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Business Data Analytics for Sustainable and Resilient Food Procurement: Integrating Expert Judgment and Objective Weighting under Uncertainty

DOI:

https://doi.org/10.63646/ADNM4507

Versions

Keywords:

Sustainable procurement; Food supply chains; Business data analytics; Supplier selection; Resilience; Hybrid weighting; Fuzzy decision support; Objective and subjective criteria weighting

Abstract

Food manufacturers increasingly face procurement decisions in which sustainability, continuity, food safety, social responsibility, and cost must be evaluated under uncertain and conflicting evidence. This study develops a business data analytics framework for sustainable and resilient food procurement by integrating expert judgment with objective weighting under uncertainty. The framework converts linguistic supplier assessments into uncertainty-aware evaluation scores, combines subjective criteria weights from cross-functional experts with objective weights derived from the information structure of supplier data, and ranks suppliers through a robust multi-normalization scoring process. A food-manufacturing case with six anonymized suppliers demonstrates how the framework identifies a preferred supplier while also generating diagnostics for supplier development, backup sourcing, and resilience investment. Results show that resilience and social responsibility criteria become decisive when supplier continuity, labor compliance, recovery capability, and traceability are explicitly modeled. Sensitivity analysis across subjective-objective weighting scenarios confirms the stability of the leading supplier and reveals where lower-ranked suppliers remain vulnerable. The study contributes to business data analytics by reframing supplier selection as an auditable analytics system rather than a one-time ranking exercise. It also provides managerial guidance for designing sustainable-resilient procurement scorecards, data-governance routines, and supplier improvement portfolios in disruption-sensitive food supply chains.

How to Cite

Xiao, X., Zhou, Y., & Wei, Y. (2025). Business Data Analytics for Sustainable and Resilient Food Procurement: Integrating Expert Judgment and Objective Weighting under Uncertainty. Journal of Business and Data Analytics, 3(4), 25-44. https://doi.org/10.63646/ADNM4507

References

Ahi, P., & Searcy, C. (2013). A comparative literature analysis of definitions for green and sustainable supply chain management. Journal of Cleaner Production, 52, 329–341. https://doi.org/10.1016/j.jclepro.2013.02.018

Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016). How to improve firm performance using big data analytics capability and business strategy alignment? International Journal of Production Economics, 182, 113–131. https://doi.org/10.1016/j.ijpe.2016.08.018

Amindoust, A., Ahmed, S., Saghafinia, A., & Bahreininejad, A. (2012). Sustainable supplier selection: A ranking model based on fuzzy inference system. Applied Soft Computing, 12(6), 1668–1677. https://doi.org/10.1016/j.asoc.2012.01.023

Ambulkar, S., Blackhurst, J., & Grawe, S. (2015). Firm's resilience to supply chain disruptions: Scale development and empirical examination. Journal of Operations Management, 33–34, 111–122. https://doi.org/10.1016/j.jom.2014.11.002

Aday, S., & Aday, M. S. (2020). Impact of COVID-19 on the food supply chain. Food Quality and Safety, 4(4), 167–180. https://doi.org/10.1093/fqsafe/fyaa024

Bag, S., Wood, L. C., Xu, L., Dhamija, P., & Kayikci, Y. (2020). Big data analytics as an operational excellence approach to enhance sustainable supply chain performance. Resources, Conservation and Recycling, 153, 104559. https://doi.org/10.1016/j.resconrec.2019.104559

Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (2019). Supply chain risk management and artificial intelligence: State of the art and future research directions. International Journal of Production Research, 57(7), 2179–2202. https://doi.org/10.1080/00207543.2018.1530476

Beske, P., Land, A., & Seuring, S. (2014). Sustainable supply chain management practices and dynamic capabilities in the food industry: A critical analysis of the literature. International Journal of Production Economics, 152, 131–143. https://doi.org/10.1016/j.ijpe.2013.12.026

Brandenburg, M., Govindan, K., Sarkis, J., & Seuring, S. (2014). Quantitative models for sustainable supply chain management: Developments and directions. European Journal of Operational Research, 233(2), 299–312. https://doi.org/10.1016/j.ejor.2013.09.032

