Forecasting Demand for Automotive Spare Parts Using Sales Trend Analysis: Evidence from the Indian Two- and Four-Wheeler Aftermarket
Komal Naik
Student
Dept. of MBA., Sipna College of Engineering and Technology, Amravati 444607, Amravati (MS.), India
guptabhoomika152@gmail.com
Prof. A.A.Umbarkar2
Asst. Professor,
Dept. of MBA., Sipna College of Engineering and Technology, Amravati 444607
Amravati (MS.), India
aaumbarkar@sipnaengg.ac.in
Abstract
Accurate demand forecasting remains a persistent challenge in the automotive spare-parts aftermarket due to intermittent demand patterns, seasonal fluctuations, and high product variety. These challenges are particularly pronounced in emerging economies, where small and medium enterprises often rely on intuition-based inventory planning. This study examines the effectiveness of sales trend analysis in forecasting demand for two- and four-wheeler automotive spare parts in the Indian aftermarket context. Using a descriptive and analytical research design, the study integrates historical sales data with primary insights collected from employees and stakeholders involved in sales, inventory, and procurement activities. Time-series techniques, including trend analysis, moving averages, and exponential smoothing, are applied to identify demand patterns and generate short-term forecasts. The empirical results reveal a significant positive relationship between historical sales trends and future demand, demonstrating that even simple and interpretable forecasting methods can substantially improve demand predictability and inventory planning. The findings highlight the managerial value of data-driven forecasting for reducing stockouts and excess inventory in resource-constrained environments. By providing empirical evidence from the Indian two- and four-wheeler aftermarket, the study contributes to demand forecasting literature and offers practical guidance for regional spare-parts distributors seeking scalable and implementable forecasting solutions.
Keywords: Automotive spare parts, Demand forecasting, Sales trend analysis, Time-series forecasting, Inventory management, Indian automotive aftermarket, Two- and four-wheeler industry
