Key Points

• Predictive analytics allows for proactive decision-making, as opposed to reacting when things go wrong.
• Utilizing AI is just one way organizations can supercharge their predictive modeling efforts.
• Predictive modeling can be used throughout all stages of a Six Sigma project.

Predictive analytics is one of the most powerful tools at your disposal when it comes to decision-making in Six Sigma. Data drives quite a bit of any business’s operations under Six Sigma, so it stands to reason that leveraging historical data to try and gain a future advantage would be a boon for any organization. Organizations can readily leverage technology to prevent defects before they occur, correct processes, and so forth.

It takes a deft hand to leverage predictive analytics, however, and that’s something we’ll discuss in-depth over the course of today’s article. Proactive decision-making isn’t just a possibility, but one of the most useful tools in your box. We’ll look at how analytics can play into DMAIC, and why you’ll want to make use of any sort of predictive tools going forward.

Enhancing DMAIC

DMAIC projects can be seen as generally reactive. You’re addressing a problem, improving a process, or ultimately trying to implement some sort of fix on a shortcoming. While this works just fine in the confines of most business operations, that doesn’t always have to be the case. Proactive DMAIC cycles can have your team tackling problems before they even arise.

While none of us can tell the future, data can tell us quite a bit about potential and likely outcomes. So, let’s look a little more closely at a typical proactive DMAIC project.

Define

The Define phase of any project is going to identify early warning signs or emerging trends. A general example would be analyzing customer feedback to determine impending churn or analyzing sensor data from production equipment to detect a failure. Predictive analytics can be rather flexible at this phase, allowing teams to pick and choose projects as needed.

Since you’re better equipped to act instead of react, you’ve got more room to plan and adjust. Priority should be given to projects with higher impacts in terms of cost, quality, and customer satisfaction if left as-is.

Measure

Measuring is all about data and metrics, as I’m sure you know. However, real-time gathering of data through more advanced collection infrastructures can make predictive analytics take place across the entire organization. Instead of relying on number crunching on historical data, you can have reliable streams of data from all aspects of production.

This can help to establish a baseline. While we typically rely on benchmarks to understand optimal conditions for any workflow, a baseline built on predictive modeling can help to set more realistic expectations and goals. While lofty planning and strategizing are fine, predictive analytics can help businesses stay grounded about what is achievable in the here and now.

Analyze

Analytics, as we think of them currently, rely on number crunching, typically by specialized staff. The Analyze phase is typically marked by the use of historical data to identify potential outliers, deviations, and defects. Proactive analysis through the use of predictive analytics takes a far more in-depth approach.

When used for something like root-cause analysis, machine learning can pinpoint what is leading to failures or defects in a way that most data scientists won’t grasp right away. Further, running simulations based on predictive models can allow teams to see the impact and understand the changes in performance for any potential fixes to a problem.

Improve

Can you imagine implementing improvements before the problem even makes itself known? With predictive analytics, you can schedule routine maintenance and repairs on machinery without having to wait for performance to degrade in the first place.

Simulating fixes and improvements can allow teams to get a full grasp on how a process is going to function. This helps to illuminate the potential rate of defects, pain points, and other vital data points to plan ahead for the future.

Control

Statistical process control, or SPC, is a fine way of handling and maintaining the status quo for a newly improved process. However, predictive analytics stretches well beyond what you might expect, allowing for adjustments well ahead of time. Predictive models can readily inform teams of when a process is expected to move out of spec, making sure they’re catching things before they go pear-shaped.

Applications and Benefits

Using predictive modeling for any sort of work under Six Sigma has some noticeable benefits. While we’ve covered a few of those, there are quite a few benefits to leveraging predictive modeling and analytics for your organization.

Predictive Maintenance

While we often dread the check engine light in our cars, a real-time flow of information from any machines on the production line can do quite a bit. This allows teams to schedule maintenance before costly failures and repairs set in, saving time and money in the process.

Demand Forecasting

We can’t readily tell the future when it comes to customer expectations. However, real-time data collection and the use of predictive analytics can allow us to better anticipate what comes next. Demand forecasting has the benefit of maintaining optimal levels of inventory for both deliverables and the materials needed in the first place.

Risk Mitigation

You can’t prevent risk altogether, but you can certainly mitigate some of the more severe elements with predictive analytics. Teams can and should leverage any sort of predictive modeling to avoid supply chain disruptions, quality non-conformance, and develop strategies to maintain continuity and minimize the overall impact of any unforeseen events.

Quality Forecasts

Understanding the potential for deviations from expected quality is a powerful thing. This saves your team from costly rework, leaves your customers happier, and minimizes waste. Predictive modeling can paint a realistic picture of what to expect from a given production line, better informing teams on what is happening with their manufacturing workflows.

Technologies

You’re not going to be relying on the likes of Microsoft Excel to do any sort of predictive analytics. As with most advanced technological tools and disciplines, it is going to require the familiarization with new tools and software. However, the benefits it brings certainly warrant the need for teams to get educated and up to speed.

Machine Learning

While machine learning and artificial intelligence are being marketed as a cure-all for most organizations, that ignores the power they possess for any sort of predictive analytics. Bespoke machine learning algorithms can readily be trained and will grow more robust over time. As your organization is constantly generating data, the predictive analytics ML algorithms you’re leveraging can become far more powerful and comprehensive.

Statistical Modeling

There are a few different statistical modeling methods at your disposal. Common types in use for any sort of predictive analytics can include time series analysis, multivariate analysis, and hypothesis testing. For most analysts working at Six Sigma organizations, these should be fairly well-understood concepts by and large.

Big Data

Big data is one of the most powerful and misunderstood tools at the disposal of predictive analytics. Leveraging the vast series of data points on offer from any big data service can allow an organization to ably navigate the shifting tides of customer demands. Further, these diverse data points can cast a wider net on your organization than internal or small data might yield.

Other Useful Tools and Concepts

Ready to start the work week right? You might want to take a closer look at what role leadership plays in TQM. TQM is one of the older approaches available to businesses looking to maximize their output and increase satisfaction, and leadership naturally plays a valuable role in the success of any initiatives.

Additionally, you might want to learn how you can build a solid business case for Lean Six Sigma at any non-profit organization. While Lean Six Sigma might not seem like a natural fit for the non-profit sector, it is well-suited for use in any industry with the right adaptations. Learn how to structure this vital business document, and you’re ready to take on any initiative you might be planning.

Conclusion

Predictive analytics can give any Six Sigma organization a competitive edge when it comes to navigating the future. While it won’t hand you a crystal ball to see what comes next, you’re well equipped to make informed, proactive decisions throughout any stage of a Six Sigma project.