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In the article Lean and Big Data: How Manufacturing Is Getting Even Leaner, the author is examining case studies of lean manufacturers using big data to become leaner and more efficient.  According to McKinsey & Co., big data could be worth tens of billions of dollars for Lean manufacturers in the automotive, chemical, FMCG and pharmaceutical industries.  According to the article, a Manufacturing Execution System can generate big data on accuracy of downtime, waste, and WIP which is data that is traditionally used in lean systems.  The overall goal of any lean manufacturer is improved processes specifically through reduced waste and increased efficiencies.  It’s beginning to look like big data can be a great asset in this lean journey.

The cases described outline how big data has become so useful to manufacturers.  One instance was a two-billion-dollar company that uses big data analytics to analyze and understand the habits of repeat customers.  Another instance is that of Intel using predictive analytics to reduce the number of quality control tests on each processor, saving them 3 million dollars in manufacturing costs on that line of processors.  One final case is that of a steel manufacturer using the Monte Carlo simulation with historical data to identify previously unknown bottlenecks.  After the subsequent process improvements, the steel manufacturer saw a 20% increase in throughput.

Finally, big data can help manufactures form answers to two of their most complex questions –

Who is buying what, when, and at what price?

How can we connect what consumers hear, read and view to what they buy and consume?

It all boils down to customer loyalty and sales.  The managers helping make this transformation see the opportunity for lean data to improve their business, and they are taking advantage of it.  They know what data to use and how to use it, which is translating to significant cost savings and separation from the competition.  Those who don’t employ big data are seriously becoming at risk of being left behind.


Can you think of a reason why a lean manufacturer wouldn’t want to employ big data?

Do you think the biggest obstacle in using big data is know what data to actually use?

Do you know of any cases where useless big data was used poor decisions were made?