Getting Predictive: What It Takes To Succeed With Analytics and IoT Initiatives in the “Industry 4.0” era

Getting Predictive With Analytics and IoT Initiatives

In recent years, the pace of data-driven transformation in operations has been relentless. And although it’s still relatively early in the “Industry 4.0” era, process digitalization using Internet of things (IoT) and analytics technologies to improve operational performance has become top priority for many manufacturers. In fact, according to the Aberdeen Group, best-in-class manufacturers are well ahead of the curve in using IoT and analytics solutions (Figure 1).

Manufacturers employing predictive analytics and IoT solutions

Figure 1: How best-in-class manufacturers are employing analytics and IoT solutions.

 

Whether or not you have jumped on the digitalization bandwagon, the value of having clear visibility into operational performance are undeniable. For example, in past posts we’ve talked about how just integrating data and aligning people and departments to stay on track in pursuit of daily goals can lead to a 5 – 10% performance boost. And the more effective you get at using your data, the more opportunities you can uncover for improving performance, quality and productivity in pursuit of revenue growth. Best-in-class manufacturers get this. That’s why they are moving to take advantage of IoT sensors and predictive analytics and other capabilities and technologies. After all, if you ignore data integration, analysis tools and sensors entirely, you’re effectively choosing to leave money on the table.

If you feel like you’re falling behind or aren’t sure where to start when it comes to analytics tools and sensors, this post will help you understand three critical points:

  • What types of initial projects using these technologies have the greatest potential payoffs
  • The challenges around realizing the true value from analytics and IoT
  • How to set up your operations for success.

Stepping inside a “virtual factory”

IoT and Analytics: Better Manufacturing Decisions in the Era of Industry 4.0 brief

Download: IoT And Analytics: Better Manufacturing Decisions in The Era Of Industry 4.0

A recent Aberdeen Group report highlights several powerful use cases in a digital factory that is enabled by analytics and IoT technologies, including the ability to:[1]

  • Manage machines, processes, and people with speed and agility
  • Monitor factory assets in real time by analyzing historical operational data to predict failure and fix it before it occurs
  • Use video to monitor quality in real time
  • Quickly simulate and compare the results of retooling an entire product line ‘on the fly’

Of course, success with these types of advanced analytics and IoT initiatives doesn’t happen overnight. It’s best to start with small steps and build on them as you see returns from your initial efforts. And the great thing about employing analytics, sensors, and other related technologies—when done right—is the way they can have a “snowball” effect in uncovering new efficiencies or business opportunities. For example, data analysis or analytics tools provide a way to more accurately identify potential issues in your processes that might be ideally suited for IoT initiatives. But there is an order in which things need to happen. It all starts with data integration and being able to use the data you already have on hand more effectively.

Understanding the potential of analytics in operations

While the uses for analytics in operations are really only limited by your imagination, there are a handful of areas with significant potential for any manufacturer, including:

  • Monitoring for equipment failure
  • Understanding cost of sales adjustments, such as labor and material variances
  • Identifying the most productive place or line to make a product
  • Identifying the true costs to produce and serve a customer
  • Identifying patterns and trends (it’s important to recognize that no solution will ever provide the “perfect” forecast)

None of these areas require the use of sensors. Initially, analyzing data that you are capturing from across existing systems and/or through manual measurement processes should help you make some initial performance or efficiency gains. In some cases, such as monitoring for equipment failure or identifying the most productive place to make a product, sensors providing real-time data can significantly enhance your ability to identify issues or opportunities in a timely manner.

The keys to realizing value from digitalization efforts

Most vendors talk a lot about the business value of “actionable insights,” but they often brush over the level of effort that is required to garner true value from analytics and IoT initiatives. The reality is that many organizations currently aren’t even capturing significant amounts of useful information from existing systems in the right way. The overarching issue is that most of their data from MES, ERP, time clocking, quality and other systems, is siloed within the systems.  That means they cannot use it to analyze relationships in data from across the enterprise without substantial time and effort involving Excel spreadsheets or Access databases.

If this sounds familiar, then you need to work through a couple of layers of challenges before you will be able to realize value from advanced analytics capabilities. The first is integrating your data from across systems and taking steps to ensure the data is accurate so you can rely on it for trustworthy analysis. Once you’ve integrated your data, then you need to have the right people with the right skills on-board to analyze the data or to create the algorithms to analyze the data. And if you are fortunate enough to have these first two elements addressed, then you must have the processes in place to respond to issues or opportunities or you are no better off.  For example, if you can identify that a machine is about to go down but don’t have the processes in place to create an emergent work order, then the machine will probably fail anyway.

First things first: Focus on strategy and getting to all of your data

The first step in successfully moving toward more digitally driven operations is all about defining a clear strategy for what you want to accomplish and getting closer to all of your data. And remember: more advanced capabilities don’t necessarily come with bigger payoffs. For example, some studies have shown very limited improvements from machine learning capabilities versus time-tested multiple linear regression techniques. So, if your organization is not already using advanced statistical analysis, you could drive a large opportunity with simpler approaches. From a capabilities standpoint it’s about being able to link transactional data to specific metrics and performance (KPIs). And then it’s about prescriptive insights, and that’s a big focus of Dploy Solutions.

Dploy Solutions is a comprehensive operations-specific solution that enables you to drive sustainable performance within your organization. It provides the best of all worlds as we enhance our analytical capabilities and combine them with the ability that we already have to capture and monitor your data from across systems to evaluate the effectiveness of actions. And it is an ideal platform for becoming a digitally driven organization.

Industry 4.0 is no longer a vision. Best-in-class firms are using prescriptive analytics and IoT to make better decisions regarding assets, products, processes, and operations, and it’s driving significant returns. Discover more about the possibilities and why digitization should be a top priority for your business in this Aberdeen Group brief.

[1] IoT And Analytics: Better Manufacturing Decisions in The Era Of Industry 4.0, Aberdeen Group, August 2017.

 

ABOUT THE AUTHOR
Ken Koenemann, VP of Supply Chain and Technology at TBM Consulting Group, is a 25+ year veteran of manufacturing, operational excellence and supply chain optimization.  At TBM, Ken is actively leading the effort to our suite of services to include emerging technologies that improve productivity and convert complex data into information for improved decision making.

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