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It is frequently the case that sales forecasts are available at the detailed product level for only a relatively short time horizon. For the rest of the forecast horizon, only aggregate sales forecasts at the product family level are available. The problem addressed in this paper is how to fit a forecast simulation model to a history of these aggregate and disaggregate forecasts. Our approach to develop such a model is to combine a forecast update model with a forecast disaggregation model. The forecast update model is called the Martingale model of forecast evolution. The parameters of the two models must be estimated from historical forecast data. It is this statistical parameter estimation problem that occupies the major part of our investigation. We recommend an estimation technique based on the method of moments. Keywords: Forecasting, disaggregation, forecast evolution, Martingale, 1. Introduction Heath and Jackson (1994) use a simulation model for the manner in which sales forecasts evolve over time in order to estimate the need for safety stock in [...]

The semiconductor industry has been one of the driving forces of the 'new' economy. It boasts of the exponentially growing performance of semiconductor devices, coupled with rapidly decreasing chip prices; [...]

We perform four experiments to evaluate several features of capacity planning approaches used in practice and make concrete suggestions for practitioners. We use an optimal capacity planning technique (FIFEX) with industrial demand and capacity data for realistic conclusions. The first experiment studies the cost effects of allowing for capacity expansion only at special times such as at the beginning of each month or each quarter. We find that these effects are not great if there is at least one capacity expansion opportunity about every six months. The second experiment determines the cost implications of capacity planning heuristics. We demonstrate that FIFEX delivers solutions which cost 5-10% less than heuristic solutions. The third experiment examines the sensitivity of costs against the frequency of forecasting and planning. Our experiments indicate that costs can be decreased by 2%-7% by doubling forecasting and planning frequency. We suggest that practitioners forecast and plan at least once every quarter. The final experiment compares joint optimization of tool and floor-shell expansions with sequential optimization. 3%-9% of costs can be saved by using the joint optimization technique FIFEX instead of sequential optimization. We note that although cost savings are relatively small in percentages, they correspond to tens of million dollars. Index Terms--Business planning, manufacturing economics, production management, procurement.

In today's business environment, unpredictable economic and noneconomic forces can affect firms' operational costs and discount factors, as well as demand. In this paper, we incorporate these uncertainties into a single-product, periodic-review, finite-horizon stochastic inventory system by modeling operational costs, discount factors, and demands as stochastic processes that evolve over time. We study three stockout protocols and establish conditions under which (s, S) inventory policies are optimal when discount factors, operational costs, and demands are stochastic and correlated both to one another and over time. Examples are provided to demonstrate nontrivial optimal policies in the absence of these sufficient conditions. [ABSTRACT FROM AUTHOR]

We develop multidimensional balancing algorithms to compute provably near-optimal capacity-expansion policies. Our approach is computationally efficient and guaranteed to produce a policy with total expected cost of no more than twice that of an optimal policy. We overcome the curse of dimensionality by introducing novel cost-separation schemes to separate the lost-sales cost of the system into exact monotonic subparts. This is the first approximation technique for multimachine, multiproduct systems facing stochastic, nonstationary, and correlated demands. To show the generality of this separation technique, we apply it to the capacity-expansion problem under two different production planning models: monotone production and revenue-maximizing production. We make the assumptions of minimal inventory and lost sales. [ABSTRACT FROM AUTHOR]

Proposes a model for multiple resource types used for multiple product families. Mathematical formulation of the strategic capacity planning problem; Discussion on demand modeling; Calculation of the expected value of the instantaneous lost sales cost; Outline of an efficient divide-and-conquer algorithm.