Master Planning for Supply Chain Network When Considering Bill of Material and Production Environment

In a competitive business environment, it is very important to coordinate the policies and behavior of organizations and facilities in supply chain. Different production environments will lead different types of inventory, and it also causes different storage locations. Therefore, the production environment in supply chain has significant impact on the overall planning outcome.
This study discusses three types of production environment: Make-to-Stock, Assemble-to-Order, Make-to-Order. Considering multiple final products, multi-level BOM, limited capacity and multiple discrete periods, this study focuses on “Master Planning” of “Advance Planning and Scheduling”, which is to determine a production and distribution plan of a supply chain network to fulfill all demands.
The objective of this study is to minimize total delay cost, total inventory cost, and total setup cost. Owing to the great influence of cost structure configuration on production environment’s planning outcome, the cost should be taken into consideration when planning demands to optimize the performance of the overall supply chain. In previous study, “Mixed Integer Programming” is a popular way to solve the supply chain problems. In this study, a MIP model is also proposed. However, when the problem gets more complex, the MIP model becomes insolvable due to the time and computer resources it required. Therefore, this study proposes a heuristic algorithm named “Production Environment Master Planning Algorithm, PEMPA” to solve the problem efficiently.
The algorithm consists of three steps: (1) Do the preliminary procedures. (2) Demand grouping and sorting. Demands will be grouped according to the required products and the capacity of the production tree, and then demands will be sorted by due date or quantity within the group, finally all group will be sorted by due date. (3) Planning demands sequentially until all demands are fulfilled. First, the cost for each link is set and the minimum cost production tree is found. Then, based on the capacity and the cost, PEMPA will choose the best shipping time and the most economical planning methods. To show effectiveness and efficiency of the heuristic algorithm, a prototype is constructed and scenario analysis is illustrated.