Brans, J. P., & Vincke, P. (1985). A preference ranking organisation method: The PROMETHEE method for multiple criteria decision-making. Management Science, 31(6), 647–656. https://doi.org/10.1287/mnsc.31.6.647

Büyüközkan, G., & Çifçi, G. (2011). A novel fuzzy multi-criteria decision framework for sustainable supplier selection with incomplete information. Computers in Industry, 62(2), 164–174. https://doi.org/10.1016/j.compind.2010.10.009

Büyüközkan, G., & Göçer, F. (2018). Digital supply chain: Literature review and a proposed framework for future research. Computers in Industry, 97, 157–177. https://doi.org/10.1016/j.compind.2018.02.010

Carter, C. R., & Rogers, D. S. (2008). A framework of sustainable supply chain management: Moving toward new theory. International Journal of Physical Distribution & Logistics Management, 38(5), 360–387. https://doi.org/10.1108/09600030810882816

Chai, J., Liu, J. N. K., & Ngai, E. W. T. (2013). Application of decision-making techniques in supplier selection: A systematic review of literature. Expert Systems with Applications, 40(10), 3872–3885. https://doi.org/10.1016/j.eswa.2012.12.040

Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, 114(1), 1–9. https://doi.org/10.1016/S0165-0114(97)00377-1

Chen, C. T., Lin, C. T., & Huang, S. F. (2006). A fuzzy approach for supplier evaluation and selection in supply chain management. International Journal of Production Economics, 102(2), 289–301. https://doi.org/10.1016/j.ijpe.2005.03.009

Choi, T. M., Wallace, S. W., & Wang, Y. (2018). Big data analytics in operations management. Production and Operations Management, 27(10), 1868–1883. https://doi.org/10.1111/poms.12838

Christopher, M., & Peck, H. (2004). Building the resilient supply chain. International Journal of Logistics Management, 15(2), 1–14. https://doi.org/10.1108/09574090410700275

Chen, Y., Lu, Y., Bulysheva, L., & Kataev, M. Y. (2024). Applications of blockchain in Industry 4.0: A review. Information Systems Frontiers, 26(5), 1715–1729. https://doi.org/10.1007/s10796-022-10248-7

Dweiri, F., Kumar, S., Khan, S. A., & Jain, V. (2016). Designing an integrated AHP based decision support system for supplier selection in automotive industry. Expert Systems with Applications, 62, 273–283. https://doi.org/10.1016/j.eswa.2016.06.030

Dubey, R., Gunasekaran, A., Childe, S. J., Bryde, D. J., Giannakis, M., Foropon, C., Roubaud, D., & Hazen, B. T. (2020). Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism. International Journal of Production Economics, 226, 107599. https://doi.org/10.1016/j.ijpe.2019.107599

Fosso Wamba, S., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234–246. https://doi.org/10.1016/j.ijpe.2014.12.031

Fosso Wamba, S., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365. https://doi.org/10.1016/j.jbusres.2016.08.009

Govindan, K., Rajendran, S., Sarkis, J., & Murugesan, P. (2015). Multi criteria decision making approaches for green supplier evaluation and selection: A literature review. Journal of Cleaner Production, 98, 66–83. https://doi.org/10.1016/j.jclepro.2013.06.046

Govindan, K., & Sivakumar, R. (2016). Green supplier selection and order allocation in a low-carbon paper industry: Integrated multi-criteria heterogeneous decision-making and multi-objective linear programming approaches. Annals of Operations Research, 238(1), 243–276. https://doi.org/10.1007/s10479-015-2004-4

Gupta, M., & George, J. F. (2016). Toward the development of a big data analytics capability. Information & Management, 53(8), 1049–1064. https://doi.org/10.1016/j.im.2016.07.004

Gunasekaran, A., Papadopoulos, T., Dubey, R., Fosso Wamba, S., Childe, S. J., Hazen, B., & Akter, S. (2017). Big data and predictive analytics for supply chain and organizational performance. Journal of Business Research, 70, 308–317. https://doi.org/10.1016/j.jbusres.2016.08.004

Galanakis, C. M. (2020). The food systems in the era of the coronavirus pandemic crisis. Foods, 9(4), 523. https://doi.org/10.3390/foods9040523

