Lean Six Sigma and Data Analytics: on why they make such a good pair.

That Lean Six Sigma strategies make room for other techniques to be deployed in its wake, for enhanced process improvement possibilities is one of the many attributes that make businesses and industries go for them.

Data analytics essentially help extract meaningful information and discover patterns and trends, and identify factors of influence and root causes from extremely large sets of data collected from various sources pertaining to the industry or business of interest. Lean methods, as we know, are all about gathering every possible data pertaining to a chosen process, so everything that goes about the process is analyzed and appraised effectively.

One plus one is two, and here we have data analytics working hand in hand with Lean Six Sigma methods, to capture, process, and extract meaningful data patterns, trends, and relationships. Integrating Lean Six Sigma with data analytics is a significant step that has rendered identification of Six Sigma projects quicker and easier.

Why is identifying Six Sigma projects important?

The 'D' in the DMAIC process, that is the core structure of Lean Six Sigma Methods, stands for 'Define', as you'd know. This phase is defined by identification of the problems in the business or process and in devising a plan of action to override these defects.

In many organizations Lean Six Sigma projects are identified on an ad-hoc basis to resolve immediate concerns, or through an annual project identification exercise. In either case, it’s a reactive process. Ideally, an organization should adopt a proactive process based on real time trends and data patterns that are dynamic. Data analytics serves to identify these trends which will empower the organization to perform proactive and systematic project identification.

Now, any business process will possess a huge trove of data. Not all of it could be measured and analysed, even after classification- it is technically a possibility, but it absorbs a lot of time, effort and other resources, which is inherently the opposite of anything 'lean'. It is essential to stratify, prioritize, and identify the bottlenecks in the process, and the factors that critically enhance the efficiency of a process. This is where data analytics come in.

Through structured and systematic analysis of huge volumes of unstructured, seemingly random data, data analytics capture relationships such as cause-and-effect, and help identify bottlenecks with greater precision and accessibility. This is particularly useful in case of real-time data that keeps expanding with time.

These conclusions as captured by data analytics are valuable inputs, that help the stakeholders, decision makers and the Lean Six Sigma consultant identify potential Six Sigma Projects, draw a structured project plan, develop a process map, and set the project rolling towards betterment in terms of delivery, efficiency, and productivity, all in good time.