Leveraging predictive analytics to enhance food safety risk management in supply chains: A conceptual framework

Reason Masengu, Chenjerai Muchenje, Benson Ruzive

Article ID: 10114
Vol 9, Issue 1, 2025

VIEWS - 1251 (Abstract)

Abstract


Food safety in supply chains remains a critical concern due to the complexity of global distribution networks. This study develops a conceptual framework to evaluate how food safety risks influence supply chain performance through predictive analytics. The framework identifies and minimizes food safety risks before they cause serious problems. The study examines the impact of food safety practices, supply chain transparency, and technological integration on adopting predictive analytics. To illustrate the complex dynamics of food safety and supply chain performance, the study presents supply chain transparency, technological integration, and food safety practices and procedures as independent variables and predictive analytics as a mediator. The results show that supply chain managers’ capacity to anticipate and control risks related to food safety can be improved by predictive analytics, leading to safer food production and distribution methods. The research recommends that businesses create scalable cloud-based predictive model solutions, combine data sources, and employ cutting-edge AI and machine learning tools. Companies should also note that strong, data-driven approaches to food safety require cooperative data sharing, regulatory compliance, training initiatives and ongoing improvement.


Keywords


food safety; supply chain performance; predictive analytics; supply chain efficiency

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References

  1. Abdolazimi, O., Salehi Esfandarani, M., Salehi, M., & Shishebori, D. (2020). Robust design of a multi-objective closed-loop supply chain by integrating on-time delivery, cost, and environmental aspects, case study of a Tire Factory. Journal of Cleaner Production, 264, 121566. https://doi.org/10.1016/J.JCLEPRO.2020.121566
  2. Adedoyin Tolulope Oyewole, Chinwe Chinazo Okoye, Onyeka Chrisanctus Ofodile, & Emuesiri Ejairu. (2024). Reviewing predictive analytics in supply chain management: Applications and benefits. World Journal of Advanced Research and Reviews, 21(3), 568–574. https://doi.org/10.30574/wjarr.2024.21.3.0673
  3. Ali, K., Showkat, N., & Chisti, K. A. (2022). Impact of Inventory Management on Operating Profits: Evidence from India. Journal of Economics, Management and Trade, 28(9), 22–26. https://doi.org/10.9734/jemt/2022/v28i930435
  4. Alkahtani, M., Omair, M., Khalid, Q. S., Hussain, G., Ahmad, I., & Pruncu, C. (2021). A covid-19 supply chain management strategy based on variable production under uncertain environment conditions. International Journal of Environmental Research and Public Health, 18(4), 1–23. https://doi.org/10.3390/ijerph18041662
  5. Al-Mhasnah, A. M., Salleh, F., Afthanorhan, A., & Ghazali, P. L. (2018). The relationship between services quality and customer satisfaction among Jordanian healthcare sector. Management Science Letters, 8(12), 1413–1420. https://doi.org/10.5267/j.msl.2018.10.003
  6. Andarwati, M. (2018). Analysis of Factors Affecting the Successof Accounting Information Systems Based on Information Technology on SME Managementsas Accounting InformationEnd User. 98.
  7. Anisatul, A., & Handoko, F. N. (2020). The Application of Haccp (Hazard Analysis Critical Control Point) in Food Production Department. Jurnal Sosial Humaniora Terapan, 2(2). https://doi.org/10.7454/jsht.v2i2.84
