Warehouse optimisation using demand data analytics - a case study-based approach. (1st December 2020) - Raghuram, P.; Singh, Abhijeet
Pickup and delivery schedules in a warehouse depend on factors like order frequency, pick locations, manpower and layout of the warehouse. Warehouse data regarding demand, stock keeping units, layout and daily operations are collected. Data analytics can be used to process the demand data of the warehouse to find out SKU frequency, pick location, and daily demand. In this paper, the layout of an electronic warehouse handling a daily transaction of more than 10 million orders and anticipated expansion is analysed. The layout and pick locations are modified resulting in footprint optimisation and responsiveness. Modification of rack arrangements, redesign of the 'forward area' by changing conveyors in the forward area from a series to a parallel layout have reduced the travel distance by 79% and the workforce is reduced by 73%. The demand data can thus be analysed periodically leading to reduced lead times and reduced costs.