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This paper describes a case study of selected inventory accounts maintained by a large manufacturer of rocket engines. It was hypothesized that a significant improvement in average inventory levels, number of purchase orders, and number of stockouts could be obtained by augmenting existing methods of inventory management with certain OR based techniques. These techniques included exponential smoothing of demand forecasts and statistical determination of optimum safety stock levels. Computerized simulation was used to determine: (1) the most effective parameter (alpha) values for use in the exponential smoothing formulas, (2) an estimate of the potential dollar savings obtainable if the proposed methods were implemented, and (3) a means by which these methods could be implemented. A simulation model was developed for two particular inventory accounts, its correspondence with the real world was tested, and the alpha values and anticipated dollar values were determined. Additionally, a set of curves were developed for use by inventory management to assist in determining an optimized policy for varying values of purchase order cost, inventory carrying rate, and cost of stockouts. [ABSTRACT FROM AUTHOR]

Network scheduling models typically assume that estimates of individual activities are stable and are not improved or degraded as progress is made in a project and the time to undertake a particular activity comes near. The formulation of scheduling models is usually compatible with this assumption about estimates and excludes the effect of actions such as rescheduling which might be taken in response to changes in estimation error. The present paper examines the nature of subjective estimates in an actual development project. Error characteristics as determined from prior estimates and actual outcomes are analyzed using a multiple regression model. Several variables are significant in their association with changes in estimation accuracy as activities are reestimated. Contrary to previously reported results there is a strong learning phenomenon. Implications for future work are discussed. [ABSTRACT FROM AUTHOR]