R K Amit

To achieve the 'Zero emission mandate', India put forward the Electric Mobility Vision 2030 which aims that in 2030, Electric vehicles will contribute around 30% of total automobile sales. Uncertainty in the availability of battery raw materials and lack of battery manufacturing facility can be the bottlenecks in achieving the Vision 2030. In this paper, we develop a system dynamics model to analyze the effect of complex demand and supply dynamics of critical battery materials in the avenue of Electric Mobility Vision 2030. Our study indicates that fluctuations in the price of key battery materials - Lithium and Cobalt - can be a major deterrent in realizing the Vision 2030. Hence we need to model and predict the Demand vs. Supply dynamics so that we can take proactive steps towards mitigating the supply risks and realize the vision.

In this study, we model the classical newsvendor ordering preferences under ambiguity. The extant literature on normative models in the newsvendor setting assumes decision-making under risk, where decision-maker has exact knowledge of the probabilities associated with the outcomes. In several business situations, the demand distribution is often incomplete or unknown. This results in decision-making under ambiguous situations. Decision theory recognizes the difference between exact probabilities and more realistic ambiguous probabilities. In his seminal paper, Scarf (1958) develops a max-min approach for the newsvendor with incomplete demand information. In the Scarf model, the newsvendor is assumed to be risk-neutral and ambiguity averse. In the recent experimental literature, it has been observed that the newsvendor behavior is not consistent with the Scarf model, and exhibits pull-to- center bias and other biases. This motivates our research to develop quantitative models under ambiguity to describe the observed biases in the literature.

Due to sustainability concerns, there has been increasing emphasis on adoption of electric mobility across the globe. The current Lithium ion battery chemistry that is mainly used for electric mobility has Lithium, Cobalt, Nickel, and Manganese as the key constituents. As the supply chains of these battery materials are disguised and concentrated in few countries, there are concerns about availability and increasing prices that can impede the electric mobility revolution. Furthermore, Lithium ion batteries have competing demand from the clean energy industry. On the other side, the battery prices are falling due to scale effects and improvement in battery technologies, which will further increase the demand for batteries. Hence, it is important to understand the supply-demand dynamics of the Lithium ion battery materials for transition from traditional mobility to electric mobility. In this paper, we attempt to understand the supply-demand dynamics using a system dynamics modeling framework. The system dynamics model enables us to capture the nonlinear, time-delayed feedbacks that operate in the Lithium ion battery ecosystem.