Improving supply chain resilience from the perspective of information processing theory

PurposeSupply chain resilience (SCR) has attracted much attention in the context of the high uncertainty caused by the coronavirus disease 2019 (COVID-19), local regional conflicts and natural disasters. Based on information processing theory (IPT), this study investigates the role of supply chain information processing capability in enhancing SCR through supply chain governance (SCG), under different conditions of environmental uncertainty.Design/methodology/approachThe hypothetical model is tested by using hierarchical regression on the primary samples collected from the Chinese manufacturing industry.FindingsThe results indicate that supply chain information processing capability has a significant positive effect on SCR. Also, SCG plays a mediating role between supply chain information processing capability and SCR. Furthermore, environmental uncertainty positively moderates the effect of supply chain information acquisition and supply chain information analysis on relational governance. However, environmental uncertainty only positively moderates the effect of supply chain information analysis on contractual governance.Originality/valueThis is the first study to explain the effect of information processing capability on SCR from the supply chain perspective, while also exploring the mediating role of SCG between SCR and supply chain information processing capacity, based on IPT.

Gauging urban resilience in the United States during the COVID-19 pandemic via social network analysis.

Social distancing policies and other restrictive measures have demonstrated efficacy in curbing the spread of the COVID-19 pandemic. However, these interventions have concurrently led to short- and long-term alterations in social connectedness. Comprehending the transformation in intracity social interactions is imperative for facilitating post-pandemic recovery and development. In this research, we employ social network analysis (SNA) to delve into the nuances of urban resilience. Specifically, we constructed intricate networks utilizing human mobility data to represent the impact of social interactions on the structural attributes of social networks while assessing urban resilience by examining the stability features of social connectedness. Our findings disclose a diverse array of responses to social distancing policies regarding social connectedness and varied social reactions across U.S. Metropolitan Statistical Areas (MSAs). Social networks generally exhibited a shift from dense to sparse configurations during restrictive orders, followed by a transition from sparse to dense arrangements upon relaxation of said orders. Furthermore, we analyzed the alterations in social connectedness as demonstrated by network centrality, which can presumably be attributed to the rigidity of policies and the inherent qualities of the examined MSAs. Our findings contribute valuable scientific insights to support informed decision-making for post-pandemic recovery and development initiatives.

Improved Artificial Neural Network with High Precision for Predicting Burnout among Managers and Employees of Start-Ups during COVID-19 Pandemic

Notwithstanding the impact that the Coronavirus pandemic has had on the physical and psychological wellness of people, it has also caused a change in the psychological conditions of many employees, particularly among organizations and privately owned businesses, which confronted numerous limitations because of the unique states of the pandemic. Accordingly, the current review expected to implement an RBF neural network to dissect the connection between demographic variables, resilience, Coronavirus, and burnout in start-ups. The examination technique was quantitative. The statistical populace of the review is directors and representatives of start-ups. In view of the statistical sample size of the limitless community, 384 of them were investigated. For information gathering, standard polls incorporating MBI-GS and BRCS and specialist-made surveys of pressure brought about by Coronavirus were utilized. The validity of the polls was affirmed by a board of specialists and their reliability was affirmed by Cronbach's alpha coefficient. The designed network structure had ten neurons in the input layer, forty neurons in the hidden layer, and one neuron in the output layer. The amount of training and test data were 70% and 30%, respectively. The output of the neural network and the collected results were compared with each other, and the designed network was able to classify all the data correctly. Using the method presented in this research can greatly help the sustainability of companies. © 2023 by the authors.

Additive manufacturing integration in E-commerce supply chain network to improve resilience and competitiveness

In light of recently increased e-commerce, also a result of the COVID-19 pandemic, this study examines how additive manufacturing (AM) can contribute to e-commerce supply chain network resilience, profitability and competitiveness. With the recent competitive supply chain challenges, companies aim to decrease cost performance metrics and increase responsiveness. In this work, we aim to establish utilisation policies for AM in a supply chain network so that companies can simultaneously improve their total network cost and response time performance metrics. We propose three different utilisation policies, i.e. reactive, proactive – both with 3D printing support – and a policy excluding AM usage in the system. A simulation optimisation process for 136 experiments under various input design factors for an (s, S) inventory control policy is carried out. We also completed a statistical analysis to identify significant factors (i.e. AM, holding cost, lead time, response time, demand amount, etc.) affecting the performance of the studied retailer supply chain. Results show that utilising AM in such a network can prove beneficial, and where the reactive policy contributes significantly to the network performance metrics. Practically, this work has important managerial implications in defining the most appropriate policies to achieve optimisation of supply network operations and resilience with the aid of AM, especially in times of turbulence and uncertainty. © 2022 The Authors

