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A multi-model methodology for forecasting sales and returns of liquefied petroleum gas cylinders

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Abstract

In the liquefied petroleum gas (LPG) cylinder business, one of the most important assets is the LPG cylinder. This work addresses the asset acquisition planning for the LPG cylinder business of a company from the energy sector which has recently started this activity. In order to make the acquisition plan, it was necessary to forecast the sales and the LPG cylinder return rate. For that purpose, an ensemble method using time series techniques, multiple linear regression models and artificial neural networks was employed. Sales forecast was obtained using time series techniques, in particular, moving averages and exponential smoothing. Then, forecast of bottled propane gas sales and return rate was also addressed through multiple linear regression and artificial neural networks. A probability density function was defined for each of the different approaches. Afterward, using Monte Carlo simulation, the forecast values are obtained by a linear combination of the probability density functions, thus producing the final forecast. Results show that the company’s expectation of growth is larger than that predicted by the proposed methodology, which means the company should reflect on its current asset acquisition strategy. By combining different approaches, the proposed multi-model methodology allowed to obtain an accurate forecasting, without requiring a lot of historical data.

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Notes

  1. http://www.apetro.pt.

  2. http://www.ogimet.com.

  3. https://www.ipma.pt.

  4. The air temperature in (\(^\circ \hbox{C}\)) was multiplied by a constant factor for a better comparison with the sales.

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Acknowledgements

This work was partially supported from COST Action TD1409, Mathematics for Industry Network (MI-NET), supported by COST (European Cooperation in Science and Technology). Aldina Correia and Eliana Costa e Silva were partially supported by Portuguese funds through CIICESI - Center for Research and Innovation in Business Sciences and Information Systems, reference UID/GES/04728/2020. Magda Monteiro and Rui Borges Lopes were partially supported by Portuguese funds through the CIDMA - Center for Research and Development in Mathematics and Applications, and the Portuguese Foundation for Science and Technology (FCT - Fundação para a Ciência e a Tecnologia), references UIDB/04106/2020 and UIDP/04106/2020. We would like to thank Ana Sapata from University of Évora, and Cláudio Henriques, Fábio Henriques and Mariana Pinto from University of Aveiro for their contributions during the European Study Group.

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Authors and Affiliations

  1. CIICESI – Center for Research and Innovation in Business Sciences and Information Systems, ESTG – School of Management and Technology, Polytechnic of Porto, Felgueiras, Portugal

    Aldina Correia & Eliana Costa e Silva

  2. LEMA – Mathematical Engineering Lab, CEOS.PP – Centre for Organisational and Social Studies of P.Porto, ISCAP – Accounting and Business School, Polytechnic of Porto, Porto, Portugal

    Cristina Lopes

  3. CIDMA – Center for Research and Development in Mathematics and Applications, ESTGA – Águeda School of Technology and Management, University of Aveiro, Aveiro, Portugal

    Magda Monteiro

  4. CIDMA – Center for Research and Development in Mathematics and Applications, DEGEIT – Department of Economics, Management, Industrial Engineering and Tourism, University of Aveiro, Aveiro, Portugal

    Rui Borges Lopes

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  1. Aldina Correia
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  2. Cristina Lopes
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  3. Eliana Costa e Silva
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  4. Magda Monteiro
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Correspondence to Cristina Lopes.

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Correia, A., Lopes, C., Costa e Silva, E. et al. A multi-model methodology for forecasting sales and returns of liquefied petroleum gas cylinders. Neural Comput & Applic 32, 12643–12669 (2020). https://doi.org/10.1007/s00521-020-04713-0

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