Open AccessAnalyzing the Manufacturing Supply Chain Performance for Urgent Item During COVID-19 Outbreak
Author(s) -
Ali Arishi,
Kannan Krishnan,
Vatsal Maru
Publication year - 2021
Publication title -
industrial and systems engineering review
Language(s) - English
Resource type - Journals
ISSN - 2329-0188
DOI - 10.37266/iser.2021v9i1.pp15-31
Subject(s) - supply chain , computer science , covid-19 , resilience (materials science) , pandemic , supply and demand , identification (biology) , survivability , margin (machine learning) , risk analysis (engineering) , operations research , business , engineering , machine learning , economics , marketing , medicine , computer network , physics , botany , disease , pathology , biology , infectious disease (medical specialty) , thermodynamics , microeconomics
As COVID-19 pandemic spreads in different regions with varying intensity, supply chains (SC) need to utilize an effective mechanism to adjust spike in both supply and demand of resources, and need techniques to detect unexpected behavior in SC at an early stage. During COVID-19 pandemic, the demand of medical supplies and essential products increases unexpectedly while the availability of recourses and raw materials decreases significantly. As such, the questions of SC and society survivability were raised. Responding to this urgent demand quickly and predicting how it will vary as the pandemic progresses is a key modeling question. In this research, we take the initiative in addressing the impact of COVID-19 disruption on manufacturing SC performance overwhelmed by the unprecedented demands of urgent items by developing a digital twin model for the manufacturing SC. In this model, we combine system dynamic simulation and artificial intelligence to dynamically monitor SC performance and predict SC reaction patterns. The simulation modeling is used to study the disruption propagation in the manufacturing SC and the efficiency of the recovery policy. Then based on this model, we develop artificial neural network models to learn from disruptions and make an online prediction of potential risks. The developed digital twin model is aimed to operate in real-time for early identification of disruptions and the respective SC reaction patterns to increase SC visibility and resilience.