Tag Archives: data collection

If you Aren’t Measuring It You Aren’t Managing It

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A favorite axiom in management is, “If you aren’t measuring it, you aren’t managing it”. Just as driving a car with your eyes closed will result in disaster, running a business without some sort of performance feedback will result in business disaster.

The collection and use of data is important because things are rarely what they seem to be. Data helps us separate what is really happening from what we think is happening (or what we want to be happening). When we make decisions based on how things feel or how they have always been, we are operating in the “as we think it is” world. This is a prescription for disaster. The successful business operates in the real world. We call this the “as-is” world.

The measure phase of a Six Sigma process improvement project focuses on characterizing the current performance of a business process, which is the current reality. In this phase, the Six Sigma project team is trying to accomplish two things. First is to establish an “as-is” performance measurement for the process. Second, is to use the data to begin looking for potential causes of defects.

Some of the important activities of the Measure phase are:

  • Developing a data collection plan and following it
  • Performing a measurement system analysis
  • Calculating performance indicators for the process from the data collected
  • Control charting

The objective is to measure the process’ impact on the customer’s CTQ (Critical to Quality) issues. The result is the characterization of the process’ performance from the customer’s perspective. This becomes the process’ story in the “as-is” world.

Collecting Data

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Collecting data for analysis is more than a statistical process. All of the math in the world will not compensate for not understanding the behavior of the process you are trying to measure.  Not everything is settled in numbers.  Some things will be discovered in context.  For example, “We really have problems when it is raining.”

 As a result, data collection plans embody four qualities of collected data that are essential to optimize its usefulness. These qualities have to do with the data’s ability to represent the process’ performance.

 

  • There must be sufficient data to see the process’ behavior.
  • The data must be relevant.
  • The data must be representative of the process’ normal operating conditions.
  • The data must be contextual.

 

Sufficient

There must be sufficient observations to see patterns of variation and shifting central tendency in the process’ output. As part of building a data collection plan, the team will seek to understand the process’ history so that all expected sources of variation are captured.

Consideration must also be give to the size of the performance gap that the team is trying to measure. As the size of the gap gets smaller, the number of samples needed to measure the gap, with statistical confidence, increases.

 Relevant

 The data must be relevant to the problem that is being investigated. For example, if a process associated with back injuries is being analyzed, data regarding the availability of safety glasses will likely not be relevant. The central question or objective behind the data collection plan will be to point to what data needs collected.

 The data must also be relevant to an important business metric. Since data collection is an expensive process, the project team should give due diligence to verifying the relevance of the data that they want to collect. The buy-in of stakeholders and process owners will waver if they discover that the team’s focus has drifted away from the central core of the project.

 Representative

 The data must represent the entire range of actual operating conditions of the process. For example, if checkout cycle times are being studied, data must representative of all levels of customer loading.

 Operating conditions can include a multitude of factors. Some examples are the time of day, sales or promotions, experience of employees, changes in process inputs, and so forth. The smart project team will brainstorm a list of the potential factors that must be considered when building the data collection plan.

 Contextual

 Contextual information pertains to conditions that surround, but are not part of, the process and can affect its performance. By collecting this information, we add relevance to the data. For example, if the checkout cycle time was longer than usual on a given day, you may also wish to know how many cashiers were on duty, what the customers were buying, and weather conditions. This sheds light on how the process behaves under various conditions.

 Summary

 To keep cost down and improve the story telling ability of data, a comprehensive data collection plan will be needed. Process owner participation will improve the quality of the plan. Owners of peripheral processes will also make a valuable contribution since they are not directly involved in the process improvement effort (forest or trees effect).