Redesign resilient production and distribution networks in the food catering industry

The catering industry is responsible for producing and distributing meals in schools, hospitals, and companies' cafeterias. Dietary pattern changes and smart working caused by the Covid pandemic highly affect the food catering industry. The demand contraction put in crisis the typical distributed productive structure of such a sector. A 30% revenue reduction from 2019 to 2020 called for the widespread network of production plants to cut corners. Production centralization leads to economies of scale but increases the logistics costs of distribution. We present a real case study of production network downsizing in the catering industry, focusing on optimizing logistics and fixed capacity costs. Meals distribution has tight time constraints due to the consumers-imposed time windows and the microbiological safety rules-imposed time windows. The proposed methodology integrates the vehicle routing problem developed by the company into a network costs optimizations problem. This optimization model exploits the pre-optimized routes defined by the company, chooses which plants to open, and performs the customer-route pairing for each picked production site. Different allocation scenarios of the same demand distribution are defined and analyzed in the case study, allowing a quantitative comparison between different redesign strategies. Results show how the mileage varies among the centralized scenarios, with a cost differential from +61% to + 29% compared to the distributed scenario. The comparison of pre and post covid demand scenarios shows how the model cushions the logistics costs: the optimal choices of the routes allows a 22% traveling reduction in the 38% post-pandemic demand reduction of the same service area. The model supports the decision-making process in the network redesign of the food catering industry by analyzing how alternative location-allocation scenarios deal with different demand patterns. © 2022, AIDI - Italian Association of Industrial Operations Professors. All rights reserved.

Redesign resilient production and distribution networks in the food catering industry

The catering industry is responsible for producing and distributing meals in schools, hospitals, and companies' cafeterias. Dietary pattern changes and smart working caused by the Covid pandemic highly affect the food catering industry. The demand contraction put in crisis the typical distributed productive structure of such a sector. A 30% revenue reduction from 2019 to 2020 called for the widespread network of production plants to cut corners. Production centralization leads to economies of scale but increases the logistics costs of distribution. We present a real case study of production network downsizing in the catering industry, focusing on optimizing logistics and fixed capacity costs. Meals distribution has tight time constraints due to the consumers-imposed time windows and the microbiological safety rules-imposed time windows. The proposed methodology integrates the vehicle routing problem developed by the company into a network costs optimizations problem. This optimization model exploits the pre-optimized routes defined by the company, chooses which plants to open, and performs the customer-route pairing for each picked production site. Different allocation scenarios of the same demand distribution are defined and analyzed in the case study, allowing a quantitative comparison between different redesign strategies. Results show how the mileage varies among the centralized scenarios, with a cost differential from +61% to + 29% compared to the distributed scenario. The comparison of pre and post covid demand scenarios shows how the model cushions the logistics costs: the optimal choices of the routes allows a 22% traveling reduction in the 38% post-pandemic demand reduction of the same service area. The model supports the decision-making process in the network redesign of the food catering industry by analyzing how alternative location-allocation scenarios deal with different demand patterns. © 2022, AIDI - Italian Association of Industrial Operations Professors. All rights reserved.

The evolution of structural resilience of global oil and gas resources trade network

This paper establishes anetwork resilience evaluation framework of the global oil and gas resourcetrade network in 2010, 2015 and 2020. The results are as follows: The links of the oil trade network present a gradually shrinking trend, and the gas tradenetwork presents a trend of close connection first and then reduced. In termsof network density, network centrality, network connectivity and network size, the structural resilience of the oil and gas resource trade network displays ashrinking trend, and the gas trade network is superior. Concerninginvulnerability and recovery, the resilience of the oil trade network issuperior to the natural gas trade network. In simulated attacks, the oil tradenetwork reveals a higher resilience towards intentional attacks, and the gastrade network shows a higher resilience towards random attacks. The Strait ofHormuz and the Strait of Malacca are critical chokepoints in the networks.

