A framework for predicting the delivery status of health commodities and data-driven healthcare supply chain management based on support vector machine technique and bayesian optimization: Case of the global healthcare supply chain of the United States Agency for International Development

Document Type : Original Research

Authors
Farid Daneshgar 1
Ali Rajabzadeh 2
Mohammad Ali Afsharkazemi 3
1 PhD candidate in Information Technology Management, Department of Information Technology, Faculty of Management and Economics, Science and Research Unit, Islamic Azad University, Tehran, Iran.
2 Professor,Department of Industrial Management, Faculty of Management, Tarbiat Modares University, Tehran, Iran.
3 Associate Professor, Department of Industrial Management, Faculty of Management and Economics, Science and Research Unit, Islamic Azad University, Tehran, Iran.
Abstract
Objective: Timely delivery of medications, medical equipment, and other essential supplies is critical to patient care and can often be life-saving. Delivery delays in the healthcare supply chain can lead to increased costs and operational challenges for healthcare organizations and affect patient care and financial stability. Efficient and reliable supply chain management is critical to reduce these risks and ensure integrated performance in the health industry. This research addresses the delay in the delivery of health commodities in the global health supply chain of the United States Agency for International Development. It presents a framework based on the support vector machine technique and Bayesian optimization to predict the delivery status of health commodities. It also determines the features that have had the greatest impact in predicting the status of commodities delivery for data-driven health supply chain management.

Method: The study's research method is design science, which presents a framework based on the support vector machine technique and Bayesian optimization to predict the delivery status of health commodities. It also compares the performance of different classification algorithms to predict the transportation status.

Findings: The results indicate that the presented framework based on the support vector machine technique and Bayesian optimization leads to a classification accuracy of 95%, outperforming other techniques to predict delivery delay. The results showed that the features of the destination country, shipping method, supplier, and production location are the most influential features in predicting the delivery status.
Keywords
Machine learning
prediction model
delivery delay
healthcare supply chain

Subjects

[1] Arora M, Gigras Y. Importance of supply chain management in healthcare of third world countries. International Journal of Supply and Operations Management. 2018;5(1):101-6.
[2] Privett N, Gonsalvez D. The top ten global health supply chain issues: perspectives from the field. Operations Research for Health Care. 2014;3(4):226-30.
[3] Lugada E, Komakech H, Ochola I, Mwebaze S, Olowo Oteba M, Okidi Ladwar D. Health supply chain system in Uganda: current issues, structure, performance, and implications for systems strengthening. Journal of pharmaceutical policy and practice. 2022;15(1):14.
[4] Nichols JA, Herbert Chan HW, Baker MA. Machine learning: applications of artificial intelligence to imaging and diagnosis. Biophysical reviews. 2019;11:111-8.
[5] دانشگر, فرید, قطری رز, کاظمی ا. کسب دانش از زنجیره تامین سلامت: روندها، تحلیل، نگرانی ها، پاسخ ها به همه گیری کویید-19. پژوهش های نوین در تصمیم گیری. 2024.
[6] Hippold S. Gartner Predicts the Future of Supply Chain Technology Stamford: Gartner; 2021 [updated April 14, 2021; cited 2024 July 2th]. Available from: https://www.gartner.com/en/articles/gartner-predicts-the-future-of-supply-chain-technology.
[7] Abbas K, Afaq M, Ahmed Khan T, Song W-C. A blockchain and machine learning-based drug supply chain management and recommendation system for smart pharmaceutical industry. Electronics. 2020;9(5):852.
[8] Leite H, Lindsay C, Kumar M. COVID-19 outbreak: Implications on healthcare operations. The TQM Journal. 2020;33(1):247-56.
[9] Van Andel B. A machine learning approach to shipment consolidation. MaRBLe. 2018;2.
[10] Milovancevic M, Petkovic D. Adaptive neuro fuzzy estimation of important factors for e-commerce product shipment delivery
8th International Conference on Transportation and Logistics; 3 December; Nis, Serbia2021. p. 53.
[11] Keung KL, Lee CK, Yiu YH, editors. A machine learning predictive model for shipment delay and demand forecasting for warehouses and sales data. 2021 ieee international conference on industrial engineering and engineering management (ieem); 2021: IEEE.
[12] Alnahhal M, Ahrens D, Salah B. Dynamic lead-time forecasting using machine learning in a make-to-order supply chain. Applied Sciences. 2021;11(21):10105.
[13] Garg R, Kiwelekar AW, Netak LD. Logistics and Freight Transportation Management: An NLP based Approach for Shipment Tracking. Pertanika Journal of Science & Technology. 2021;29(4).
[14] Ubaid A, Hussain F, Saqib M. Container shipment demand forecasting in the Australian shipping industry: A case study of Asia–Oceania trade lane. Journal of Marine Science and Engineering. 2021;9(9):968.
[15] Hathikal S, Chung SH, Karczewski M. Prediction of ocean import shipment lead time using machine learning methods. SN Applied Sciences. 2020;2(7):1272.
[16] Ermagun A, Punel A, Stathopoulos A. Shipment status prediction in online crowd-sourced shipping platforms. Sustainable Cities and Society. 2020;53:101950.
[17] Wen Y-H. Shipment forecasting for supply chain collaborative transportation management using grey models with grey numbers. Transportation Planning and Technology. 2011;34(6):605-24.
[18] Polim R. Real-Time Supply Chain Analytics-Shipment Duration Prediction. 2016.
[19] Steinberg F, Burggräf P, Wagner J, Heinbach B, Saßmannshausen T, Brintrup A. A novel machine learning model for predicting late supplier deliveries of low-volume-high-variety products with application in a German machinery industry. Supply Chain Analytics. 2023;1:100003.
[20] Salari N, Liu S, Shen Z-JM. Real-time delivery time forecasting and promising in online retailing: When will your package arrive? Manufacturing & Service Operations Management. 2022;24(3):1421-36.
[21] Özdemir R, Taşyürek M, Aslantaş V. Improved Marine Predators Algorithm and Extreme Gradient Boosting (XGBoost) for shipment status time prediction. Knowledge-Based Systems. 2024;294:111775.
[22] Peffers K, Tuunanen T, Rothenberger MA, Chatterjee S. A design science research methodology for information systems research. Journal of management information systems. 2007;24(3):45-77.
[23] Cristianini N, Shawe-Taylor J. An introduction to support vector machines and other kernel-based learning methods: Cambridge university press; 2000.
[24] Anderson MR, Cafarella M, editors. Input selection for fast feature engineering. 2016 IEEE 32nd International Conference on Data Engineering (ICDE); 2016: IEEE.
[25] Victoria AH, Maragatham G. Automatic tuning of hyperparameters using Bayesian optimization. Evolving Systems. 2021;12:217-23.
[26] Daneshgar F, Hoseini V. A New Framework of Credit Scoring System Based On Support Vector Machine for Credit Risk Management: Evidence from Banks and Financial Institutions. Journal of Economic & Management Perspectives. 2017;11(3):557-65.
[27] Frazier PI. A tutorial on Bayesian optimization. arXiv preprint arXiv:180702811. 2018.
[28] Linardatos P, Papastefanopoulos V, Kotsiantis S. Explainable ai: A review of machine learning interpretability methods. Entropy. 2020;23(1):18.
[29] Molnar C. Interpretable machine learning: Lulu. com; 2020.
Organizational Resources Management Researches
Volume 14, Issue 3
November 2024
Pages 45-72