SMU Data Science Review
Abstract
The automotive industry seeks effective ways to forecast consumer demand to avoid overstocking, waste, underproduction, and employee underperformance. Modeling future demand for vehicles is standard, however parts & accessories are a significant subset of overall automotive revenue. There is no industry standard for predicting the quantity of accessories sold or revenue. This paper seeks to use the best industry forecasting methods and research practices to build a predictive model that forecasts vehicle accessory sales. The time-series forecasting model utilizes Toyota Motor Corporation data in a first attempt to predict accessory sales.
Recommended Citation
Cadena, Eric; Albright, Kevin; Wang, Harry; and Ajmera, Satvik
(2023)
"Forecasting Accessory Demand in the Automotive Industry,"
SMU Data Science Review: Vol. 7:
No.
2, Article 5.
Available at:
https://scholar.smu.edu/datasciencereview/vol7/iss2/5
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