Hazen, B. T., Boone, C. A., Ezell, J. D., & Jones-Farmer, L. A. (2014). Data quality for data science, predictive analytics, and big data in supply chain management. International Journal of Production Economics, 154, 72–80. https://doi.org/10.1016/j.ijpe.2014.04.018

Hobbs, J. E. (2020). Food supply chains during the COVID-19 pandemic. Canadian Journal of Agricultural Economics, 68(2), 171–176. https://doi.org/10.1111/cjag.12237

Ho, W., Xu, X., & Dey, P. K. (2010). Multi-criteria decision making approaches for supplier evaluation and selection: A literature review. European Journal of Operational Research, 202(1), 16–24. https://doi.org/10.1016/j.ejor.2009.05.009

Hohenstein, N. O., Feisel, E., Hartmann, E., & Giunipero, L. (2015). Research on the phenomenon of supply chain resilience: A systematic review and paths for further investigation. International Journal of Physical Distribution & Logistics Management, 45(1/2), 90–117. https://doi.org/10.1108/IJPDLM-05-2013-0128

Ivanov, D. (2020). Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak. Transportation Research Part E: Logistics and Transportation Review, 136, 101922. https://doi.org/10.1016/j.tre.2020.101922

Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: Extending the supply chain resilience angles towards survivability. International Journal of Production Research, 58(10), 2904–2915. https://doi.org/10.1080/00207543.2020.1750727

Kache, F., & Seuring, S. (2017). Challenges and opportunities of digital information at the intersection of big data analytics and supply chain management. International Journal of Operations & Production Management, 37(1), 10–36. https://doi.org/10.1108/IJOPM-02-2015-0078

Kamalahmadi, M., & Parast, M. M. (2016). A review of the literature on the principles of enterprise and supply chain resilience. International Journal of Production Economics, 171, 116–133. https://doi.org/10.1016/j.ijpe.2015.10.023

Keshavarz Ghorabaee, M., Zavadskas, E. K., Olfat, L., & Turskis, Z. (2015). Multi-criteria inventory classification using a new method of Evaluation Based on Distance from Average Solution. Informatica, 26(3), 435–451. https://doi.org/10.15388/Informatica.2015.57

Keršuliene, V., Zavadskas, E. K., & Turskis, Z. (2010). Selection of rational dispute resolution method by applying new step-wise weight assessment ratio analysis. Journal of Business Economics and Management, 11(2), 243–258. https://doi.org/10.3846/jbem.2010.12

Luthra, S., Govindan, K., Kannan, D., Mangla, S. K., & Garg, C. P. (2017). An integrated framework for sustainable supplier selection and evaluation in supply chains. Journal of Cleaner Production, 140, 1686–1698. https://doi.org/10.1016/j.jclepro.2016.09.078

Lu, Y. (2025). The current status and developing trends of Industry 4.0: A review. Information Systems Frontiers, 27(1), 215–234. https://doi.org/10.1007/s10796-021-10221-w

Lu, Y., Ivanov, L. A., Wang, F., Pisarenko, Z. V., & Ye, C. (2024). Management analytics: A bibliometric analysis. Nanotechnologies in Construction, 16(3), 257–266. https://doi.org/10.15828/2075-8545-2024-16-3-257-266

Lu, Y., Pisarenko, Z. V., Yang, L., Ivanov, L. A., & Ye, C. (2024). Advancing decision-making: The role of management analytics in modern business practices. Nanotechnologies in Construction, 16(5), 431–440. https://doi.org/10.15828/2075-8545-2024-16-5-431-440

Lu, Y., Sigov, A. S., Ratkin, L., Ivanov, L. A., & Zuo, M. (2023). Quantum computing and industrial information integration: A review. Journal of Industrial Information Integration, 35, 100511. https://doi.org/10.1016/j.jii.2023.100511

Lu, Y. (2021). Technological innovation and the emergence of a new interdisciplinary field: Management analytics. Nanotechnologies in Construction, 13(3), 181–192. https://doi.org/10.15828/2075-8545-2021-13-3-181-192

Lu, Y., & Ning, X. (2020). A vision of 6G–5G’s successor. Journal of Management Analytics, 7(3), 301–320. https://doi.org/10.1080/23270012.2020.1802622

