further improvement is feasible.
May 10, 16 09:24 PM
A Quality Control Plan is a documented description of the activities needed to control a process or product. The objective of a QCP is to minimize variation.
May 10, 16 08:49 PM
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May 10, 16 07:28 PM
The Weibull distribution is applicable to make population predictions around a wide variety of patterns of variation.