The customer is a US Central Distribution Center for lenses, serving a leading manufacturer of luxury eyewear. The center manages an extensive inventory of 100,000 SKUs and employs a dedicated workforce of 80 people.
Business Challenge
Optimizing Picking and Sorting Strategies for Rapid Automation Payback.
In addressing our client’s challenge, we focussed on defining the most effective picking policy and sorting parameters to achieve swift payback for automation. Our approach involved Dynamic Warehouse Simulation, where we rapidly simulated warehouse operations, considering forecasted volume. The key factors included:
- Area Segmentation: Analyzing different warehouse zones.
- Picking Modes: Evaluating various picking methods.
- Sorting Technology: Assessing sorting and packing options.
- Parameter Mix: Identifying optimal combinations.
Approach
The project included the following steps:
- Analyzing Order Cut-Off Time Consistency vs. Service Level: We examined a substantial data sample to assess how well order cut-off times align with service level requirements. This analysis focused on daily peak operations.
- Efficient Simulation Modeling for Pick and Pack Operations: In a remarkably short timeframe, we created a simulation model that accurately represents pick and pack operations. Our model considers both manual and technology-assisted operations.
- Strategic Stakeholder Discussions for Optimal Picking Policy: Engaging with key stakeholders, we explored various alternatives for the picking policy. Our goal: gain consensus on the best approach, ultimately minimizing payback time for automation.
Results
- Up to $ 1.2 M savings foreseen in 2 years (~$0.8 m in the 1st year)
- Reduction in pay back time
- Developed rules for order bundling to run the sorting technology starting from the picking approach
- Defined a simulation model to represent the picking sizing according to different pathways and different rule for bundling orders in the mission