  8. Anser, M. K., Khan, M. A., Awan, U., Batool, R., Zaman, K., Imran, M., Sasmoko, Indrianti, Y., Khan, A., & Bakar, Z. A. (2020). The role of technological innovation in a dynamic model of the environmental supply chain curve: Evidence from a panel of 102 countries. Processes, 8(9). https://doi.org/10.3390/pr8091033
  9. Apeji, U. D., & Sunmola, F. T. (2022). ScienceDirect ScienceDirect Principles and Factors Influencing Visibility in Sustainable Supply Principles and Factors Influencing Visibility in Sustainable Supply Chains Chains. Procedia Computer Science, 200, 1516–1527. https://doi.org/10.1016/j.procs.2022.01.353
  10. Assery, S., Tjahjono, H. K., Palupi, M., & Dzakiyullah, N. R. (2020). The role of conflict resolution on supply chain performance. International Journal of Scientific and Technology Research, 9(3), 4007–4011.
  11. Balwant, P. T. (2020). Training and Development of Instructor-Leadership : An Instructional Systems Design Approach. Journal of Human Services: Training, Research, and Practice Volume, 6(1).
  12. Belhadi, A., Kamble, S. S., Venkatesh, M., Chiappetta Jabbour, C. J., & Benkhati, I. (2022). Building supply chain resilience and efficiency through additive manufacturing: An ambidextrous perspective on the dynamic capability view. International Journal of Production Economics, 249, 108516. https://doi.org/10.1016/J.IJPE.2022.108516
  13. Benzidia, S., Makaoui, N., & Bentahar, O. (2021). The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance. Technological Forecasting and Social Change, 165, 120557. https://doi.org/10.1016/J.TECHFORE.2020.120557
  14. Bratt, C., & Sroufe, R. (2021). Implementing Strategic Sustainable Supply Chain Management.
  15. Brun, A., Karaosman, H., & Barresi, T. (2020). Supply chain collaboration for transparency. Sustainability (Switzerland), 12(11). https://doi.org/10.3390/su12114429
  16. Bui, T. D., Tsai, F. M., Tseng, M. L., Tan, R. R., Yu, K. D. S., & Lim, M. K. (2021). Sustainable supply chain management towards disruption and organizational ambidexterity: A data driven analysis. Sustainable Production and Consumption, 26, 373–410. https://doi.org/10.1016/J.SPC.2020.09.017
  17. Côrte-Real, N., Ruivo, P., & Oliveira, T. (2020). Leveraging internet of things and big data analytics initiatives in European and American firms: Is data quality a way to extract business value? Information & Management, 57(1), 103141. https://doi.org/10.1016/J.IM.2019.01.003
  18. Dai, B., Nu, Y., Xie, X., & Li, J. (2021). Interactions of traceability and reliability optimization in a competitive supply chain with product recall. European Journal of Operational Research, 290(1), 116–131. https://doi.org/10.1016/J.EJOR.2020.08.003
  19. De Boeck, E., Jacxsens, L., Kurban, S., & Wallace, C. A. (2020). Evaluation of a simplified approach in food safety management systems in the retail sector: A case study of butcheries in Flanders, Belgium and Lancashire, UK. Food Control, 108. https://doi.org/10.1016/j.foodcont.2019.106844
  20. Del Giudice, M., Chierici, R., Mazzucchelli, A., & Fiano, F. (2020). Supply chain management in the era of circular economy: the moderating effect of big data. International Journal of Logistics Management, 32(2), 337–356. https://doi.org/10.1108/IJLM-03-2020-0119
  21. Dias, E. G., de Oliveira, L. K., & Isler, C. A. (2022). Assessing the effects of delivery attributes on e-shopping consumer behaviour. Sustainability (Switzerland), 14(1), 1–19. https://doi.org/10.3390/su14010013
