Among the most hyped technologies in the industrial landscape today are artificial intelligence, machine learning, and Big Data. At the core of these advancements is analytics, with the ability to glean key information to make better decisions and drive industrial transformation. A recent LNS Research survey of nearly 6,200 plants compared analytical progress among industrial organizations today to efforts two years ago. The results have shown that there has been a significant increase in formal analytics programs across industrial companies, which is very good news; but the study also reveals that manufacturers still have more data work to do.
As a result of the survey, LNS Research, in conjunction with MESA International, has just released its latest biennial report, “Analytics That Matter in 2020: A New World.” The comprehensive study provides an in-depth analysis on the use of analytics by industrial companies and offers specific recommendations for improvement. Moreover, the study looks at the impact the COVID-19 pandemic has had on manufacturers.
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According to the survey conducted this year, among industrial companies, there has been a 52% increase in use of formal analytics programs. Furthermore, there has also been a 102% increase in diagnostic capabilities, and a 66% increase in predictive capabilities. These are significant improvements in the use of analytics, which is critical to developing an industrial transformation strategy and accelerating its success.
While growth in formal analytics and these types of capabilities are promising, one area that continues to lag behind is prescriptive analytics. The LNS Research survey reveals there has only been a 39% increase in prescriptive capabilities in the past two years—well behind the increases in diagnostic and predictive analytics. Prescriptive analytics differs from its diagnostic and predictive counterparts by offering specific recommendations—i.e., what should we do—with predicted outcomes. This capability makes prescriptive analytics particularly important in readying an organization for future action and if/when situations.
For industrial companies seeking continuous improvement and ways to transform its operations, there are big opportunities within prescriptive capabilities. Prescriptive is the only type of analytics that suggests specific actions. However, for it to work, an organization’s analytics must be reliable and trusted. Real-time optimization (RTO) in continuous process control is a common example of prescriptive analytics that is based on well-proven, first principle mathematical models that are understood by the process engineers who use them. RTO existed even before analytics was used in manufacturing, and as such, some may not consider it prescriptive. However, RTO provides the much-needed trust in analytics to make prescriptive effective.
The bottom line for manufacturers seeking industrial transformation and operational improvements is that the answers are at your fingertips—in your own data. The key is to ensure your data is first trustworthy, available broadly, and demonstrates value. Then, employ the various types of analytics—including prescriptive analytics—to create effective action. Utilizing analytics is core to promoting a data-centric and learning-based organization, and is the primary pre-requisite for successful industrial transformation.
