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Efficient Emission Reduction through Dynamic Mode Selection

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Published on Tuesday, 01 June 2021

Melvin Drent’s PhD research focuses on dual-sourcing strategies; particularly the application of such strategies to create carbon efficient supply chains and is supervised by Prof. Joachim Arts. In his recent paper, “Efficient Emission Reduction through Dynamic Mode Selection,” Melvin and fellow Master students Ranit Sinha and Poulad Moradi study how companies can use a decision model to efficiently source suppliers while meeting carbon emissions targets. The paper has been submitted to a recognised journal in the field and is currently undergoing peer review.

Summary of the research

Reducing the carbon footprint of global supply chains is a challenging attempt for many companies in different industries. Not only are governmental emission regulations increasingly stringent, consumers are also becoming more and more environmentally conscious. Companies should therefore integrate carbon emissions explicitly in their supply chain decision making.

In this paper, we consider a company that sells an assortment of products, each of which is sourced from two suppliers. These suppliers differentiate in terms of their carbon emissions, lead times, and procurement costs. The company needs to decide when to order how much from which supplier such that total holding, backlog, and procurement costs are minimised while the total carbon emissions from transportation for the entire assortment remains below a certain target level. Assuming a dual-index policy for each product, we formulate this decision problem as a mixed integer linear program that we solve through Dantzig-wolfe decomposition.

We benchmark our decision model against two state-of-the art approaches in a large test bed based on real-life carbon emissions data. In the first benchmark, referred to as the mode selection approach, inventories can only be replenished from one supplier. In the second benchmark, referred to as the blanket approach, inventories can be replenished from both suppliers but a carbon emission constraint is imposed on each product individually. Hence, relative to our decision model, the first benchmark lacks the flexibility of both suppliers while the second benchmark makes sourcing decisions for each product individually rather than holistically for the entire assortment.

The numerical analysis shows that our decision model can outperform both benchmark approaches significantly at moderate carbon emission targets.  Our decision model has particularly good performance when the products in the assortment have (i) relatively small cost differences between the regular and the expedited suppliers, (ii) require relatively low service levels, and/or (iii) inhibit relatively high demand volatility. Our analysis provides managerial insights into how our dual-sourcing multi-product approach facilitates bringing down logistics costs increase during supply chain carbon reduction and to what extent different parameters play a role in the alteration of this gain.

About Melvin

Melvin Drent joined the Luxembourg Centre for Logistics and Supply Chain Management as a full-time PhD student in November 2017. While writing his Master thesis in integrated spare part control and repair optimization in a complex network, he was supervised by the LCL Associate Prof. Joachim Arts.

His thesis was awarded with the Dow Chemical prize for the best Master thesis in the field of Operations Management and Logistics defended at the Eindhoven University of Technology in the academic year 2016-2017.

The LCL is very proud of the cutting-edge research work performed by Melvin Drent.