Journal of Computer Technology & Applications

Journal of Computer Technology & Applications Review Article

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

  1. Aarati Haribhai Bhojani CS & IT Department, Atmiya University, Rajkot
  2. Divyesh Prafulbhai Gohel CS & IT Department, Atmiya University, Rajkot

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
  • Big data analytics
  • Internet of things (IoT)
  • Machine Learning
  • demand forecasting
  • inventory optimization
  • spare parts management

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