Inventory management has been anything but simple over the last three years. A global pandemic, labor shortages and product recalls, among other factors, have led to the so-called “bullwhip effect,” leaving supply chains with either too much or not enough inventory to meet customer needs.
According to Citi analyst Paul Lejuez, 11 out of 18 retailers saw inventory levels increase by 10% more than sales in Q1 2022 compared to Q1 2019. So, let’s take a look at the timeline of how retailers, manufacturers and distributors have experienced inventory fluctuations over the last three years.
The Inventory Timeline
Q1: COVID-19 began a long string of supply and demand variabilities. Essential home goods such as toilet paper and entertainment items such as puzzles and bikes surged in demand. With labor and raw material shortages and logistics capacity issues, demand was outstripping supply and consumers found themselves staring at a lot of empty store shelves.
This is where the bullwhip effect first came into play. that had previously cut off supply due to low demand now had severe shortages, inhibiting them from meeting suddenly surging demand.
Q2: To correct for the misalignment, when demand inevitably slowed, in a continuation of the bullwhip effect.
Q3/Q4: By the end of 2020, most retailers had enabled omnichannel fulfillment for their customers, enabling a boom in ecommerce and, once again, an increase in demand.
Q1: With an increase in retailers ramped up supply to meet expected demand. However, labor shortages impacting transportation cause shipments to be delayed, causing more inventory misalignment.
Q2-Q4: Retailers started ordering inventory increasingly earlier to ensure demand could be met during peaks. However, this resulted in a significant amount of inventory on hand, often for months before it was ready to be sold.
Q1/Q2: Two years after COVID first hit, as constraints lifted and inventory began to balance out, inflation hit with a vengeance and the pendulum swung once again. With increased supply and decreased demand, retailers are now attempting to offset this imbalance by applying promotions to boost lagging sales and avoid an excess of inventory.
Historical Forecasting Trap
The common trend over the last three years is a consistent reliance on historical data when forecasting and ordering inventory. have considered the level of demand from a recent point in time, ordering inventory under the assumption that it will remain consistent. But if we’ve learned anything from the last three years, it’s that demand is anything but, and there are countless uncontrollable factors impacting how much supply you have and when you receive it.
This was exhibited clearly each time retailers based inventory decisions on a recent increase or decrease in demand, only to be left with too much or too little when the pendulum swung again. As well, uncontrollable market factors inhibited them from receiving inventory in time to meet demand. Ultimately, historical forecasting has proven highly ineffective in creating a robust inventory forecasting plan.
Successful Inventory Forecasting and Positioning
Forecasting is only one piece of a successful inventory management strategy. An equally, if not more important aspect is inventory positioning. Since historical forecasting can’t be relied on for ordering and positioning, what is effective? If a retailer can’t look at previous data to determine future outcomes, what can they do? This is where artificial intelligence and machine learning (AI/ML) and what-if analysis come into play.
Artificial intelligence and machine learning are used to gather and analyze data to predict future patterns. They provide a much larger dataset, including hundreds of attributes which can help manufacturers, distributors and omnichannel retailers make forward-looking, informed decisions on the what, how much and where of inventory purchasing decisions.
Over the last three years, supply chain leaders have learned that contingency plans are a must, because unforeseen circumstances can be catastrophic to supply chain operations. Leveraging what-if analysis, they can examine demand patterns and signals to understand what changes should be made to the plan in the case of an unexpected market or world event.
World-class Sales, Inventory and Operations Planning (SIOP) teams develop detailed contingencies supported by modeling the financial and operational results of various responses. Many planning systems support the ability to try different inventory strategies and approaches to determine what to change and what the effects might be. SIOP teams should review and update contingencies on a biannual basis to keep contingency plans relevant.
Future-focused inventory forecasting and positioning that equips supply chains to meet promised SLAs is not an easy task but is absolutely critical for managing and meeting customer expectations. In uncertain times, successful organizations will make critical business decisions that leverage cutting-edge technology, such as AI/ML and what-if analysis, to create a dynamic, flexible and resilient inventory management strategy.
Nate Rosier is SVP, consulting group leader in the strategy practice at enVista