The “as-is” simulation showed the performance of the current service and repair operations for new, repaired, and failed products. The supply chain optimization model covered over 150,000 Repair Material Authorizations (RMAs), 20 third party logistics providers (3PLs), 15 OEM locations, three logistics centers, 100 depots or remote stocking locations (RSLs), and four call centers. The supply chain optimization model also measured the impact of the design on order lead time, inventory levels, work-in-process (WIP), repair costs, scrap value, and call center utilization. To provide a realistic picture of the true cost of transporting products between countries, the model also included inventory, costs, and taxes related to transportation, with transit times broken down by 3PL provider. After verifying the as-is model by using actual data from the previous 12 months, five “to-be” supply chain network design alternatives were created using Arena’s supply chain optimization software. The to-be alternatives reflected changes to the company’s logistics structure, inventory management strategy, call center management, inventory replenishment strategy, and repair strategy.
The supply chain optimization model showed that only two of the five alternative designs met all of the company’s goals, but only one of the two had the lowest cost with the highest customer service. This final design projected a 39% reduction in repaired and failed product inventory value and carrying cost. The model illustrated how repair costs could be reduced if the manufacturer repaired failed parts only when needed. Additionally, it showed how engineering change management (ECO) costs could be lowered and where excess and obsolete inventory could be eradicated. The design also projected a 16% reduction in transportation costs by using direct ship as a transport option and instituting advanced ship notices (ASNs). The supply chain optimization model illustrated how to save costs by consolidating inbound and outbound shipments, consolidating 3PLs, and sharing resources across Europe. The model determined that a forward and reverse logistics supply chain would maximize service and minimize costs for handling new, repaired, and failed products. Arena helped discover the ideal logistics strategy and network design for sharing resources and channels in the manufacturer’s supply chain. The savings anticipated as a direct result of using Arena’s supply chain simulation software was well over $50 million.