COMPARATIVE ANALYSIS OF EXPONENTIAL SMOOTHING MODELS FOR SALES PREDICTION AND SUPPLY MANAGEMENT IN E-COMMERCE
Abstract
In the growing era of e-commerce, stock management is crucial. Problems arise in forecasting sales in order to achieve effective stock management. This research uses the time series analysis method by focusing on comparing the accuracy of three forecasting methods: Single Exponential Smoothing (SES), Double Exponential Smoothing (DES), and Triple Exponential Smoothing (TES/Holt-Winter). This research provides a solution by comparing the performance of the three methods based on the Mean Absolute Error (MAE) results and prediction graphs. The goal is to determine the most accurate forecasting method using the time series analysis method with several stages, namely data preprocessing, train/test split, modeling, and performance metrics measurement. based on the test results show MAE SES 1077, DES 96, and TES (Holt-Winter) 101. Although DES has a lower MAE, TES (Holt-Winter) provides better accuracy, especially through prediction graph analysis. Holt-Winter is recognized as the most effective method in forecasting future sales, reliable for proper stock management in the dynamic e-commerce industry. This approach is expected to improve efficiency and accuracy in enterprise stock management, support the growth of online businesses, and contribute to the literature and practice of stock management. The use of time series analysis methods, especially Holt-Winter, is considered an important strategic step to optimize sales prediction, positively impact stock management, and create a competitive advantage in a growing market
Downloads
References
D. Hernikawati, “Analisis Dampak Pandemi COVID-19 terhadap Jumlah Kunjungan pada Situs E-Commerce di Indonesia Menggunakan Uji T Berpasangan,” J. Stud. Komun. dan Media, vol. 25, no. 2, p. 191, 2021, doi: 10.31445/jskm.2021.4389.
J. Vasilev, and T. Milkova, " Optimisation Models for Inventory Management with Limited Number of Stock Items ," Logistics, vol. 6, no. 54, pp. 1-12, 2022, doi: 10.3390/logistics6030054
I. Yulian, D. Sri Anggraeni, and Q. Aini, “Penerapan Metode Trend Moment Dalam Forecasting Penjualan Produk Cv. Rabbani Asyisa,” JURTEKSI ( J. Teknol. dan Sist. Inf. ), vol. 6, no. 2, pp. 2407–1811, 2020, doi: 10.33330/jurteksi.v6i2.443.
R. Pugliese, S. Regondi, and R. Marini, "Machine learning-based approach: global trends, research directions, and regulatory standpoints," Data Science and Management, vol. 4, pp. 19-29, 2021, doi: 10.1016/j.dsm.2021.12.002
F. A. Prodhan, J. Zhang, S. S. Hasan, T. P. P. Sharma, and H. P. Mohana, "A review of machine learning methods for drought hazard monitoring and forecasting: Current research trends, challenges, and future research directions," Environmental Modelling & Software, vol. 149, p. 105327, 2022, doi: 10.1016/j.envsoft.2022.105327
M. F. Asrozy, I. Hartami Santi, and D. Fanny Hebrasianto Permadi, “Pengkombinasian Metode Fifo Dan Metode Fefo Pada Sistem Aplikasi Pengeluaran Stok Barang,” JATI (Jurnal Mhs. Tek. Inform., vol. 6, no. 1, pp. 59–66, 2022, doi: 10.36040/jati.v6i1.4282.
H. Pan, and H. Zhou, " Study on convolutional neural network and its application in data mining and sales forecasting for E-commerce," Electronic Commerce Research, vol. 20, pp. 297-320, 2020, doi: 10.1007/s10660-020-09409-0
V. Wineka Nirmala, D. Harjadi, and R. Awaluddin, “Sales Forecasting by Using Exponential Smoothing Method and Trend Method to Optimize Product Sales in PT. Zamrud Bumi Indonesia During the Covid-19 Pandemic,” Int. J. Eng. Sci. Inf. Technol., vol. 1, no. 4, pp. 59–64, Aug. 2021, doi: 10.52088/ijesty.v1i4.169.
M. Nababan, J. Harly, L. H. Bintang, R. A. Rizal, and Aisyah, “Comparison of Single Exponential Smoothing Method With Double Exponential Smoothing Method Prediction of Salt Sales,” Journal of Information Systems and Computer Science Prima, vol. 6, no. 2, pp. 1-5, 2023, doi: 10.34012/jurnalsisteminformasidanilmukomputer.v6i2.3366.
