Efficient emission reduction through dynamic supply mode selection

Reducing the carbon footprint of global supply chains is a challenge for many companies. Governmental emission regulations are increasingly stringent, and consumers are increasingly environmentally conscious. Companies should therefore integrate carbon emissions in their supply chain decision making. In this paper, we study the inbound supply mode and inventory management decision making for a company that sells an assortment of products. Stochastic demand for each product arrives periodically and unmet demand is backlogged. Each product has two distinct supply modes that differ in terms of their carbon emissions, speed, and costs. The company needs to decide when to ship how much using which supply mode such that total holding, backlog, and procurement costs are minimized while the emissions associated with different supply modes across the assortment remains below a target level. We assume that shipment decisions for each product are governed by a dual-index policy for which we optimize the parameters. 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. Relative to our decision model, the first benchmark lacks the flexibility to dynamically ship products with two supply modes while the second benchmark makes supply mode decisions for each product individually. Our computational experiment shows that our decision model can outperform the first and second benchmark by up to 15 and 40 percent, respectively, for moderate carbon emission reduction targets.