  22. Dominguez, R., Cannella, S., Ponte, B., & Framinan, J. M. (2020). On the dynamics of closed-loop supply chains under remanufacturing lead time variability. Omega, 97, 102106. https://doi.org/10.1016/J.OMEGA.2019.102106
  23. Durach, C. F., Blesik, T., von Düring, M., & Bick, M. (2021). Blockchain Applications in Supply Chain Transactions. Journal of Business Logistics, 42(1), 7–24. https://doi.org/10.1111/jbl.12238
  24. Franke, G., & Sarstedt, M. (2019). Heuristics versus statistics in discriminant validity testing: a comparison of four procedures. Internet Research, 29(3), 430–447. https://doi.org/10.1108/INTR-12-2017-0515/FULL/XML
  25. Gołaś, Z. (2020). The effect of inventory management on profitability: Evidence from the Polish food industry: Case study. Agricultural Economics (Czech Republic), 66(5), 234–242. https://doi.org/10.17221/370/2019-AGRICECON
  26. Gray, G., Cooke, G., Murnion, P., Rooney, P., & O’Rourke, K. C. (2022). Stakeholders’ insights on learning analytics: Perspectives of students and staff. Computers & Education, 187, 104550. https://doi.org/10.1016/J.COMPEDU.2022.104550
  27. Gupta, P., Hurburgh, C. R., Bowers, E. L., & Mosher, G. A. (2022). Application of fault tree analysis: Failure mode and effect analysis to evaluate critical factors influencing non-GM segregation in the US grain and feed supply chain. Cereal and Grains Association, 99(6), 1394–1413. https://doi.org/10.1002/cche.10601
  28. Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203/FULL/XML
  29. Hashmi, A. (2022). Factors Affecting the Supply Chain Resilience and Supply Chain Performance. South Asian Journal of Operations and Logistics, 1(2), 53–73. https://doi.org/10.57044/sajol.2022.1.2.2212
  30. Hazen, B. T., Russo, I., Confente, I., & Pellathy, D. (2020). Supply chain management for circular economy: conceptual framework and research agenda. International Journal of Logistics Management, 32(2), 510–537. https://doi.org/10.1108/IJLM-12-2019-0332
  31. Helo, P., & Shamsuzzoha, A. H. M. (2020). Real-time supply chain—A blockchain architecture for project deliveries. Robotics and Computer-Integrated Manufacturing, 63, 101909. https://doi.org/10.1016/J.RCIM.2019.101909
  32. Ibrahim, O. O. (2020). Introduction to Hazard Analysis and Critical Control Points (HACCP). EC Microbiology, 4, 93–99.
  33. Insfran-Rivarola, A., Tlapa, D., Limon-Romero, J., Baez-Lopez, Y., Miranda-Ackerman, M., Arredondo-Soto, K., & Ontiveros, S. (2020). A systematic review and meta-analysis of the effects of food safety and hygiene training on food handlers. Foods, 9(9). https://doi.org/10.3390/foods9091169
  34. Joseph, H. M. S. C. R. S. G. (2024). Advanced Issues in Partial Least Squares Structural Equation Modeling. Sage Publication Inc.
  35. Kamboj, S., Gupta, N., Bandral, J. D., Gandotra, G., & Anjum, N. (2020). Food safety and hygiene: A review. International Journal of Chemical Studies, 8(2), 358–368. https://doi.org/10.22271/chemi.2020.v8.i2f.8794
  36. Kondaveeti, H. K., Simhadri, C. G., Yasaswini, G. L., & Shanthi, G. K. (2023). The use of artificial intelligence in the food industry: From recipe generation to quality control. Impactful Technologies Transforming the Food Industry, 116–134. https://doi.org/10.4018/978-1-6684-9094-5.CH008
  37. Kravenkit, S., & So-In, C. (2022). Blockchain-Based Traceability System for Product Recall. IEEE Access, 10(September), 95132–95150. https://doi.org/10.1109/ACCESS.2022.3204750
  38. Krejcie, R., & Morgan, D. W. (1970). Determining Sample Size for Research Activities. Educational and Psychological Measurement, 30, 607-610. http://www.sciepub.com/reference/145556
