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Assessing project criticality in a project portfolio (PP) is of great practical significance to improve robustness from damage. While project criticality assessment has increased diversity in approaches, the understanding of vulnerable project impacts is still limited. To promote a better understanding of assessing project criticality, a vulnerability measurement model is constructed.

First, integrating the tasks, projects and corresponding relationships among them, a project portfolio network (PPN) is constructed. Second, the project's vulnerability is measured by combining the topological structure and functional attributes. Third, project criticality is assessed by the vulnerability measurement results. Lastly, the proposed model is applied in a numerical example to illustrate its suitability and effectiveness.

For academia, this study provides a novel perspective on project vulnerability measurement and expands project criticality assessment tools. For practitioners, the straightforward model provides an effective tool for assessing project criticality and contributes to enhancing project portfolio management (PPM).

The impact of the task on the project is considered in this study. Topological structure and functional attributes are also integrated for measuring project vulnerability due to the impact of random attacks in an uncertain environment, providing a new perspective on the requirements of project criticality assessment and the measurement of project vulnerability.

The manufacturing industry and the producer service industry have a high degree of industrial correlation, and their integration will cause changes in the complex industrial network topology, which is an important reason for the synergistic effect. This paper describes the topology of industrial systems using complex network theory; further, it discusses how to identify the criticality and importance of industrial nodes, and whether node characteristics cause synergistic effects.

Based on the input-output data of China in 2007, 2012 and 2017, this paper constructs the industrial complex network of 30 Chinese provinces and cities, and measures the regional network characteristics of the manufacturing industry. The fixed-effect panel regression model is adopted to test the influence of agglomeration degree and centrality on synergies, and its adjustment mechanism is explored.

The degree of network agglomeration in the manufacturing industry exerts a negative impact on the synergistic effect, while the centrality of the network exerts a significant promoting effect on the synergistic effect. The results of adjustment mechanism test show that enhancing the autonomous controllable ability of the regional industrial chain in the manufacturing industry can effectively reduce the effect of network characteristics on the synergistic effect.

Based on input-output technology, this paper constructs a complex industrial network model, however, only basic flow data are used. Considerable in-depth and detailed research on the economic and technological connections within the industry should be conducted in the future. The selection of the evaluation index of the importance of industrial nodes also needs to be further considered. For historical reasons, it is also difficult to obtain and process data when carrying out quantitative analysis; therefore, it is necessary to make further attempts from the data source and the expression form of evaluation indicators.

In a practical sense this has certain reference value for the formulation of manufacturing industrial policies the optimization of regional industrial layout and the improvement of the industrial development level. It is necessary to formulate targeted and specialized industrial development strategies according to the characteristics of the manufacturing industry appropriately regulate the autonomous controllable ability of the industrial chain and avoid to limit the development of industries which is in turn limited by regional resources. Industry competition and market congestion need to be reduced industry exchanges outside the region encouraged the industrial layout optimized and the construction of a modern industrial system accelerated.

The above research results hold certain reference importance for policy formulation related to the manufacturing industry, regional industrial layout optimization and industrial development level improvement. Targeted specialized industrial development strategies need to be formulated according to the characteristics of the manufacturing industry; the autonomous controllability of the industrial chain needs to be appropriately regulated; limitation of regional resources needs to be avoided as this restricts industrial development; and industry competition and market congestion need to be reduced. Agglomeration of production factors and optimization of resource allocation is an important part of a beneficial regional economic development strategy, and it is also an inevitable choice for industrialization to develop to a certain stage under the condition of a market economy. In alignment with the research conclusions, effective suggestions can be put forward for the current major industrial policies. In the process of promoting the development of the manufacturing industry, it is necessary for regional governments to carry out unified planning and guidance on the spatial layout of each manufacturing subsector. Regional governments need to effectively allocate inter-industry resources, better share economies of scale, constantly enhance the competitive advantages and competitiveness of development zones and new districts and promote the coordinated agglomeration and development of related industries with input industries. Industrial exchanges outside the region should be encouraged, the industrial layout should be optimized and the construction of a modern industrial system should be accelerated.

Complex network theory is introduced to study the industrial synergy effect. A complex industrial network of China's 30 regions is built and key network nodes are measured. Based on the dimensionality of the “industrial node – industrial chain – industrial complex network”, the research path of industrial complex networks is improved.

Severe disruptions to maritime supply chains, including port closures, congestion and shortages in shipping capacity, have occurred during the COVID-19 pandemic. This paper’s purpose is to explore flexibility-based countermeasures that enable actors in maritime supply chains to mitigate the effects of disruptions with different characteristics.

Semi-structured interviews were conducted with shipping lines, shippers, forwarders and ports. Data on the COVID-19 pandemic's effects and countermeasures were collected and compared with data regarding the 2016–2017 Gothenburg port conflict.

