Posted By Jeff Moad, November 04, 2016 at 12:27 PM, in Category: The Adaptive Organization
When you boil it down, much of the focus on the Industrial Internet of Things (IIoT) and Manufacturing 4.0 is about one thing: Getting clean, accurate, and actionable real time data to people in the supply chain and on the plant floor who can do something with it to fix or avoid problems.
This seems pretty intuitive, but it’s not really the way manufacturers have looked at or used data historically. Although most plants generate tons of data, it often hasn’t been widely accessed in real time to make decisions. It often hasn’t been very clean or actionable. And it hasn’t been the primary driver of decision-making. Manufacturing executives, like those in other industries, have typically spent a lot of time arguing about whose data is correct, then making decisions that reflect their considerable experience—otherwise known as gut feeling—as much as the data.
So the notion that decisions will be primarily driven by data, that decisions will be based on real-time data, and that operators on the plant floor—not just managers and executives--will engage in data-driven decision-making is something new for a lot of manufacturers.
There is evidence that this kind of data-driven decision-making can lead not only to better decisions but greater corporate performance. A recent survey of 418 senior executives by McKinsey & Company showed a strong correlation between companies that make extensive use of customer data analytics and those that outperform their competition in terms of sales, sales growth, profit, and ROI.
We see many manufacturers are looking at how to cash in on real-time data to make better decisions faster. And, not surprisingly, we see many initially focusing on the technology side of the equation; how to put in place the IT infrastructure and technology people necessary to transform, cleanse, aggregate, and provide prescriptive context for real-time data generated by machines on the plant floor.
But recent conversations among members of the Manufacturing Leadership Council show that effectively using real-time data and analytics to make better decisions is not just a technology issue. In a very real way, effectively embracing data-drive decision-making requires a significant cultural shift.
Or, as the McKinsey study states, “Having a culture that values and acts on customer analytics is critical. Investments in IT and skilled employees are also important, but investments alone will not deliver value. Leadership that expects fact-based decisions and an organization that can quickly translate those facts into action are more likely to win than those companies that focus mostly on IT.”
But that shift can be challenging for contributors across the enterprise. In many instances, for example, executives and managers prove reluctant to adopt data-driven decision-making because they feel it devalues the experience and expertise they bring to the table from many successful years on the job. Executives from one large automotive OEM recently told ML Council members that it took a good deal of explanation, training, and messaging from senior leadership to get some of these long-time managers to accept greater reliance on data-drive decision-making.
And it’s not just the managers who sometimes resist the change. Line workers are often less than enthusiastic, ML Council members say, because they fear the data will be used to find fault with their performance.
Many manufacturers are apparently facing similar challenges moving to a data-driven decision-making culture. A recent report in the Harvard Business Review found that, while the shift to data-driven decision-making among manufacturers has boomed in recent years—from 11% of plants implementing it in 2005 to 30% in 2010—successful adoption has been highly uneven. Larger manufacturing enterprises with more educated workers and more advanced information technology, for example, have been more successful adopting data-drive decision-making. Others not so much.
So what can manufacturers do to create a culture that encourages and benefits from data-driven decision-making? Here are five steps that will be important:
- Senior leadership must actively back the strategic importance of the shift to data-driven decision-making and model it by being seen as engaging in it themselves and expecting the same from others. It also means the leadership must accept investments in real time data and analytics infrastructure as strategic. As one member of the ML Council recently said, “I spend zero time measuring what we save from data analytics or OIT [operations information technology] versus base performance. I’ve kind of mentally progressed through that and, clearly, the senior leadership of the company has progressed through that as well.”
- Senior leadership must also be very clear about where they want to see real-time data and data-driven decision-making applied and what improvements they expect. One very large automotive member of the Council, for example, is prioritizing improvements in plant equipment maintenance, logistics, and plant flexibility (the ability to support both mass, built-to-stock production and much more customized production within the same production system.)
- Manufacturers need to create standard data models and policies that can be applied globally and that define the important data elements that will be tracked, who needs to see what real-time data, and how and by whom decisions will be made against it.
- Manufacturers need to invest in education and training for operators on the plant floor so they can understand not just the meaning of the real-time data to which they are being exposed, but also the impacts their data-driven decisions will have on end-to-end production processes.
- Manufacturers will need to present real-time data to line workers in formats that are easy and quick to consume and act on. The kinds of reports and spreadsheets that managers typically use to consume analytics don’t fly on the plant floor. Simpler is better.
Certainly human experience, insight, and even intuition won’t and shouldn’t disappear from the inputs that go into operational decision-making by manufacturers. But, increasingly, the use of real-time data will drive and improve operational decision-making. As that happens, manufacturers that can modify their decision-making cultures will have a leg up.
Written by Jeff Moad
Jeff Moad is Research Director and Executive Editor with the Manufacturing Leadership Community. He also directs the Manufacturing Leadership Awards Program. Follow our LinkedIn Groups: Manufacturing Leadership Council and Manufacturing Leadership Summit