Q

Does CEP deserve a spot among top trends in manufacturing?

If you're wondering whether the hype around CEP is real, the answer is a resounding 'yes.' Here's a look at why complex event processing is making inroads into manufacturing.

If you've read recent lists of manufacturing trends, you'll have seen that complex event processing is scoring...

a top spot -- and for good reason.

The term complex event processing is used to describe analytical software that processes information from multiple sources about events and develops conclusions or predictions based on those events. CEP looks at patterns on which it can infer the likelihood of future occurrences. The goal of CEP is to identify opportunities or threats early enough to allow the user to prepare for or avoid their occurrence.

In a way, all forecasting can be considered "event processing" if you consider sales transactions to be events. Forecasting that encompasses additional sources of input, extrinsic factors like demographics, economic data, product lifecycles or leading indicators can be considered "complex" event processing. But that's splitting hairs.

The CEP that is making recent lists of trends in manufacturing is a subset -- or perhaps a renaming -- of analytics that manufacturers are using in conjunction with big data and the Industrial Internet of Things (IIoT), which are two other trends in manufacturing business leaders would do well to research. The old executive information dashboards and business intelligence systems are morphing into increasingly sophisticated analytical engines on the way to predictive analytics, i.e., complex event processing.

The real questions are whether CEP is finding its way into manufacturing organizations and whether it has value. The answer to both is a resounding "yes". To address the second question first, anything that can provide advance warning -- or at least a probabilistic forecast -- of market trends, demographic shifts, or any future threats or opportunities is certainly of great value to any business leader.

As far as the evolution and adoption of CEP, that is well underway, but not yet at the point where CEP use is anywhere near widespread. As with any new technology, there are barriers to early adoption, primarily complexity and price. Sophisticated analytics are quite technical and early adopters have needed data scientists and other technical resources to implement and operate the systems.

But as with most other technologies, these systems are becoming more affordable as the number of users increases and as they are packaged into analytics modules designed to integrate with commonly used ERP systems. This evolution is also "commercializing" the tools so that a wider range of people can employ them -- i.e., business users with a little help from IT -- no data scientists required.

You can expect to see more CEP and predictive analytics capabilities migrating into existing business intelligence applications, resulting in a new wave of packaged, user-friendly and affordable add-on modules for use with the major installed ERP systems in the market.

Next Steps

Choose the right supply chain analytics

Dynamics adds mobile-friendly UI, analytics

Understanding IoT data

How to develop an architecture for implementing CEP

This was last published in March 2016

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