The cognitive supply chain
In recent decades, businesses have initiated supply chain improvements by focusing on transactional excellence. Companies have streamlined internal processes on a static cycle often tied to daily metrics. After shaving internal inefficiencies as finely as possible, they worked to reduce friction in the handoff to the next partner, and so on down the line. While these transactional optimisations deliver tangible results, they can only be repeated so many times until the effort required dwarfs the return. But now the prevalence of AI is creating opportunities to look beyond these improvements for competitive advantages.
Looking at the broader picture for potential strategic advantages is also key to Formula E racing. Using its AI-based Lap Estimate Optimizer (LEO), a scenario engine that analyses weather, track conditions, and driver positions, Genpact helps Envision Racing better predict the number of laps in a race. This not only helps the team decide how hard to push to protect a position or when to overtake a competitor, it allows it to improve its strategies race to race. The focus shifts from analysing the previous race for minute improvements to predicting approaches and outcomes across future races.
The evolution of these digital technologies is also enhancing prediction in supply chains. With an influx of data, cognitive supply chains are emerging, shifting the focus away from narrow transactions towards a bigger growth agenda. By capturing, storing, processing, and sharing relevant data across partners, demand is clearer, planning more accurate, inventory leaner, and disruptions more easily avoided. The same types of advanced algorithms used by Envision Racing can generate far more accurate forecasting models in supply chains, including balancing inventory levels against demand volatility and customer service agreements. Anticipation, rather than reaction, becomes the norm.