Tuesday, October 28, 2014

Big Data in Manufacturing: Is the Emperor wearing clothes?

By Patrick Weber, MBA PMP, MESA Member

Today I attended LNS Research’s “Global State of MOM” webinar; quite a bit of good information that I’m still digesting (kudos to Matt Littlefield & company). There is a bit I find hard to swallow though – Big Data in manufacturing. Perhaps I’m just so far out of the loop on this that I’m just not comprehending the obvious, but I can’t see a real business case for Big Data in the manufacturing environment, nor can I see plants investing in the infrastructure required to support Big Data solutions.

Big Data is (of course) making a big splash in the business press. In June of 2014, Forbes magazine quoted several sources stating that Big Data analytics, services, and infrastructure will grow at a 30% rate over the next five years – what software, hardware, or integration vendor wouldn't want part of that pie? But beyond the buzz, I question if there is real business value for Big Data at the plant floor level.

Manufacturing has always produced volumes of data; SPC, batch records, lot traceability, maintenance records, machine down time, material flow, root cause analysis, design of experiments – the list is huge. But “a great deal of data” isn’t the same thing as “Big Data”; you don’t need Hadoop and MapReduce with petabytes of storage on multiple servers and ultra-high speed networking to deal with manufacturing data analytics.

Sorting through some of the reasons given for interest in Big Data during the LNS webcast:

  • Better forecasts – This has some potential, but the data isn’t generated on the plant floor. This information comes from the marketplace, and the need for better forecasting isn't unique to manufacturing. Better visibility into customer demand in near real-time should result in better capabilities to collaborate with suppliers, warehouses, and logistics. But is “Big Data” really the answer for improving forecasting at the plant level? Maybe I just need to see a good example of this in practice, but until then I remain skeptical.
  • Better understand multiple metrics – I suspect this is more related to Enterprise Manufacturing Intelligence (EMI) tools than Big Data, and that there is confusion in the user community distinguishing between analytics and Big Data. I could be wrong, but I don’t think software vendors are doing much to clear up this confusion.
  • Service and support customers faster – I would examine the existing business processes first before implementing a Big Data solution here. I don’t believe the lack of actionable information is what’s causing service/support issues.
  • Real time manufacturing analytics – Again, I think there’s confusion between Big Data and analytics. MES/MOM, Data warehouses, and historians are sufficient for this; does a plant really need a Big Data infrastructure to provide analytical insights?
  • Correlate manufacturing and business performance – Honestly, I don’t know why this is different from “Better understand multiple metrics” and “Real time manufacturing analytics”. Aren't these things done to ensure correlation of manufacturing and business performance?

At this point, I remain a solid Big Data curmudgeon, hoping someone more enlightened will share their insights. The folks at A.T. Kearney have stated “While this massive wave of [Big] data promises to transform both top and bottom lines, few organizations have been able to operationalize and monetize this promise for their enterprise.” I believe efforts to integrate Big Data into manufacturing will prove this true.

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