Published: 2024-04-01
Optimizing E-commerce Inventory to prevent Stock Outs using the Random Forest Algorithm Approach
DOI: 10.35870/ijsecs.v4i1.2326
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Abstract
This research investigates the effectiveness of the Random Forest algorithm in optimizing e-commerce inventory management. In a digital business that continues to grow, inventory management is crucial for smooth operations and customer satisfaction. The Random Forest algorithm, a development of the CART method by applying bagging techniques and random feature selection, was tested to predict inventory. An experimental design is used to test the algorithm's performance algorithms performance, using data relevant to the observed inventory variables. The analysis involves evaluating the performance of algorithms in predicting and preventing stockouts. The results show that the Random Forest algorithm provides more accurate inventory predictions than traditional methods. Comparison with linear and rule-based regression reveals higher accuracy, making this algorithm a promising choice for e-commerce inventory management. These findings imply that the Random Forest Algorithm can be an effective solution in overcoming the complexity and fluctuations of digital markets. Practical recommendations include a deep understanding of the data, engagement of trained human resources, and training strategies for optimal use of these algorithms. This research also contributes to the literature by expanding understanding of the application of the Random Forest algorithm in various contexts, including forest basal area prediction, supply chain management, and backorder prediction. In conclusion, the Random Forest algorithm has great potential to improve e-commerce inventory management, opening up opportunities for broader application in the digital business world. Proactive adoption of these algorithms can have a positive impact on operational efficiency, customer satisfaction, and a company's competitiveness in an ever-evolving market.
Keywords
Ecommerce ; Inventory Management ; Random Forest Algorithm ; Inventory Prediction ; Out of Stock
Article Information
This article has been peer-reviewed and published in the International Journal Software Engineering and Computer Science (IJSECS). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
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Issue: Vol. 4 No. 1 (2024)
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Section: Articles
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Published: %750 %e, %2024
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License: CC BY 4.0
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Copyright: © 2024 Authors
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DOI: 10.35870/ijsecs.v4i1.2326
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Achmad Ridwan
Information Systems Study Program, Universitas Muhammadiyah Kudus, Kudus Regency, Central Java Province, Indonesia
Ully Muzakir
Computer Science Study Program, Faculty of Science, Technology and Health Sciences, Universitas Bina Bangsa Getsempena, Banda Aceh City, Aceh Province, Indonesia
Safitri Nurhidayati
Economics and Business Study Program, Universitas Muhammadiyah Berau, Berau Regency, East Kalimantan, Indonesia
-
Shi, Y., Wang, T., & Alwan, L.C. (2020). Analytics for cross-border e-commerce: inventory risk management of an online fashion retailer. Decision Sciences, 51(6), 1347-1376. https://doi.org/10.1111/deci.12429
-
Zhou, J. (2023). Logistics inventory optimization method for agricultural e-commerce platforms based on a multilayer feedforward neural network. Pakistan Journal of Agricultural Sciences, 60, 487-496.
-
Tang, Y.M., Chau, K.Y., Lau, Y.Y., & Zheng, Z. (2023). Data-Intensive Inventory Forecasting with Artificial Intelligence Models for Cross-Border E-Commerce Service Automation. Applied Sciences, 13(5), 3051. https://doi.org/10.3390/app13053051
-
Pramodhini, R., Kumar, S., Bhardwaj, S., Agrahari, N., Pandey, S., & Harakannanavar, S.S. (2023, July). E-Commerce Inventory Management System Using Machine Learning Approach. In 2023 International Conference on Data Science and Network Security (ICDSNS) (pp. 1-7). IEEE. https://doi.org/10.1109/ICDSNS58469.2023.10245500
-
Luo, X., Lu, X., & Li, J. (2019). When and how to leverage e-commerce cart targeting: The relative and moderated effects of scarcity and price incentives with a two-stage field experiment and causal forest optimization. Information Systems Research, 30(4), 1203-1227. https://doi.org/10.1287/isre.2019.0859
-
Hua, J., Yan, L., Xu, H., & Yang, C. (2021, August). Markdowns in e-commerce fresh retail: a counterfactual prediction and multi-period optimization approach. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 3022-3031). https://doi.org/10.1145/3447548.3467083
-
Wang, W. (2023). A IoT-Based Framework for Cross-Border E-Commerce Supply Chain Using Machine Learning and Optimization. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3347452
-
Sharma, S., Deepika, D., & Singh, G. (2021, December). Intelligent Warehouse Stocking Using Machine Learning. In 2021 IEEE International Conference on Mobile Networks and Wireless Communications (ICMNWC) (pp. 1-6). IEEE. https://doi.org/10.1109/ICMNWC52512.2021.9688530
-
Schulz, J.W. (1996). ABC Analysis: A Simple and Effective Inventory Management Tool. Journal of Business Logistics, 17(1).
-
Womack, J.P., Jones, D.T., & Roos, D. (1990). Just-in-Time: A Revolutionary Approach to Inventory Management. Free Press.