Spatial, capacity, service and temporal flexibility emerged as the primary countermeasures, whilst important characteristics of disruptions were geographical spread, duration, uncertainty, criticality, the element of surprise and intensity. Spatial flexibility was exercised in both disruptions by switching to alternative ports. During the COVID-19 pandemic, ensuring capacity flexibility included first removing and then adding vessels. Shipping lines exercising service flexibility prioritised certain cargo, which made the spot market uncertain and reduced flexibility for forwarders, importers and exporters that changed carriers or traffic modes. Experience with disruptions meant less surprise and better preparation for spatial flexibility.

Understanding how actors in maritime supply chains exercise flexibility-based countermeasures amid disruptions with different characteristics can support preparedness for coming disruptions.

Comparing flexibility-based measures in a pandemic versus port conflict provides insights into the important characteristics of disruptions and the relevance of mitigation strategies. The resilience of maritime supply chains, although underexamined compared with manufacturing supply chains, is essential for maintaining global supply chain flows.

This study examines the firm-level financial consequences caused by supply chain disruptions during COVID-19 and explores how firms' supply chain diversification strategies, including diversified suppliers, customers and products, moderate the negative effect on firm performance.

Based on data drawn from 222 publicly traded firms in China, the authors use event study methodology to estimate the effects of supply chain disruptions on the financial performance of affected firms. Regression analyses are conducted to examine the moderating effects of supply chain diversification.

Firms affected by supply chain disruptions during COVID-19 experienced a significant decline in shareholder value in two weeks and a subsequent decrease in operating performance in one year. Diversified suppliers, customers and products act as shock absorbers to alleviate the negative effects. Further regression shows a substitution effect between customer and product diversification. Cross-industry comparisons reveal that service firms experienced more loss than manufacturing firms. Customer diversification mitigates the adverse effects of supply chain disruptions for both manufacturing and service firms. Supplier diversification exerts a noteworthy role in manufacturing firms, while product diversification is beneficial for service firms.

The study provides empirical evidence on the magnitude of financial consequences of supply chain disruptions during COVID-19 in both the short term and long term and enriches the current understanding of how to build resilience from the supply chain diversification perspective.

With the development of science and technology, more creators are trying to use new crafts to represent the cultural trends of the social media era, which makes cultural heritage innovative and new genres emerge. This compels the academic community to examine craft from a new perspective. It is very helpful to understand the hidden representational structure of craft more deeply and improve the craft innovation system of cultural and creative products that we deconstruct the craft based on Complex Network and discover its intrinsic connections.

The research crawled and cleaned the craft information of the top 20% products on the Forbidden City’s cultural and creative products online and then performed Complex Network modeling, constructed three craft representation networks among function, material and technique, quantified and analyzed the inner connections and network structure of the craft elements, and then analyzed the cultural inheritance and innovation embedded in the craft representation networks.

The three dichotomous craft representation networks constructed by combining function, material and technique: (1) the network density is low and none of them has small-world characteristics, indicating that the innovative heritage of the craft elements in the Forbidden City’s cultural and creative products is at the stage of continuous exploration and development, and multiple coupling innovation is still insufficient; (2) all have scale-free characteristics and there is still a certain degree of community structure within each network, indicating that the coupling innovation of craft elements of the Forbidden City’s cultural and creative products is seriously uneven, with some specific “grammatical combinations” and an Island Effect in the network structure; (3) the craft elements with high network centrality emphasize the characteristics of decorative culture and design for the masses, as well as the pursuit of production efficiency and economic benefits, which represent the aesthetic purport of contemporary Chinese society and the ideological trend of production and life.

The Forbidden City’s cultural and creative products should continue to develop and enrich the multi-coupling innovation of craft elements, clarify and continue their own brand unique craft genes, and make full use of the network important nodes role.

External stakeholder risks (ESRs) caused by unfavorable behaviors hinder the success of project portfolios (PPs). However, due to complex project dependency and numerous risk causality in PPs, assessing ESRs is difficult. This research aims to solve this problem by developing an ESR-PP two-layer fuzzy Bayesian network (FBN) model.

A two-layer FBN model for evaluating ESRs with risk causality and project dependency is proposed. The directed acyclic graph (DAG) of an ESR-PP network is first constructed, and the conditional probability tables (CPTs) of the two-layer network are further presented. Next, based on the fuzzy Bayesian network, key variables and the impact of ESRs are assessed and analyzed by using GeNIe2.3. Finally, a numerical example is used to demonstrate and verify the application of the proposed model.

The proposed model is a useable and effective approach for ESR assessment while considering risk causality and project dependency in PPs. The impact of ESRs on PP can be calculated to determine whether to control risk, and the most critical and heavily contributing risks and project(s) in the developed model are identified based on this.

