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Spare Parts Sales Forecasting for Mining Equipment: Methods Analysis and Evaluation

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Intelligent Systems Design and Applications (ISDA 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 418))

Abstract

The work is devoted to finding the optimal solution for predicting the sales of spare parts for a supplier of mining equipment. Various methods and approaches were analyzed such as Croston’s method, zero forecast, naive forecast, moving average forecast to forecasting sales of commodity items with variable demand. The market of commercial offers on this topic was studied, conclusions were drawn regarding which method is best suited to solve the problem, and a forecast model was also built. As a result, software was developed to create correlation matrices that allow you to select relevant positions for further training and building forecasting models. As a result of the experiments, a correlation matrix was built, with the help of which it is possible to determine goods dependent on each other. The models are currently being finalized, since they can make a forecast with high accuracy only for those positions whose sales were at least ten for a specific period.

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Acknowledgments

The research has been supported by the Russian State Research # FFZF-2022-0005.

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Authors and Affiliations

  1. ITMO University, Saint-Petersburg, 197001, Russia

    Egor Nikitin

  2. SPC RAS, Saint-Petersburg, 199178, Russia

    Alexey Kashevnik & Nikolay Shilov

Authors
  1. Egor Nikitin
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  2. Alexey Kashevnik
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  3. Nikolay Shilov
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Corresponding author

Correspondence to Egor Nikitin .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Niketa Gandhi

  3. Institut fĂĽr Wirtschaftsinformatik, Fachhochschule Nordwestschweiz, Olten, Switzerland

    Thomas Hanne

  4. Department of Computer Science and information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  5. Federal University of Bahia, Ondina, Brazil

    Tatiane Nogueira Rios

  6. Nantong University, Nantong Shi, Jiangsu, China

    Weiping Ding

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Nikitin, E., Kashevnik, A., Shilov, N. (2022). Spare Parts Sales Forecasting for Mining Equipment: Methods Analysis and Evaluation. In: Abraham, A., Gandhi, N., Hanne, T., Hong, TP., Nogueira Rios, T., Ding, W. (eds) Intelligent Systems Design and Applications. ISDA 2021. Lecture Notes in Networks and Systems, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-96308-8_38

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