Now more than ever, retailers are focused on how to prepare for and respond to sudden, significant increases in demand and changes in consumer buying behavior. Demand forecasting has been a large part of recent conversations that I’ve seen.
However, the suddenness of the COVID-19 event, and its effects on behavior, mean that demand forecasting alone is not enough. The key is how to react to massive disruptions in the supply chain while and after they are happening and how to put that to use moving forward. To do this, full inventory visibility is a must.
End-to-end inventory visibility is essential
Research from December 2019 revealed that 76% of retailers named real-time inventory visibility as a key focus area for improvement in the supply chain. Further, 36% of retailers named visibility as a current business challenge.1 In the events since Jan 2020, these issues have only come into sharper focus.
Moving forward, the vital component for every retailer’s supply chain is full and accurate visibility into their inventory. Taking into account true inventory availability across the supply chain when responding to forecasted quantities gives you the ability to not only confidently predict changes in demand but the agility to respond rapidly when they hit unexpectedly. Further, it enables you to support complex logistics networks, sourcing and pulling product from multiple locations and/or vendors during a period of disruption.
Incorporating AI helps retailers respond to critical events
Demand forecasting systems that include AI and machine learning drive continuous improvement of demand and forecast accuracy. Algorithms are continuously retrained using historical, current and contextual data without requiring user intervention. AI-enabled systems remove the need for manual data manipulation and process all the disparate data sources in seconds as well as deliver actionable insights to the entire enterprise on any device.
This enables buyers, analysts and demand planners to spend less time chasing data and reports and more time on critical tasks. Some of these tasks include sourcing new products, reviewing urgent orders and analyzing various KPIs such as service levels, out-of-stocks/missed sales and forecast alerts.
Turning today’s pain into tomorrow’s preparation
Managing events like the COVID-19 outbreak means also managing the short and long-term impacts on buyer behavior and demand. By identifying the disruptive event through its duration, the AI engine will be able to segment that period in demand as well as analyze contextual data that drove specific spikes during the event. It will also be able to analyze specific products that were in high and low demand that may be unusual.
This means that the AI will be able to fully understand the event as an anomaly that would not normally exist in the demand cycle. Further, it will be able to incorporate historical data pre event, the data during the event and the incoming data post event. This results in a full understanding of the impacts of the disruption on shopper behavior in the near and short term.
Learn more about COVID-19 and its impacts on the state of retail on our Insights Hub Page.
1 RSR Research, The Retail Supply Chain: Designing New Ways to Satisfy Customer Demand, March 2020