With the process that is in need of improvement defined, the team now has to determine the data that are required to analyze the performance of that process (see Figure 2.11). A data collection plan will be required regardless of whether the team is able to rely on historical data or needs to develop and implement a short-term data collection process. A data
collection plan provides a clear, documented strategy for gathering reliable data for the project and gives all the members of the team a common reference point for the collection.
Figure 2.11 Overview of Analysis Steps What data should the team collect?
While the team may still be focused on the process output or what the customer needs or wants (the project CTQs), such as faster service, getting the right answer the first time on the phone, or no defects in the product, this is also the ideal time to start collecting data on
process variables and process input variables (Xs) (see Figure 2.12).
Figure 2.12 Data Collection for Process Xs and Ys
Data collection can be costly and time-consuming, and so it is best to collect what the team may deem critical now, as this will help you save time in the Analyze phase. The best way to generate a good list of possible Xs is by using tools like process mapping,
brainstorming, and a fishbone diagram. These tools help you identify points or variables that may contribute to the variation or customer issue that you are trying to resolve.
Data Collection Plan
When developing a data collection plan, there are four key milestones to consider (see Figure 2.13):
Figure 2.13 Data Collection Milestones 1. Establishing data collection goals
2. Developing operational definitions and procedures 3. Ensuring data consistency and stability
4. Actual data collection and monitoring
The main objectives of the first step are to ensure that you have a complete list of the process Ys and Xs that you want to collect. This step is also focused on making certain that there is consensus on why the team needs to collect the data—what are we trying to measure and gauge?
For the second step, the team needs to define an operational definition for each data
element—a precise description of how to get a value for the characteristic that you are trying to measure so that there is no ambiguity concerning what to measure and how to measure it.
In this step, it is also important to develop a data collection template or form. The last
element to be considered in this step is data sampling. It is often impossible, impractical, or too costly to collect all the data from every aspect of your process. When there are too many data (thousands of items to evaluate), when too much time would be required to investigate all the data (each event lasts weeks or months), or when measurement is costly (it involves a destructive process or single-use items), it is important to consider data sampling.
A good sampling plan must be:
• Representative of all segments (for example, locations, sizes, days of the week, months, or shifts)
• Of adequate size—while there are mathematical equations that can determine the exact sample size needed, the guidelines in Table 2.4 can provide a rough estimate
Table 2.4 Adequate Sample Sizes
• Free from bias. Avoid collecting only when doing so is convenient, omitting shifts or collecting for a short time (ignoring long-term effects) or from responsive employees The last point to consider in data sampling is the scheme that you will use to sample.
There are four possible options:
• Random. With this option, each unit has an equal chance of being selected. It is a good option for homogeneous populations (see Figure 2.14).
Figure 2.14 Random Sampling
• Stratified random. When data are needed from multiple areas, this ensures that all groups that are of interest are adequately represented. The sample size for each group is generally proportional to the relative size of the group (see Figure 2.15).
Figure 2.15 Stratified Random Sampling
• Rational subgrouping. Rational subgroups are samples from similar processes (for example, comparing cycle time from production lines 1, 2, and 3) or items from the same process over time (for example, morning shift vs. afternoon shift). The word rational implies there is reasoning behind the choice of subgroup size and interval. If selected correctly, subgroups help identify variation within the group and between the subgroups (see Figure 2.16).
Figure 2.16 Rational Subgroup Sampling
• Systematic sampling. Sample every nth unit (for example, every fourth unit). This method helps guard against time-related biases (see Figure 2.17).
Figure 2.17 Systematic Sampling
Milestone 3 of the data collection plan ensures that the way in which the data have been collected is reliable and accurate. This concept will be covered in more detail in the upcoming chapter.
As for the last step in the data collection plan, once the team has ensured that the
measurement system is adequate for the data that needs to be collected, the people collecting the data need to be trained on how, when, and where to track the required information. It is best to conduct a pilot of the data collection to ensure that everyone has been adequately trained prior to full implementation.
Summary of Step 7: Develop a Data Collection Plan
A data collection plan helps the team provide the answer to questions like: What data do we need? What is the time frame for collecting them? Who will collect the data? Using what mechanism? A good data collection plan provides a clear, documented strategy for gathering reliable data for the project. While developing the plan, the team needs to ensure that the collection process yields reliable and accurate data. It also needs to ensure that the data are representative of the entire population, that the sample is of adequate size, and that the data are free of biases. The quality of the data is critical, as they are used in the Analyze phase to enable the team to better understand the capability of the process and help uncover
opportunities for improvement. Poorly collected data can steer the team in the wrong direction, with adverse impact on the company.