British mathematician Clive Humby has been credited with coining the axiom “Data is the new oil.” His pithy expression is frequently repeated – usually without attribution – in the context of people collecting and hoarding all sorts of information. And data prospectors have been coming out of nowhere to drill for it. But Humby’s phrasing didn’t end there; it continued. He went on to say that, “It’s valuable, but if unrefined, it cannot be used. It has to be changed into gas, plastic, chemicals, etc., to create a valuable entity that drives profitable activity. So must data be broken down and analyzed for it to have value.”

Humby was right. And today, with the volume of data exploding, his insight is more important than ever. For example, in manufacturing, the fast-growing industrial internet of things allows us to attach sensors to every component of the industrial process. The result has been that an unfathomable amount of data is being collected every day. However, as in the case of oil, that gusher of data needs to be processed, analyzed, and turned into digestible insights that can be used constructively by the organization’s personnel.

In Context

But who, exactly, can use it and for what purposes? The answer is open-ended; essentially every function in a product-making business can benefit from data-driven insights. But they can only do it when that data is curated, combined, analyzed, and interpreted in a context that’s meaningful to each specific function or task.

For instance, if your goal is to reduce the energy used by a particular piece of machinery, the measurement of its power consumption needs to be understood in the context of data regarding its speed, feed, position, and other process parameters distinctive to that machine. By the same token, other functions such as design, planning, inspection, and maintenance each require their own carefully curated datasets to relate some impact of importance to information concerning relevant variables such as that topic’s features, capabilities, or other characteristics.

There are lots of opportunities for organizations to benefit from data-derived insights. Aspects of industrial operation that have seen tremendous value from data-driven decision-making include increasing yields, better quality, improved safety, reduced waste, easier compliance, fewer recalls, and operational savings. Many of these outcomes result from managers having better information and, as a result, making better operational choices. But, in an era when automation has become a significant part of most manufacturing processes, data is also being used to guide the operation of autonomous equipment using machine learning and artificial intelligence.

And while data collection is important, it’s just the start. A data lake without the means of extracting intelligence from it is more like a data swamp. The real magic is by removing intelligence from it to improve manufacturing performance. And today there are commercial firms, which specialize in doing just that for their clients.

The Ledger

However, most of the data I’ve been referring to here is information that deals with inventory, equipment, and personnel already on site. Those are important. But there’s also the elephant in the room. It has to do with data concerning materials coming in from outside through a company’s supply chain. And what’s missing in most companies is a material ledger – a system with the level of detail required to track incoming material attributes, their provenance, quality variances, energy use, waste, inventories, production bottlenecks, and all the other factors affecting how money is spent transforming materials purchased into products sold.

While a company’s income statement typically subtracts the cost of goods sold from the revenues coming in, those costs are usually aggregated in ways that give little visibility into its materials. And those materials, in many cases, represent the single greatest expense involved in production. The conventional level of detail isn’t enough to manage material uses effectively.

For instance, things you need to know include: are you buying the suitable material? Are you paying a premium for qualities that don’t provide value for you? Are you applying the right amount of energy? Are you using the best production formulas? Are you consuming too much material or too little? What are your product tracing capabilities in the event of a recall? And what is the cost of waste in your process?

With the right data, all of those questions and more can be answered in actionable ways. Yet all of that is part of a bigger picture: gathering data concerning material flow during each phase of the production process is the first step toward becoming a data-transformed, Industry 4.0 organization. Of course, the data you collect will require appropriate analysis and interpretation. But there are resources available to help you refine the crude oil of raw data into practical, high-value business assets. And now is an excellent time to take advantage of them.

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Source: https://www.iotforall.com/contextualizing-data-the-key-to-achieving-industry-4-0