  39. Kudashkina, K., Corradini, M. G., Thirunathan, P., Yada, R. Y., & Fraser, E. D. G. (2022). Artificial Intelligence technology in food safety: A behavioral approach. Trends in Food Science & Technology, 123, 376–381. https://doi.org/10.1016/J.TIFS.2022.03.021
  40. Kula, E., Greuter, E., Van Deursen, A., & Georgios, G. (2021). Factors Affecting On-Time Delivery in Large-Scale Agile Software Development. IEEE Transactions on Software Engineering, 48(9), 3573–3592. https://doi.org/10.1109/TSE.2021.3101192
  41. Kumar, V., & Ramachandran, D. (2021). Developing firms’ growth approaches as a multidimensional decision to enhance key stakeholders’ wellbeing. International Journal of Research in Marketing, 38(2), 402–424. https://doi.org/10.1016/J.IJRESMAR.2020.09.004
  42. Lai, P. C. (2017). THE LITERATURE REVIEW OF TECHNOLOGY ADOPTION MODELS AND THEORIES FOR THE NOVELTY TECHNOLOGY. Journal of Information Systems and Technology Management, 14(1), 21–38. https://doi.org/10.4301/S1807-17752017000100002
  43. Lee, C. S., Cheang, P. Y. S., & Moslehpour, M. (2022). Predictive Analytics in Business Analytics: Decision Tree. Advances in Decision Sciences, 26(1), 1–29. https://doi.org/10.47654/V26Y2022I1P1-30
  44. Lee, K. L., Romzi, P. N., Hanaysha, J. R., Alzoubi, H. M., & Alshurideh, M. (2022). Investigating the impact of benefits and challenges of IOT adoption on supply chain performance and organizational performance: An empirical study in Malaysia. Uncertain Supply Chain Management, 10(2), 537–550. https://doi.org/10.5267/j.uscm.2021.11.009
  45. Liu, H., Fan, L., & Shao, Z. (2021). Threshold effects of energy consumption, technological innovation, and supply chain management on enterprise performance in China’s manufacturing industry. Journal of Environmental Management, 300, 113687. https://doi.org/10.1016/J.JENVMAN.2021.113687
  46. Lohmer, J., da Silva, E. R., & Lasch, R. (2022). Blockchain Technology in Operations & Supply Chain Management: A Content Analysis. Sustainability (Switzerland), 14(10), 1–88. https://doi.org/10.3390/su14106192
  47. Lund, S.; Manyika, J.; Woetzel, J.; Bsrriball, Ed.; Krishnan, M.; Alicke, K.; Birshan, M.; George, K., Smit, S.; Swan, D.; Hultzler, K. (2020). and Rebalancing in Global Value Chains. In McKinsey Global Institute (Issue August).
  48. Ma, X., Nakab, A., & Vidart, D. (2020). Human Capital Investment and Development: The Role of On-the-job Training.
  49. Masengu, R., Al Habsi, J. S., Ruzive, B., Muchenje, C., & Tsikada, C. (2024). Food Traceability Technology and Compliance Measures in Fast Food Retails: The Mediating Effect of Supply Chain Efficiency on Consumer Trust. Studies in Systems, Decision and Control, 545, 563–576. https://doi.org/10.1007/978-3-031-65203-5_50
  50. Masengu, R., Mohamed, E. D., Benson, R., & Jouhara, A. H. (2024). Effectiveness of food quality and safety management systems in Oman’s food supply chain. https://doi.org/10.21203/RS.3.RS-3867358/V1
  51. McCarthy, R. V., McCarthy, M. M., & Ceccucci, W. (2022). Applying Predictive Analytics. Applying Predictive Analytics. https://doi.org/10.1007/978-3-030-83070-0
  52. McGreevey, J. D., Mallozzi, C. P., Perkins, R. M., Shelov, E., & Schreiber, R. (2020). Reducing Alert Burden in Electronic Health Records: State of the Art Recommendations from Four Health Systems. Applied Clinical Informatics, 11(1), 1–12. https://doi.org/10.1055/s-0039-3402715
