Search

Sales territory alignment is strongly connected to sales force performance. For example, consider two sales territories from the Midwest region of a large company. The Chicago territory leads the region in sales and as a result, the salesperson earns high commissions. The salesperson works hard and seems to be always on the road visiting customers. Yet many prospective customers from the territory have never been contacted and some existing customers complain that they are not getting sufficient service. Sales to existing customers have remained almost flat and sales to new customers are low.

Sales force structure answers two central questions: how to divide up all the sales activities among different types of salespeople, and how to coordinate and control the activities to meet the firm’s goals. Sales force structures are often represented by organization charts with accompanying job descriptions for the various roles.

The success of any company depends on its ability to acquire and retain customers. Sales forces are particularly good at helping a company accomplish this in many ways. For example, salespeople sell complex products and solutions into large key accounts with a high degree of control over the sales process. Salespeople provide two-way communication and social interaction with customers; they listen, assess needs, provide solutions, reduce complexity, handle objections, create value, and provide long-term continuing service. Salespeople are flexible; they can customize the product offering and message to a specific customer’s needs and buying process, they can smooth out rocky relationships, work cooperatively with other selling partners, and engender customer loyalty. Because of their close, personal relationship with customers, salespeople are an important information gathering resource for the firm.

Sales forces play different roles in connecting companies with their customers. Consider the cosmetics industry. Avon has almost 4 million independent sales representatives selling its wide range of cosmetics, skin care, fragrance, and other personal care products directly to consumers in over 140 countries. Revlon, on the other hand, reaches its customers through partnerships with retail outlets around the world. Revlon’s salespeople work with buyers at retail chains on issues such as product mix, logistics, and pricing. Revlon’s consultants and merchandisers spend their time in retail outlets demonstrating products, restocking merchandise, rearranging shelves, and setting up displays.

This paper presents Elimination-By-Cutoffs (EBC), an individual-level model of choice which derives choice probabilities from the location of choice alternatives in a perceptual space. EBC is a multidimensional operationalization of Tversky's (Tversky, A. 1972. Elimination by aspects: A theory of choice. Psych. Rev. 79(4) 281–299.) feature-based set-theoretic Elimination-By-Aspects (EBA) model of choice. It employs monotonic and noninteractive utilities of perceptual attributes in a sequential elimination strategy to determine choice. EBC like EBA is free from the property of independence from irrelevant alternatives. Unlike the independent multinomial logit model (IML), EBC takes into consideration the effect of product similarities on the computation of choice probabilities. An empirical application is also presented in this paper. The EBC model is parameterized from readily available data and is computationally tractable. A comparison between predictions from EBC and IML is drawn using a holdout sample. Approaches for assessing the implications of alternative product positioning strategies are also developed. Finally, extensions to nonmonotonic utility functions, interactive utility functions and other directions for further work with the EBC model are discussed.

This paper deals with the problem of scheduling items (tasks, employees, equipment, etc.) over a finite time horizon so as to minimize total cost expenditures while maintaining a predefined separation between certain items. The problem is cyclic, because the same schedule will be repeated over several consecutive time periods of equal length. Thus, requirements are present to maintain the separation of items not only within the individual time periods considered, but also between items in adjoining periods. A special purpose branch-and-bound algorithm is developed to solve this scheduling problem by taking advantage of its cyclic nature. Computational results are given.

The fundamental concepts required to understand the operation of any computerized food management information system are presented in this paper. The major components of every information system (input- processing - output) are reviewed from the standpoint of foodservice management. Although the material is placed in the context of an actual system developed by the authors, the concept is applicable to every foodservice facility. Algebraic expressions are developed to demonstrate how the computer can be utilized to obtain required information.

The sales territory alignment problem may be viewed as the problem of grouping small geographic sales coverage units into larger geographic clusters called sales territories in a way that the sales territories are acceptable according to managerially relevant alignment criteria. This paper first reviews sales territory alignment models which have appeared in the marketing literature. A framework for sales territory alignment and several properties of a good sales territory alignment are developed in the course of the review. A general sales territory alignment model which accommodates these properties is developed. A solution procedure for the general model is presented. Finally, an actual implementation of the general model is described. The implementation provides a comparison of the general model with a similar model which has been frequently cited in the marketing literature.

This paper presents an extension of Tomlin's penalties for the branch-and-bound linear mixed integer programming algorithm of Beale and Small. Penalties which are uniformly stronger are obtained by jointly conditioning on a basic variable and the non-basic variable yielding the Tomlin penalty.

The fixed charge problem is a mixed integer mathematical programming problem which has proved difficult to solve in the past. In this paper we look at a special case of that problem and show that this case can be solved by formulating it as a set-covering problem. We then use a branch-and-bound integer programming code to solve test fixed charge problems using the setcovering formulation. Even without a special purpose set-covering algorithm, the results from this solution procedure are dramatically better than those obtained using other solution procedures.

In this paper, the analytical representation of food preference is used in a separable non-linear program to yield the serving frequencies of menu items for a finite time horizon. The frequencies obtained in this way insure cost and nutritional control. Subsequently, the scheduling problem dealing with item assignments to meals and days is formulated as an integer program consisting of several transportation problems linked by weekly nutritional constraints. This problem is solved using a branch and bound algorithm which employs Lagrangian relaxation to obtain bounds and to decide on branching strategy.

