In this context, a retail company faced the need to improve the overall flow of products in one of its fresh food warehouses, which operates in a 24/7 schedule, whilst leveraging its global efficiency and maintaining the freshness of its products. For this purpose and given two alternative layouts, our main goals were:
Aiming to tackle the retailer’s problem, a digital twin was created to accurately simulate the current operation. Subsequently, modifications were made in the model to evaluate the two proposed alternative layouts. Each layout was tested for distinct levels of affluence and for various scenarios, in order to better understand the warehouse’s congestion levels and how each layout impacts the company’s key performance indicators.
All the main indicators obtained from the simulation models were gathered and delivered to the retailer, therefore leveraging a more accurate, data-driven decision regarding the distinct layouts.
Through the digital twin, the retailer obtained a bird’s-eye view and a detailed-view of the warehouse operation. Hence, the project not only conveyed a new perspective on the current functioning of the warehouse, but it also allowed a thorough analysis of the main performance indicators for the three layouts.
The project demonstrated that digital twins may help developing risk-free approaches for testing in an agile way, different layouts and scenarios.
Hence, instead of time-consuming and costly real-life experiments, a model was created to aid decision making, while allowing a better perception of the future evolution of the company.