Article Per Year (5 Year)
p-Index From 2021 - 2026
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This Author published in this journals
All Journal International Journal of Management Science and Application
Simon Suwanzy Dzreke
Federal Aviation Administration: Washington, District of Columbia, US
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Systematic Analysis of IoT, AI, Active Packaging, and Blockchain for Food Waste Reduction across the Farm-to-Fork Supply Chain Simon Suwanzy Dzreke
International Journal of Management Science and Application Vol. 4 No. 2 (2025): IJMSA
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijmsa.v4i2.441

Abstract

Global food waste (1.3 billion tons per year) is a major economic and environmental issue, contributing considerably to cash losses and greenhouse gas emissions. This study assesses the efficacy, limitations, and integration potential of four Industry 4.0 technologies—IoT sensors, AI/ML algorithms, advanced active packaging, and blockchain traceability—for waste reduction at key food supply chain stages (production, logistics, retail, and consumption). We show that each technology has different waste reduction advantages using a rigorous literature synthesis (2020-2025), techno-economic evaluation, and environmental impact analysis. Crucially, coordinated deployment unleashes synergistic potential, resulting in considerably larger systemic waste reduction than standalone applications. However, fulfilling this promise requires overcoming long-standing obstacles such as implementation costs, data needs, recyclability issues, and energy usage. The results highlight the need for coordinated policy frameworks that promote interoperable technology, standardized data protocols, and circular design principles. This study outlines a systematic approach for changing food waste from a systemic failure to a controllable engineering issue, resulting in more resilient and efficient food systems.
The AI Co-pilot: Navigating Market Turbulence and Charting a Course for Sustainable Advantage Simon Suwanzy Dzreke
International Journal of Management Science and Application Vol. 4 No. 2 (2025): IJMSA
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijmsa.v4i2.442

Abstract

This study addresses the gap in frameworks for effective human-AI collaboration in strategic decision-making during turbulent market conditions. Using a mixed-methods approach (longitudinal case studies in manufacturing, finance, and logistics; large-scale executive surveys; computational simulations), we empirically evaluate the "AI co-pilot" model, where AI augments human strategic cognition. Results show AI co-pilots improve market disruption prediction accuracy by 30-50% and reduce strategic response latency. However, these benefits critically depend on governance frameworks ensuring algorithmic accountability, dynamic trust calibration, and human agency preservation. Case studies (e.g., AI-enabled semiconductor shortage detection enabling proactive diversification) demonstrate value, while instances of algorithmic opacity highlight the necessity of human oversight. Maintaining competitive advantage requires interfaces ("algorithmic diplomacy"), balancing AI's computational power with human judgment, wisdom, and ethics. Organizations achieving this symbiosis gain superior resilience, transforming volatility into adaptive innovation opportunities.
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