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Aggregation systems for sales forecasting

Author

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  • Merigó, José M.
  • Palacios-Marqués, Daniel
  • Ribeiro-Navarrete, Belén

Abstract

Sales forecasting consists of calculating the expected sales of a specific product or company. An important issue when dealing with sales forecasting is the calculation of the average sales, usually using the arithmetic mean or the weighted average. This study introduces new methods for calculating the average sales. These methods are two modern aggregation operators: the ordered weighted average, and the unified aggregation operator. The main advantage of this approach is the possibility to deal with uncertain and complex environments in a more complete way. The study develops some key examples through multi-person and multi-criteria techniques. The study also presents a numerical example regarding the calculation of the average sales of a product in a set of countries.

Suggested Citation

  • Merigó, José M. & Palacios-Marqués, Daniel & Ribeiro-Navarrete, Belén, 2015. "Aggregation systems for sales forecasting," Journal of Business Research, Elsevier, vol. 68(11), pages 2299-2304.
  • Handle: RePEc:eee:jbrese:v:68:y:2015:i:11:p:2299-2304
    DOI: 10.1016/j.jbusres.2015.06.015
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    References listed on IDEAS

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    1. Merigó, José M. & Palacios-Marqués, Daniel & del Mar Benavides-Espinosa, María, 2015. "Aggregation methods to calculate the average price," Journal of Business Research, Elsevier, vol. 68(7), pages 1574-1580.
    2. Dalrymple, Douglas J., 1978. "Using Box-Jenkins techniques in sales forecasting," Journal of Business Research, Elsevier, vol. 6(2), pages 133-145, May.
    3. Guiwu Wei & Rui Lin & Xiaofei Zhao & Hongjun Wang, 2014. "An approach to multiple attribute decision making based on the induced Choquet integral with fuzzy number intuitionistic fuzzy information," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 15(2), pages 277-298, April.
    4. Michel Grabisch & Jean-Luc Marichal & Radko Mesiar & Endre Pap, 2011. "Aggregation functions: Means," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00539028, HAL.
    5. Huarng, Kun-Huang & Yu, Tiffany Hui-Kuang, 2014. "A new quantile regression forecasting model," Journal of Business Research, Elsevier, vol. 67(5), pages 779-784.
    6. P. J. Harrison, 1967. "Exponential Smoothing and Short-Term Sales Forecasting," Management Science, INFORMS, vol. 13(11), pages 821-842, July.
    7. Engle, R. F. & Granger, C. W. J. & Hallman, J. J., 1989. "Merging short-and long-run forecasts : An application of seasonal cointegration to monthly electricity sales forecasting," Journal of Econometrics, Elsevier, vol. 40(1), pages 45-62, January.
    8. Dalrymple, Douglas J., 1987. "Sales forecasting practices: Results from a United States survey," International Journal of Forecasting, Elsevier, vol. 3(3-4), pages 379-391.
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    1. repec:spr:waterr:v:32:y:2018:i:2:d:10.1007_s11269-017-1823-2 is not listed on IDEAS

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