Evaluation of ARIMA model performance in projecting future sales: case study on electronic products
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Abstract
Sales of electronic products are highly influenced by various internal and external factors, which require accurate prediction models to support strategic decision making. This study aims to evaluate the performance of ARIMA models in projecting future sales with a case study of electronic products, using monthly sales data collected from company reports and industry databases. The methods used include checking the stationarity of the data using the Augmented Dickey-Fuller (ADF) test, applying differentiation if necessary, and selecting ARIMA parameters based on the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) analysis. The results of the analysis show that ARIMA models successfully capture seasonal and trend patterns, with performance evaluated using accuracy metrics such as Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The implications of this study suggest the importance of considering external factors in modeling to improve prediction accuracy, as well as exploring other modeling approaches that can be more responsive to changing market dynamics.
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How to Cite
Simanungkalit, E. (2024). Evaluation of ARIMA model performance in projecting future sales: case study on electronic products. Journal of Intelligent Decision Support System (IDSS), 7(4), 309-317. https://doi.org/10.35335/idss.v7i4.274
References
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Tadayonrad, Yasin, and Alassane Balle Ndiaye. 2023. “A New Key Performance Indicator Model for Demand Forecasting in Inventory Management Considering Supply Chain Reliability and Seasonality.” Supply Chain Analytics 3:100026.
Vo, Tran Thi Bich Chau, Phan Hung Le, Nhut Tien Nguyen, Thi Le Thuy Nguyen, and Ngoc Hien Do. 2021. “Demand Forecasting and Inventory Prediction for Apparel Product Using the ARIMA and Fuzzy EPQ Model.” Journal of Engineering Science & Technology Review 14(2).
Arissinta, Insyiraah Oxaichiko, Indah Dwi Sulistiyawati, Dedy Kurnianto, and Iqbal Kharisudin. 2022. “Pemodelan Time Series Untuk Peramalan Web Traffic Menggunakan Algoritma Arima, LSTM, Dan GRU.” Pp. 693–700 in PRISMA, Prosiding Seminar Nasional Matematika. Vol. 5.
Barka, Zeineb, Ramzi Benkraiem, Taher Hamza, Faten Lakhal, and Samuel Vigne. 2023. “Institutional Investor Horizon and Stock Price Synchronicity: Do Product Market Competition and Analyst Coverage Matter?” International Review of Financial Analysis 89:102733.
Büyükşahin, Ümit Çavuş, and Şeyda Ertekin. 2019. “Improving Forecasting Accuracy of Time Series Data Using a New ARIMA-ANN Hybrid Method and Empirical Mode Decomposition.” Neurocomputing 361:151–63.
Cachon, Gérard P., Santiago Gallino, and Marcelo Olivares. 2019. “Does Adding Inventory Increase Sales? Evidence of a Scarcity Effect in US Automobile Dealerships.” Management Science 65(4):1469–85.
Chih-Yi, Su, and Lin Bou-Wen. 2021. “Attack and Defense in Patent-Based Competition: A New Paradigm of Strategic Decision-Making in the Era of the Fourth Industrial Revolution.” Technological Forecasting and Social Change 167:120670. doi: https://doi.org/10.1016/j.techfore.2021.120670.
Chouhan, Mansi, and Devesh Kumar Srivastava. 2022. “COVID-19 Outbreaks Challenges to Global Supply Chain Management and Demand Forecasting on SCM Using Autoregressive Models.” Pp. 99–117 in Innovative Supply Chain Management via Digitalization and Artificial Intelligence. Springer.
Duong, Linh N. K., Lincoln C. Wood, and William Y. C. Wang. 2018. “Effects of Consumer Demand, Product Lifetime, and Substitution Ratio on Perishable Inventory Management.” Sustainability 10(5):1559.
Hameed, Hind Khalid. 2023. “Deep Learning Algorithm for Predicting Various Retail Store Sales Using LSTM and ARIMA.” Dijlah Journal 6(4).
Hankammer, Stephan, Ruth Jiang, Robin Kleer, and Martin Schymanietz. 2018. “Are Modular and Customizable Smartphones the Future, or Doomed to Fail? A Case Study on the Introduction of Sustainable Consumer Electronics.” CIRP Journal of Manufacturing Science and Technology 23:146–55.