-
Sheffield, R.J. (2004). E-commerce Inventory Management: A Comprehensive Guide. McGraw-Hill.
-
Jevšenak, J., & Skudnik, M. (2021). A random forest model for basal area increment predictions from national forest inventory data. Forest Ecology and Management, 479, 118601. https://doi.org/10.1016/j.foreco.2020.118601
-
Brosofske, K.D., Froese, R.E., Falkowski, M.J., & Banskota, A. (2014). A review of methods for mapping and prediction of inventory attributes for operational forest management. Forest Science, 60(4), 733-756. https://doi.org/10.5849/forsci.12-134.
-
Islam, S., & Amin, S.H. (2020). Prediction of probable backorder scenarios in the supply chain using Distributed Random Forest and Gradient Boosting Machine learning techniques. Journal of Big Data, 7, 1-22. https://doi.org/10.1186/s40537-020-00345-2
-
Kilham, P., Hartebrodt, C., & Kändler, G. (2018). Generating tree-level harvest predictions from forest inventories with random forests. Forests, 10(1), 20. https://doi.org/10.3390/f10010020.
-
Ntakolia, C., Kokkotis, C., Karlsson, P., & Moustakidis, S. (2021). An explainable machine learning model for material backorder prediction in inventory management. Sensors, 21(23), 7926. https://doi.org/10.3390/s21237926.
-
Magness, D.R., Huettmann, F., & Morton, J.M. (2008). Using random forests to provide predicted species distribution maps as a metric for ecological inventory & monitoring programs. In Applications of Computational Intelligence in Biology: Current Trends and Open Problems (pp. 209-229). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-78534-7_9.
-
Sagita, P.B., Tastrawati, N.K.T., & Sari, K. (2019). Model economic order quantity (eoq) dan model optimisasi robust dalam penentuan persediaan alat suntik (spuit). E-Jurnal Matematika, 8(3), 164. https://doi.org/10.24843/mtk.2019.v08.i03.p248
-
Mulya, M.F., & Rismawati, N. (2021). Analisis dan perancangan sistem e-commerce berbasis cloud enterprise resource planning menggunakan odoo 14. Jurnal SISKOM-KB (Sistem Komputer Dan Kecerdasan Buatan), 5(1), 57-65. https://doi.org/10.47970/siskom-kb.v5i1.229
-
Nugrahani, T.S., Ardiyanto, F., & Umam, S. (2019). Cyber crime awareness: internet knowledge dan e-commerce use pada umkm di kabupaten bantul yogyakarta. Manajemen Dewantara, 3(2), 203-213. https://doi.org/10.26460/md.v3i2.6010
-
Artheswara, L.C., & Sulistiawati, A. (2020). Tingkat penggunaan e-commerce pada remaja di kota dan kabupaten bogor. Jurnal Sains Komunikasi Dan Pengembangan Masyarakat [JSKPM], 4(4), 437-448. https://doi.org/10.29244/jskpm.4.4.437-448
-
Taranenko, I., Chychun, V., Korolenko, O., Goncharenko, I.M., & Zhuvahina, I. (2021). Management of the process of e-commerce development in business on the example of the european union. Studies of Applied Economics, 39(5). https://doi.org/10.25115/eea.v39i5.4911
-
Zhang, L., Chen, X., & Fan, H. (2019). Research on logistics outsourcing e-commerce enterprise performance evaluation based on the entropy weight topsis model. Proceedings of the 1st International Conference on Business, Economics, Management Science (BEMS 2019). https://doi.org/10.2991/bems-19.2019.4
-
Li, Y. (2017). Analysis on financial management of e-commerce enterprise. Proceedings of the 2017 2nd International Conference on Education, Sports, Arts and Management Engineering (ICESAME 2017). https://doi.org/10.2991/icesame-17.2017.257
-
Zhu, Z., & Zhu, M. (2022). Evaluation method of performance of cross-border e-commerce system based on fuzzy dea model. Mobile Information Systems, 2022, 1-6. https://doi.org/10.1155/2022/1456584
-
Zafira, F., Irawan, B., & Bahtiar, A. (2024). Penerapan data mining untuk estimasi stok barang dengan metode k-means clustering. JATI (Jurnal Mahasiswa Teknik Informatika), 8(1), 156-161. https://doi.org/10.36040/jati.v8i1.8319
-
Kurniawan, S., & Diputra, A.A.A. (2023). Sistem pengelolaan stok obat berbasis internet of things (iot) untuk peningkatan efisiensi dan pelayanan konsumen di apotek pusaka asih. Jurnal Farmaku (Farmasi Muhammadiyah Kuningan), 8(2), 46-50. https://doi.org/10.55093/jurnalfarmaku.v8i2.536
-
