Problem:
A leading US-based e-commerce gift manufacturer and retailer, with a revenue of ~$3 billion and a complex distribution network of 3 manufacturing facilities, 7 fulfilment centers, faced a significant challenge during peak seasons. The company shipped 10 million parcels annually across the US. Due to perishable nature of products, the products had to be stored at different temperatures ranging from 0 degree to 72 degrees. To manage this, the retailer had to continuously compartmentalize their cold storage facilities to account for changes in demand and supply
Solution:
The retailer partnered with Lambda SCS to improve storage requirement prediction. Lambda SCS developed a simulation tool – SpacePlannerX that could account for granular supply chain complexities to predict storage requirements on a weekly basis accounting for real time changes in demand and supply.
Result:
The pilot highlighted key benefits of improved storage visibility: enabling real-time demand and supply alignment, reducing spoilage risks during peak seasons, and supporting optimized transportation costs through better planning.
Additionally, it revealed the potential to mitigate unplanned storage costs caused by demand-supply mismatches, driving overall efficiency.