Evaluating Advancements and Identifying Research Gaps in Automotive Spare Parts Demand Forecasting

Year : 2024 | Volume : 15 | Issue : 03 | Page : 47 58
    By

    Aarati Haribhai Bhojani,

  • Divyesh Prafulbhai Gohel,

  1. Assistant Professor, CS & IT Department, Atmiya University, Rajkot, Gujarat, India
  2. Assistant Professor, CS & IT Department, Atmiya University, Rajkot, Gujarat, India

Abstract

The automotive industry, a key driver of global economic activity, relies heavily on the effective management of spare parts to ensure vehicle longevity and reliability. Accurate prediction of demand for these components is imperative to uphold ideal stock levels, minimize expenditures, and elevate customer contentment. This review of literature assesses recent progressions in demand prediction methodologies for automotive spare parts, with a specific emphasis on conventional statistical methods and contemporary machine learning strategies. It underscores the noteworthy enhancements facilitated by extensive data analysis and the incorporation of Internet of Things (IoT) technologies, which have empowered more accurate and adaptable forecasting. Despite these advancements, several challenges and research gaps persist. This paper endeavors to consolidate primary progressions, conduct a critical evaluation of their efficacy, pinpoint areas lacking in research, and examine practical applications in the field through empirical cases and real-life instances. By incorporating a diverse array of prediction techniques and delving into the impact of emergent technologies, this study furnishes an all-encompassing comprehension of the present status of predictive analytics for automotive spare parts demand. Its objective is to steer forthcoming research endeavors toward filling the identified lacunae, ultimately fostering heightened operational effectiveness and client contentment within the automotive sector.

Keywords: Automotive industry, demand forecasting, spare parts management, machine learning, big data analytics, Internet of Things (IoT), inventory optimization

[This article belongs to Journal of Computer Technology & Applications ]

How to cite this article:
Aarati Haribhai Bhojani, Divyesh Prafulbhai Gohel. Evaluating Advancements and Identifying Research Gaps in Automotive Spare Parts Demand Forecasting. Journal of Computer Technology & Applications. 2024; 15(03):47-58.
How to cite this URL:
Aarati Haribhai Bhojani, Divyesh Prafulbhai Gohel. Evaluating Advancements and Identifying Research Gaps in Automotive Spare Parts Demand Forecasting. Journal of Computer Technology & Applications. 2024; 15(03):47-58. Available from: https://journals.stmjournals.com/jocta/article=2024/view=177288


References

  1. Henkelmann R. A deep learning based approach for automotive spare part demand forecasting. Is. Ovgu. DE, USA; 2018.
  2. Liu Y, Zhang Q, Fan ZP, You TH, Wang LX. Maintenance spare parts demand forecasting for automobile 4S shop considering weather data. IEEE Trans Fuzzy Syst. 2018;27:943–55. DOI: 10.1109/TFUZZ.2018.2831637.
  3. Qin XY, Ren CH, Yu Y. Auto parts sales forecast supporting business resource value-added service. 2021 International Symposium on Computer Technology and Information Science (ISCTIS), Guilin, China, 2021, pp. 158–64. DOI: 10.1109/ISCTIS51085.2021.00041.
  4. Sohrabpour V, Oghazi P, Toorajipour R, Nazarpour A. Export sales forecasting using artificial intelligence. Technol Forecast Soc Change. 2021;163:120480. DOI: 10.1016/j.techfore.2020.12
  5. Berkovec J. Forecasting automobile demand using disaggregate choice models. Transp Res Part B Methodol. 1985;19:315–29. DOI: 10.1016/0191-2615(85)90039-6.
  6. Li Z, Rose JM, Hensher DA. Forecasting automobile petrol demand in Australia: An evaluation of empirical models. Transp Res Part A. 2010;44:16–38. DOI: 10.1016/j.tra.2009.09.003.
  7. Guo C, Ge Q, Jiang H, Yao G, Hua Q. Maximum power demand prediction using fbprophet with adaptive Kalman filtering. IEEE Access. 2020;8:19236–47. DOI: 10.1109/ACCESS.2020.2968
  8. Chandriah KK, Naraganahalli RV. RNN/LSTM with modified Adam optimizer in deep learning approach for automobile spare parts demand forecasting. Multimed Tools Appl. 2021;80:26145–59. DOI: 10.1007/s11042-021-10913-0.
  9. Kulash DJ. Forecasting long-run automobile demand. In: Strategies for Reducing Gasoline Consumption Through Improved Motor Vehicle Efficiency. Transportation Research Board Special Report. 1976;(169):14–9. Available from: http://onlinepubs.trb.org/Onlinepubs/sr/sr169/sr169-003.
  10. González Vargas CA, Elizondo Cortés M. Automobile spare-parts forecasting: A comparative study of time series methods. Int J Automot Mech Eng. 2017;14:3898–912. DOI: 10.15282/ijame.14.1.7.0317.
  11. Aktepe A, Yanık E, Ersöz S. Demand forecasting application with regression and artificial intelligence methods in a construction machinery company. J Intell Manuf. 2021;32:1587–604. DOI: 10.1007/s10845-021-01737-8.
  12. Yang X, Pan B, Evans JA, Lv B. Forecasting Chinese tourist volume with search engine data. Tour Manag. 2015;46:386–97. DOI: 10.1016/j.tourman.2014.07.019.
  13. Aktepe A, Ersöz S, Lüy M. Welding process optimization with artificial neural network applications. Neural Netw World. 2014;24:655–70. DOI: 10.14311/NNW.2014.24.037.
  14. Alalawin A, Arabiyat LM, Alalaween W, Qamar A, Mukattash A. Forecasting vehicle’s spare parts price and demand. J Qual Maint Eng. 2021;27:483–99. DOI: 10.1108/JQME-03-2020-0019.
  15. İfraz M, Aktepe A, Ersöz S, Çetinyokuş T. Demand forecasting of spare parts with regression and machine learning methods: Application in a bus fleet. J Eng Res. 2023;11:100057. DOI: 10.1016/j.jer.2023.100057.
  16. Boukhtouta A, Jentsch P. Support vector machine for demand forecasting of Canadian Armed Forces spare parts. 2018 6th International Symposium on Computational and Business Intelligence (ISCBI), Basel, Switzerland, 2018, pp. 59–64. DOI: 10.1109/ISCBI.2018.00021.
Regular Issue Subscription Review Article
Volume 15
Issue 03
Received 16/08/2024
Accepted 16/09/2024
Published 07/10/2024
447


My IP

PlumX Metrics