Demand forecast updating

26-Oct-2015 17:39

This paper presents an optimization model that extends previous approaches focused on optimizing production plans to the JIT setting.

The simple median-based adjustment heuristic performs the best of all the approaches. ABSTRACT: We develop the first approximation algorithm for periodic-review perishable inventory systems with setup costs. The model allows for correlated demand processes that generalize the well-known approaches to model dynamic demand forecast updates.The structure of optimal policies for this fundamental class of problems is not known in the literature.Thus, finding provably near-optimal control policies has been an open challenge.We develop a randomized proportional-balancing policy (RPB) that can be efficiently implemented in an online manner, and we show that it admits a worst-case performance guarantee between 3 and 4.In this paper, we propose a general probabilistic model for modeling the evolution of demand forecasts, referred to as the Martingale Model of Forecast Evolution (MMFE).We combine the MMFE with a linear programming model of production and distribution planning implemented in a rolling horizon fashion.

The resulting simulation methodology is used to analyze safety stock levels for a multi-product/multi-plant production/distribution system with seasonal stochastic demand.

In the context of this application we demonstrate the importance of good forecasting.

ABSTRACT: This paper considers an auto parts supplier who receives order release updates from its customers and revises its production plan for future periods on a weekly basis.

The inaccuracy of the order releases causes significant costs in the form of premium expedited transportation, production overtime, and excess inventory.

This setting provides a rich context for studying order release variance, because the supply chain has adopted a just-in-time (JIT) approach where ideal inventory levels are kept at zero.

This leads to a high reliance on order release accuracy in order to manage production quantities.