Finite-Time Horizon Logistics Decision Making Problems: Consideration of a Wider Set of Factors - Petros Boutselis; Ken McNaught (Innovative Methods in Logistics and Supply Chain Management: Current Issues and Emerging Practices. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 18, 2014)
The newsvendor's problem (NVP) formulation is applied to many logistics problems in which the principal decision is the level of inventory which should be ordered to meet stochastic demand during a finite time-horizon. This type of decision makes demand the central variable to be examined and since the time horizon is finite, there is variable risk throughout the period. While the NVP formulation is applicable to many areas (e.g. retail business, service booking, investment in health-insurance, humanitarian aid, defence inventory for operations), modelling and research into the factors affecting demand and its uncertainty has been conducted mainly where the goal is to increase demand (e.g. price, rebate, substitutability). This paper describes ongoing work on modelling demand within the NVP framework where little prior specific demand information exists and uncertainty plays a crucial role. The suggested approach is to model demand and its uncertainty using other causally related, casespecific factors by applying Bayesian inference. Initial work in progress on a case study is outlined. In future the approach will be tested in several case studies and will adopt the innovative approach of Sherbrooke (2004) and Cohen et al (1990) for its validation, through which the model's outputs along with the real life demand data are provided as inputs to a simulation and the results compared. Thus the simulation's final output is the evaluation measure. The future expected benefit from this work is to offer decision makers an intuitive demand modelling tool within an NVP framework where modelling uncertainty is of great importance and past demand data are scarce.
10419/209188; https://econpapers.repec.org/RePEc:zbw:hiclpr:19
Keywords: 650; newsvendor; bayesian; risk; validation