Infrastructure

Assessing Dynamic Connectedness in Global Supply Chain Infrastructure Portfolios: The Impact of Risk Factors and Extreme Events ArXiv ID: 2508.04858 “View on arXiv” Authors: Haibo Wang Abstract This paper analyses the risk factors around investing in global supply chain infrastructure: the energy market, investor sentiment, and global shipping costs. It presents portfolio strategies associated with dynamic risks. A time-varying parameter vector autoregression (TVP-VAR) model is used to study the spillover and interconnectedness of the risk factors for global supply chain infrastructure portfolios from January 5th, 2010, to June 29th, 2023, which are associated with a set of environmental, social, and governance (ESG) indexes. The effects of extreme events on risk spillovers and investment strategy are calculated and compared before and after the COVID-19 outbreak. The results of this study demonstrate that risk shocks influence the dynamic connectedness between global supply chain infrastructure portfolios and three risk factors and show the effects of extreme events on risk spillovers and investment outcomes. Portfolios with higher ESG scores exhibit stronger dynamic connectedness with other portfolios and factors. Net total directional connectedness indicates that West Texas Intermediate (WTI), Baltic Exchange Dry Index (BDI), and investor sentiment volatility index (VIX) consistently are net receivers of spillover shocks. A portfolio with a ticker GLFOX appears to be a time-varying net receiver and giver. The pairwise connectedness shows that WTI and VIX are mostly net receivers. Portfolios with tickers CSUAX, GII, and FGIAX are mostly net givers of spillover shocks. The COVID-19 outbreak changed the structure of dynamic connectedness on portfolios. The mean value of HR and HE indicates that the weights of long/short positions in investment strategy after the COVID-19 outbreak have undergone structural changes compared to the period before. The hedging ability of global supply chain infrastructure investment portfolios with higher ESG scores is superior. ...

Cost-Benefit Analysis using Modular Dynamic Fault Tree Analysis and Monte Carlo Simulations for Condition-based Maintenance of Unmanned Systems ArXiv ID: 2405.09519 “View on arXiv” Authors: Unknown Abstract Recent developments in condition-based maintenance (CBM) have helped make it a promising approach to maintenance cost avoidance in engineering systems. By performing maintenance based on conditions of the component with regards to failure or time, there is potential to avoid the large costs of system shutdown and maintenance delays. However, CBM requires a large investment cost compared to other available maintenance strategies. The investment cost is required for research, development, and implementation. Despite the potential to avoid significant maintenance costs, the large investment cost of CBM makes decision makers hesitant to implement. This study is the first in the literature that attempts to address the problem of conducting a cost-benefit analysis (CBA) for implementing CBM concepts for unmanned systems. This paper proposes a method for conducting a CBA to determine the return on investment (ROI) of potential CBM strategies. The CBA seeks to compare different CBM strategies based on the differences in the various maintenance requirements associated with maintaining a multi-component, unmanned system. The proposed method uses modular dynamic fault tree analysis (MDFTA) with Monte Carlo simulations (MCS) to assess the various maintenance requirements. The proposed method is demonstrated on an unmanned surface vessel (USV) example taken from the literature that consists of 5 subsystems and 71 components. Following this USV example, it is found that selecting different combinations of components for a CBM strategy can have a significant impact on maintenance requirements and ROI by impacting cost avoidances and investment costs. ...