A New Year always brings new priorities and possibilities for retailers who understand they operate in an industry where competing successfully requires continuous improvement. This reality puts pressure on senior leaders to make strategic decisions about allocating resources across countless opportunities.
Retail is a complex industry, and that complexity means there are always processes to be optimized and efficiencies to be gained. While priorities vary among retailers, no one would dispute that supply chain is at, or near the top, of every retailers’ list. There are many reasons why, but the pandemic was a major factor because it revealed the deficiencies of supply chains and highlighted an urgent need to increase resilience.
The Right Way to Enable Resilience
One area where retailers can immediately bolster supply chain resilience is with demand forecasting and replenishment. Improving performance in this area lets retailers move beyond responding and reacting to supply chain challenges, as was the case throughout the pandemic, to anticipating possibilities and modeling optimal outcomes to be ready when future disruptive events inevitably occur.
This aspiration explains the surge in interest among retailers to improve demand forecasting and replenishment capabilities by leveraging technologies such as AI and ML. For example, when Retail Systems Research (RSR) asked retailers recently about the top three opportunities associated with an AI-enabled supply chain, the most frequent response was, “use predictive models to anticipate supply chain disruptions at the individual SKU level and recommend cost-optimized corrective actions.” A close second among the opportunities retailers identified was, “real-time and accurate visibility throughout the supply chain to better control timing and cost.”
RSR research also showed that more than half of retailers surveyed believe that insights derived from AI-enabled analytics will have a profound effect in the next three years on demand forecasting and merchandise planning and supply chain planning and management.
Make Resilience a Reality
Retailers deserve a lot of credit for the supply chain agility displayed during the pandemic. Sure, there were issues with in-stock levels and product availability, but retailers consistently moved with speed and flexibility to address new challenges as pandemic circumstances changed. This ability to react, while impressive, is different from true supply chain resilience.
Resilience is about anticipating, developing plans and using modeling technique that incorporate a wide range of variables, so when disruption occurs retailers never have to say, “I wish I knew then what I know now.” To make resilience a reality in 2023 and beyond requires a focus on several key areas, especially demand forecasting and replenishment.
This was the topic of discussion on a recent webinar with the leading U.S. media brand Chain Store Age titled, “Supply Chain Success Strategies for an Uncertain Future.” The event featured executives from RSR and SymphonyAI Retail CPG who shared insights on how to get resilience right, highlights of which included:
ONE: Improve the accuracy of demand forecasting and replenishment decisions by embracing innovation. How? By moving to advanced AI-based solutions and away from legacy statistical based forecasting solutions. What’s the difference? AI-based solutions leverage machine learning to produce a probabilistic forecast of potential outcomes versus the standard statistical approach, also known as deterministic, because a single forecast is produced.
TWO: Improve data quality to drive demand forecast and replenishment accuracy. Machine learning is a very powerful tool when applied to the vast amounts of data that retailers have available to them, but the data fed to models must be of high quality. Retailers traditionally have captured a lot of data but curation of the data, meaning the unification and cleansing, is crucial. And then retailers have to ensure it is accessible to AI-powered forecasting and replenishment solutions.
THREE: Reap the benefits of machine learning for process improvement. Traditionally, forecasting and demand planning involves statistical forecasters reviewing models, tweaking forecasts and adjusting based on areas of expertise. Those activities change when a ML driven forecasting solution is implemented because automation increases and the focus shifts to improving the quantity and quality of data.
FOUR: Take advantage of digital twins to achieve disruption avoidance. Supply chain is the perfect area to benefit from the simulation and modeling opportunities enabled by digital twins. Retailers can detect and plan for supply chain challenges and predict and optimize performance in a digital world to validate potential changes before undertaking costly and time-consuming physical changes. The concept of digital twins is especially relevant in demand forecasting and replenishment where AI-powered solutions are needed to ingest large data volumes to perform complex simulations.
A major effort is underway to increase supply chain resiliency in 2023 and a lot of that effort will focus on improvements to demand forecasting and replenishment. Some of this emphasis was in the works prior to the pandemic, but events of the past few years have created a new sense of urgency to increase the resilience of supply chains.
A replay and transcript of the webinar, “Supply Chain Success Strategies for an Uncertain Future,” is now available by clicking here.
Want to see first hand how resilience is achieved with AI-powered demand forecasting and replenishment solutions? Request a demo today.
