The Six Sigma DMAIC Framework for Marketing

The Importance of Following a Framework

Frameworks help us organize our thinking. In Six Sigma for Marketing, a framework guides us from one step in a marketing project to the next as we apply the skills necessary to improve performance. A framework also ensures that an orderly and logical flow is followed from beginning of the project to the end. The framework that is followed by the Lean Six Sigma profession, and in Lean Six Sigma for Marketing is DMAIC. In this post, I want to provide an overview of DMAIC and its components.

DMAIC

The DMAIC framework includes five stages:

• Define

• Measure

• Analyze

• Improve

• Control

These five steps provide us with a systematic problem solving methodology for moving a Lean Six Sigma marketing project from its initial stages to completion.

Define

The first step is to define the problem. This definition must answer the question: “What is the real problem that must be addressed?”

Often the answer is not obvious because the problem that is expressed may only be a symptom of an underlying and more complex problem. So the first pass at defining a problem may not uncover the real problem. Several passes may be necessary.

Measure

The second step requires that measurements be made to better understand the problem. Measurements require data. Sometimes the data is readily available, but at other times it may be have to be collected through observation, access by retrieving data stored in a database or system, or by administering questionnaires (a common practice in marketing).

Analyze

The third step focuses on analyzing the data to better understand the problem, further reveal its root cause, and point to a solution. Analysis may involve the use of spreadsheets, process maps, charts, or statistical analysis. For example, process maps, may narrow the problem to one specific step in a process. Statistical analysis may be useful in comparing process solutions to determine if one is better than the other. Analysis may also lead to solutions by pointing to inefficiencies in a process or even identifying unnecessary steps.

Improve

With analysis complete, the next step is to improve the process. Depending on the nature of the problem this can be a simple as eliminating steps, purchasing a new tool, or as complex as redesigning almost every step in a process. But behavioral issues may interfere with this step. Often those attempting to change or improve a process must contend with resistance to change where those involved in the old process resist changes to a process that has become routine and familiar.

Control

One of the fundamental challenges faced by every organization is to establish monitoring and control systems to ensure that process outputs conform to expected levels of performance. In this step, actual process results are compared with expected results and when actual results fail to conform to expectations remedial action is taken.

Measurement is critical

Measurement, the second step in the DMAIC framework process, is critical not only during the initial stages when a problem is first defined but also later, when measurements must be taken during the control stage to ensure that an operational process is performing as expected. Clearly, quite a bit depends upon measurement. It is central to data collection during the define stage and it is central to the control stage. What we would like to believe, is that we have confidence in our ability to measure what we observe or that the data we collect is accurate. Yet this is not necessarily true. If it isn't, we have a real problem, the entire Lean Six Sigma for Marketing process in which we engage may not produce the desired results.

Our ability to measure is actually limited in several ways. First, it is limited by the accuracy of the tools we use to take the measurement. Second, it is limited by the human errors we make while taking measurements.

Limitation in our tools

The tools we use to measure an outcome may be less than perfect. For example, a questionnaire to determine customer satisfaction may not accurately detect proper levels of satisfaction. In addition to imperfect tools, there is human error. Two individuals monitoring the same process may reach different conclusions. For example, two marketing managers reviewing the same resume may reach very different conclusions, one may recommend that the individual be hired, the other may conclude that the person is unqualified for the job.

Gage R&R Study

Since the measurement problems are not uncommon, and since the define and control stages rely on measurements, a formal analysis to uncover the extent of the measurement problem is often conducted. It is called a Gage R&R study.

A Gage R&R study focuses on two dimensions of the measurement process. First, it focuses on whether an individual collecting the data or performing the tests is consistent across many measurement tools. Second, it focuses on whether the variation between different people involved in data collection is consistent.

The first focus, consistency associated with a single individual, is called repeatability. The second focus, consistency across individuals performing the same task, is called reproducibility. This is the basis for the term Gage R&R, gage repeatability and reproducibility.

Precision and Accuracy

Measurement systems must be both accurate and precise. It is important to distinguish between these two words.

Precision refers to the closeness of two or more measurements to each other. If they are very close then it might be thought that the data are truly representative of the process or parts being measured. Yet this measurement by itself may not be sufficient.

Accuracy is the proximity of measurement results to the true value. It means that the data are indeed a true representation of what it is we are trying to measure.

Measurements in a data set can be precise and not accurate, they can be accurate and not precise, and they can be both accurate and precise.