Dr.

Irshadullah Asim Mohammed’s contributions to low-carbon supply chain strategies have profoundly shaped sustainable practices in global logistics, manufacturing, and energy sectors. With over 35 impactful publications on my ResearchGate profile-garnering 20,174 reads and widespread citations-my work integrates cutting-edge technologies like artificial intelligence, machine learning, and circular economy principles to drive emission reductions and build resilient, net-zero networks. These efforts address the urgent need for decarbonization amid climate challenges, enabling businesses to achieve up to 30% lower CO2 footprints while enhancing efficiency and competitiveness.
My research pioneers green supply chain management (GSCM), as explored in "Green Supply Chains: Strategies for Sustainability in Global Logistics" and "Green Supply Chain Management (GSCM): Evolving Strategies and Practices." These papers detail eco-friendly sourcing, energy-efficient transportation, and waste minimization tactics, embedding environmental safeguards into core operations to combat global warming. By advocating for renewable integration in logistics, I've provided frameworks that reduce Scope 1 and 2 emissions through optimized energy use.
A cornerstone of my contributions is the circular economy's role in waste mitigation, highlighted in "The Circular Economy in Mitigating Supply Chain Waste." This study promotes closed-loop systems for resource efficiency, minimizing environmental impacts across international chains and fostering sustainability without economic trade-offs. Complementing this, my work on Scope 3 emissions—"Scope 3 Emissions in the Global Push Toward Sustainability"—urges accountability for indirect value-chain impacts, offering strategies to trace and cut upstream/downstream carbon in trade and procurement.
AI and ML form the technological backbone of my innovations. In "Artificial Intelligence (AI) in Enhancing the CCS Supply Chain," I demonstrate AI's application in carbon capture and storage (CCS) for supplier quality and predictive analytics, ensuring low-emission supply for clean energy tech. Similarly, "AI in Transportation Routing within Reverse Logistics to Reduce CO2 Emissions" analyzes optimized routing to slash logistics emissions, while "The Integration of Artificial Intelligence (AI) into Supply Chain Operations" and "AI and ML in Predictive Analytics for Supply Chain Optimization" leverage data-driven forecasting to preempt risks, streamline inventory, and minimize energy waste. Publications like "ML for Optimizing Supply Chain Models in Sustainable Energy Storage" extend this to renewables, enhancing supplier reliability and scalability.