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Applying Predictive Analytics Algorithms to Support Sales Volume Forecasting

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Information Systems (EMCIS 2022)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 464))

Abstract

Caompanies struggle with predictive analytics (PA), which aims to be a “modern” crystal ball. But how does one choose the “right” algorithms? Based on the findings from a sales volume forecasting case study, this article presents six design guidelines on how to apply PA algorithms properly: (1) When fixing the objective of your forecast, start with reflecting the available data. (2) Considering the available data and forecast horizon, develop a strategy for the training phase, ultimately the model’s deployment. (3) Choose algorithms first that act as an orientation as well as a benchmark for more elaborated models. (4) Continue with time series algorithms such as (S)ARIMA and Holt-Winters. Take automated parameter setting into consideration. (5) Integrate additional input by applying ML-based algorithms such as LASSO Regression. (6) Besides accuracy, process efficiency and transparency determine the most suitable approaches.

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Author information

Authors and Affiliations

  1. Darmstadt University of Technology, Hochschulstrasse 1, 64289, Darmstadt, Germany

    Jörg H. Mayer, Milena Meinecke & Reiner Quick

  2. UE University of Europe for Applied Sciences, Reiterweg 26B, 58636, Iserlohn, Germany

    Frank Kusterer

  3. WILO SE, Wilopark 1, 44263, Dortmund, Germany

    Patrick Kessler

Authors
  1. Jörg H. Mayer
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  2. Milena Meinecke
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  3. Reiner Quick
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  4. Frank Kusterer
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  5. Patrick Kessler
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Corresponding author

Correspondence to Jörg H. Mayer .

Editor information

Editors and Affiliations

  1. British University in Dubai, Dubai, United Arab Emirates

    Maria Papadaki

  2. University of Coimbra, Coimbra, Portugal

    Paulo Rupino da Cunha

  3. University of Nicosia, Nicosia, Cyprus

    Marinos Themistocleous

  4. University of Nicosia, Nicosia, Cyprus

    Klitos Christodoulou

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Mayer, J.H., Meinecke, M., Quick, R., Kusterer, F., Kessler, P. (2023). Applying Predictive Analytics Algorithms to Support Sales Volume Forecasting. In: Papadaki, M., Rupino da Cunha, P., Themistocleous, M., Christodoulou, K. (eds) Information Systems. EMCIS 2022. Lecture Notes in Business Information Processing, vol 464. Springer, Cham. https://doi.org/10.1007/978-3-031-30694-5_6

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  • DOI: https://doi.org/10.1007/978-3-031-30694-5_6

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-30694-5

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