[Paper Review] Efficient Strategies on Supply Chain Network Optimization for Industrial Carbon Emission Reduction

Jihu Lei|arXiv (Cornell University)|Apr 17, 2024

Sustainable Supply Chain Management4 citations

TL;DR

This paper proposes a novel supply chain network optimization framework using Adaptive Carbon Emissions Indexing (ACEI) to dynamically reduce industrial carbon emissions by integrating real-time emissions data, regulatory shifts, and market trends. The method achieves significant carbon reduction and enhanced operational resilience across diverse industrial sectors, demonstrating robust performance under disruptions.

ABSTRACT

This study investigates the efficient strategies for supply chain network optimization, specifically aimed at reducing industrial carbon emissions. Amidst escalating concerns about global climate change, industry sectors are motivated to counteract the negative environmental implications of their supply chain networks. This paper introduces a novel framework for optimizing these networks via strategic approaches which lead to a definitive decrease in carbon emissions. We introduce Adaptive Carbon Emissions Indexing (ACEI), utilizing real-time carbon emissions data to drive instantaneous adjustments in supply chain operations. This adaptability predicates on evolving environmental regulations, fluctuating market trends and emerging technological advancements. The empirical validations demonstrate our strategy's effectiveness in various industrial sectors, indicating a significant reduction in carbon emissions and an increase in operational efficiency. This method also evidences resilience in the face of sudden disruptions and crises, reflecting its robustness.

Motivation & Objective

• To address rising industrial carbon emissions amid growing climate change concerns.
• To develop a dynamic, responsive supply chain optimization strategy that adapts to evolving environmental regulations and market conditions.
• To reduce carbon emissions while improving operational efficiency in industrial supply chains.
• To enhance resilience of supply networks against sudden disruptions through adaptive emission control.
• To validate the framework’s effectiveness across multiple industrial sectors.

Proposed method

• Introduces Adaptive Carbon Emissions Indexing (ACEI), a real-time data-driven mechanism for monitoring and adjusting supply chain operations.
• Uses live carbon emissions data to trigger instantaneous operational adjustments across the supply network.
• Integrates dynamic inputs from changing environmental regulations, market trends, and technological advancements into decision-making.
• Employs a feedback loop that continuously recalibrates supply chain parameters based on emission performance and external conditions.
• Applies a multi-sector validation approach to test the framework’s scalability and adaptability.
• Leverages computational modeling to simulate and optimize network flows under varying emission constraints.

Experimental results

Research questions

• RQ1 How can real-time carbon emissions data be effectively integrated into supply chain decision-making to reduce emissions?
• RQ2 To what extent can adaptive optimization strategies improve both emission reduction and operational efficiency?
• RQ3 How does the framework perform under sudden supply chain disruptions or regulatory changes?
• RQ4 What is the scalability of the ACEI-based optimization across diverse industrial sectors?
• RQ5 How does the method compare to static or non-adaptive supply chain optimization models in terms of emission reduction and resilience?

Key findings

• The ACEI framework achieved a significant reduction in industrial carbon emissions across multiple test sectors.
• Operational efficiency improved alongside emission reductions, indicating co-benefits of the optimization strategy.
• The system demonstrated strong resilience during simulated disruptions, maintaining low emissions and stable performance.
• The method effectively adapted to changing environmental regulations and market dynamics in real time.
• Empirical validation confirmed the framework’s robustness and scalability across different industrial contexts.
• The approach outperformed conventional static optimization models in both emission reduction and adaptability.