Using a simulation-powered digital twin to streamline daily logistical operations and minimize disruption to the retail supply chain network

What did CEVA Logistics do to cope with the surge in demand?

CEVA worked with Simul8 to create a simulation-powered digital twin linked to real-time data from the fulfillment center’s systems. It began by working with the fulfillment center’s planners to understand their daily operations and Key Performance Indicators (KPIs), such as order processing time, throughput, resource utilization, and customer satisfaction. This advanced planning helped minimize the risk of high-volume volatility within the system and set clear parameters for the simulation.

When building the digital twin, there were three phases of the fulfillment center’s operations to consider including:

Inbound phase

Using metrics such as supplier direct receive and supplier direct putaway helped evaluate the efficiency of receiving goods directly from suppliers and accurately storing them in the facility. Similarly, intra-warehouse goods received and putaway KPIs assessed the effectiveness of internal goods transfers within the center. The returns received and putaway KPIs analyzed the handling of returned goods, ensuring streamlined processes and reduced processing time.

Storage space

Efficient storage management was critical to the day-to-day operations of the fulfillment center. Using a metric such as the rotation index helped logistics managers evaluate the efficiency of inventory rotation within the storage space. The simulation-driven digital twin captured valuable data on item turnover rates and storage utilization. Analyzing the rotation index allowed for the long-term optimization of storage configurations, picking strategies, and stock placement to maximize efficiency and minimize errors.

Outbound phase

The first KPI in the outbound stage was picking. By evaluating the accuracy and efficiency of the picking process, fulfillment center managers were able to optimize strategies, warehouse layout, and resource allocation to improve order fulfillment speed. Other KPIs in the outbound phase included: sorting, packing, vector sorting – sorting items based on shipping vectors or destination – and truck loading. Analyzing these processes allowed logistics managers to optimize sorting, packing, and loading procedures; improving outbound logistics and minimizing delays.

A regular digital twin will provide a static representation of a system's current state. However, by adding simulation technology, the digital twin was able to understand the dynamic behavior of the system, its process rules, and how each element interacts with others. This technology enabled CEVA to conduct continuous ‘what if’ analysis. It facilitated advanced optimization by running thousands of permutations to evaluate and then optimize the different strategies available. In this way, the simulation-powered digital twin could inform process owners on the best corrective action to take, whether responding to different inputs, potential challenges, or changes to the operating environment. Furthermore, the robust simulation engine enables the digital twin to adapt in real-time, meaning quick adjustments can be made based on emerging data to forecast future process performance.

Implementing simulation-powered digital twin technology offered granular insights into the fulfillment center’s dynamic operations, which minimized risk, reduced downtime, and accelerated time to market.