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Soft Computing Applied to the Supply Chain Management: A Method for Daily Sales Classification and Forecasting

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Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 279)

Abstract

The supply chain management is a vast field of study where intelligent techniques can be applied to obtain better results than other approaches. Resource planning, route optimizations, stock and ordering strategies amongst others are common problems in this field of study. In this paper we will focus on how to solve the problems related to making accurate daily sales forecasting in the retail sector. Solutions to this problem must deal with two inherent complexities: the huge amount of data involved in it and the selection of accurate forecasting models. Due to the first complexity, one of the main features of the solution has to be the independence of the system from user interaction.

Keywords

  • Support vector machines
  • ARIMA
  • Time series analysis
  • Forecasting
  • Supply chain management

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Correspondence to Luis Javier García Villalba .

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García, F.T., Villalba, L.J.G., López, V. (2014). Soft Computing Applied to the Supply Chain Management: A Method for Daily Sales Classification and Forecasting. In: Wen, Z., Li, T. (eds) Practical Applications of Intelligent Systems. Advances in Intelligent Systems and Computing, vol 279. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54927-4_37

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  • DOI: https://doi.org/10.1007/978-3-642-54927-4_37

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-54926-7

  • Online ISBN: 978-3-642-54927-4

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