Competing in the World of Big Data

Better, deeper information improves competitive advantage in a variety of ways:

Margin recovery. Savings are regularly found in reduced material costs and system capacity recovery.

Product quality and safety. Teams are better equipped to understand and eliminate the true root causes of product risk and can actually address and fix these shortcomings at the start of processes, rather than merely discarding low-quality output based on post-production testing. Product quarantines can be established based on specific, objective data rather than subjective approximations.

Eliminating overlapping investment and personnel support. Identifying overlap should result in the proper allocation of both people and technology resources.

Measurable ROI. Moving from a sort of unstructured approach that characterized previous manufacturing reporting processes to a solution built for data collection and analysis enables managers to develop consistent, new methods that can result in greater efficiencies, yields, and production flexibility that take new products to market up to 30 percent faster than before, a remarkable achievement with significant effects on profits and losses.

Collaboration. The synergy of analytics and Big Data provides both macro and finely detailed views of information that allow management, operators and engineers to work together based on quick feedback in a data-driven environment unlike anything in the past.

Monetizing Assets. Attitudes toward monetizing corporate assets, such as intellectual property, have changed dramatically over the past few years. In manufacturing this move towards using analytics to mine Big Data shifts the function of those assets and the systems that generate them into profit-enabling centers rather than just insurance and a cost of doing business. This profound change has significant impact on a shop’s bottom line.

RELATING HUMANS TO ANALYTICS
Let’s cut to the chase on the relationship between technology-assisted processes and the people needed to make them succeed: No matter how advanced technology may be – here we are talking about highly advanced mathematical algorithms that analyze large pools of business information and process data – it is a costly mistake to divorce the processes that it drives from the specialized, tacit human knowledge and expertise that exists in any business.

Decoupling the two is a serious step in the wrong direction. This is not to say that relying on humans alone to guide manufacturing processes still suffices. It does not. But analytics alone cannot be the sole catalyst of this industrial knowledge. Just as manufacturers have discovered hidden, siloed data, they must also tap into and then incorporate into their analytics the extraordinary amount of tacit knowledge that resides (often hidden) in their workforce.

Robinson says, “To some degree you look as much as possible to use data that comes out of sensors, data inherent to the process. This is the basis for the analytics that guide process improvements and standards. But the way that people interact with that process tells you a lot about what they know and believe to be true. When a skilled, experienced operator shortens a cycle time, you must ask “why” and sit down with that operator to understand and capture why that choice was made, then combine their human expertise with the sensor data to create new operating procedures and processes.”

Capturing this knowledge is even more important when one considers natural employee attrition and retirement. Expertise that walks out the door cannot easily – if ever – be recovered and can significantly affect operations and business in the long term. Expertise gained through analytics must be “socialized” among managers, engineers and other workers so that it may be transferred to manufacturing processes.

These ultimately means the types of workers found in manufacturing today must dispel the old competitive stereotype of highly repetitive, task-oriented types of jobs and replace them with the new competitive model of high level, analytical problem solvers.

RETURN ON INVESTMENT
Big Data has a measurable ROI: Moving from traditionally unstructured approaches often found in older shops to a structured system designed for data collection and analysis can enable a manufacturer to develop new, consistent processes with 14 percent higher production yields.

Think about it. Software empowers teams of operators, engineers, and management to work collaboratively and identify areas of improvement in a data-driven culture predicated on quick and accurate feedback. With higher yields, products can go to market more quickly. Shops can tighten quality specifications. Margins are recovered. Savings are found. Material costs decline.

Each of these returns on investment provide competitive advantages that can be used to reinvest in the shop’s workers and product lines, buffer against price pressures from competition and other market factors, and properly allocate capital based on new business plans and process strategies. And don’t forget expansion.

Perhaps most interesting, these newly found efficiencies can help shops on the supply side both accelerate their speed to market and provide more accurate data to their buyers to improve customer loyalty and credibility within the supply chain.

Manufacturers have come far in their use of data and analytics, but not far enough. Critical information still remains hidden, making it difficult, if not impossible, to drive improvements or increase production without adding equipment or people. Shops that fail to use Big Data will eventually become seen as less reliable supply chain partners and, at the end of the day, they will become dinosaurs.

– Ben Kerschberg is the founder of BK Advisory Group, LLC (New York, NY) and Consero Group LLC, 4915 St. Elmo Avenue, Suite 100, Bethesda, MD 20814, 202-595-9300, Fax: 202-595-9301, [email protected], benkerschberg.com.

So what are your thoughts about competing with Big Data? Do you agree with Kerschberg or not? I’m anxious to see where you stand.

¹ Jacques Bughin, John Livingston & Sam Marwaha, Seizing The Potential of Big Data (McKinsey Quarterly Oct. 2011).