Optimal Order Allocation Under Risks Of Supplier Delay, Production Disruption And Stochastic Demand: A Case Study Of Arevo Inc.

Abstract

This study investigates the optimal order allocation strategy for Arevo Inc., considering the risks associated with supplier delay, production disruption, and stochastic demand. The research methodology employed Particle Swarm Optimization (PSO) and Exact method to optimize the order allocation decisions. The study design involved analyzing historical data, simulating different scenarios, and evaluating the performance of the proposed strategies. The procedures included collecting data on historical supplier delays, production disruptions, and demand patterns. A mathematical model was developed to integrate these factors and optimize the order allocation decisions. The PSO and Exact method were implemented to find the globally optimal solution. The results of the analysis demonstrated the effectiveness of the proposed approach. By considering the risks of supplier delay, production disruption, and stochastic demand, the optimized order allocation strategy improved Arevo Inc.'s overall performance. The simulations showed that the proposed strategy minimized total ordering costs, reduced the supply risk, and also improved customer satisfaction. In conclusion, the findings of this study have several potential implications. Firstly, the optimal order allocation strategy can help Arevo Inc. mitigate the risks associated with supplier delay, production disruption, and stochastic demand. Secondly, the proposed approach can be generalized to other companies operating in similar industries facing similar risks. Based on the results, some recommendations can be made for Arevo Inc. and other companies. Firstly, it is essential to establish strong supplier relationships and monitor supplier performance regularly to minimize the risks of supplier delay. Secondly, implementing efficient production planning and scheduling systems can help reduce the chances of production disruptions. Lastly, adopting robust demand forecasting methods and using safety stock buffers can help manage the risks associated with stochastic demand.