Now retailers not only stand to capture a short-term opportunity by putting the right product in front of the customer at the right time, with the right price that the customer desires but also to delight them with something that they themselves didn't even realize they wanted. That is, retailers can move away from traditional forecasting based on purely historical data; thinking about who the customer will be, not who the customer was.
Once this is in place, businesses can synchronize core functions around a single view of the customer to better enhance responsiveness to future demand states, and begin collaboratively solving problems that the customer truly cares about.
Use case example: Using AI to improve demand forecasting and promotions planning
Retailers have the opportunity to bring together market and customer insight data via online advertisement clicks, visits to social media sites and behaviors.
Typically, promotions are static and tied to historical data rather than market trends. Sales teams are unable to keep up with competition in the fast-changing world due to inaccurate predictive data models, leading to ineffective trade investments and failed customer negotiations. To optimize promotions to negotiate with customers more effectively and react to critical events, sales teams need support to make proactive decisions.
By embedding AI and ML into supply chain business decisions, retailers can:
- Anticipate short-term demand by combining real-time customer insight data with other market data to feed into demand sensing capabilities.
- Locate sourcing options and channels, and feed into the demand and forecasting process requirements for promotions.
- Compare promotional pricing intelligence as part of the demand shaping scenario modeling and optimize based on desired outcomes.
- Integrate this new intelligence with end-to-end planning and production activities to deliver a step change in availability of product for promotions.
- Reallocate and relocate existing stock and production plans more quickly than before.
- Dynamically adjust safety stocks to reflect the new situation, constraints and lead times.
- Evaluate better routing optimization decision opportunities to help guarantee delivery precision.