SUPPLY CHAIN MANAGEMENT : A REVIEW OF ANOMALY DETECTION TECHNIQUES AND THE BENFORD'S LAW

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LUSI ELVIANI RANGKUTI, FARIDA KHAIRANI LUBIS, ISKANDAR MUDA

Abstract

With technological advances and continued economic growth in modern society, this is just one of many implementations that can turn the tide of the supply chain industry. Traditional models of managing the transfer of physical goods have failed to overcome inefficiencies. Machine learning measures offer another way to ensure that goods are delivered to customers faster with fewer delays and damage. clearly there is a huge gap between traditional data monitoring practices and the demands of modern enterprises. In the supply chain space, insights must be delivered instantly to ensure deliveries are made on time. Machine learning will not only help improve visibility of the supply of goods but will also actively improve the transfer of goods from suppliers to customers. The results of this literature survey on supply chains using anomaly detection are that more use of LSTM LSTM Autoencoder and OCSCM algorithms also use methods to optimize hyperparameters for hybrid algorithms to detect anomalies located in time series data.

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References

Acharya, A., Singh, S. K., Pereira, V., & Singh, P. (2018). Big data, knowledge co-creation and decision making in the fashion industry. International Journal of Information Management, 42, 90–101.

Adewumi, A.O., & Akinyelu, A.A. (2018). A survey of machine learning and nature-based credit card fraud detection techniques. International Journal of Systems Assurance Engineering and Management, 8, 937–953.

A. FarzadT and A. Gulliver. (2020). Unsupervised log message anomaly detection. ICT Express, 6(3):229–237.

Aljohani, A. (2023). Predictive Analytics and Machine Learning for Real-Time Supply Chain Risk Mitigation and Agility. Sustainability , 15 (20), 15088. https://doi.org/10.3390/su152015088

An, J., & Cho, S. (2015). Variational Autoencoder-based Anomaly Detection using Reconstruction Probability. SNU Data Mining Center.

Antic, J., Costa, J.P., Cernivec, A., Cankar, M., Martincic, T., Potocnik, A., Ratkajec, H., Elguezabal, G.B., Leligou, N., Lakka, A., Boigues, IT, & Morte, EV (2023). Runtime security monitoring by an interplay between rule matching and deep learning-based anomaly detection on logs. 2023 19th International Conference on the Design of Reliable Communication Networks, DRCN 2023 . https://doi.org/10.1109/DRCN57075.2023.10108105

Chen, Y.M., Chen, T.Y., & Li, J.S. (2023). A Machine Learning-Based Anomaly Detection Method and Blockchain-Based Secure Protection Technology in Collaborative Food Supply Chain. International Journal of E-Collaboration , 19 (1), 1–24. https://doi.org/10.4018/IJeC.315789

Das A., Gottlieb S., Ivanov, D. (2019). 'Managing disruptions and the ripple effect in digital supply chains: empirical case studies', in Ivanov D. et al. (Eds. ) Handbook of the Ripple Effects in the Supply Chain. Springer, New York, pp. 261-285.

Darmawan, D., Kurniady, D. A., Komariah, A., Tamam, B., & Pallathadka, H. (2022). Introduce a new mathematical approach to inventory management in production processes under constrained conditions. Foundations of Computing and Decision Sciences, 47(4), 421-431.

Dubey, R., Altay, N., Gunasekaran, A., Blome, C., Papadopoulos, T., Childe S.J. (2018) 'Supply chainagility , adaptability and alignment: empirical evidence from the Indian auto components industry'. International Journal of Operations & Production Management 38 (1), 129-148.

El-Khchine, R., Amar, A., Guennoun, Z.E., Bensouda, C., & Laaroussi, Y. (2018). Machine learning for supply chain's big data: State of the art and application to social networks' data. MATEC Web of Conferences , 200 . https://doi.org/10.1051/matecconf/201820000015

El Sima, A.H, Siregar, C (2020). Inventory Accounting System Analysis and Design (Overview about Providing Good Service to Consumers). Turkish Online Journal of Qualitative Inquiry. 11(4). 1088-1097.

Garvey, M.D., Carnovale, S., & Yeniyurt, S. (2015). An analytical framework for supply network risk propagation: A Bayesian network approach. European Journal of Operational Research , 243 (2), 618–627. https://doi.org/10.1016/j.ejor.2014.10.034.

