The manufacturing sector has come far in its use of analytic data, but not far enough. Ben Kerschberg of the BK Advisory Group explains why manufacturers who do not embrace a complete vision of using this tool will soon find themselves at a significant competitive disadvantage.
As more shops monitor their machines through MTConnect, the communications protocol for CNC machine tools and other production equipment, they are learning how to compete in the openly hidden world of Big Data, where overwhelming pools of business information and process data can be captured, communicated, aggregated, stored, and analyzed to uncover and use even deeper data that has remained hidden or unappreciated.
As the Internet, cloud computing and MTConnect become more prominent in manufacturing operations, Ben Kerschberg says traditional shops must recognize why Big Data is no longer a domain reserved for the IT department. In fact, his article “Using Big Data and Business Intelligence To Spur Operational Excellence” in Forbes magazine is nothing less than a warning of a day fast approaching when manufacturers that fail to tap into and use this resource will be stranded.
The following is an excerpt of Kerschberg’s thought-provoking insights into how manufacturers will compete tomorrow. Let’s think through this together with him:
One in six American jobs is still in or directly tied to manufacturing, the result of its tremendous multiplier effect: every job in manufacturing generates more than 2.5 jobs elsewhere in the economy. Because of its increasingly sophisticated technologies and the processes it employs, U.S. manufacturing relies on a more educated workforce and pays higher wages and better benefits than other sectors.
However, numerous factors challenge the health of manufacturing. Rising external costs in our global economy may well top the list. Domestic factors, including corporate tax rates, rising health care costs, regulatory compliance and energy prices, add a stunning 18 percent to manufacturers’ costs relative to our major trading partners.
In these economic times, shops must examine their existing business practices and ask difficult questions: How can we work smarter? How can we improve our processes? What are we leaving on the table? How can we regain our competitive advantage through operational excellence?
Some of the answers to these questions are evolving through the analysis of Big Data that mines for hidden patterns and insights to higher efficiencies, to the point they now make it clear that large-scale data gathering and analytics are quickly becoming a new frontier of competitive differentiation.1
But these answers require a different way of thinking: Even the largest manufacturers will not realize and maximize this competitive opportunity until they recognize the importance of – and invest in – the convergence of analytics, Big Data and the cloud infrastructure upon which it depends.
This can be difficult because the “correct” data is not easy to identify and analyze. Even technologically savvy organizations have critical data residing in antiquated legacy systems that may not be integrated. “Much information is siloed inside isolated datasets with different custodians or in enterprise resource planning (ERP) tools,” says Sean Robinson, a global industry manager of GE Intelligent Platforms (Charlottesville, VA) that uses Big Data in the manufacturing sector.
Without significant investment, manufacturers will miss critical opportunities to achieve operational excellence by marshaling and using one of their most important corporate assets: information (data).
The new manufacturing strategy is to (1) find hidden data, specifically real time data at both aggregate and finely detailed levels; (2) apply advanced computational methods to mine that data; (3) understand the interconnectedness of the multivariable data; and then (4) optimize traditional manufacturing processes accordingly. A shop’s ability to achieve these goals depends on good information and analytics that lead to actionable intelligence.
Multivariable analysis depends on (1) data transparency that allows data from different manufacturing functions to be integrated so that executives can form a holistic view of the processes that was never possible before; (2) process visibility that allows managers to see how processes unfold as they happen, which allows for real-time adjustments; and (3) data visualization that allows end users, such as plant managers, to actually see the hidden data and its value. As analytics are applied to Big Data, it is especially important that the output be analyzed mathematically and represented visually because the data is dynamic in real time.
This analysis reveals both informational assets and shortcomings. Any shop can economically benefit from an integrated “big picture” view of operations that measures all sorts of production metrics and uncovers inefficient areas that are being accepted simply because they do not realize it, or other areas where stable processes are being unintentionally disrupted. Better information like this improves continuous improvement and focuses on margin recovery instead of capacity improvement alone.
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