Hartanti, Dwi, and Hanifah Permatasari. 2024. “Enhancing Sales Performance through ARIMA-Based Predictive Modeling: Insights and Applications Model.” Journal of Information Systems and Informatics 6(3):1646–62.
Júnior, Domingos S. de O. Santos, João F. L. de Oliveira, and Paulo S. G. de Mattos Neto. 2019. “An Intelligent Hybridization of ARIMA with Machine Learning Models for Time Series Forecasting.” Knowledge-Based Systems 175:72–86.
Kumar, Lalji, Sudhakar Khedlekar, and U. K. Khedlekar. 2024. “A Comparative Assessment of Holt Winter Exponential Smoothing and Autoregressive Integrated Moving Average for Inventory Optimization in Supply Chains.” Supply Chain Analytics 8:100084.
Le, Trang. 2024. “Enhancing Predictive Capabilities of ARIMA Models by Hybridization: A Case Study on OMXH25 Index.”
Li, Chunquan, Yaqiong Chen, and Yuling Shang. 2022. “A Review of Industrial Big Data for Decision Making in Intelligent Manufacturing.” Engineering Science and Technology, an International Journal 29:101021.
Li, Yao, Yugang He, and Renhong Wu. 2023. “Traversing the Macroeconomic Terrain: An Exploration of South Korea’s Economic Responsiveness to Cross-Border e-Commerce Production Technology Alterations in the Global Arena.” Sustainability 15(15):11719.
Liu, Jiaying, and Bin Liu. 2023. “Commodity Pricing and Replenishment Decision Strategy Based on the Seasonal ARIMA Model.” Mathematics 11(24):4921.
Mosavi, Amir, Pinar Ozturk, and Kwok-wing Chau. 2018. “Flood Prediction Using Machine Learning Models: Literature Review.” Water 10(11):1536.
Plaza, Malgorzata, Iulian David, and Farid Shirazi. 2018. “Management of Inventory under Market Fluctuations the Case of a Canadian High Tech Company.” International Journal of Production Economics 205:215–27. doi: https://doi.org/10.1016/j.ijpe.2018.09.007.
Rahaman, Md Atiqur, and M. D. HASANUJAMMAN BARI. 2024. “Predictive Analytics for Strategic Workforce Planning: A Cross-Industry Perspective from Energy and Telecommunications.” International Journal of Business Diplomacy and Economy 3(2):14–25.
Ruhiat, Dadang. 2018. “Pengaruh Faktor Musiman Pada Pemodelan Deret Waktu Untuk Peramalan Debit Sungai Dengan Metode Sarima.” Teorema: Teori Dan Riset Matematika 2(2):117–28.
Schaffer, Andrea L., Timothy A. Dobbins, and Sallie-Anne Pearson. 2021. “Interrupted Time Series Analysis Using Autoregressive Integrated Moving Average (ARIMA) Models: A Guide for Evaluating Large-Scale Health Interventions.” BMC Medical Research Methodology 21:1–12.
Singh, Ram Kumar, Meenu Rani, Akshaya Srikanth Bhagavathula, Ranjit Sah, Alfonso J. Rodriguez-Morales, Himangshu Kalita, Chintan Nanda, Shashi Sharma, Yagya Datt Sharma, and Ali A. Rabaan. 2020. “Prediction of the COVID-19 Pandemic for the Top 15 Affected Countries: Advanced Autoregressive Integrated Moving Average (ARIMA) Model.” JMIR Public Health and Surveillance 6(2):e19115.
Sirisha, U. M. 2022. “Profit Prediction Using ARIMA, SARIMA and LSTM Models in Time Series Forecasting: A Comparison.” IEEE Access 10:124715–27. doi: 10.1109/ACCESS.2022.3224938.
Tadayonrad, Yasin, and Alassane Balle Ndiaye. 2023. “A New Key Performance Indicator Model for Demand Forecasting in Inventory Management Considering Supply Chain Reliability and Seasonality.” Supply Chain Analytics 3:100026.
Vo, Tran Thi Bich Chau, Phan Hung Le, Nhut Tien Nguyen, Thi Le Thuy Nguyen, and Ngoc Hien Do. 2021. “Demand Forecasting and Inventory Prediction for Apparel Product Using the ARIMA and Fuzzy EPQ Model.” Journal of Engineering Science & Technology Review 14(2).
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