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Manufacturing 3.0 Powered By Big Data & Analytics

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The last transformation in manufacturing was powered largely by quality processes like six sigma

The key goals have always been efficient, error free maximised yield. Today Manufacturing is at the edge of what was to be achieved and is now getting ready for dest another leap that will transform the efficiency, yield and quality aspects of manufacturing once again. Be ready for Manufacturing 3.0

The new age for manufacturing is typically challenging because of diverse and complex inputs and the expectation of customised output , on demand coupled with small sets of ever growing output.

The use of advanced analytics can help an Operations Manager to understand what mix of components can give him the best yield in a  given situation, or one can deep dive and check historical data to understand correlation between 2 or more variables to check errors , quality and volume.

A recent McKinsey report has illustrated the above with an example of a European Chemical Manufacturer which underwent a process overhaul in 60s and assumed that there is not much room for further improvement. However when the company used neural network techniques to analyse coolant pressures, temperatures, quantity, and carbon dioxide flow. The analysis revealed a number of previously unseen sensitivities—for instance, levels of variability in carbon dioxide flow prompted significant reductions in yield. By resetting its parameters accordingly, the chemical company was able to reduce its waste of raw materials by 20 percent and its energy costs by around 15 percent, thereby improving overall yield.

(http://www.mckinsey.com/business-functions/operations/our-insights/how-big-data-can-improve-manufacturing)

Companies that want to leverage advanced analytics need to first consider the amount of data that they have. For example companies that have a long process cycle need to analyse multiple years data, hence they need to invest in commensurate storage, processing and analytical infrastructure which will be different for companies that have minute by minute process data which can be analysed to get insights very frequently.

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