Big Data In Manufacturing

The big data era has only just appeared, yet the practice of advanced analytics is existing in years of mathematical research as well as scientific application.

It can be a critical tool for realizing developments in yield, particularly in any manufacturing environment where process complication, process variability as well as capacity restraints are present. Here is how big data is revolutionizing manufacturing.

  1. Developing unexpected insights

Even in manufacturing operations which are considered best in class, the utility of advanced analytics may show out more chances to increase yield. This was the case at one established European maker of functional and specialty chemical for lots of industries, including paper, detergents as well as metalworking. It motivated a strong history of process advances since the 1960s, and its average yield was higher than industry benchmarks as well. In fact, staffers were skeptical that there was so much room for improvement.

Nevertheless, several unexpected insights turned up when the company used neural network techniques, which is a form of advanced analytics based on the way the human brain processes information, in order to measure as well compare the relative impact of different production inputs on yield. Among the factors which it examined were coolant pressures, temperatures, quantity and carbon dioxide flow. The analysis pointed out a number of previously unseen sensitivities. Levels of variability in carbon dioxide flow prompted significant reductions in yield is an example. By resetting its parameters, the chemical company was capable of reducing its waste of raw materials by twenty percent as well as its energy cost by around fifteen percent, by which the company can improve overall yield. It is now adding advanced process controls so as to implement its basic systems and leading production automatically.

  1. Capitalizing on Big Data

Considering how much data the company has at its disposal is the critical first step for producers who want to take advantage of advanced analytics to develop yield. Most companies gather vast troves of process data but only utilize them for tracking purposes, not at a basis for advancing operations. For these players, the challenge is to invest in the systems as well as skill suites which will allow them the ability to optimize their use of existing process information. For example, centralizing or indexing data from a wide variety of sources so that they can be analyzed more easily and employing data analysts who are trained for spotting patterns as well as drawing actionable insights from information.

What is more, some companies, especially those with months or years-long production cycles, have too little data to make sense when put under an analyst’s lens. The challenge for senior leaders at these organizations will be taking a long term attention and investing in systems and practices in order to gather more data. They can invest incrementally. For example, collecting information about one particularly important or complicated process step within the larger chain of activities, and then applying intricate analysis to that part of the process.

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