Development of a Logistics Decision Support Tool for Small and Medium Companies to Evaluate the Impacts of Environmental Regulations in California

Research Team: Miguel Jaller (lead), Carlos Otero, and Anmol Pahwa

University: UC Davis

Problem Statement: Vehicles moving goods to supply rapidly increasing demand in urban areas generate a number of negative externalities. To mitigate these impacts, California agencies have developed a number of initiatives―in the form of fuel taxes, voucher incentives, vehicle manufacturer mandates, and regulatory emissions policies―that aim to improve freight efficiency, foster the use of zero and near-zero emission vehicles, and improve economic competitiveness. While these actions are potentially effective for sustainability, the immediate economic impacts on businesses are not necessarily understood. To fill this gap, this proposal aims to develop a logistics decision-support tool to evaluate such impacts in terms of changes in operational efficiency, and fleet replacement, facility, and operational costs.

Project Description: The study will evaluate the impacts from freight-related regulations or plans with pre- established goals of environmental efficiency and fleet composition in California. The research team will quantify the potential trade-offs between logistics costs and emission reductions. Considering the objective of developing a decision support tool for small and medium businesses, and the variability within the types of businesses and logistics structures, the study will review the most representative supply chains, cargoes, and businesses in the region. The Supply Chain Operation Reference (SCORE®) model will be utilized to develop generalizable case studies. Secondly, current and upcoming regulations and plans in the State will be reviewed. Third, the team will develop the decision support tool. The tool will capture the policy implications in terms of logistics costs and generated transport emissions, as well as the overall operational and financial decisions at the tactical level. The mathematical model will use the Joint Replenishment Inventory model as the basis for the optimization. Finally, the team will select the most relevant case studies identified from the typological analyses, and, considering data availability and the potential for the generalization of findings, perform empirical analyses.

Status: In Progress

Budget: $79,985