Inventory is a retailer’s most expensive and important resource. As such, determining where each SKU should go to maximize profit while balancing customer demands is a challenging task. We’ve written before about how to manage weeks of supply and turn, how to plan for a first store (or pop-up location) and the changing roles in planning teams, but today we’re tackling the question of inventory rationing.
Sometimes referred to as allocation, replenishment, distribution, stock balancing or broadly inventory management, in this article, we’re focused on the use case where a retailer knows they don’t have enough inventory to support the demand across all locations and must make the hard decision about where those precious few last pieces will go.
Overall Store Rationing Rational
The goal of store rationing is to distribute the available inventory to the stores that are most likely to benefit from it, while also minimizing the negative impact on other stores. In general, stores that are performing well and have higher customer demand will receive priority for inventory allocation, while stores with lower performance and demand may receive less inventory or none at all.
Low inventory unfortunately happens to even the best retailers, but they react quickly with data: historic sales, inventory turn, store performance, footprint, customer demand and market trends. Retailers often use sophisticated merchandise planning software (like Toolio) and algorithms to make these decisions.
Four Store Rationing Strategies
Although it’s tempting to only look at sales data to make allocation decisions, here are some additional strategies teams look to employ to make the most of the inventory they do have.
- Sales data analysis: As already mentioned, this one’s a no-brainer. The most common strategy analyzes sales data to determine which stores have the highest demand for a particular product. Retailers can use tools like Toolio to identify which stores have sold the most of a particular product in the past, and allocate inventory accordingly. We specifically have functionality around the concept of Good Weeks and True Demand to assess this.
- Customer demand forecasting: Another approach would use customer demand forecasting techniques to predict which stores are likely to have the highest demand for a particular product in the future. Using historical sales data, market trends, and other factors, retailers can forecast demand (very easily in Toolio) and transfer inventory accordingly.
- Proximity to distribution centers: Another factor to consider is the proximity of each store to distribution centers or warehouses. Especially for seasonal items, time is of the essence. Stores that are closer to distribution centers may be given priority for inventory allocation, as it is easier and more cost-effective to transport inventory to these stores.
- Seasonal trends: Retailers can also use seasonal trends to determine which stores should receive inventory. For example, stores in warmer climates may require more inventory of summer apparel and accessories, while stores in colder climates may require more inventory of winter apparel and accessories. We cover more on these strategies in our new store allocation article.
Toolio is built for retailers to solve scenarios exactly like this one. Here are some ways we help:
- Don’t get into this situation to begin with. With Toolio, we help allocators ensure they have the right inventory to meet demand automatically. Replenishment of a core best seller shouldn’t need a lot of human involvement. Instead, the system (Toolio) should make all the suggestions (including PO creation for additional stock) and an allocator should only need to review, approve and deploy.
- Create minimum presentation quantities. Presentation priority is a concept that ensures all stores have an opportunity to sell. In many instances lower performing stores may be shorted inventory which creates a self fulfilling prophecy for the demand potential. In some instances, ensuring stores have a minimum presentation quantity can improve overall store performance.
- Employ time based allocation parameters. Time based allocations ensure that lead times are accounted for so that each store has a fair pick of the available inventory regardless of the lead time to the destination, again ensuring stores have equal opportunity to sell.
- Retrending. Store/SKU retrend ensures that after initial allocation of stock, stores true demand is the primary driver of allocation rationing maximizing the potential sell through and minimizing markdown.
- All your data in one system of truth. Visibility is key to any allocation solution and projections can ensure users know about issues before they materialize, this allows for allocation strategy decisions to be made that can mitigate a potential future shortage or stock out.
Final Thoughts: Other considerations
Artificial intelligence (AI) and machine learning (ML) are major buzzwords across all factions of retail and allocation is no different. The use cases for AI in allocation are well established and not a pipe dream. The reality is that allocators with thousands of styles and hundreds or thousands of stores with multiple sizes each simply have no chance of manually doing this work. Allocation is a task prime for automation by the best possible calculation of demand and stock placement. However, while most decisions can and should be left up to the machines, it’s important to still invest in experienced people and equip them with proper tooling that is flexible enough to allow allocators to apply their experience and change strategy to better direct the automatic calculation.
Our final tip is about communication. Back office teams should keep their front line store managers and sales associates informed about inventory shortages and rationing decisions to ensure that they can in turn manage customer expectations and provide accurate information to shoppers.
Ultimately, the strategy used to determine which stores receive inventory will depend on a variety of factors, including the nature of the product, the distribution network, and the goals of the retailer. By using a combination of data-driven analysis and strategic decision-making, retailers can ensure that inventory is allocated efficiently and effectively to maximize sales and profitability.