Forecast Demand for Staffing in Retail / DC / Warehouse

Business Problem

For companies that operate distribution centers or warehouses, improving their utilization of resources is critical to maximizing efficiency along the supply chain. However, a source of disruption towards efficiency is uncertainty towards demand, which frequently leads to inefficiencies in staffing resources. As described by McKinsey, staffing needs in distribution centers can have daily variabilities of up to 50%. Since many in the workforce are unionized, companies cannot simply hire workers by the day, and need to let them know of their work schedules in advance by several weeks. The combination of high variability and long lead times makes it difficult for supply chain managers to make accurate decisions on how many workers they should staff. As a result, most end up overstaffing their distribution centers or warehouses which leads to underutilized resources that compile into high operating costs.

Intelligent Solution

With AI, you are able to more accurately forecast the volume of inbound shipments required over the next month to allocate the right levels of staffing. By learning the historical flow of goods through your distribution centers and understanding the most influential external factors, such as holiday seasons and weather patterns, AI will accurately forecast your demand in a way that takes into account the multi-dimensionality of the real world. Unlike traditional forecasting methods that are largely based on linear or static assumptions, AI’s ability to leverage large amounts of complex data reduces the variability of forecasts so you can be certain that the staffing allocations you make that are based on these forecasts will not result in over or under staffing.