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Valeriy V. Davnis
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Decision Sciences, Operations Research & Management Engineering Environmental Science Industrial & Manufacturing Engineering Transportation

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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 Vol 9, No 2 (2020): International Journal of Supply Chain Management (IJSCM)
Publisher : International Journal of Supply Chain Management

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Abstract

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.
Basics of Building and Analyzing Adaptively Targeted Forecast Models for Supply Chain Management Valeriy V. Davnis; Viktoriya I. Tinyakova; Tatyana V. Karyagina; Igor S. Frolov; Nadezhda F. Sivtsova
International Journal of Supply Chain Management Vol 9, No 3 (2020): International Journal of Supply Chain Management (IJSCM)
Publisher : International Journal of Supply Chain Management

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Abstract— Forecasting is an under estimated field of research in supply chain management. To describe the methodology for building adaptively targeted forecast models based on the recursive least squares method and to show the possibility of using these models in economic analysis. Two cases were studied, which include targeting by a single given target value and by a target trajectory described by several consecutive values. It was shown that for the autoregressive model in the case of setting several target values, the multistep procedure of the recursive least squares method is not applicable. It was also possible to clarify the necessity of introducing changes into the adaptive regression analysis scheme for the case when the adaptively targeted model is built on the basis of the autoregressive one. Procedures for building adaptively targeted models for supply chain management of setting target conditions have been proposed. The adaptive regression analysis technique has been modified for the case of an adaptively targeted autoregressive model.
Co-Authors Igor S. Frolov Manya A. Ziroyan Nadezhda F. Sivtsova Sun Xingyuan Tatyana V. Karyagina Viktoriya I. Tinyakova