Is Big Data applicable to Industrial environments ?
- Do we really need a “big data set” with strong analytics to extract knowledge, or we only need a small set of data ?
- Do we need to create a strong structure (data model) or can we have non structured data (“data lake”) ?
A friend of mine shared his experience in a recent project. They decided to apply Big data to an Industrial manufacturing process.
- They installed a big server with a lot of processing power and a huge amount of memory.
- They install best-in-class analytical engine.
They selected a coating process. The process is similar to this:
In this process, the product is heated by a hot air current, when the setpoint is reached then spray “paints” the product, and when the thickmess is the appropiated, the spay stops and the air is cooling down the product.
The first analytics was intended to establish the robustnes of the process and the influence of the average temperature in the thickness coverage.
The outcome was the average temperature was 50°C, well below the specs.
Of course, the user rejected inmediately the validity of the result.
They reviewed the calculations and brought a data statistician on board, that concluded the data was not following neither a normal nor a chi square distribution.
After all this, they decided to bring a Process Expert on board. He said the data has to be structured in three independent phases
Now, the “Smart Data” basic statistics could be easily applied to each of the phase, with very basics statistical tools and obtaining very quick results.
The conclusions of my friend were:
- The number of data needed for extracting meaningful conclusions in industrial processes is normally very low
- The data has to be structured, because the process itself is heavily structured, and following phisical laws
- And the most important: If you do not know your process….don’t ask a big data engine, ask to your process experts !
What do you think ? What are your experiences on this field ? Please leave a comment
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