Deterministic demand assessment

This is the most precise method of dispatching assuming that primary demand which is triggered via bills of materials as secondary demand consists of actual customer orders. 

If the primary demand was obtained on the basis of forecasts, the demand-driven determination of requirements presents a misleading accuracy. 

In this case the consumption-driven planning and scheduling of purchased parts and components is to be preferred over the apparent accuracy. The forecast is dangerous at the production level because of the high value of the products and inaccurate due to the limited consumption frequency. The forecast suitability constantly improves at the lower production stages because 

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As a matter of consistency, the same calculation of priority would have to be made in ERP systems for the deterministic demand and order calculations for stocks that are not readily available or procurable stocks. Here, too, priorities must be assessed as to which urgent orders are to receive the limited inventory. Surprisingly enough, this does not happen. This is a situation that no one could thoroughly comprehend. 

In a deterministic planning environment the most likely scenario, here scenario 2, would be considered the base case and the optimization model would be solved based on this scenario. The optimal decision would be to open facility 1 in period 1 and facility 3 in period 2 leading to a total profit of 2,590. To assess the robustness of this network to alternative demand scenarios the profit achievable with this configuration in case of the alternative demand scenarios can be assessed. In the example, for scenario 1 the overall profit would be 1,640 and for scenario 3, 2,765 respectively. Considered individually, the optimal decision for scenario 1 would be to open only facility 1 with a total profit of 1,880 and for scenario 3 to open both facilities 1 and 2 in period 1 with a total profit of 2,931. In order to explicitly incorporate the uncertainties caused by the different realization probabilities of the three demand scenarios, the optimization model can be extended into a two-stage decision with recourse ... 

To assess the importance of the stochastic formulation, the deterministic model using the nominal demand values has been first solved and compared with its stochastic counterpart. The results obtained are detailed in Table 1. The schedules are not shown for space reasons, but they are significantly different. In addition, it is noted that the makespan of the deterministic model is shorter, because the model does not generate inventory to hedge from adverse scenarios, as the stochastic one does. 

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