Статья

Enhancing Metadata Management And Data-Driven Decision-Making In Sustainable Food Supply Chains Using Blockchain And AI Technologies

Anber Abraheem Shlash MohammadDigital Marketing Department, Faculty of Administrative and Financial Sciences, Petra University, JordanAmmar Mohammad Al-RamadanAssistant Professor - Faculty of Hospitality and Tourism Management, Department: Hospitality and Culinary arts, Al-Ahliyya Amman University – JordanSulieman Ibraheem Shelash Al-HawaryElectronic Marketing and Social Media, Economic and Administrative Sciences Zarqa University, JordanBadrea Al OrainiBusiness Administration Department. Collage of Business and Economics, Qassim University, Qassim – Saudi ArabiaAsokan VasudevanFaculty of Business and Communications, INTI International University, 71800 Negeri Sembilan, MalaysiaMuhammad Turki AlshuridehDepartment of Marketing, School of Business, The University of Jordan, Amman 11942, JordanQian ChenFaculty of Liberal Arts, Shinawatra UniversityImad AliGNIOT Institute of Management Studies, Greater Noida, Uttar Pradesh, India

2025en

ABI

Аннотация

Sustainability in food supply chains is a critical global challenge, particularly in resource-constrained regions like Jordan, where operational inefficiencies and environmental concerns are prevalent. This study explores the integration of blockchain and artificial intelligence (AI) technologies to enhance metadata management, forecast sustainability metrics, and support decision-making in Jordan’s food supply chains. Blockchain's ability to improve metadata accuracy, standardization, and traceability, combined with AI’s predictive capabilities, offers a powerful solution for addressing sustainability challenges.MethodsThe research employed a mixed-methods approach, combining real-time data from blockchain transaction logs, AI-generated forecasts, and stakeholder surveys. Blockchain data from platforms like Hyperledger Fabric and Ethereum provided insights into metadata accuracy and traceability. AI models were developed using machine learning techniques, such as linear regression, to forecast food waste reduction, carbon footprint reduction, and energy efficiency. Multi-Criteria Decision Analysis (MCDA), using AHP and TOPSIS, was applied to evaluate trade-offs among sustainability goals.ResultsThe results revealed significant improvements in metadata accuracy (from 83% to 96.66%) and reductions in traceability time (from 4.0 to 2.35 hours) following blockchain implementation. AI models demonstrated high predictive accuracy, explaining 88%, 81%, and 76% of the variance in food waste reduction, carbon footprint reduction, and energy efficiency, respectively. ConclusionThis study underscores the transformative potential of blockchain and AI technologies in achieving sustainability goals. By fostering transparency, predictive insights, and data-driven decision-making, these innovations can address key challenges in Jordan’s food supply chains, offering actionable strategies for stakeholders.

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Идентификаторы

DOI: 10.56294/dm2025683

Цитирования и источники

Цитирований: 5Использованных источников: 0

Показатели — AkademScholar