Allocators have an operational challenge like no other in retail. They have to place each product in the ideal store location, at the right time, while meeting multiple business constraints, considering product attributes, and hyper-localized demand — at the same time minimize markdowns, maximize sell-through, and extend profits.
With so many variables to balance, all changing constantly, retailers need to be armed with data-driven decision-making capabilities. Celect Allocation Optimization delivers intelligent recommendations to optimize store allocations to help drive margin, full price sell through and inventory turnover improvements by ensuring that each location has the right inventory aligned to cover the hyper-localized demand.
For a long time, retailers relied on averages to manage their business (for example, all large doors are assigned the same amount of inventory, etc.). Advances in artificial intelligence and machine learning (AI/ML) over the last five years now make it possible to inform inventory allocation decisions by considering how consumers like to shop, product nuances, and a product's relationship to its surrounding assortment.
Use insights into customer preferences and selling trends to allocate the right mix of merchandise. Celect's patented AI/ML approach to handling sparse data and understanding customer preferences enable allocators to address products that are new or have limited history.
Most retail allocation systems enable users pick from a line of styles and then allocate one line-item at a time. With this approach, allocation decisions don’t consider the context of other styles as each decision is made in isolation from one another (rather than looking at the whole pool of inventory and their context to one another). Celect optimizes across multiple styles at once to make much more informed, accurate allocation decisions with a full view of how each style interacts with each other.
Celect Allocation Optimization augments existing allocation systems (e.g., JDA Allocation, MID, Oracle Allocation, SAP, or home-grown systems) by using AI/ML technology to optimize the allocations.