The Efficiency of the Price, Budget, and Mixed Approaches Under Varying a Priori Information Levels for Decentralized Planning

Organizations have a choice of information and planning procedures to use for a coordinated plan. In this paper, we investigate the efficacy of price and/or budget planning approaches, where the first proposal can be determined from a combination of historical plans, external prices, rules of thumb, and other data. The problem is to consider which planning approach and information structure are best. The experimental approach is to simulate the behavior of the planning approaches for a coordinated decentralized organization using decomposed mathematical programming models. The model is adapted from a Danish slaughterhouse. Many organizations use budgets and/or transfer prices to set coordinated plans. But optimization as defined mathematically is not a major concern. Organizations do consider a few alternative plans in an iterative process. We investigate the efficiency of the decomposition models as organizational analogues during the first five iterations to simulate organizational planning cycles. A changing environment for prices and availabilities is generated experimentally by perturbing these environmental parameters. A random number generator is used to yield the statistical sample. The experimental results are compared in terms of relative profitability for the organization. A nonparametric statistical test is used to analyze the experimental data. The results, are, in part, surprising. For a changing environment, historical information, (i.e., a past solution) is generally inferior to a combination of less precise information on current transfer prices, reasonable rules of thumb, and capacity constraints. However, more current information on prices and budgets yields better plans. Further, we found the transfer price approach yields more nearly optimal plans in the first few iterations than a combination of prices and budgets which in turn, is more efficient than budgets only.