Supply Chain Resiliency

Experience the power of our supply chain management model, designed to handle diverse scenarios with utmost efficiency. Whether you're dealing with a finite horizon, settings involving a single decision [decision, information, stop], dual decisions [decision, information, decision, stop], a random number [stopping when a condition is met], or even an infinite number of possibilities, our model has got you covered. Moreover, we have expanded its capabilities to tackle a range of new SCM challenges that begin with fresh information preceding the initial decision, commonly known as a "context." With the integration of natural language processing, users can seamlessly chat with the model and unlock valuable insights within their supply chains.

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Recycling

The primary objective of our model is to leverage the capabilities of high-performance computing and natural language processing to unlock invaluable insights pertaining to battery supply chains and recycling decisions, thereby advancing the development of a more sustainable energy storage supply chain. Specifically, the model empowers users to: [1] benchmark production using recycled materials against production employing virgin materials, providing a comprehensive assessment of the benefits and trade-offs associated with battery recycling; [2] estimate the cost and environmental impacts of prevailing industrial practices across the battery supply chain, identifying critical cost and environmental focal points, and evaluating the potential implications of business decisions and market dynamics; and [3] benchmark novel technologies/processes against existing practices within the battery industry, examining the potential changes in cost and environmental impacts as the new technology/process scales up.

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