Habbe, A. H., Prawira, I. F. A., Hasibuan, R. M., & Dhumale, N. R. (2023, February). Machine Learning Pose Detection Kit Implementation in Taspen Android Application. In 2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS) (pp. 554-558). IEEE. https://ieeexplore.ieee.org/abstract/document/10073816

Hosseini, S., Ivanov, D., & Dolgui, A. (2019). Review of quantitative methods for supply chain resilience analysis. Transportation Research Part E: Logistics and Transportation Review , 125 (December 2018), 285–307. https://doi.org/10.1016/j.tre.2019.03.001

Ivanov, D., Dolgui, A., Sokolov, B. (2019). 'The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics'. International Journal of Production Research, 57(3), 829-846.

Kraus, C., & Valverde, R. (2014). A data warehouse design for the detection of fraud in the supply chain by using the Benford's law. American Journal of Applied Sciences , 11 (9), 1507–1518. https://doi.org/10.3844/ajassp.2014.1507.1518

Li, DUN, Crespi, N., & Minerva, R. (nd). Detecting Potential Market Corner Risk of WTI: A Hybrid Algorithm Anomaly Detection Approach Detecting Potential Market Corner Risk of . 0–22.

Liu, K.-S.; Lin, M.-H.; Dwijendra, N.K.A.; Carrillo Caballero, G.; Alviz-Meza, A.; Cárdenas-Escrocia, Y. (2023). An Application of Machine Learning to Estimate and Evaluate the Energy Consumption in an Office Room. Sustainability. 15, 1728. https://doi.org/10.3390/su15021728

Lorenc, A., Czuba, M., & Szarata, J. (2021). Big Data Analytics and Anomaly Prediction in the Cold Chain to Supply Chain Resilience. FME Transactions , 49 (2), 315–326. https://doi.org/10.5937/fme2102315L

Nguyen, H.D., Tran, K.P., Thomassey, S., & Hamad, M. (2021a). Forecasting and Anomaly Detection approaches using LSTM and LSTM Autoencoder techniques with the applications in supply chain management. International Journal of Information Management , 57 (October), 102282. https://doi.org/10.1016/j.ijinfomgt.2020.102282

Nguyen, H.D., Tran, K.P., Thomassey, S., & Hamad, M. (2021b). Forecasting and Anomaly Detection approaches using LSTM and LSTM Autoencoder techniques with the applications in supply chain management. International Journal of Information Management , 57 (November). https://doi.org/10.1016/j.ijinfomgt.2020.102282

Rajan, S. D., Vavilapalli, S., Hasan, S., Kumar, R., Rafa, N., (2022, December). A Survey on the Impact of Data Analytics and Machine Learning Techniques in E-commerce. In 2022 5th International Conference on Contemporary Computing and Informatics (IC3I) (pp. 1117-1122). IEEE. https://ieeexplore.ieee.org/abstract/document/10072652

Saragih, F., HS, W. H., & Sumitra, A. (2022). Public Perception of Fraudulent Financial Statements in Pharmaceutical Sub Sector. Journal of Pharmaceutical Negative Results, 1577-1584. https://doi.org/10.47750/pnr.2022.13.S09.194

S. Chen and H. Liao. Bert-log: (2022). Anomaly detection for system logs based on pre-trained language models. Applied Artificial Intelligence, 36(1):2145642.

Sihombing, E., Erlina, & Rujiman (2019). The effect of forensic accounting, training, experience, work load and professional skeptic on auditors ability to detect of fraud. International Journal of Scientific and Technology Research, 8(8), 474-480. https://www.ijstr.org/paper-references.php?ref=IJSTR-0819-20847

Tran, K. P., Nguyen, H. Du, & Thomassey, S. (2019). Anomaly detection using Long Short Term Memory Networks and its applications in Supply Chain Management. IFAC-PapersOnLine , 52 (13), 2408–2412. https://doi.org/10.1016/j.ifacol.2019.11.567

Zunic, E., Tucakovic, Z., Delalic, S., Hasic, H., & Hodzic, K. (2020). Innovative Multi-Step Anomaly Detection Algorithm with Real-World Implementation: Case Study in Supply Chain Management. 2020 IEEE/ITU International Conference on Artificial Intelligence for Good, AI4G 2020 , 9–16. https://doi.org/10.1109/AI4G50087.2020.9311045