Skip to main content
SpringerLink
Log in

Demand Forecasting for Freight Transport Applying Machine Learning into the Logistic Distribution

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

The use of information technologies such as Transportation Management System (TMS) is crucial to improve the transportation process in a company, offering potential results that include optimization of shipments, an increase in cost savings and customer satisfaction. The TMS, along with data analysis techniques such as Machine Learning (ML) and/or Data Mining (DM) are useful tools for demand forecasting, facilitating strategic, tactical and operational decision making, which ultimately improves the logistics distribution performance. For this, this work proposes a methodology that will compare three prediction methods (traditional statistical method, hybrid method and artificial neural network), which will be tested through the Mean Squared Error (MSE) indicator to determine which one has more accurate results in the forecast of demand for freight transport. The experimental results obtained in this research show that the historical data saved in a TMS can be used for reliably freight transportation demand forecasting. Each of the three methods proposed in this paper has been an effective tool in resolving time series forecasting problems, and when Artificial Neuronal Network (ANN) was used, better results are yielded.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. It must be taken into account that the short term usually covers periods until of 3 months, the medium term of 3 to 18 months and the long term will be greater than 1 year [8].

References

  1. Nettsträters A, Geißen T, Witthaut M, Ebel D, Schoneboom J, Ten Hompel, M (eds), (2015) Cloud Computing for Logistics pp 1–11, https://doi.org/10.1007/978-3-319-13404-8

  2. Peerless Research Group, Transportation Management Systems. Tech. rep., Supply Chain Management Review, Logistics Management for Oracle (2014). https://www.logisticsmgmt.com/wp_content/oracle_wp_transmangmt_0814b.pdf

  3. Ross DF (2016) Introduction to Supply Chain Management Technologies, 2nd edn. CRC Prees, New York

    Google Scholar 

  4. Martel A, Klibi W (2016) In: Designing Value-Creating Supply Chain Networks. https://doi.org/10.1007/978-3-319-28146-9_5. Springer, Berlin, pp 161–206

  5. Ballou R (2004) Logística: Administración de la cadena de suministro. Pearson Educación, México, México. https://docs.google.com/file/d/0B5MTkAxQgJnKVnZoWkxQSWVRaTg/view

    Google Scholar 

  6. BluJay Solutions Ltd, How to turn big data into actionable intelligence. The four step process to continuous cost savings. Tech. rep., BluJay Solutions Ltd (2018). https://www.blujaysolutions.com/wp-content//2018/02/WP_Actionable-Intelligence_0218.pdf

  7. Dragu V, Dinu O, Oprea C, Roman EA (2017) IOP Conference series: materials science and engineering 252(1). https://doi.org/10.1088/1757-899X/252/1/012050

  8. Heizer J, Render B (2009) Principios de Administración de Operaciones Séptima edición. PEARSON EDUCACIÓN, México

    Google Scholar 

  9. Chatfield C (2000) Time-Series forecasting, vol 1 (Chapman & Hall/CRC

  10. Lim C, McAleer M (2002) . Tour Manag 23(4):389. https://doi.org/10.1016/S0261-5177(01)00098-X

    Article  Google Scholar 

  11. Sebastian N, Britos P, Bibiana B, Garcia-Martinez R (2003) . Reportes Técnicos en Ingeniería del Software 5(1):1. https://www.researchgate.net/publication/26520179_Pronostico_de_Ventas_Comparacion_de_Prediccion_entre_Redes_Neuronales_y_Metodos_Estadisicos

    Google Scholar 

  12. Castro A, Torres I (2008) . Comercio Exterior 58(11):794. https://dialnet.unirioja.es/servlet/articulo?codigo=2778864

    Google Scholar 

  13. Xu X, Qi Y, Hua Z (2010) . Expert Systems with Applicationspplications 37(6):4313. https://doi.org/10.1016/j.eswa.2009.11.069

    Article  Google Scholar 

  14. Ortiz CE (2012) Modelos econométricos y de redes neuronales para predecir la oferta maderera en méxico: ARIMA vs NAR y ARMAX vs NARX. Tesis de maestría en ciencias, Universidad Autónoma Metropolitana

