Lean Practices In The Big Data Era
With the combination of advanced analytics and lean management practices, enterprises can make better decisions, solve critical problems and get a greater ROI, states a recent McKinsey article. A good example given by authors Rajat Dhawan and his associates is that of sophisticated modeling that can help in identifying waste, thereby, enabling organizations in opening up new frontiers where lean problem solving can support continuous improvement.
The authors note that Powerful data-driven analytics also can help to solve previously unsolvable (and even unknown) problems that undermine efficiency in complex manufacturing environments, such as, hidden bottlenecks, operational rigidities, and areas of excessive variability.
Lean is not new
Even though in the big data era, lean practices may appear to be new, many enterprises in the chemicals, electronics, mining and metals, and pharmaceuticals already implemented lean data practices since 1990s, even though their approaches were more traditional.
“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, the authors report.
Focus: People and processes
However they also note that even these companies have to adapt to newer environment and shift from the traditional approaches to get the most from data-driven lean production. This is no longer about technology but involves people and the process
Dhawan and his team points out that by connecting 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.
Identifying the real problem
The article mentions a pharmaceutical company that wanted to get to the root causes of variability in an important production process. Operators suspected that some 50 variables were involved but couldn’t determine the relationships among them to improve overall efficiency. Working closely with data specialists, the operators used neural networks (a machine-learning technique) to model the potential combinations and effects of the variables. Ultimately, it determined that five of them mattered most. Once the primary drivers were clear, the operators focused their efforts on optimizing the relevant parameters and then managing them as part of routine plant operations. This helped the company to improve yields by 30 percent.
Similarly, a leading steel producer used advanced analytics to identify and capture margin-improvement opportunities across its production value chain. It began with a Monte Carlo simulation to model ranges of possible outcomes and their probabilities. The steelmaker focused on what it thought was the principal bottleneck in an important process, where previous continuous-improvement efforts had already helped raise output by 10 percent. When statisticians analyzed the historical data, however, they recognized that the process suffered from multiple bottlenecks, which shifted under different conditions. The part of the process that the operators traditionally focused on had a 60 percent probability of causing problems, but two other parts could also cripple output, they found out. With this new understanding, the company conducted structured problem-solving exercises to find newer, more economical ways of making improvements.
Data for continuous improvement
These examples suggest that the key to applying advanced analytics in lean-production environments is to view data through the lens of continuous improvement and not as an isolated series of one-offs. The ability to solve previously unsolvable problems and make better operational decisions in real time is a powerful combination. More powerful still is using these advantages to encourage and empower frontline decision making.
Dhawan and his team emphasized that CXOs should continue to play an active role in applying advanced analytics in lean-production. They state that the information and data required for many big data initiatives already exist in silos around companies—in shop-floor production logs, maintenance registers, real-time equipment-performance data, and even vendor performance-guarantee sheets. In some cases, data may come from outside partners or databases. Determining what to look for, where to get it, and how to use it across a dispersed manufacturing network requires senior management knowledge and support, they say.
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