  53. McMaster, M., Nettleton, C., Tom, C., Xu, B., Cao, C., & Qiao, P. (2020). Risk Management: Rethinking Fashion Supply Chain Management for Multinational Corporations in Light of the COVID-19 Outbreak. Journal of Risk and Financial Management, 13(8). https://doi.org/10.3390/jrfm13080173
  54. Mohammadi-Nasrabadi, F., Salmani, Y., & Esfarjani, F. (2021). A quasi-experimental study on the effect of health and food safety training intervention on restaurant food handlers during the COVID-19 pandemic. Food Science and Nutrition, 9(7), 3655–3663. https://doi.org/10.1002/fsn3.2326
  55. Montecchi, M., Plangger, K., & West, D. C. (2021). Supply chain transparency: A bibliometric review and research agenda. International Journal of Production Economics, 238, 108152. https://doi.org/10.1016/J.IJPE.2021.108152
  56. Moral-Pajares, E., Martínez-Alcalá, C., Gallego-Valero, L., & Caviedes-Conde, Á. A. (2020a). Transparency index of the supplying countries’ institutions and tree cover loss: Determining factors of EU timber imports? Forests, 11(9), 1–16. https://doi.org/10.3390/F11091009
  57. Moral-Pajares, E., Martínez-Alcalá, C., Gallego-Valero, L., & Caviedes-Conde, Á. A. (2020b). Transparency index of the supplying countries’ institutions and tree cover loss: Determining factors of EU timber imports? Forests, 11(9), 1–16. https://doi.org/10.3390/F11091009
  58. Munir, M., Jajja, M. S. S., Chatha, K. A., & Farooq, S. (2020). Supply chain risk management and operational performance: The enabling role of supply chain integration. International Journal of Production Economics, 227, 107667. https://doi.org/10.1016/J.IJPE.2020.107667
  59. Otitolaiye, V. O., & Abd Aziz, F. S. (2024). Bibliometric analysis of safety management system research (2001–2021). Journal of Safety Research, 88, 111–124. https://doi.org/10.1016/j.jsr.2023.10.014
  60. Overbosch, P., & Blanchard, S. (2023). Principles and Systems for Quality and Food Safety Management. Food Safety Management: A Practical Guide for the Food Industry, Second Edition, 497–512. https://doi.org/10.1016/B978-0-12-820013-1.00018-8
  61. Parast, M. M. (2020). The impact of R&D investment on mitigating supply chain disruptions: Empirical evidence from U.S. firms. International Journal of Production Economics, 227, 107671. https://doi.org/10.1016/J.IJPE.2020.107671
  62. Patidar, A., Sharma, M., & Agrawal, R. (2021). Prioritizing drivers to creating traceability in the food supply chain. Procedia CIRP, 98, 690–695. https://doi.org/10.1016/J.PROCIR.2021.01.176
  63. Purwanto, A., Sulistiyadi, A., Primahendra, R., Kotamena, F., Prameswari, M., & Ong, F. (2020). Does quality, safety, environment and food safety management system influence business performance? Answers from indonesian packaging industries. International Journal of Control and Automation, 13(1), 22–35.
  64. Raspor, P. (2008). Total food chain safety: how good practices can contribute? Trends in Food Science & Technology, 19(8), 405–412. https://doi.org/10.1016/J.TIFS.2007.08.009
  65. Rincon-Ballesteros, L., Lannelongue, G., & González-Benito, J. (2024). Cross-Continental Insights: Comparing Food Safety Management Systems In Europe And Latin America. Food Control, 110552. https://doi.org/10.1016/j.foodcont.2024.110552
  66. Roemer, E., Schuberth, F., & Henseler, J. (2021). HTMT2–an improved criterion for assessing discriminant validity in structural equation modeling. Industrial Management and Data Systems, 121(12), 2637–2650. https://doi.org/10.1108/IMDS-02-2021-0082
  67. Rosak-Szyrocka, J., & Abbase, A. A. (2020). Quality management and safety of food in HACCP system aspect. Production Engineering Archives, 26(2), 50–53. https://doi.org/10.30657/pea.2020.26.11