R. Rachmat and S. Suhartono, “Comparative Analysis of Single Exponential Smoothing and Holt’s Method for Quality of Hospital Services Forecasting in General Hospital,” Bull. Comput. Sci. Electr. Eng., vol. 1, no. 2, pp. 80–86, Aug. 2020, doi: 10.25008/bcsee.v1i2.8.
D. A. Adeyinka and N. Muhajarine, “Time series prediction of under-five mortality rates for Nigeria: comparative analysis of artificial neural networks, Holt-Winters exponential smoothing and autoregressive integrated moving average models,” BMC Med. Res. Methodol., vol. 20, no. 1, p. 292, Dec. 2020, doi: 10.1186/s12874-020-01159-9.
A. Lusiana and P. Yuliarty, “Penerapan Metode Peramalan (Forecasting) Pada Permintaan Atap di PT X,” Ind. Inov. J. Tek. Ind., vol. 10, no. 1, pp. 11–20, Jun. 2020, doi: 10.36040/industri.v10i1.2530.
B. Lim and S. Zohren, “Time-series forecasting with deep learning: a survey,” Philos. Trans. R. Soc. A Math. Phys. Eng. Sci., vol. 379, no. 2194, p. 20200209, Apr. 2021, doi: 10.1098/rsta.2020.0209.
D. Chicco, M. J. Warrens, and G. Jurman, “The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation,” PeerJ Comput. Sci., vol. 7, p. e623, Jul. 2021, doi: 10.7717/peerj-cs.623.
A. Subasi, M. F. El-Amin, T. Darwich, and M. Dossary, "Permeability prediction of petroleum reservoirs using stochastic gradient boosting regression," Journal of Ambient Intelligence and Humanized Computing, vol. 13, pp. 3555–3564, 2020, doi: 10.1007/s12652-020-01986-0.
F. S. Aritonang, I. M. Sarkis, and A. Situmorang, “Peramalan Penyediaan Jumlah Vaksin Untuk Balita Dengan Metode Trend Projection di Dinas Kesehatan Kabupaten Toba,” J. Ilm. Sist. Inf., vol. 2, no. 1, pp. 39–45, 2022.
J. Luiten et al., “HOTA: A Higher Order Metric for Evaluating Multi-object Tracking,” Int. J. Comput. Vis., vol. 129, no. 2, pp. 548–578, Feb. 2021, doi: 10.1007/s11263-020-01375-2.
G. Fitrinanda and A. Djunaidy, “Peramalan Harga Saham PT Adaro Energy Indonesia Tbk yang Mempertimbangkan Faktor Kurs Dolar Amerika Menggunakan Bidirectional Long-Short Term Memory,” J. Tek. ITS, vol. 11, no. 3, Dec. 2022, doi: 10.12962/j23373539.v11i3.92124.
N. Hirschauer, S. Grüner, and O. Mußhoff, "The p-Value and Statistical Significance Testing," In: Fundamentals of Statistical Inference. SpringerBriefs in Applied Statistics and Econometrics, pp. 63–96, 2022, doi: 10.1007/978-3-030-99091-6_6
N. Q. Dat, N. T. Ngoc Anh, N. Nhat Anh, and V. K. Solanki, “Hybrid online model based multi seasonal decompose for short-term electricity load forecasting using ARIMA and online RNN,” J. Intell. Fuzzy Syst., vol. 41, no. 5, pp. 5639–5652, Nov. 2021, doi: 10.3233/JIFS-189884.
N. Andriani, S. Wahyuningsih, and M. Siringoringo, "Application of Double Exponential Smoothing Holt and Triple Exponential Smoothing Holt-Winter with Golden Section Optimization to Forecast Export Value of East Borneo Province," Jurnal Matematika, Statistika dan Komputasi (JMSK), vol. 18, no. 3, pp. 475-483, 2022, doi: 10.20956/j.v18i3.17492
A. Jierula, S. Wang, T-M. OH, and P. Wang, "Study on Accuracy Metrics for Evaluating the Predictions of Damage Locations in Deep Piles Using Artificial Neural Networks with Acoustic Emission Data," Applied Sciences, vol. 11, no. 5, pp. 1-20, 2021, doi: 10.3390/app11052314
S. N. Afiyah, F. Kurniawan, and N. L. Aqromi, “Rice Production Forecasting System in East Java Using Double Exponential Smoothing Method,” Procedia Eng. Life Sci., vol. 1, no. 2, Jul. 2021, doi: 10.21070/pels.v1i2.988.
Abstract viewed = 62 times
PDF downloaded = 51 times
Copyright (c) 2024 Aji Saeful, Fandy Setyo Utomo, Yuli Purwati, Mohd Sanusi Azmi
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
-a.jpg)
-b.jpg)
