A major grocery chain optimizes order preparation and scheduling for curbside pickup
A grocery chain struggled with high labor costs to maintain a good customer experience with its digital grocery ordering service. They faced issues handling resources and scheduling and preparing orders on time. Inaccurate in-store estimates on how long it would take to pull items and prepare an order were part of the problem. The inaccurate estimates made the process complex, impacting the store employees’ ability to find the right items on shelves. Also, they faced challenges creating optimal routes to pick up items and estimating the time needed to fill the carts. These inefficiencies in the process caused cascading lateness impacting the overall delivery performance.
Neal built a simulation to replicate the order picking process and tested various improvements to the order sequence. We developed a model that predicts the time required to prepare a customer order taking into consideration the items in the order, the location of items in the store, the other orders in the queue, etc. This model could also recommend item pick sequences and create an optimal route through the store for pickers.
Now, the customer has a more accurate model to estimate the time needed to pull items for pickup. The model helped improve batching and sequencing logic to better sequence the customer orders. Ultimately, this resulted in an improved on-time delivery performance at the store, leading to decreased labor costs and improved customer satisfaction.