Sanatin, S., Asfi, M., Amroni, A., & Nas, C. (2023). Perancangan sistem informasi persediaan stok obat dengan metode safety stok dan rop di apotek pasuketan cirebon. Jurnal Manajemen Sistem Informasi, 1(2), 75-80. https://doi.org/10.51920/jurminsi.v1i2.145
-
Hidayat, W.W. (2022). Economic order quantity sebagai informasi pemasaran pada perusahaan makanan. Makro: Jurnal Manajemen Dan Kewirausahaan, 7(2), 166. https://doi.org/10.53712/jmm.v7i2.1592
-
Gunarti, T.S., Tujni, B., & Solikin, I. (2022). Desain e-forecasting menggunakan metode weighted moving average (wma) pada jimmy fish. KRESNA: Jurnal Riset Dan Pengabdian Masyarakat, 2(1), 45-52. https://doi.org/10.36080/jk.v2i1.19
-
Sari, N.L., Saputra, H., & Sinaga, H.D.E. (2021). Implementasi supply chain management berbasis web untuk pengelolaan stok dan distribusi spare part handphone pada erwin ponsel. J-Com (Journal of Computer), 1(2), 103-108. https://doi.org/10.33330/j-com.v2i1.1207
-
Saputri, N.A.O., & Huda, N. (2020). Implementasi sistem informasi prediksi hasil penjualan perangkat komputer menggunakan metode double exponential smoothing. Jurnal Media Informatika Budidarma, 4(3), 806. https://doi.org/10.30865/mib.v4i3.2253
-
Syahril, M., Kusnasari, S., Muhazir, A., Hutasuhut, M., & Purwadi, P. (2023). Menentukan pola pembelian konsumen pada toko grosiran el-shop menggunakan algoritma fp-growth. Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika Dan Komputer), 22(1), 34. https://doi.org/10.53513/jis.v22i1.7517
-
Maghfiroh, A., & Riptiono, S. (2019). Analisis pengaruh variety of selection terhadap e impulse buying dengan web browsing sebagai mediasi (studi pada konsumen bukalapak di kabupaten kebumen). Jurnal Ilmiah Mahasiswa Manajemen, Bisnis Dan Akuntansi (JIMMBA), 1(1), 25-38. https://doi.org/10.32639/jimmba.v1i1.389
-
Nugroho, A., & Rilvani, E. (2023). Penerapan metode oversampling smote pada algoritma random forest untuk prediksi kebangkrutan perusahaan. Techno.Com, 22(1), 207-214. https://doi.org/10.33633/tc.v22i1.7527
-
Priantama, Y., & Siswa, T.A.Y. (2022). Optimasi correlation-based feature selection untuk perbaikan akurasi random forest classifier dalam prediksi performa akademik mahasiswa. JIKO (Jurnal Informatika Dan Komputer), 6(2), 251. https://doi.org/10.26798/jiko.v6i2.651
-
Budianti, L., & Suliadi, S. (2022). Metode weighted random forest dalam klasifikasi prediksi kelangsungan hidup pasien gagal jantung. Bandung Conference Series: Statistics, 2(2), 103-110. https://doi.org/10.29313/bcss.v2i2.3318.
-
Shen, Y., Wang, L., Jian, W., Shang, J., Wang, X., Ju, L., … & Zhou, X. (2021). Big-data and artificial-intelligence-assisted vault prediction and evo-icl size selection for myopia correction. British Journal of Ophthalmology, 107(2), 201-206. https://doi.org/10.1136/bjophthalmol-2021-319618
-
Wang, Y., Peng, G., Sharshir, S. W., & Kandeal, A. (2021). The weighted values of solar evaporation’s environment factors obtained by machine learning. ES Materials & Manufacturing. https://doi.org/10.30919/esmm5f436
-
Golden, C. E., Rothrock, M. J., & Mishra, A. (2019). Comparison between random forest and gradient boosting machine methods for predicting listeria spp. prevalence in the environment of pastured poultry farms. Food Research International, 122, 47-55. https://doi.org/10.1016/j.foodres.2019.03.062
-
Chen, J., Zhong, Z., Feng, Q., & Liu, L. (2022). The multimodal emotion information analysis of e-commerce online pricing in electronic word of mouth. Journal of Global Information Management, 30(11), 1-17. https://doi.org/10.4018/jgim.315322
-
Tian, L., & Wang, X. (2022). A dynamic prediction neural network model of cross-border e-commerce sales for virtual community knowledge sharing. Computational Intelligence and Neuroscience, 2022, 1-11. https://doi.org/10.1155/2022/2529372
-
Meng, G., & Wen, Z. X. (2023). Fault diagnosis of rolling bearing based on the combination of vmd-esa and the optimized gbrf. Third International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2023). https://doi.org/10.1117/12.3008559
-
Ogutu, J. O., Piepho, H., & Schulz-Streeck, T. (2011). A comparison of random forests, boosting and support vector machines for genomic selection. BMC Proceedings, 5(S3). https://doi.org/10.1186/1753-6561-5-s3-s11
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