Conference Agenda

Utilizing game theory, we examine how a capital-constraint supplier optimizes profit through a two-stage supply chain finance, including purchase order financing and factoring, when confronting potential exogenous liquidity shocks. We analyze suppliers' strategic decisions on whether responding to shocks in two stages and their impact on overall supply chain profitability.

We examine how financing arrangements and frictions affect supply chain resilience investments. In our game-theoretic model of a buyer, a supplier, and a bank, the supplier can invest in resilience, which reduces the supplier's production losses in case of a shock. The supplier is financially constrained and faces two financing frictions: moral hazard costs (the resilience investment is unobservable) and bankruptcy costs (future cash flows are lost in bankruptcy). We compare two financing arrangements: Under commercial loan financing, the supplier requests a loan from the bank; under buyer-intermediated financing (BIF), the supplier also obtains a loan from the bank, but the buyer guarantees the repayment and proposes the loan terms. Under commercial loan financing, we find that moral hazard costs can lead to credit rationing, limiting the supplier's resilience investments. In contrast, bankruptcy costs can accentuate these investments and change the direction of the moral hazard effect on investments. Bankruptcy costs can even motivate the supplier to invest more in resilience than she would without financing frictions. The buyer benefits from resilience and offers BIF only if it mitigates credit rationing. When BIF is offered, it always increases resilience. Surprisingly, financing frictions can increase the expected value for one of the firms: either the buyer or the supplier. The supplier's bankruptcy costs benefit the buyer when the supplier uses a commercial loan. Moral hazard costs can benefit the supplier when they motivate the buyer to offer BIF.

The built environment is developing building codes and regulations to adapt to climate change, e.g. to factor in increased flooding risks, stronger wind loads, and extreme heat events. This implies the usage of new technologies to be integrated into buildings or infrastructure (e.g. sensors to monitor structural health), or changes in material selection (self-sensing concrete, biomass-based insulation materials, etc.) and construction practices (bioswales to mitigate heat island effects, stilt-elevated structures to protect from flooding, etc.), impacting supply chains. To mitigate these climate-induced disruptions, urban planners are challenged to design and plan supply chains that can optimally trade-off resilient and sustainable objectives. Two key elements of this planning are 1) proactivity in constructing climate-adapted buildings and strategic upgrades of existing vulnerable infrastructure, and 2) contingency plans for rapid response and recovery from infrastructure failures due to unforeseen climate events. This study presents a simulation-optimization framework for resilient and sustainable supply chain design, integrating economic, environmental, social, and resilience considerations. The framework includes a GIS-based spatio-temporal analysis of climate-induced demand for infrastructure upgrades and a multi-objective model. The model is applied to the case of Barcelona’s road network renovation, using Nature Based Solutions and recycled glass asphalt to mitigate the heat island effect. The model’s robustness and scenario performance are demonstrated through sensitivity analyses. This study contributes to the field by presenting a simulation-optimization framework for resilient and sustainable supply chain design in the face of climate change. The model’s application to Barcelona’s road network renovation demonstrates its practical utility and robustness.

Our study employs a combination of network science and simulation techniques to reveal the hidden critical nodes in a supply network, empowering firms to make informed decisions for resilience strategies. We examine a product-level supply network and reveal that a disruption at a medium purchasing volume supplier could induce more significant consequences than a disruption at suppliers with high purchasing volumes. We apply a linear regression model to validate the non-linear relationship between node strength and lost sales, as well as between node strength and Time-To-Survive (TTS). Metrics such as weighted betweenness, weighted eigenvector, and weighted Katz centrality are used for analysis. The insights of our study offer practical implications for supply chain managers to identify the most vulnerable nodes within the network. Moreover, the proposed technique allows for quantifying TTS without relying on supplier data contributing to the resilience assessment methods.