(471d) A Quantitative Framework Towards Managing Risks and Disruptions in Energy and Manufacturing Supply Chains

Today’s energy and manufacturing supply chains are exposed to a plethora of risks and disruptions, including but not limited to, extreme weather events, natural disasters, cyberattacks, wars and infectious diseases [1]. The recent COVID-19 pandemic and other “black swan” disruptions have exposed the infrastructural and managerial shortcomings of globalized supply chains, highlighted by their inability to respond effectively to such novel risks [2]. To tackle the increased frequency and intensity of these new types of disruptions, it is imperative to design and operate the next generation of supply chains taking into account along with efficiency, their resilience and sustainability footprints. Holistic quantitative frameworks to address these emerging challenges in the realm of supply chain networks are currently lacking.

To this end, we present an first effort towards a quantitative decision-aiding framework to identify trade-offs and leverage synergies among economic, resilience, and sustainability objectives for energy and manufacturing supply chains. The framework encompasses: (1) partial and complete failure of network components; (2) consequences of cascading disruptions on network performance and planning; and (3) dynamic modeling to gauge the evolution of network performance over multiple time horizons. The capabilities of the proposed methodology are demonstrated by examining the performance of a distribution network under demand and capacity disruptions [3]. A multi-objective optimization problem is formulated to capture trade-offs among economic (transportation and holding costs), resilience (stored inventories), and service level (fulfilled demand) objectives. Furthermore, a sensitivity analysis is presented to determine the effect of specified service levels on economic and resilience performance. (250 words)

References

[1]. El-Halwagi, M. M., Sengupta, D., Pistikopoulos, E. N., Sammons, J., Eljack, F., & Kazi, M.-K. (2020). Disaster-Resilient Design of Manufacturing Facilities Through Process Integration: Principal Strategies, Perspectives, and Research Challenges. Frontiers in Sustainability, 1.

[2]. Iakovou, E., & White, C. (2020). How to build more secure, resilient, next-gen US supply chains. Brookings Institute TechStream; https://www.brookings.edu/techstream/how-to-build-more-secure-resilient-next-gen-u-s-supply-chains/ .

[3]. Ivanov, D., Pavlov, A., & Sokolov, B. (2014). Optimal distribution (re)planning in a centralized multi-stage supply network under conditions of the ripple effect and structure dynamics. European Journal of Operational Research, 237(2), 758–770.