Lu, Y., & Zheng, X. (2020). 6G: A survey on technologies, scenarios, challenges, and the related issues. Journal of Industrial Information Integration, 19, 100158. https://doi.org/10.1016/j.jii.2020.100158

Lu, Y., Zheng, X., Li, L., & Xu, L. D. (2020). Pricing the cloud: A QoS-based auction approach. Enterprise Information Systems, 14(3), 334–351. https://doi.org/10.1080/17517575.2019.1669827

Lu, Y. (2019). Artificial intelligence: A survey on evolution, models, applications and future trends. Journal of Management Analytics, 6(1), 1–29. https://doi.org/10.1080/23270012.2019.1570365

Lu, Y. (2019). The blockchain: State-of-the-art and research challenges. Journal of Industrial Information Integration, 15, 80–90. https://doi.org/10.1016/j.jii.2019.04.002

Lu, Y., & Xu, L. D. (2019). Internet of Things (IoT) cybersecurity research: A review of current research topics. IEEE Internet of Things Journal, 6(2), 2103–2115. https://doi.org/10.1109/JIOT.2018.2869847

Lu, Y. (2018). Blockchain and the related issues: A review of current research topics. Journal of Management Analytics, 5(4), 231–255. https://doi.org/10.1080/23270012.2018.1516523

Lu, Y. (2017). Cyber physical system (CPS)-based Industry 4.0: A survey. Journal of Industrial Integration and Management, 2(3), 1750014. https://doi.org/10.1142/S2424862217500142

Lu, Y. (2017). Industry 4.0: A survey on technologies, applications and open research issues. Journal of Industrial Information Integration, 6, 1–10. https://doi.org/10.1016/j.jii.2017.04.005

Mardani, A., Jusoh, A., Nor, K. M., Khalifah, Z., Zakwan, N., & Valipour, A. (2015). Multiple criteria decision-making techniques and their applications: A review from 2000 to 2014. Economic Research-Ekonomska Istraživanja, 28(1), 516–571. https://doi.org/10.1080/1331677X.2015.1075139

Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445–455. https://doi.org/10.1016/S0377-2217(03)00020-1

Pagell, M., & Shevchenko, A. (2014). Why research in sustainable supply chain management should have no future. Journal of Supply Chain Management, 50(1), 44–55. https://doi.org/10.1111/jscm.12037

Pagell, M., & Wu, Z. (2009). Building a more complete theory of sustainable supply chain management. Journal of Supply Chain Management, 45(2), 37–56. https://doi.org/10.1111/j.1745-493X.2009.03162.x

Pagell, M., Krumwiede, D. W., & Sheu, C. (2007). Efficacy of environmental and supplier relationship investments: Moderating effects of external environment. International Journal of Production Research, 45(9), 2005–2028.

Pamucar, D., & Ćirović, G. (2015). The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC). Expert Systems with Applications, 42(6), 3016–3028. https://doi.org/10.1016/j.eswa.2014.11.057

Pamucar, D., Stević, Ž., & Sremac, S. (2018). A new model for determining weight coefficients of criteria in MCDM models: Full Consistency Method (FUCOM). Symmetry, 10(9), 393. https://doi.org/10.3390/sym10090393

Pettit, T. J., Fiksel, J., & Croxton, K. L. (2010). Ensuring supply chain resilience: Development of a conceptual framework. Journal of Business Logistics, 31(1), 1–21. https://doi.org/10.1002/j.2158-1592.2010.tb00125.x

Ponomarov, S. Y., & Holcomb, M. C. (2009). Understanding the concept of supply chain resilience. International Journal of Logistics Management, 20(1), 124–143. https://doi.org/10.1108/09574090910954873

Queiroz, M. M., Ivanov, D., Dolgui, A., & Fosso Wamba, S. (2020). Impacts of epidemic outbreaks on supply chains: Mapping a research agenda amid the COVID-19 pandemic. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03685-7

Rao, P., & Holt, D. (2005). Do green supply chains lead to competitiveness and economic performance? International Journal of Operations & Production Management, 25(9), 898–916. https://doi.org/10.1108/01443570510613956

Rizou, M., Galanakis, I. M., Aldawoud, T. M. S., & Galanakis, C. M. (2020). Safety of foods, food supply chain and environment within the COVID-19 pandemic. Trends in Food Science & Technology, 102, 293–299. https://doi.org/10.1016/j.tifs.2020.06.008

