THE AGE OF ANALYTICS
Five best practices for predictive operations at scale Case study example illustrates challenges and opportunities
By Nikunj Mehta, Ph.D.
Figure 1: Best practices to guide digital transformation. All graphics courtesy: Falkonry
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igitalization of production and process operations has the potential to boost profit margins by three to five percentage points — but only if people can make the new technologies work at scale. According to a McKinsey survey, nearly 30% of executives reported active pilot projects, while 71% expected significant increase in AI investment. However, the survey found that progress remained slow. Most companies don’t have infrastructure for sourcing data and scaling artificial intelligence (AI) initiatives. Experts often address AI transformation at large enterprises. However, the focus has been on vision, strategy, people and culture. While important, these overlook other, multidimensional, factors necessary to succeed at scale. Successful at scale Working with some of the largest oil & gas and other industrial companies, we at Falkonry have observed that “transformation doesn’t happen from the inside out — it grows on you from the outside in.”
Transforming upstream and downstream operations from reactive to predictive processes, where quality, equipment and process-line issues can be prevented before they occur, is the goal for many of these companies. If you are embarking upon a digital transformation initiative — or if you are already in the process of implementing one — it’s time to step back and look at lessons learned from leaders in this space. From these lessons, here are five best practices (See figure 1) to guide transformation. BEST PRACTICE 1: Mass-adoptable technology Without mass-adoptable technology, there would be no transformation. Mass-adoptable AI technology doesn’t imply simple core tech, but technology where complexity is minimized or hidden from users. Using the technology shouldn’t require data scientists, or historical and labeled data. Such features — which make the technology easy to use, repeatable, and able to deliver ROI in a shorter time — are what drive its adoption. BEST PRACTICE 2: Ardent advocates Most organizations with critical operations are risk averse and are more comfortable making incremental improvements. Transformations require executive champions who are ardent advocates and furnish the necessary mandate and resources to reduce risk for engineering and operations teams. Of the reasons cited as causes of failure in enterprise transformation, basic challenges in human and team behavior top the list. Strong advocates ensure transformations that matter succeed despite these challenges.
OIL&GAS ENGINEERING FEBRUARY 2020 • 17
