This Dynamic Simulation exercise aimed to meet following Compelling
We developed a network model and used ALX Network Optimization (NO) engine to obtain an optimal network structure. We provided full flexibility to model in term of intersite (Plant to DCs, and between DCs), and direct customer shipping (DC to Wholesalers) connectivity, so that NO engine comes up with optimal network design to minimize transportation and warehousing costs. The results revealed that new recommended design has a USD 390M/month (JPY 43MM/month) saving vs. current network from Intersite shipping.
For building a network optimization model of an FMCG company in Indonesia, we applied anyLogistix supply chain software. The combination of network optimization and dynamic simulation capabilities in one package would allow us to measure every aspect of supply chain performance and would provide more accurate and transparent decision making. A powerful set of in-package experiments would save time when designing and testing new policies.
The final network design and policies, compared to the current design, showed better performance in terms of service level and cost saving. Average inventory level dropped 35% and total costs decreased to 20%. The designed structure was proposed to the executives, received positive feedback, and was advised for implementation. .
In the new supply chain design, the company has 3 DCs to fulfil customers in China South region. Therefore, a dynamic sourcing logic is designed to assign orders to one of 3 DCs. MRP replenishment policy based on unconsumed forecast is modelled to calculate inventory replenishment. Using ALX extension, we built an agent-based simulation model to address following problem statements:
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