    Google Scholar 

  15. Narváez J, Jairo JDF, Valencia M (2013) . Revista Ingeniería Industrial 1(1):63. https://revistas.upb.edu.co/index.php/industrial/article/view/2309/2059

    Google Scholar 

  16. Jiménez JM (2013) Pronóstico de demanda de llamadas en los call center, utilizando redes neuronales artificiales. Ph.D. thesis, Universidad de Piura. https://pirhua.udep.edu.pe/handle/11042/1745

  17. Babu CN, Reddy BE (2014) . Appl Soft Comput 23:27. https://doi.org/10.1016/j.asoc.2014.05.028

    Article  Google Scholar 

  18. Pereira C, Pereira CR, Catapan A, Ortato U, Vieira W (2014) . WSEAS Transactions on Business and Economics 11(1):608

    Google Scholar 

  19. Venkatesh K, Ravi V, Prinzie A, Van den Poel D (2014) . Eur J Oper Res 232(2):383. https://doi.org/10.1016/j.ejor.2013.07.027

    Article  Google Scholar 

  20. Ruelas EA, Laguna JA (2014) Ingeniería Industrial. Actualidad y Nuevas Tendencias 12:91. http://www.redalyc.org/articulo.oa?id=215037911008

    Google Scholar 

  21. Matsumoto M, Ikeda A (2015) . Journal of Remanufacturing 5 (1):1. https://doi.org/10.1186/s13243-015-0010-y

    Article  Google Scholar 

  22. Szoplik J (2015) . Energy 85:208. https://doi.org/10.1016/j.energy.2015.03.084

    Article  Google Scholar 

  23. Amini MH, Kargarian A, Karabasoglu O (2016) . Electr Power Syst Res 140:378. https://doi.org/10.1016/j.epsr.2016.06.003

    Article  Google Scholar 

  24. Cheng CY, Chiang KL, Chen MY (2016) . J Med Syst 40(10):217. https://doi.org/10.1007/s10916-016-0571-9

    Article  Google Scholar 

  25. Tsai F. m., Huang LJW (2017) . Int J Prod Res 55(17):5001. https://doi.org/10.1080/00207543.2015.1112046

    Article  Google Scholar 

  26. Lao O., Rivas A., Caridad M., Marrero F. (2017) . Ciencias Holguín 23:1. http://www.redalyc.org/articulo.oa?id=181549596004

    Google Scholar 

  27. Ghalehkhondabi I, Ardjmand E, Weckman GR, Young WA (2017) . Energy Systems 8 (2):411. https://doi.org/10.1007/s12667-016-0203-y

    Article  Google Scholar 

  28. Bilegan IC, Crainic TG, Gendreau M (2008) . European Journal Of Operational Research 13:1

    Google Scholar 

  29. Rahman M, Jafarzadeh-Ghoushchi S, Wahab DA, Jafarzadeh-Ghoushji M (2014) . Life Science Journal 11(2002):146. http://www.lifesciencesite.com/lsj/life1102s/025_B00046life1102s14_146_154.pdf

    Google Scholar 

  30. Karlaftis MG, Vlahogianni EI (2011) . Transportation Research Part C: Emerging Technologies 19(3):387. https://doi.org/10.1016/j.trc.2010.10.004

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Facultad de Ingenieria Mecanica y Electrica, Universidad Autonoma de Nuevo Leon, San Nicolas de los Garza, NL, 66445, Mexico

    Tomás Eloy Salais-Fierro & Jania Astrid Saucedo Martínez

Authors
  1. Tomás Eloy Salais-Fierro
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jania Astrid Saucedo Martínez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jania Astrid Saucedo Martínez.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Salais-Fierro, T.E., Martínez, J.A.S. Demand Forecasting for Freight Transport Applying Machine Learning into the Logistic Distribution. Mobile Netw Appl 27, 2172–2181 (2022). https://doi.org/10.1007/s11036-021-01854-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-021-01854-x

Keywords

Search

Navigation