Analysis of Demand Forecasting on CV Unggul Jaya Aki Using Naïve and Exponential Smoothing Methods
DOI:
https://doi.org/10.29407/ba69x237Abstract
Research aim: Analyze the demand forecasting calculation and determine the level of accuracy between the Naïve and Exponential Smoothing methods at CV Unggul Jaya Aki.
Design/Method/Approach: A Quantitative Descriptive Approach was used in this study, and the data used were historical sales data of automotive batteries from December 2024 to November 2025. Forecasting accuracy is measured using Mean Absolute Percentage Error (MAPE).
Research Finding: The Naif method yielded an estimated 460 units with a MAPE of 12.93%, while the Exponential Smoothing method produced 623.06 units with a MAPE of 14.13% based on analysis data. Thus, the Naif method has a smaller error rate and is more accurate for cases with small business scale.
Theoretical contribution/Originality: This study contributes to the literature on forecasting methods for small and medium-sized enterprises (SMEs) in the automotive sector, in particular comparing simple quantitative methods for demand fluctuations.
Practitioner/Policy implication: The results of this study provide demand forecasting for CV Unggul Jaya Aki to control inventory more effectively, minimizing the risk of overstock and understock.
Research limitation: This study is limited to historical sales data from one company (CV Unggul Jaya Aki) without considering other external factors, such as national inflation, natural disasters, or price competition between competitors that can cause changes in the historical sales data of this company.
Keywords:
Demand Forecasting, Naive Methods, Exponential Smoothing, MAPE, Inventory ManagementDownloads
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