Abstract Today’s supply chain is becoming complex and fragile. Hence, supply chain managers need to create and unlock the value of the smart supply chain. A smart supply chain requires connectivity, visibility, and agility, and it needs be integrated and intelligent. The digital twin (DT) concept satisfies these requirements. Therefore, we propose creating a DT-driven supply chain (DTSC) as an innovative and integrated solution for the smart supply chain. We provide background information to explain the DT concept and to demonstrate the method for building a DTSC by using the DT concept. We discuss three research opportunities in building a DTSC, including supply chain modeling, real-time supply chain optimization, and data usage in supply chain collaboration. Finally, we highlight a motivating case from JD.COM, China’s largest retailer by revenue, in applying the DTSC platform to address supply chain network reconfiguration challenges during the COVID-19 pandemic. 

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Digital twin-driven smart supply chain

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The Covid-19 pandemic impact on people’s lives has been devastating. Around the world, people have been forced to stay home, resorting to the use of digital technologies in an effort to continue their life and work as best they can. Covid-19 has thus accelerated society’s digital transformation towards Industry 4.0 (the fourth industrial revolution). Using scientometric analysis, this study presents a systematic literature review of the themes within Industry 4.0. Thematic analysis reveals that the Internet of Things (IoT), Artificial Intelligence (AI), Cloud computing, Machine learning, Security, Big Data, Blockchain, Deep learning, Digitalization, and Cyber–physical system (CPS) to be the key technologies associated with Industry 4.0. Subsequently, a case study using Industry 4.0 technologies to manage the Covid-19 pandemic is discussed. In conclusion, Covid-19,is clearly shown to be an accelerant in the progression towards Industry 4.0. Moreover, the technologies of this digital transformation can be expected to be invoked in the management of future pandemics.

The application of industry 4.0 technologies in pandemic management: Literature review and case study

Long-term sequelae of symptomatic infection caused by SARS-CoV-2 are largely undiscovered. We performed a prospective cohort study on consecutively hospitalized Sars-CoV-2 patients (March-May 2020) for evaluating COVID-19 outcomes at 6 and 12 months. After hospital discharge, patients were addressed to two follow-up pathways based on respiratory support needed during hospitalization. Outcomes were assessed by telephone consultation or ambulatory visit. Among 471 patients, 80.9% received no respiratory support during hospitalization; 19.1% received non-invasive ventilation (NIV) or invasive mechanical ventilation (IMV). 58 patients died during hospitalization, therefore 413 were enrolled for follow-up. At 6 months, among 355 patients, the 30.3% had any symptoms, 18.0% dyspnea, 6.2% neurological symptoms. Fifty-two out of 105 had major damages in interstitial computed tomography images. NIV/IMV patients had higher probability to suffer of symptoms (aOR = 4.00, 95%CI:1.99-8.05), dyspnea (aOR = 2.80, 95%CI:1.28-6.16), neurological symptoms (aOR = 9.72, 95%CI:2.78-34.00). At 12 months, among 344, the 25.3% suffered on any symptoms, 12.2% dyspnea, 10.1% neurological symptoms. Severe interstitial lesions were present in 37 out of 47 investigated patients. NIV/IMV patients in respect to no respiratory support, had higher probability of experiencing symptoms (aOR = 3.66, 95%CI:1.73-7.74), neurological symptoms (aOR = 8.96, 95%CI:3.22-24.90). COVID-19 patients showed prolonged sequelae up to 12 months, highlighting the need of follow-up pathways for post-COVID-19 syndrome. 

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“Long COVID” results after hospitalization for SARS-CoV-2 infection

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The problem is the vaccination of a large number of people in a short time period, using minimum space and resources. The tradeoff is that this minimum number of resources must guarantee a good service for the patients, represented by the time spent in the system and in the queue. The goal is to develop a digital twin which integrates the physical and virtual systems and allows a real-time mapping of the patient flow to create a sustainable and dynamic vaccination center. Firstly, to reach this goal, a discrete-event simulation model is implemented. The simulation model is integrated with a mobile application that automatically collects time measures. By processing these measures, indicators can be computed to find problems, run the virtual model to solve them, and replicate improvements in the real system. The model is tested in a South Tyrol vaccination clinic and the best configuration found includes 31 operators and 306 places dedicated for the queues. This configuration allows the vaccination of 2164 patients in a 10-h shift, with a mean process time of 25 min. Data from the APP are managed to build the dashboard with indicators like number of people in queue for each phase and resource utilization.

https://www.mdpi.com/2071-1050/13/13/7396/pdf

Digital Twin of COVID-19 Mass Vaccination Centers

Industry 4.0 at the Service of Public Health Against the COVID-19 Pandemic.

E-commerce is a continuously growing sector significantly affected by sustainability issues during the last few years. To deal with economic, environmental and social sustainability aspects, e-commerce platforms consolidate orders to pick-up several requests from the same location, defining the so-called Few-to-Many Pick-up and Delivery Vehicle Routing Problem (F-M VRPPD). The proposed contribution addresses the optimisation of this problem by developing a multi-objective simulated annealing algorithm distinguished by four tailored Local Search (LS) operators specifically developed to increase the probability to identify feasible solutions and decrease the computational time. This algorithm is validated with several instances of a case study e-commerce platform based in an European mountain region. Firstly, the original LS operators are compared to benchmark literature ones to solve identical problems, reporting better performance in 84% of these instances. Furthermore, for the most relevant scenarios significant results are presented and discussed concerning the economic, environmental and social performance of the defined solutions according to the characteristics of the instances, as the routes height profile and the drivers’ metabolic energy consumption. The tri-dimensional Pareto frontiers suggest how through a slight worsening in the economic objective function it is possible to improve the social one by up to 18.3% on average. 

Multi-objective optimisation for sustainable few-to-many pickup and delivery vehicle routing problem

As all sectors, e-commerce should consider social sustainability which is gaining importance within society. To integrate these themes into the logistics of e-commerce platforms, a mathematical model is proposed for a socially and economically sustainable Vehicle Routing Problem (VRP) that optimizes the drivers energy consumption along with delivery costs. The model is solved through Simulated Annealing and is validated with a realistic case study for an e-commerce platform. The results show that by assigning a small weight to the social sustainability dimension, the platform can improve by 20% this aspect with a worsening in the economic performances of just 4%. 

Riccardo Tronconi

Papers

Digital Twin of COVID-19 Mass Vaccination Centers

Industry 4.0 at the Service of Public Health Against the COVID-19 Pandemic.

Multi-objective optimisation for sustainable few-to-many pickup and delivery vehicle routing problem

Social-economic sustainable Vehicle Routing Problem for local e-commerce platforms