Retailers today are facing a wild conundrum. On one hand, the costs of raw materials, retail spaces, labor wages, ocean freight charges, and every other variable with respect to the cost of production are increasing dramatically. However, on the other hand, shoppers are increasingly turning savvier than ever and will simply not accept drastic price hikes, as their shopper loyalty reduces consistently. As retailers look to reduce operating costs to stabilize margins, inventory allocation is one area that retailers cannot afford to neglect!
Ultimately, to survive in retail, decision-makers need to switch to automation and advanced predictive analytics to drive efficiencies across every aspect of their operations. However, as shoppers leverage the convenience of online shopping channels, as well as brick-and-mortar stores, it is important for retailers to analyze localized demand and prevent overstocking and markdowns, especially when stores are full.
What does an effective inventory allocation strategy look like?
In complex retail businesses, with multiple warehouses, SKUs, and several e-commerce channels, it is important that the retail allocation strategy be strong enough to be executed effectively across the entire enterprise. After all, store allocation does have a profound impact on the overall sell-through rate and markdowns/overstocks.
Retail inventory allocation is all about figuring out the right service levels per location and managing inventory accordingly. However, a solid retail allocation strategy must take into account local demand factors by channel, if there are inflationary pressures, and other variables such as geopolitical issues, or constraints that may be out of the retailer’s control. The result is an optimal store allocation that increases sales through increased availability while minimizing the risk of markdowns and understocks!
By allocating inventory effectively across the SKUs, as per localized demand and intelligent clustering methods, modern retailers make sure that the right stock is available at the right time and in the right stores. Essentially, an optimal inventory allocation will improve customer satisfaction and increase margins. However, what if the inventory is not allocated correctly?
If the allocation strategy does not resonate with the overall business strategy, it can impact the sales and performance of the whole retail brand. Many of the resulting problems are very visible such as empty shelves in certain stores while other locations suffer from cluttered aisles and overstocking, resulting in poor margins.
So, how should businesses approach inventory allocation when their stores are already full?
Achieve the optimal allocation with AI & ML-powered retail inventory allocation
For retailers in the current omnichannel environment, acquiring the optimum retail inventory allocation across a multitude of channels has never been more difficult… or costly!
With expectations from shoppers to fulfill their exceptional demands and availability, how can retailers attain a balanced inventory allocation across all SKUs?
In this article, we explore how you can adopt a more strategic approach to inventory allocation even when your stores are full.
- Inventory Allocation in the near term
Several retailers were often compelled to make difficult trade-offs on how to implement and stabilize inventory to best meet shopper demand and protect margins during the peak months of inflation. Pulling levers such as prioritizing markets for inventory allocation and ensuring promotional plans that resonate with the pipeline of available and in-demand inventory can help businesses mitigate unprecedented disruptions and be resilient through the holiday season. This can be done through demand forecasting, i.e. predicting the demand prior to upcoming seasonal events.
2. Automatically account for store/market-level variation
In order to stock up on inelastic or essential inventory, retailers should focus on purchase-order flows accordingly—for instance, by filling up the frontal floor sets with products that are in demand during peak seasons. Taking a total cost of ownership (TCO) view of supply-chain decisions can aid retailers to understand and appropriately address critical inventory requirements. This will need retailers to lean on advanced analytics capabilities. In the long term, investments in advanced analytics can eventually help them to automate the risk-assessment process and ensure profound assessment based on real-time insights into inventory levels and recent supply-chain situations.
3. Operate cross-functionally to change buying and planning behaviors
Retailers should craft longer-term decisions on how to streamline purchases, optimize assortment, and ensure they are reducing disruptions to deliveries. They should also factor in multiple variables such as seasonality, climate, local demands, unprecedented events, national issues, and so on.
Catalyzing timelines and inventory planning can impact freight availability and rates directly, i.e. the faster the retailers submit orders, the more likely the orders are to be accepted. Similarly, decreasing SKU complexity can reduce the possibilities of stock-outs and overstocks by building more flexibility further down the supply chain.
Inventory allocation across an integrated network can ensure retailers acquire items closer to their ultimate SKUs, thereby, decreasing costs and increasing the likelihood of acquiring promised service levels. By resorting to advanced analytics that enable seamless collaboration across the supply chain, merchandising, financial planning, and allocation functions, as well as a holistic view of how decisions in each function affect the entire retail environment.
Retailers should adopt AI and ML-powered retail inventory allocation software with automation (including robotics) to achieve better service and reduce TCO to the network. Integrated analytics platforms can ensure end-to-end visibility by bringing together insights from pre-season, in-season, and post-season merchandise to ensure a TCO to view across the go-to-market process.
Our AI/ML retail inventory allocation software, InventorySmart instantly drives improvements in the replenishment process, warehouse operations, and beyond. This powerful solution, when amalgamated with our robust demand forecasting tool -ADA, helps retailers avoid the randomness and inaccuracy of traditional demand forecasts to improve inventory planning efficiency.