Carbon footprint measurement and analysis of a multi-modal logistics network

Abstract

General Motors (GM) is one of the world's largest automobile manufacturing companies and does business in over 120 countries, requiring a complex operations network. Operating with a focus on environmental impact has become a strategic pillar within the company, both in its products and in its supply chain. Specifically, the GM global logistics organization is driving toward greater emissions visibility and the identification of carbon dioxide reduction opportunities within its network. Key objectives of this thesis work include creating business tools and processes to record global logistics emissions data, which will allow GM to more accurately report logistics emissions and reduction efforts to shareholders, track network emissions over time, pinpoint carbon reduction opportunities that align with cost savings efforts, and understand and mitigate future risks to the business. The approach taken to address the above objectives unfolds into three distinct work streams: (1) implementation of industry-recognized methods and processes, (2) development of a global carbon footprint measurement model, and (3) emissions analysis of network change activities. Industry research and data analysis along with internal cost and network data were used to develop carbon measurement tools. These tools are capable of estimating mass emissions (tons C02) generated by logistics operations globally as well as the increase or decrease in mass emissions generated by individual network change events (e.g., changes in mode, mileage, shipment frequency, etc.). Furthermore, through close collaboration with logistics providers, GM fulfilled the necessary requirements to become an official shipper partner of the USEPA SmartWay program. Immediate benefits of the project work include using the resulting data for global reporting and benchmarking purposes, providing management with a new set of information that can be used to strengthen network change proposals, and tracking improvements in overall network emissions as well as the performance of individual providers. Long term benefits include stronger relationships with providers, reputational and governmental risk mitigation, and cost savings from increased fuel efficiency of operations.