The multinomial bound is a nonparametric bound for a finite population total when most elements have a value of zero and the remaining elements have positive values, such as occur in accounting and in threshold problems in the physical and biological sciences. Up to now, computational difficulties have restricted use of the multinomial bound to cases where the sample contains eight or less errors. The use of clustered errors described in this paper extends use of the multinomial bound to cases where the sample contains up to 25 errors, with only moderate loss in tightness of the bound.

Deciding which items to include in a sales catalog and how much space should be allocated to each item is a major planning problem facing many large retailers and catalog firms. The steps in the catalog planning process include compiling a list of candidate items, developing one or several ad size “strategies” for each item, estimating the sales and contribution which would be realized as a result of each strategy, and then selecting a set of strategies which effectively allocate the space available in the catalog to some subset of items. This paper describes the planning process in detail. An integer programming model is presented which incorporates merchandising policy constraints in optimally choosing the items and ad strategies to be included in the catalog. The organizational implications of using such a model are also explored. A simple example of the application of the model to planning the tennis equipment lines of a major mass merchandiser appears as an appendix.

Computer assistance for menu planning has evolved to the stage where analytical representations of food preferences can be used in a mathematical optimization procedure. The model presented in this paper can be used to determine serving frequencies of menu items while ensuring budgetary and nutritional control.

This paper addresses the time management problem confronted by sales representatives. The sales representative planning his itinerary must decide the best way to ration time among the accounts comprising his territory. The time management problem is formulated as an integer program whereby each admissible call frequency for each account is represented by a zero-one decision variable. A branch-and-bound integer programming algorithm for this problem is presented. The algorithm is unique in that two integer programming formulations of the problem are used simultaneously in the search procedure and an approximation-cum-relaxation is evaluated at each branch in the search. Computational testing of the algorithm shows that it can solve many realistic time management problems optimally in fractions of a second.

A practical conceptual framework for sales resource allocation modeling is presented in this paper. A literature review of sales resource allocation models is described in terms of this framework. The conceptual framework also lends itself to several integer programming models which may be used to address the variety of sales resource allocation decisions faced by every sales organization. A general model for sales resource allocation is developed which incorporates multiple sales resources, multiple time periods and carryover effects, non-separability, and risk. Several actual model implementations are discussed which illustrate the practical application of the integer programming models. The model implementations utilize recent advances in integer programming theory which enables sales managers and sales representatives to quickly develop and evaluate alternative sales resource allocation strategies.

A specially-structured integer programming model, called the multiple-choice nested knapsack model, is developed in this paper. The model is characterized by variables which are partitioned into multiple-choice sets and resource constraints which are nested across the multiple-choice sets. The application of the model in the areas of merchandise catalog planning and sales resource allocation is described. An algorithm for the model is presented and the results of the computational testing of the algorithm are provided.

The multiple-choice knapsack problem is defined as a binary knapsack problem with the addition of disjoint multiple-choice constraints. The strength of the branch-and-bound algorithm we present for this problem resides with the quick solution of the linear programming relaxation and its efficient, subsequent reoptimization as a result of branching. An implemented version of this algorithm has performed well on a large set of test problems. We cite computational results as well as a comparison with a previously reported algorithm. Several useful applications of the multiple-choice knapsack problem are also suggested.

This paper reports the results of experiments designed to establish the nature of the functional relations between the measure of preference for a menu item and the time history of its previous consumption. Experimental data support the hypothesis that such a relation indeed exists and a mathematical representation of the phenomenon is feasible. Evidence is presented that the preference-time function is conceived by most people as being monotonically increasing and concave. There is a distinction, however, between preference measures defined on relative and absolute time scales, with the latter one accounting for the effect of repetitiveness and providing a link between the interpretation of food preference and frequency ratings of individuals. The experimental data indicate that the preferred frequency of serving is the locus of the maximum of a function that expresses the realization of the time-averaged value of preference over absolute time. Methods are outlined for the routine estimation of these functions from the results of simple questionnaires involving only three or four questions per item. The feasibility of such questionnaires for a large population and involving a large number of menu items is also established.

This paper presents the application of a modified mixed-integer programming algorithm to plan menus in which the portion size of the menu items can vary over a specified positive range. Previous mathematical programming formulations of menu planning problems have either required the variables representing menu items to be bivalent, or have formulated the problem with food groups as decision variables and no integer requirements at all. The former gives rise to a zero-one programming problem and the latter to a “feed-mix” problem. In many instances, a more realistic formulation would require that if a menu item is offered, its portion size must be between a specified upper and lower bound. Although this paper addresses itself chiefly to menu planning, it is readily seen that problems in capital budgeting may be tractable with a similar formulation. It is shown how a branch-and-bound algorithm for mixed-integer programming of the type proposed by Beale and Tomlin can be modified to solve the quasi-integer programming problem resulting from a variable portion size formulation of menu planning problems.

A computational procedure is presented to explore and formulate the quantitative relations between the preference distribution of a population for menu items and a corresponding set of optimum choice-groups on a selective menu schedule. The investigation is limited to the decision rules applicable for the determination of a single choice group, and it is carried out in two stages. First, it is shown that for any preference matrix a unique process of choicegroup augmentation exists with the properties that the augmented choicegroup will provide a maximum increase in population preferences along with the probability estimates of selections. Second, it is shown that if the preference matrix is updated in the function of time and predicted selections, a multistage process of chociegroup generation can determine a sequence of choicegroups with optimal properties of concerning the size or preference contribution involved. A computer program which operates on these principles is attached, and is used for column generating functions of constrained optimization models of selective menu scheduling.