A System Dynamics Model for Studying the Resiliency of Supply Chains and Informing Mitigation Policies for Responding to Disruptions

Economic shocks are unanticipated events that have widespread impact on an economy and can lead to supply chain disruptions that propagate from one region to another. The COVID-19 pandemic is a recent example. Simulations have been applied to study the impact of COVID-19 shocks on supply chains at the macro level using various approaches. This research has developed a hybrid System Dynamics and Input/Output simulation to model the economic impact of various types of supply chain disruptions. The hybrid model provides results that match historical performance of the U.S. economy under COVID-19 shocks and provides reasonable results when applied to investigate U.S. dependence on foreign trade. Its graphical nature also supports a decision support tool that will allow policymakers to explore the costs and benefits of various policy decisions designed to mitigate the impact of a broad set of potential supply chain disruptions. © 2022 IEEE.

A SIMULATION FRAMEWORK FOR STUDYING FOREIGN RELIANCE ON REGIONAL SUPPLY CHAINS AT THE INDUSTRY LEVEL

Simulations incorporating economic input/output models have been applied recently to assess the extent of labor shocks from COVID 19 and their impact on supply chains at the macro level. Research is being done to extend these simulations for application to other scenarios of economic shocks beyond what was triggered through COVID related labor reductions. The problem of foreign supply chain dependency is of particular concern to localized regions as a significant portion of their economy is dependent on supplies from overseas. The extended simulation approach proposed here aims to optimize the degree to which the increased inventory supply targets allow for improved economic productivity and the ideal allocation per industry which most efficiently achieves this mitigation. This paper considers the application of the proposed simulation framework to study the regional dependence on the Asian supply chain. The case study presented in this paper demonstrates the economic insight that can be obtained through simulation analysis to support regional government decision making for the state of Alabama. © 2022 Society for Modeling & Simulation International (SCS)

A Simulation Framework For Studying Foreign Reliance On Regional Supply Chains At The Industry Level

Simulations incorporating economic input/output models have been applied recently to assess the extent of labor shocks from COVID 19 and their impact on supply chains at the macro level. Research is being done to extend these simulations for application to other scenarios of economic shocks beyond what was triggered through COVID related labor reductions. The problem of foreign supply chain dependency is of particular concern to localized regions as a significant portion of their economy is dependent on supplies from overseas. The extended simulation approach proposed here aims to optimize the degree to which the increased inventory supply targets allow for improved economic productivity and the ideal allocation per industry which most efficiently achieves this mitigation. This paper considers the application of the proposed simulation framework to study the regional dependence on the Asian supply chain. The case study presented in this paper demonstrates the economic insight that can be obtained through simulation analysis to support regional government decision making for the state of Alabama. © 2022 SCS.

Regionalized Modeling of Supply Chain Resiliency for Analyzing Incentive Options

This work examines the regional effects of COVID-19 supply chain shocks and potential inventory strategies to sustaining overall economic productivity through lockdowns. We introduce a multi-region extension to the economic production model proposed by Pichler, et al. [15] that was used to assess the extent of Covid-related shocks to gross output through modeling the interdependency between regional and national economies at the industry level. Our extended modeling approach aims to optimize, through genetic search, the degree to which the increased inventory supply targets allow for improved economic productivity and the ideal allocation per industry which most efficiently achieves this mitigation. The approach also integrates a new data regionalization procedure which we apply to a case study of the Alabama state economy. This application is shown to identify a set of major manufacturing and service sectors, where additional inventories enable greater sustained productivity across the Alabama region. This regional analysis of the Alabama economy highlighted the importance of sectors such as chemical, petroleum, food and beverage, and vehicle manufacturing and public administration, construction, management, transportation, and healthcare towards maintaining economic productivity. The ability to quantity regional production impacts from inventory allocations is leading to starting points for determining local government policies that target their most sensitive industries. © 2022 IEEE.

Assessing the impact of community-based exercise on healthcare utilization among older adults in the leap study

Background: Social isolation in older adults drives increased morbidity, mortality, and Medicare costs. The Leveraging Exercise to Age in Place (LEAP) study seeks to engage community-dwelling older adults (age≥50) in community-based exercise classes to improve social connectedness. LEAP has yielded statistically significant changes in measures of social isolation, including 3.3% increase in the Duke Social Support Index (n=382, p<0.0001) and 6.9% decrease in the UCLA 3-Item Loneliness Scale (n=382, p<0.0001). We seek to assess whether LEAP also impacts healthcare utilization rates. Methods: Since 2018, LEAP has enrolled 382 participants via health system or self referrals. Of these, 184 were members of the Cedars-Sinai Health Network (Los Angeles, California) and consented to review of their medical records. Using encounters in the EMR, we analyzed healthcare utilization across 3 indicators: ED visits, inpatient hospital admissions, and average length of stay. We compared encounters 6 months before class start date with those 6 months after. We excluded participants who did not complete class (<50% attendance);had inaccessible records;and/or were deceased before the 6 months after date. We also excluded those completing classes after March 19, 2020, when LEAP classes became virtual due to COVID-19. From this, we identified n=65 eligible participants. Results: There were no statistically significant changes in healthcare utilization. McNemar analysis resulted in two-tailed p=1.00 for ED visits and two-tailed p=0.75 for inpatient hospital admissions. Paired t-test analysis on average length of stay resulted in p=0.79. Conclusion: While LEAP has shown success in enhancing social connectedness in older adults, its effect on healthcare utilization has not reached statistical significance. This may be due to many factors. Analyses excluded those outside of Cedars-Sinai Health Network. Meaningful trends may also not be detectable yet. Future aims include assessing data over time, expanding sample size, and employing costbased metrics. LEAP remains a viable option to combat social isolation;further evaluation is required to explore the extent of its impact on healthcare utilization.