The need for a good forecast estimate is imperative for managing flows in a supply chain. For this, it is necessary to make forecasts and integrate them into the flow control models, in particular in contexts where demand is very variable. However, forecasts are never reliable, hence the need to give a measure of the quality of these forecasts, by giving a measure of the forecast uncertainty linked to the estimate made. Different forecasting models have been developed in the past, particularly in the statistical area. Before going to our application on real industrial cases which highlights a prospective study of demand forecasting and a comparative study of sales price forecasts, we begin, in the first section of this chapter, by presenting the forecasting models, as well as their validation and monitoring.
Companies are nowadays challenged to offer high service levels while minimising inventory costs in an ever-increasing competitive market. One of the keys is to manage and improve the product flow in the distribution network continuously. In this paper, Demand Driven Distribution Resource Planning (DDDRP) is a proposed model for product flow management in distribution networks. It allows to optimise the flow by managing customer demand fluctuations. A literature review about flow management policies is presented, and then a case study is provided to make a comparison of the DDDRP concept with conventional management methods such as Distribution Resource Planning (DRP). To achieve this comparison, a discrete event simulation (DES) is adopted to measure the effectiveness of each model regarding the demand fluctuations, using key performance indicators. The simulation gives empirical results and illustrates the interests and benefits of the DDDRP approach in terms of inventory costs and service levels. The originality of this document concerns the assessment of Demand-Driven Distribution as a new approach of management and opens up new opportunities for optimising inventory and product flow in distribution networks.
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