Summary.
Process management, which swept the business world in the 1990s and then fell out of favor, is experiencing a renaissance, thanks to AI. The two reinforce each other: AI helps firms significantly scale up improved processes, and well-managed processes make it easier to obtain the high-quality data needed to train AI. Combining them can generate huge productivity gains—but it requires a lot of change management. In this article the authors outline seven steps companies can follow to bring together people, data, analytics, and technology—in particular, AI—to revamp processes and achieve increasingly higher business performance.
When Mars Wrigley decided to digitize its supply chain, it invested in several AI and analytics capabilities. It built a digital twin of its production line (a virtual replica simulating its operations in real time) and fed data from it into a machine-learning model to predict the line’s output and reduce overfilling and waste. It worked with a “decision intelligence” vendor, Aera Technology, to create visualizations of the data, generate recommendations about preventive maintenance, and automate some operational decisions. It hired Kinaxis, a vendor whose AI software gave the staff suggestions on how to balance supply and demand, automate invoice processing, and increase truck utilization by 15%. As a result of all these improvements, the company was able to fill orders more quickly, and customer service ratings rose by a couple of percentage points.