Welcome to the MIT SCM Research Symposium 2024 and thank you for joining us! We hope you find the day exciting and informative. To enhance your experience, we are utilizing this platform in order to help identify projects of top interest to you. Below you will find not only the schedule, but also the abstracts and attached executive summaries of each project. Additionally, you can better acquaint yourself with the student presenters and advisors through their profiles. Thank you again and we look forward to hosting you.
Recognition of the need to reduce greenhouse gases and carbon footprints has led us to investigate what actions are available to companies that rely on fleets for business purposes. There is a strong alignment for the collective contributions of all actors, i.e. nations, firms, and individuals, to limit the annual warming to below 2 degrees Celsius. Our sponsor company, with a global fleet of over 25,000 vehicles primarily comprised of Internal Combustion Engine (ICE) vehicles, is committed to significantly decarbonizing its fleet by 2030 to mitigate its CO2 emissions footprint and contribute to global warming reduction. This goal is to be achieved while maintaining operational excellence and within the company’s economic and operational constraints. To this end, our study first identified optimal locations for transitioning fleets from ICEs to Electric Vehicles (EVs), considering the geographical scope of the 50 US states plus the District of Columbia. Using Machine Learning Clustering techniques, we included endogenous factors (age of fleet, number of vehicles ) and exogenous factors (laws and incentives, temperature, gas price, and electricity price) to identify how to rank states according to their impact. Then, a logistic growth function, with a growth rate factor derived from 5 metrics, was applied to model the timing and strategy of EV implementation: Total Cost of Ownership (TCO), driving range, refueling, CO2 emissions, value-perception. We found that the adoption of EVs in a global corporation with a significantly large fleet is equally dependent on both endogenous and exogenous factors. Furthermore, to reap optimal benefits, the number of EVs in the company’s fleet mix should be gradually increased over the target period. Combining these 2 approaches allows the company to maintain control over operational performance objectives and predict future TCO and decarbonization implications. The model's applicability extends beyond the studied region to other geographical, political, and economic contexts, such as Europe or East Asia.
