When big data goes lean

Posted on: April 28th, 2014 by bobmorris

When Big DataHere is a brief excerpt from an article written by Rajat Dhawan, Kunwar Singh, and Ashish Tuteja for the McKinsey Quarterly, published by McKinsey & Company. They are convinced that the combination of advanced analytics and lean management could be worth tens of billions of dollars in higher earnings for large manufacturers. A few leading companies are showing the way. To read the complete article, check out other resources, learn more about the firm, obtain subscription information, and register to receive email alerts, please click here.

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The application of larger data sets, faster computational power, and more advanced analytic techniques is spurring progress on a range of lean-management priorities. Sophisticated modeling can help to identify waste, for example, thus empowering workers and opening up new frontiers where lean problem solving can support continuous improvement. Powerful data-driven analytics also can help to solve previously unsolvable (and even unknown) problems that undermine efficiency in complex manufacturing environments: hidden bottlenecks, operational rigidities, and areas of excessive variability. Similarly, the power of data to support improvement efforts in related areas, such as quality and production planning, is growing as companies get better at storing, sharing, integrating, and understanding their data more quickly and easily.

Pioneers in the application of advanced-analytics approaches, some borrowed from risk management and finance, are emerging in industries such as chemicals, electronics, mining and metals, and pharmaceuticals. Many are lean veterans: these companies cut their teeth during the 1990s (when sagging prices hit a range of basic-materials companies hard) and more recently doubled down in response to rising raw-materials prices. The benefits they’re enjoying—an extra two to three percentage points of margin, on top of earlier productivity gains (from conventional lean methods) that often reached 10 to 15 percent—suggest that more big data applications will be finding their way into the lean tool kits of large manufacturers. Indeed, our work suggests that, taken together, the new uses of proven analytical tools could be worth tens of billions of dollars in EBITDA [earnings before interest, taxes, depreciation, and amortization] for manufacturers in the automobile, chemical, consumer-product, and pharmaceutical industries, among others (exhibit).

Nonetheless, to get the most from data-fueled lean production, companies have to adjust their traditional approach to kaizen (the philosophy of continuous improvement). In our experience, many find it useful to set up special data-optimization labs or cells within their existing operations units. This approach typically requires forming a small team of econometrics specialists, operations-research experts, and statisticians familiar with the appropriate tools. By connecting these analytics experts with their frontline colleagues, companies can begin to identify opportunities for improvement projects that will both increase performance and help operators learn to apply their lean problem-solving skills in new ways.

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Here’s a direct link to the complete article.

Rajat Dhawan is a director in McKinsey’s Delhi office, where Kunwar Singh is an associate principal; Ashish Tuteja is an associate principal in the Mumbai office.

The authors wish to thank Abhishek Anand, Rajat Gupta, Snehanshu Mahto, Dev Ramchandani, Aman Sethi, Saurabh Srivastava, and Abhishek Tikmani for their contributions to the analysis underpinning this article.

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