Abstract: Method and apparatus suitable for demand forecasting. The invention can enable sales forecasting “by item, by size, by location”. The invention features combining a demand profile and a demand model into a single encompassing model which is capable of projecting demand for an identified set of merchandise.

Abstract: Sensitivity information about the average back-ordered sales and inventory levels of a back-order (R,s,S) inventory system is generated with respect to the two decision variables s and S. This information is used for what-if analysis and optimization of the decision variables s and S. The computer implemented algorithm provides sensitivity of both average inventory levels and average back orders with respect to changes in s and q (where q=S-s), for a back-order inventory simulation for any specified demand distribution. Further, the sensitivity information provided by the algorithm is exact when compared to a finite-difference simulation for the case of both discrete and continuous, stationary and non-stationary demands.

Abstract: Sensitivity information about average lost sales and inventory levels of a lost-sale (s,S) inventory system with respect to the two decision variables s and S is used for "what-if" analysis and optimization of the decision variables. Computer software implements an algorithm that provides sensitivity of average inventory levels as well as average lost sales with respect to changes in s and q(q=S-s) for a lost-sale inventory simulation for any specified demand distribution. The sensitivity information provided is exact when compared to a finite-difference simulation for the case of both discrete and continuous, stationary and non-stationary demands. A finite difference estimate of the sensitivity is obtained by running two simulations where the parameter in question (s or S) differ by a chosen small number, and then calculating the resulting difference in the performance measure of interest, in this case average inventory and lost sales.

Abstract: A computer implemented method provides decision support for how much of an inventory item to order and when to order where the review period is different from a basic time unit for demand realization and updates. For cases where the review period is greater than the basic time unit for describing the system, the (s,S) rule is replaced with an enhanced policy for deciding when to order and how much to order. This policy becomes necessary as in practice items are often reviewed at different frequencies, even though demand realization and inventory updates may be done every day. Given a periodic review inventory system and given a set of (s,S) values, the computer implemented method produces lower average inventory costs for the same achieved service level. Alternatively, for a given average investment in inventory, the method will result in higher service level than the corresponding (s,S) policy.