(160d) Sustainable Supply Chain Design Under Uncertainty within the “Safe and Just Space”: Modeling Framework and a Case Study of Li-Ion Battery

The global impact of human activities on the Earth's biophysical processes and resources is a matter of significant concern. Within the domain of supply chain (SC) management and enterprise-wide optimization, it is increasingly evident that stakeholders must prioritize the creation of sustainable value to maintain competitiveness [1]. This requires simultaneous optimization of environmental and social objectives. The "Safe and Just" (SJS) framework delineates the ecological ceiling and social foundation for humanity, with the space in between referred to as the "safe and just operating space". This framework has garnered significant attention from policymakers and holds the potential to unite stakeholders around a comprehensive vision of sustainable development [2,3]. Uncertainties are inherent in supply chain design and quantitative measurements of environmental impacts, presenting a pervasive challenge [4]. Developing a supply chain design framework that operates within the SJS space while effectively addressing uncertainty is imperative. This work aims to bridge this research gap and contribute to the advancement of sustainable supply chain practices.

The planetary boundary framework has been used for ecological ceiling quantification however at product level it relies on subjective downscaling which has low geographical resolution and could lead to misleading results with high variability. The techno-ecological synergy framework which relies on scientific data and biophysical models has been used in defining the ecological ceiling for each process inside the supply chain. Social foundations have also been quantified through this framework [5]. This model has been applied at a life cycle scale for the supply chain meaning processes from raw materials extraction to the generation of final products are captured. In order to consider the effects of uncertainties, a two-stage mixed integer linear programming stochastic model is proposed to capture uncertainties in both raw material supply and final market demand. Stochastic optimization is a scenario-based approach for optimization under uncertainty and in this approach, uncertainties are captured by a number of discrete realizations [4]. In the first stage, where to produce which product and how much to produce is decided while the second stage determines the quantity of products to be shipped. The major constraints are due to capacity requirements and mass balance (supply chain and LCA network). While constraining the social foundations for each process, we looked into different ways of defining the objective functions and constraints to respect ecological ceiling: 1) minimizing the overall net emission of the whole supply chain (emission subtracts ecological threshold); 2) minimizing the percentage of overshooting (emission larger than ecological threshold) processes in different regions. The L-shaped method is applied for solving the design problem.

This framework is implemented and applied to a case study on the design of the Li-ion battery supply chain. Electric vehicles (EVs) have emerged as a promising solution to address sustainability challenges, particularly global warming [6]. The Li-ion battery, a crucial component of EVs, significantly contributes to greenhouse gas (GHG) emissions. Mineral production, a key input for Li-ion batteries, is susceptible to water supply and governmental policies. Additionally, market demands for Li-ion batteries are influenced by factors such as technological advancements and market competition, modeled here as stochastic variables. Scenarios are generated using Monte Carlo simulation based on historical mineral supply and battery demand data from the past decade. In the first stage, the model minimizes net production emissions, while in the second stage, it focuses on minimizing net transportation emissions. Social foundation constraints are applied in both stages, with penalty costs assigned to unsatisfied demands. To address risk aversion, we also formulated this it as a minimax problem. The results of this model are compared with the current Li-ion battery supply chain of Tesla, providing valuable insights into the effectiveness of the proposed framework.

The novelty of this work includes the development of a supply chain design model operating within the safe and just space while accounting for uncertainty alongside the algorithm to solve it. Notably, optimization occurs at the life cycle scale. By applying this framework to the Li-ion battery supply chain, it underscores the imperative of integrating the ecological ceiling and social foundation in facilitating the transition toward a more sustainable future.

Reference

1. Wheeler, Jonathan, et al. "Designing biomass supply chains within planetary boundaries." AIChE Journal 67.4 (2021): e17131.
2. Turner, Rachel A., and Jane Wills. "Downscaling doughnut economics for sustainability governance." Current Opinion in Environmental Sustainability 56 (2022): 101180.
3. Fanning, Andrew L., et al. "The social shortfall and ecological overshoot of nations." Nature sustainability 5.1 (2022): 26-36.
4. Gao, Jiyao, and Fengqi You. "Modeling framework and computational algorithm for hedging against uncertainty in sustainable supply chain design using functional-unit-based life cycle optimization." Computers & Chemical Engineering 107 (2017): 221-236.
5. Aleissa, Yazeed M., and Bhavik R. Bakshi. "Possible but rare: Safe and just satisfaction of national human needs in terms of ecosystem services." One Earth 6.4 (2023): 409-418.
6. Parmesan, Camille, Mike D. Morecroft, and Yongyut Trisurat. Climate change 2022: Impacts, adaptation and vulnerability. Diss. GIEC, 2022.