  68. Sarstedt, M., Hair, J. F., Nitzl, C., Ringle, C. M., & Howard, M. C. (2020). Beyond a tandem analysis of SEM and PROCESS: Use of PLS-SEM for mediation analyses! International Journal of Market Research, 62(3), 288–299. https://doi.org/10.1177/1470785320915686/ASSET/IMAGES/LARGE/10.1177_1470785320915686-FIG2.JPEG
  69. Schniederjans, D. G., Curado, C., & Khalajhedayati, M. (2020). Supply chain digitisation trends: An integration of knowledge management. International Journal of Production Economics, 220, 107439. https://doi.org/10.1016/J.IJPE.2019.07.012
  70. Schuitemaker, R., & Xu, X. (2020). Product traceability in manufacturing: A technical review. Procedia CIRP, 93, 700–705. https://doi.org/10.1016/J.PROCIR.2020.04.078
  71. Seyedan, M., & Mafakheri, F. (2020). Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities. Journal of Big Data, 7(1). https://doi.org/10.1186/s40537-020-00329-2
  72. Sghir, N., Adadi, A., & Lahmer, M. (2023). Recent advances in Predictive Learning Analytics: A decade systematic review (2012–2022). In Education and Information Technologies (Vol. 28, Issue 7). Springer US. https://doi.org/10.1007/s10639-022-11536-0
  73. Sharma, R., Kamble, S. S., Gunasekaran, A., Kumar, V., & Kumar, A. (2020). A systematic literature review on machine learning applications for sustainable agriculture supply chain performance. Computers & Operations Research, 119, 104926. https://doi.org/10.1016/J.COR.2020.104926
  74. Shekarian, E. (2020). A review of factors affecting closed-loop supply chain models. Journal of Cleaner Production, 253, 119823. https://doi.org/10.1016/J.JCLEPRO.2019.119823
  75. Sheng Liu, L. H., & Zuo-Jun, M. S. (2021). On-Time Last-Mile Delivery : Order Assignment with On-Time Last-Mile Delivery : Order Assignment with. Management Science, 67(7), 4095–4119.
  76. Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J. H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322–2347. https://doi.org/10.1108/EJM-02-2019-0189/FULL/PDF
  77. Tirkolaee, E. B., Sadeghi, S., Mooseloo, F. M., Vandchali, H. R., & Aeini, S. (2021). Application of Machine Learning in Supply Chain Management: A Comprehensive Overview of the Main Areas. Mathematical Problems in Engineering, 2021(Ml). https://doi.org/10.1155/2021/1476043
  78. Wamba, S. F., & Queiroz, M. M. (2020). Blockchain in the operations and supply chain management: Benefits, challenges and future research opportunities. International Journal of Information Management, 52, 102064. https://doi.org/10.1016/J.IJINFOMGT.2019.102064
  79. WHO. (2022). Food safety. World Helath Organisation. https://www.who.int/news-room/fact-sheets/detail/food-safety
  80. Yang, M., Fu, M., & Zhang, Z. (2021). The adoption of digital technologies in supply chains: Drivers, process and impact. Technological Forecasting and Social Change, 169, 120795. https://doi.org/10.1016/J.TECHFORE.2021.120795
  81. Yilmaz, C., Varnali, K., & Kasnakoglu, B. T. (2016). How do firms benefit from customer complaints? Journal of Business Research, 69(2), 944–955. https://doi.org/10.1016/J.JBUSRES.2015.08.038
  82. Zheng, M., Li, Y., Su, Z., Fan, Y. Van, Jiang, P., Varbanov, P. S., & Klemeš, J. J. (2022). Supplier evaluation and management considering greener production in manufacturing industry. Journal of Cleaner Production, 342, 130964. https://doi.org/10.1016/J.JCLEPRO.2022.130964


DOI: https://doi.org/10.24294/jipd10114

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