Picking and palletizing goods is an expensive part of warehousing. One solution is to automate this process. However, making a sizable investment into automation without knowing whether it will be able to fulfill demand or the estimated payback period of that system poses a substantial risk. In order to address this problem for a grocery distribution center, the team designed a discrete event simulation based on historical order data. This was then used to create a flexible “black box” tool that simulates potential future states of the warehouse. The tool calculates the payback period of a proposed automation solution under different demand and operational scenarios for the candidate SKU portfolio across the ambient warehouse. The solution also specifies which of these SKUs should be prioritized for automation. With this information, the company can better understand the operational benefits of their proposed solutions and projected time to profitability.
