Adaptively Targeted Models of Economic Forecast-ing by Supply Chain Management - Viktoriya I. Tinyakova; Valeriy V. Davnis; Manya A. Ziroyan; Sun Xingyuan (International Journal of Supply Chain Management, 2020)
Abstract- Forecasting is an under estimated field of research in supply chain management. Uncertainties inherent in customer demands make it difficult for supply chains to achieve just-in-time inventory replenishment, resulting in loosing sales opportunities or keeping excessive chain-wide inventories. In this paper, we propose two adaptive inventory-control models for a supply chain consisting of one supplier and multiple retailers. Development of an approach that provides the opportunity to construct models for the formation of multidimensional options that describe the forecast image of regions and municipalities. Agreeing with the thesis "the future grows out of the past," the patterns of the past cannot be fully transferred to the future. They should be adjusted in accordance with the existing ideas about the future. To accomplish this opportunity, it is proposed to provide the model of each process with an adaptive mechanism and express the idea of the future with the help of target settings, at which the adaptive change of models is aimed. There has been theoretically substantiated the methodology for constructing adaptively targeted models. Results showed that from cutting costs to keeping consumers happy, forecasting is a vital component of supply chain management, helping companies fill orders on time, avoid unnecessary inventory expenses and plan for price fluctuations.