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

Year : 2024 | Volume :15 | Issue : 03 | Page : –
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 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 endeavours towards 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 (jocta)]

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):-.
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):-. 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. 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 Transactions on Fuzzy Systems. 2018 Apr 30;27(5):943-55.
  3. Qin XY, Ren CH, Yu Y. Auto parts sales forecast supporting business resource value-added service. In2021 International Symposium on Computer Technology and Information Science (ISCTIS) 2021 Jun 4 (pp. 158-164). IEEE.
  4. Sohrabpour V, Oghazi P, Toorajipour R, Nazarpour A. Export sales forecasting using artificial intelligence. Technological Forecasting and Social Change. 2021 Feb 1;163:120480.
  5. Berkovec J. Forecasting automobile demand using disaggregate choice models. Transportation Research Part B: Methodological. 1985 Aug 1;19(4):315-29.
  6. Li Z, Rose JM, Hensher DA. Forecasting automobile petrol demand in Australia: An evaluation of empirical models. Transportation Research Part A: Policy and Practice. 2010 Jan 1;44(1):16-38.
  7. Guo C, Ge Q, Jiang H, Yao G, Hua Q. Maximum power demand prediction using fbprophet with adaptive Kalman filtering. IEEE Access. 2020 Jan 20;8:19236-47.
  8. Chandriah KK, Naraganahalli RV. RNN/LSTM with modified Adam optimizer in deep learning approach for automobile spare parts demand forecasting. Multimedia Tools and Applications. 2021 Jul;80(17):26145-59.
  9. Kulash DJ. Forecasting long-run automobile demand. Transportation Research Board Special Report. 1976(169). Available form https://trid.trb.org/View/65958
  10. Vargas CG, Cortés ME. Automobile spare-parts forecasting: A comparative study of time series methods. International Journal of Automotive and Mechanical Engineering. 2017;14(1):3898-912.
  11. Aktepe A, Yanık E, Ersöz S. Demand forecasting application with regression and artificial intelligence methods in a construction machinery company. Journal of Intelligent Manufacturing. 2021 Aug;32(6):1587-604.
  12. Yang X, Pan B, Evans JA, Lv B. Forecasting Chinese tourist volume with search engine data. Tourism management. 2015 Feb 1;46:386-97.
  13. Aktepe A, Ersöz S, Lüy M. Welding process optimization with artificial neural network applications. Neural Network World. 2014 Nov 1;24(6):655.
  14. Alalawin A, Arabiyat LM, Alalaween W, Qamar A, Mukattash A. Forecasting vehicle’s spare parts price and demand. Journal of quality in maintenance engineering. 2021 Jul 12;27(3):483-99.
  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. Journal of Engineering Research. 2023 Jun 1;11(2):100057.
  16. Boukhtouta A, Jentsch P. Support vector machine for demand forecasting of canadian armed forces spare parts. In2018 6th International Symposium on Computational and Business Intelligence (ISCBI) 2018 Aug 27 (pp. 59-64). IEEE.
Regular Issue Subscription Review Article
Volume 15
Issue 03
Received 16/08/2024
Accepted 16/09/2024
Published 07/10/2024
Views: 60

Check Our other Platform for Workshops in the field of AI, Biotechnology & Nanotechnology.
Learn more
Check Out Platform for Webinars in the field of AI, Biotech. & Nanotech.
Learn more