Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/147600
Authors: 
Sprecher, Arno
Year of Publication: 
1999
Series/Report no.: 
Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 514
Abstract: 
Sales force management decisions belong to the major issues considered in marketing research. Among others, the alignment of sales territories has been one of the main field of work for years. Recent results have reported a strong impact of the alignment of sales territories on profit, and, thus, have directed the focus from the balancing approach to profit maximization. One of the latest models proposed is the so-called sales force deployment problem. Employing a sales response function the sales force deployment problem simultaneously considers several interacting subproblems: (1) Sales force sizing, (2) sales force location, (3) sales territory alignment, and (4) sales effort allocation are the subjects of investigation. We provide a heuristic solution approach that builds on genetic concepts. The approach is evaluated on a set of benchmark instances with sizes of practical relevance. The approach produces solutions of competitive quality at far less CPU-time than required by the state-of-the-art procedure. Moreover, simple modification of the concepts allow to deal with alternated problem settings as well. First, the per-period fixed cost of setting up a sales center can be considered as a discrete function of the amount of selling time made available. Second, the balancing approach can be portrayed. Additionally, the concepts can support man-machine interactions in an online decision support system required to adjust sales territory alignments, e.g., when new products are launched, markets shift or mergers change the portfolio of the company. As such, the operations can be employed by a decision maker in a step-by-step approach to manipulate given territory alignments.
Subjects: 
Sales Force Management
Decision Support
Mathematical Programming
Genetic Algorithm
Experimental Evaluation
Document Type: 
Working Paper

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