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This paper constructs a model to explain the determinants of the extent of product cost bias which results when a firm employs a conventional two-stage overhead cost allocation system. The extent of product cost bias has previously been hypothesized to be a function of (1) production technology heterogeneity, (2) unit input costs, and (3) product mix. Our model provides an explicit framework within which to analyze such factors and their interactions. In particular, we derive an expression for the firm's economic loss from product cost distortion as a function of each product's squared bias and the extent of product market competition. In turn, the squared bias is shown to be a function of the heterogeneity of the production technology, unit input costs, and the product mix. In addition, the paper uses the model described above to develop and evaluate algorithms which managers can use as practical guides in choosing the best allocation base at each overhead cost pool. Such algorithms are potentially valuable to firms in two ways. First, in designing the firm's cost system, particularly the overhead cost pool structure, the algorithms can help managers to quantify the magnitude of cost distortion induced by alternative allocation bases, thus allowing improved tradeoffs to be made. Second, once an overhead cost pool design has been chosen, the proposed algorithms can also be used in selecting the best allocation base at each cost pool. [ABSTRACT FROM AUTHOR]

Despite the clear prescription from economic theory that a firm should set price based only on variable costs, firms routinely factor fixed costs into pricing decisions. We show that full-cost pricing (FCP) can achieve the optimal price. FCP marks up variable cost with the contribution margin per unit, which, in equilibrium, includes the fixed cost. FCP converges to the optimal price when the firm can estimate its equilibrium income. We compare FCP to alternative pricing algorithms that require less information, but converge to optimal price under more narrow conditions than FCP. [ABSTRACT FROM AUTHOR]

Influence diagrams are presented as a complement to analytical methods for solving capacity-planning and pricing problems under uncertainty. An influence diagram model that permits continuous decision variables is used to determine optimal decision rules for a single product monopolist making capacity and price selections once per period. Solutions under different scenarios based on the timing of actual demand observations and the ability to adjust initial capacity are compared to previous solutions in the literature. The results demonstrate that optimal decision rules for capacity and price determined using influence diagrams closely approximate those found using analytical approaches. The influence diagram is used to determine the effect of changing demand function parameters on the optimal solutions. Additionally, the influence diagram model can be adapted to solve capacity-planning problems that do not meet the assumptions required to obtain analytical solutions or heuristics in previous studies. [ABSTRACT FROM AUTHOR]