Optimization Model for Cross-Functional Decision Making: A Computational Business Intelligence Approach

Latest manufacturing technologies enhance cross-functional interaction between manufacturing and marketing. In spite of increasingly emphasizing on the aspect of end user’s demand, many production decision-making processes do not take into account only the dynamic nature of the marketer. Here, an attempt has been made to bridge the gap between marketing and partially integrated production problem, with the objective of developing mathematical model that can act as an optimizer in an add-on advanced planning system within an enterprise. Basic idea of this research is the integration of work on determining production and raw material batch sizes under different ordering and delivery assumptions for heuristically evaluating the two-stage batch production problem. Production rate is considered to be a decision variable. Integrated unit production cost function is formulated by considering the various pertinent factors. Proposed model is developed simultaneously by formulating constrained maximization problem for marketing division and minimization problem for production division. Considering the complexities for highly non-linear optimization problem, a Computational Intelligence approach is successfully developed and implemented. The model is practical in nature and may be used as an add-on optimizer that co-ordinates distinct function with an aim of maximizing the profit function in any firm.