How Responsible Innovation Builds Business Network Resilience to Achieve Sustainable Performance During Global Outbreaks: An Extended Resource-Based View

The recent application of responsible innovation has driven firms to become risk-resistant and sustainable in order to counter the effects of global outbreaks like diseases or other severe situations. However, there is little research examining the role of responsible innovation in promoting firms' sustainable development. Based on the extended resource-based view (ERBV), this article examines the impact of responsible innovation on firms' sustainable performance via business network resilience. In the context of the COVID-19 pandemic, we use data from 422 Chinese manufacturing firms and find that responsible innovation has a positive effect on firms' sustainable performance and that business network resilience plays a mediating role in this relationship. Additionally, this article shows that both absorptive capacity and social media adoption not only moderate the relationship between responsible innovation and business network resilience, as well as the relationship between business network resilience and sustainable performance, but that they also moderate the mediating effect of business network resilience on the relationship between responsible innovation and sustainable performance. Using the ERBV perspective, this article contributes to the existing literature by extending current discussions on the relationship between responsible innovation and sustainable performance into the area of business network resilience while also elaborating on the roles of absorptive capacity and social media adoption in moderating this relationship.

Evaluation and Influencing Factors of Network Resilience in Guangdong-Hong Kong-Macao Greater Bay Area: A Structural Perspective

Currently, urban crises are spreading, even tending to be magnified along the urban networks. Improving urban network resilience can effectively reduce the loss and cope with sudden disasters. Based on the dimensions of regional resilience and the framework of urban network, a new evaluation system of network resilience, including economic, social, and engineering networks, was established to assess the network resilience of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from a structural perspective. We analyzed the spatial characteristics and influencing factors of network resilience using social network analysis and quadratic assignment procedure. The results were as follows: (1) regional difference was biggest in GBA's economic network strength while smallest in its transportation network strength, and the east bank of the Pearl River represented an extremely resilient connection axis;(2) the structures of network resilience and its subsystems were heterogeneous, and the connection paths of network resilience were more heterogeneous and diversified than those of the subsystems;(3) network resilience presented an obvious core-edge structure, and the spatial correlation and spillover effect between blocks were substantial;and (4) geographical proximity, as well as differences in economic development, urban agglomeration, and market development, had a significant impact on network resilience. This study provides a more systematic approach to evaluate the regional network resilience, and the results provide references for the construction of bay areas in developing countries.

Impact of the COVID-19 Epidemic on Population Mobility Networks in the Beijing-Tianjin-Hebei Urban Agglomeration from a Resilience Perspective

As an important symbol and carrier of regional social and economic activities, population mobility is a vital force to promote the re-agglomeration and diffusion of social and economic factors. An accurate and timely grasp on the impact of the COVID-19 epidemic on population mobility between cities is of great significance for promoting epidemic prevention and control and economic and social development. This study proposes a theoretical framework for resilience assessment, using centrality and nodality, hierarchy and matching, cluster, transmission, and diversity to measure the impact of the COVID-19 epidemic on population mobility in the Beijing-Tianjin-Hebei (BTH) urban agglomeration in 2020-2022, based on the migration data of AutoNavi and social network analysis. The results show that the COVID-19 epidemic had different impacts on the population network resilience of the BTH urban agglomeration based on the scale and timing. During the full-scale outbreak of the epidemic, strict epidemic prevention and control measures were introduced. The measures, such as social distancing and city and road closure, significantly reduced population mobility in the BTH urban agglomeration, and population mobility between cities decreased sharply. The population mobility network's cluster, transmission, and diversity decreased significantly, severely testing the network resilience. Due to the refinement of the epidemic control measures over time, when a single urban node was impacted, the urban node did not completely fail, and consequently it had little impact on the overall cluster, transmission, and diversity of the population mobility network. Urban nodes at different levels of the population mobility network were not equally affected by the COVID-19 epidemic. The findings can make references for the coordination of epidemic control measures and urban development. It also provides a new perspective for the study of network resilience, and provides scientific data support and a theoretical basis for improving the resilience of BTH urban agglomeration and promoting collaborative development.

Network resilience in the financial sectors: advances, key elements, applications, and challenges for financial stability regulation

Security against systemic financial risks is the main theme for financial stability regulation. As modern financial markets are highly interconnected and complex networks, their network resilience is an important indicator of the ability of the financial system to prevent risks. To provide a comprehensive perspective on the network resilience of financial networks, we review the main advances in the literature on network resilience and financial networks. Further, we review the key elements and applications of financial network resilience processing in financial regulation, including financial network information, network resilience measures, financial regulatory technologies, and regulatory applications. Finally, we discuss ongoing challenges and future research directions from the perspective of resilience-based financial systemic risk regulation.