What is prescriptive analytics?
Prescriptive analytics is considered by many to be the most advanced form of data analytics, positioned top-right on the Gartner data analytics ascendancy model. In theory, it builds on organizations’ use of advanced analytics, like descriptive analytics and predictive analytics. The main difference of prescriptive analytics is its focus on what could happen, by using data to consider various possible options and probabilities, and then advise users how they can cause certain outcomes to occur.
Like other types of advanced analytics for manufacturing, prescriptive analytics relies on big data gathered from industrial IoT (IIoT) devices, plant sensors, and multiple other sources. Descriptive analytics helps manufacturers understand what has happened in the plant, and predictive analytics helps to forecast what could occur in the plant in the near future.
Prescriptive analytics is typically used to identify the best option in a broad field of choices. It uses advanced modeling based on artificial intelligence (AI) and machine learning (ML) or deep learning (DL) to examine the potential consequences of different courses of action, and then recommend the best path to take.
Prescriptive analytics in process manufacturing
Prescriptive analytics is out of reach for process manufacturing plants, since there are vast numbers of variables which fluctuate frequently, most out of the control of the user, making the advanced modeling of possible outcomes that prescriptive analytics embodies nearly moot. To maximize operational efficiency, typically predictive analytics or predictive monitoring are the ideal choice.
With so many complex variables, prescriptive analytics is largely unavailable to the process manufacturing industry at the moment. There are only a few specific areas where process manufacturing can take advantage of prescriptive analytics. And while some process manufacturers have said that they are implementing prescriptive analytics, for the most part, these are simply advanced implementations of predictive analytics.
It is important not to get stuck in the loop of thinking that one type of analytics is better than another, rather than identifying the type of analytics that is best for the job at hand. For example, in order to be aware of what is happening in the plant, descriptive analytics may be the best solution to use. To enhance manufacturing production and predict maintenance issues: predictive analytics. Neither type of analytics is better than the other, simply different tools for different jobs.