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A conceptual model for demand management in the assemble‐to‐order environment
Author(s) -
Kern Gary M.,
Guerrero Hector H.
Publication year - 1990
Publication title -
journal of operations management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.649
H-Index - 191
eISSN - 1873-1317
pISSN - 0272-6963
DOI - 10.1016/0272-6963(90)90146-5
Subject(s) - demand management , computer science , context (archaeology) , popularity , schedule , capacity management , process (computing) , operations research , order (exchange) , risk analysis (engineering) , build to order , demand forecasting , conceptual model , operations management , process management , production (economics) , business , economics , microeconomics , psychology , paleontology , social psychology , computer network , macroeconomics , finance , engineering , biology , operating system , database
The term demand management encompasses the activities required to accept customer orders and promise them productive capacity. This activity is central to effective manufacturing planning and control. The popularity of just‐in‐time systems suggests that manufacturers are steadily losing authority for establishing order delivery dates, further confounding the demand management process. No reported research has suggested how this process can be performed. This paper presents a rigorous description of the demand management process in the assemble‐to‐order environment, as a mathematical model. The description provided in this model should be viewed as an initial attempt to define the complex challenges that arise in dealing with demand management on an operational level. The focus of this model is short‐term decision making involved in controlling demand management—accepting orders and dispatching capacity to fill the orders. The problem is presented in the context of the assemble‐to‐order environment which requires the monitoring of both a final assembly schedule and a master production schedule. The factors involved in effective decision making for demand management including capacity availability, relative costs, and the relative timing of prospective customer orders are discussed. A simple example is presented to illustrate the performance of the mathematical model.