Automotive Kit Demand Forecasting Using Advanced Forecasting Models: A Data-Driven Approach for Optimal Demand Forecasting

Abstract

This study addresses the major challenges of forecasting automotive kit items(parts of vehicles) by enhancing the delivery of the products and managing the inventory. The kit items vary as per customers and it is unique on its own, where the uniqueness determines the vehicle parts. Customers are the major role players who provide the business hence, this study highlights various factors contributing to the customer’s choice of kit items with features consisting of vehicle name, original equipment manufacturer (OEM), Item Description (collection of vehicle parts) type of product (brand of vehicle) and monthly allotment of each kit item as per customer starting from 2021 April to 2024 January. We conducted an extensive analysis to assess a range of time series analysis techniques for predicting kit demand within the automotive industry, the methods we investigated encompassed Autoregressive (AR), Autoregressive Moving Average (ARMA) ,Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA), Simple Exponential Smoothing (SES), Holt's Linear Trend Method - Double Exponential Smoothing, Triple Exponential Smoothing - Holt Winters, Long Short-Term Memory (LSTM) and advanced forecasting models such as prophet in evaluating the accuracy of these models, we employed key metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE), this study aims to drive significant progress in the automotive industry by optimising inventory management reducing storage costs and improving delivery efficiency to ensure smooth business operations moreover the integration of visually engaging dashboards for real-time analysis of projected values plays a pivotal role in identifying crucial monthly demand trends this integration not only enhances operational efficiency but also fosters enriched customer engagement thereby facilitating sustained advancement within the automotive sector.