Sarkis, J. (2012). A boundaries and flows perspective of green supply chain management. Supply Chain Management: An International Journal, 17(2), 202–216. https://doi.org/10.1108/13598541211212924

Sarkis, J., Zhu, Q., & Lai, K. H. (2011). An organizational theoretic review of green supply chain management literature. International Journal of Production Economics, 130(1), 1–15. https://doi.org/10.1016/j.ijpe.2010.11.010

Schoenherr, T., & Speier-Pero, C. (2015). Data science, predictive analytics, and big data in supply chain management: Current state and future potential. Journal of Business Logistics, 36(1), 120–132. https://doi.org/10.1111/jbl.12082

Seuring, S., & Müller, M. (2008). From a literature review to a conceptual framework for sustainable supply chain management. Journal of Cleaner Production, 16(15), 1699–1710. https://doi.org/10.1016/j.jclepro.2008.04.020

Simpson, D., Power, D., & Samson, D. (2007). Greening the automotive supply chain: A relationship perspective. International Journal of Operations & Production Management, 27(1), 28–48. https://doi.org/10.1108/01443570710714529

Touboulic, A., & Walker, H. (2015). Theories in sustainable supply chain management: A structured literature review. International Journal of Physical Distribution & Logistics Management, 45(1/2), 16–42. https://doi.org/10.1108/IJPDLM-05-2013-0106

Tukamuhabwa, B. R., Stevenson, M., Busby, J., & Zorzini, M. (2015). Supply chain resilience: Definition, review and theoretical foundations for further study. International Journal of Production Research, 53(18), 5592–5623. https://doi.org/10.1080/00207543.2015.1037934

Vachon, S., & Klassen, R. D. (2006). Extending green practices across the supply chain: The impact of upstream and downstream integration. International Journal of Operations & Production Management, 26(7), 795–821. https://doi.org/10.1108/01443570610672248

Verdouw, C. N., Wolfert, S., Beulens, A. J. M., & Rialland, A. (2016). Virtualization of food supply chains with the Internet of Things. Journal of Food Engineering, 176, 128–136. https://doi.org/10.1016/j.jfoodeng.2015.11.009

Xu, L. D., Lu, Y., & Li, L. (2021). Embedding blockchain technology into IoT for security: A survey. IEEE Internet of Things Journal, 8(13), 10452–10473. https://doi.org/10.1109/JIOT.2021.3060508

Yakovleva, N. (2007). Measuring the sustainability of the food supply chain: A case study of the UK. Journal of Environmental Policy and Planning, 9(1), 75–100. https://doi.org/10.1080/15239080701255005

Yu, W., Chavez, R., Jacobs, M. A., & Feng, M. (2018). Data-driven supply chain capabilities and performance: A resource-based view. Transportation Research Part E: Logistics and Transportation Review, 114, 371–385. https://doi.org/10.1016/j.tre.2017.04.002

Zhu, Q., Sarkis, J., & Geng, Y. (2005). Green supply chain management in China: Pressures, practices and performance. International Journal of Operations & Production Management, 25(5), 449–468. https://doi.org/10.1108/01443570510593148

Zhu, Q., Sarkis, J., & Lai, K. H. (2013). Institutional-based antecedents and performance outcomes of internal and external green supply chain management practices. Journal of Purchasing and Supply Management, 19(2), 106–117. https://doi.org/10.1016/j.pursup.2012.12.001

Zimmer, K., Fröhling, M., & Schultmann, F. (2016). Sustainable supplier management: A review of models supporting sustainable supplier selection, monitoring and development. International Journal of Production Research, 54(5), 1412–1442. https://doi.org/10.1080/00207543.2015.1079340

Zavadskas, E. K., Turskis, Z., & Kildienė, S. (2014). State of art surveys of overviews on MCDM/MADM methods. Technological and Economic Development of Economy, 20(1), 165–179. https://doi.org/10.3846/20294913.2014.892037

Zhang, C., & Lu, Y. (2021). Study on artificial intelligence: The state of the art and future prospects. Journal of Industrial Information Integration, 23, 100224. https://doi.org/10.1016/j.jii.2021.100224