Lean Six Sigma and DMAIC Method

Lean management aims to eliminate the waste in processes by removing activities that do not add value to customer expectations. Six Sigma focuses on minimizing defects by reducing process variations. Although these two continuous-improvement methodologies started their journey separately, it didn’t take long for practitioners to realize that they work together like a charm. Since the early 2000s, countless organizations have been improving their processes using Lean Six Sigma (LSS) tools.

LSS relies on a step-by-step improvement approach called the Define-Measure-Analyze-Improve-Control cycle, often abbreviated to the acronym DMAIC methodology. It’s so popular that songs have been written about it (you’ll probably recognize the melody).
So, how does DMAIC work? Let’s study all five phases of this intuitive and practical methodology.

Define

LSS emphasizes continually improving production and business processes to eliminate waste and minimize variability. It is also a bottom-up approach that justifiably assumes that the best improvement ideas come from those with first-hand knowledge of the job.

If an organization has created a culture that empowers and equips employees with the skills to improve the existing processes in which they are directly involved, those workers should propose numerous improvements to those processes. Implementing them all is usually not viable due to time and resource constraints.

The define phase offers a mechanism to assess and prioritize proposals based on a cost-benefit analysis. Management examines the improvement ideas applying such criteria as:

• What is the problem?
• What process(es) are involved?
• Is there any urgency attached?
• Does it have a direct effect on customer experience?
• How much budget and resources are required?
• How long would it take to tackle it?
• Can we quantify the ROI (return on investment)? If so, what is the estimate?

The company decides which improvement proposal to implement in a problem statement. The team now has the necessary information to move to the next DMAIC phase, which is the measure phase.

Measure

The team responsible for an LSS project needs data that explain the problems they are trying to solve. As a colleague of mine is fond of saying, “What gets measured, gets done.”

The measure phase is about determining what inputs the team needs to analyze the current state of the processes. These indicators will serve as a baseline against which the team can assess their achievements in the following phases.

Organizations use software systems to record everyday business transactions that include sales, purchasing, HR, accounting, production control, inventory management, and shipments. Ideally, a single, sophisticated database integrates the data collected by these systems. An essential task in the measure phase is to figure out whether the type of data already available is sufficient for the appropriate analysis to drive problem-solving.

If it turns out that the team needs different data types that are not readily available, then they must work on effective ways of collecting those data. It can be as straightforward as developing a checklist that some employee(s) will fill out using pen and paper or a simple mobile app. Sometimes, it may require collaboration with the IT department to customize software applications to collect the necessary metrics.

The measure phase can be as brief as a day’s work if the data needed is already available, or it could take months if the team decides that it needs to collect new data. Once the team has decided what performance indicators it will use and has a data collection plan, the next step in the DMAIC method is the analyze phase.

Analyze

LSS practitioners have several quantitative and qualitative techniques in their toolbox to execute the analyze phase, during which they dig deeper into the problems to discover their root causes. It is vital not to fall into the trap of focusing on the symptoms.
Commonly used qualitative analysis tools include:

• Flow charts are effective visual representations of the business or manufacturing processes. They display the interdependencies, decision points, material and information flows along the process steps in an easily understandable way.
• Cause-and-effect diagrams, also known as fishbone diagrams due to their visual resemblance to a fish skeleton, are especially useful in figuring out the root causes of problems. These diagrams enable teams to brainstorm the potential triggers of an issue using a systematic approach. The team members, who have experience in the process that is the subject of the improvement project, engage in discussions in a structured manner by continually asking ‘why’ questions until hitting the spark that triggered the fire.

Commonly used quantitative analysis tools include:

• Histograms are powerful column charts that outline the frequency distribution of certain occurrences, such as different types of defects in a given product or the number of customers served each hour of the day. They assist the teams in distinguishing the areas on which they need to concentrate.
• Pareto charts are a specific type of histogram that orders the columns from the highest frequency to the lowest. It is also possible to assign dollar values to columns displaying the cost of different quality defects. The idea here is to use the Pareto principle, which suggests, as a rule of thumb, that 20% of different types of occurrences (vital few) cause 80% of all problems. This allows teams to focus first on relatively few issues to address the majority of problems.
• Scatter diagrams are an analytical tool to assess the relationship between two variables. For example, a company can use a scatter diagram to examine the correlation between the monthly total hours of overtime work and the total days of absenteeism.
• Value stream mapping is an effective means of figuring out the wastes in the system, especially those related to inventory, transportation, and waiting. Improvement teams can use this tool to identify and visually illustrate the tasks that add value directly to customers and those that do not add any value (waste).

When the team has determined the root causes, it is time to move on to the improve phase.

Improve

At the improve phase, the team discusses the potential ways to tackle the root causes of the problem. It is a common practice to prioritize the causes based on a cost-benefit analysis and start with those that offer the most benefit and require fewest resources. As described above, Pareto charts are ideal for determining priorities.

The team develops an implementation plan if they settle on self-evident improvement ideas that won’t require additional analytical analysis to demonstrate their potential effectiveness.

Sometimes, the process is not that straightforward, and the team may decide to run some experiments to study the usefulness of the proposed solution ideas. This approach, called the design of experiments, follows the scientific method, by which analysts work with controlled and independent variables to test their hypotheses. The experiments will reveal whether a proposed solution will offer promising results. The team will then work on an implementation plan.

Implementing the selected solutions can become challenging when they impact several employees’ everyday routines, especially if they are not part of the improvement team. Whether those employees were part of the team or not, management should ensure that they understand why the changes are necessary and how the improved process will boost productivity, making their jobs easier.

The team should also set realistic, measurable targets for the performance indicators and baselines established in the measure phase. Those targets will become essential reference points during the next DMAIC step, the control phase.

Control

The control phase serves three primary purposes:

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