Supply chain planning with scenario based probabilistic demand

Abstract

Raw materials ordering policy and the manufacturing lot size for fixed interval deliveries of manufactured products to multiple customers, considered to as a key factor in managing the supply chain logistic more economically whenever probabilistic demand exists. In this paper we developed a weighted probabilistic total cost function in a manufacturing center which supplies manufactured products to multiple customers, with a fixed-quantity at a fixed time-interval to each of the customers while total demand of the customers have a probabilistic behavior and maybe changes during planning horizon. An optimal multi-ordering policy for procurement process of raw materials in a single manufacturing system used to minimize the total acquired cost due to raw materials and manufactured products inventories regarding to different predicted demand alternatives in the production time horizon. The carried over inventory of manufactured products that remained from the previous cycle has been used as initial manufactured products inventory and as a result production schedule shifted ahead for the next cycle. A closed-form solution for the minimal total cost for the entire inventory-production system formulated. The algorithm considered as the solution finding procedure for multiple customer systems with probabilistic future demand rate.