What is DMAIC?
Sometimes in the business world we hear jargon that goes in one ear and out the other. ‘DMAIC’ (pronounced ‘duh-may-ik’) is a good example. Quite simply, the acronym – which stands for ‘Define, Measure, Analyse, Improve and Control’ – is a data-driven problem-solving approach involving incremental refinements and optimisations to products, designs and processes.
Motorola engineer Bill Smith created DMAIC in the 1980s as part of the Six Sigma methodology, which relies on measurement and analysis to make operations as efficient as possible. Its goal? Process improvement.
What are the different steps of the DMAIC process?
Smith designed DMAIC’s five interconnected steps to have a cumulative effect: Build on information and data generated in previous stages and iterate upon your discoveries. (Pro tip: Lots of our clients love the real-time analytics they can gather with DocSend!)
The define phase establishes what the problem is and what you need to solve it. Sounds obvious, but this part of the process – stating the issue, goal and scope necessary to achieve it – is crucial. By defining, you’ll learn which elements are critical to quality, or ‘CTQs’. Team leaders and project managers usually develop a SIPOC – Suppliers; Inputs; Process; Outputs; Customers – diagram for their team or project charter document.
Understand the problem? Great; let’s outline how you’re going to measure and observe the changes you make to it. The purpose of the Measure phase is to establish your existing process performance and determine which data you’ll analyse. (From there, you can use a data collection plan to monitor your performance as you make changes and to compare results at the end of the project.)
You should now have a baseline of data that you can use to start making process-related decisions. Congrats! Welcome to the analyse phase, when you study that data. You and your team will build a current process map using the data to understand where the issues in your existing process began. (No need to use one of the complicated Six Sigma tools, either; fishbone diagrams and Pareto charts are perfectly sufficient, commonly used methods for conducting root cause analysis.) Once you’ve identified several root causes, involve your team to help determine the focus of your DMAIC process moving forward.
It’s time to start making actual process improvements. During the Improve phase, work with your team to find creative solutions to implement and measure. Brainstorming and having effective meetings are critical. Once you have solutions in mind, you’ll need to test, fail-proof and implement them. Plan-Do-Check-Act or ‘PDCA’ cycles are a common method for this, as is Failure Mode and Effects Analysis, or ‘FMEA’, to anticipate possible issues. Finally, create a detailed implementation plan to guide how you fix existing process issues.
The last step in DMAIC should help you verify and sustain the success of your solution. In the Control phase, your team creates – you guessed it – a control plan in order to continually reassess the impacts of implemented process changes. You should also create a response plan to act upon in the event that performance begins to fall again. Being able to look back on how you conducted improvements and which solutions you made can be invaluable. In these moments, having proper documentation and version control on the improvement process is vital.
Word to the wise: if you’ve already heard of DMAIC, you may have heard of a different initial step, known as the Recognise phase. It’s a minor semantic distinction; the two are essentially identical.
What are the advantages of DMAIC?
In industry, the adoption of Six Sigma and DMAIC helped drive a lean manufacturing approach with the goal of eliminating waste, defects and over-production. (Curiously, the name Six Sigma is derived from the statistical model used: In statistics, a standard deviation is also referred to as a ‘Sigma’ or σ.)
Manufacturers found that the more standard deviations there were between their average and acceptable quality limits, the less likely they were to go over those limits. Thus, Six Sigma, or six standard deviations, became the gold standard in defining production limits, reducing the number of defects and improving processes.
This statistical approach to identifying and solving root causes of problems – along with other methodologies like Kaizen – was vital to improvement in manufacturing in the ’90s and 2000s.
Whether or not you work in manufacturing, the core benefit of DMAIC is that it’s incredibly rigorous. Problem-solving experiments with new processes can often end up not yielding definitive answers as to what’s changed. By using DMAIC, you’re in a better position to assess and measure progress using data.
DMAIC’s standardised, five-step framework keeps everyone abreast of every stage of the process. By producing documentation to summarise all decisions and progress made, you can move seamlessly from step to step. And by having a definitive process, you can help guide teams in accordance with their current objectives.
Its fans love that DMAIC is iterative and ongoing: The Control phase of the process necessitates that process owners continue to monitor the impacts of optimisation. With that data monitoring plan in place, your new process data naturally forms the baseline for a new Measure phase. The DMAIC methodology is able to continually identify problems or impacts on business processes – plus a way to immediately target those issues.
When should you use DMAIC?
DMAIC requires team alignment and time to understand. As a manager, it’s worth asking, ‘Which process or team stands to benefit most from DMAIC?’
Keep in mind that it may not necessarily be the right option for your problem, industry and organisational culture. Easily identifiable process issues that have prospective simple solutions backed by compelling data mean a full DMAIC process may not be necessary.
When the process problem is more complex or high-risk, such as when you can’t afford a performance decrease, DMAIC can be a smart tactic. Even if it entails a bigger budget than you might want, the process – when properly implemented – should produce results.
Fixing problems with DMAIC
Though it can be daunting to attempt a DMAIC project for the first time, especially if your organisation has major issues, it’s an academic way to attack a problem – similar to the scientific process. By focusing on discovery, iteration and incremental improvement, you should find the best practices for your teams – which can lead to measurable success for your company.