Bodo Wiegand heads the Lean Management Institute, which is the German affiliate of the Lean Enterprise Institute. In his latest newsletter, on Wiegand’s Watch, he explains how he feels manufacturers should respond to the German government’s Industry 4.0 initiative.
“[…]When it comes to the manufacturing industry specifically, IoT is poised to make a tectonic shift in the industry. As manufacturing remains one of the larger economic drivers across the globe, one can anticipate that IoT is set to disrupt this important, interconnected global market.”
Sourced through Industry Week
Michel Baudin‘s comments:
In Manufacturing it is, perhaps, fitting that disruption by a largely wireless technology should be heralded with a picture of a 1990s vintage maze of cables. This article is part of an Industry Week special report about the Industrial Internet of Things (IIoT), with informercials from suppliers like Dell and Intel, and articles about applications in various settings, including the GE case I reviewed earlier this week. I take the authors’ word about what this technology can do. The question in my mind is what Manufacturing will do, given its past unwillingness or inability to take advantage of available technology.
Whether you manage operations with paper and pencil as in 1920 or use the state of the art in information technology (IT), you need clean data. If you don’t have it, you will suffer all sorts of dysfunctions. You will order materials you already have or don’t need, and be surprised by shortages. You will make delivery promises you can’t keep, and ship wrong or defective products. And you will have no idea what works and what doesn’t in your plant.
I have never seen a factory with perfect data, and perhaps none exists. Dirty data is the norm, not the exception, and the reason most factories are able to ship anything at all is that their people find ways to work around the defects in their data, from using expediters to find parts that aren’t where the system thought they were, to engineers who work directly with production to make sure a technical change is implemented. Mei-chen Lo, of Kainan University in Taiwan, proposed a useful classification of the issues with data quality. What I would like to propose here is pointers on addressing them.