Base Stock Policy for Production Environments When Considering Uncertain Factors
Date Issued
2011
Date
2011
Author(s)
Chen, Teng-Wei
Abstract
Recent years, variations and uncertainties of supplies and demands are two main difficulties in doing businesses for many companies. An organization who satisfies customer demand in lower cost and faster speed gains the upper hand. A product can have different designs of BOM, which in turn correspond to different kinds of production environments. In this study, we compare the different BOM designs and production environments, and determine the optimal base-stock policy for each supply chain member who is allowed to stock inventories and compare the profits generated by MTS and ATO under the uncertain conditions such as demand, demand frequency, and lead time.
This study formulates a stochastic model and solves the model using the simulation method to compare the impacts caused by the different production environments and different inventory policies. However, as the problem size increases, the search range grows exponentially. It becomes impractical to conduct a global search due to the considerable time and computer resources. Therefore, this study proposes a heuristic algorithm, called the Leader’s Base-Stock Policy Algorithm (LBSPA) to solve this problem effectively.
The main process of the algorithm in this study can be divided into three phases: Leader Finding, Leader and Followers’ (R, Q) setting, Simulation and searching a proper solution. In Leader Finding, we classify the supply chain members into leaders and followers, and develop ruled based selecting mechanisms to identify the leader. The rest of the supply chain members are defined as followers. In Leader and Followers (R, Q) setting, we define the search range of the leader’s base stock policy and followers’ (R, Q) which are based on leader’s policy. In the last step, we run the simulation process according to the base stock policies which are defined in advance, and search the proper solution. Although we have defined the ranges of R and Q, it takes lots of time to simulate every single combination of R and Q. Instead of conducting a global search method, this study develops three-level interval search to solve the problem more efficiently. To show the effectiveness and efficiency of LBSPA, a prototype is constructed and a scenario analysis is conducted.
Subjects
Supply Chain Management
Heuristic Algorithm
Production Environment
Bill of Material
Base-Stock Policy
Type
thesis
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