This study extends prior research on PP risk in terms of stakeholders. ESRs that have received limited attention in the past are explored from an interaction perspective in the PP domain. A new two-layer FBN model considering risk causality and project dependency is proposed, which can synthesize different dependencies between projects.

Research and development (R&D) projects are often pursued through a project portfolio (PP). R&D PPs involve many stakeholders, and without proactive management, their interactions may lead to conflict risks. These conflict risks change dynamically with different stages of the PP life cycle, increasing the challenge of PP risk management. Existing conflict risk research mainly focuses on source identification but lacks risk assessment work. To better manage the stakeholder conflict risks (SCRs) of R&D PPs, this study employs the dynamic Bayesian network (DBN) to construct its dynamic assessment model.

This study constructs a DBN model to assess the SCRs in R&D PP. First, an indicator system of SCRs is constructed from the life cycle perspective. Then, the risk relationships within each R&D PPs life cycle stage are identified via interpretative structural modeling (ISM). The prior and conditional probabilities of risks are obtained by expert judgment and Monte Carlo simulation (MCS). Finally, crucial SCRs at each stage are identified utilizing propagation analysis, and the corresponding risk responses are proposed.

The results of the study identify the crucial risks at each stage. Also, for the crucial risks, this study suggests appropriate risk response strategies to help managers better perform risk response activities.

This study dynamically assesses the stakeholder conflict risks in R&D PPs from a life-cycle perspective, extending the stakeholder risk management research. Meanwhile, the crucial risks are identified at each stage accordingly, providing managerial insights for R&D PPs.

Currently, China’s manufacturing industry chain still faces the danger of chain breakage due to the persistent “lack of technology” issue. The definition and detection of key nodes in the industry chain are significant to the enhancement of the stability of the industry chain. Therefore, detecting the key nodes in the manufacturing industry chain is necessary.

A complex network based on the links amongst listed manufacturing enterprises is built, and the authors analyse the network’s basic characteristics and vulnerability, taking into account the impact of scientific and technological innovation on the stability of the industry chain.

It is found that the high structural characteristic of midstream nodes in the naval architecture and marine engineering equipment industry chain determines their importance to stability, and the key status of upstream nodes is reflected in the weakness of technological innovation. The upstream nodes should focus on improving their independent innovation and R&D capability, whilst the midstream nodes should maintain a close supply–demand cooperation relationship.

The key node detection model for industry chain stability is constructed by considering various factors from the perspective of network and technological innovation. Empirical study is conducted to verify effectiveness of proposed method.

This paper aims to conduct a detailed analysis of the industrial practices currently being used in the geothermal energy industry and to determine whether they are contributing to any limitations. A HAZOP-based upgradation model for improvement in existing industrial practices is proposed to ensure the removal of inefficient conventional practices. The HAZOP-based upgradation model examines the setbacks, identifies its causes and consequences and suggests improvement methods comprising of modern-day technology.

This paper proposed a HAZOP-based upgradation model for improvement in existing industrial practices. The proposed HAZOP model identifies the drawbacks brought on by conventional practices and suggests improvements.

The study reviewed the challenges geothermal power plants currently face due to conventional practices and suggested a total of 22 upgradation recommendations. From those, a total of 11 upgradation modules comprising modern digital technology and Industry 4.0 elements were proposed to improve the existing practices in the geothermal energy industry. Autonomous robots, augmented reality, machine learning and Internet of Things were identified as useful methods for the upgradation of the existing geothermal energy system.

If proposed recommendations are incorporated, the efficiency of geothermal energy generation will increase as cumulating setbacks will no longer degrade the work output.

The proposed recommendation by the study will make way for Industry 4.0 integration with the geothermal energy sector.

The paper uses a proposed HAZOP-based upgradation model to review issues in existing industrial practices of the geothermal energy sector and recommends solutions to overcome operability issues using Industry 4.0 technologies.

Recently, the serviceability of the transportation infrastructure in urban areas has become crucial. Any impact of the hazardous conditions on the urban road network causes significant disruption to the functioning of the urban region, making the city’s resilience a point of concern. Thereby, the purpose of the study is to examine the city’s recovery capacity to absorb the impacts of adverse events like urban floods.

This study examines the road network resilience for an urban flood event for zones proposed by the Municipal Corporation to develop multiple central business districts. This study proposes a novel approach to measure the resilience of road networks in an urban region under floods caused due to heavy rainfall. A novel Road Network Resilience Index (RNRI) based on the serviceability of the road network during floods is proposed, estimated using Analytic Hierarchy Process - Multiple Criteria Evaluation (AHP-MCE) approaches by using the change in street centrality, impervious area and road network density. This study examines and analyses the resilience of road networks in two conditions: flood and nonflood conditions. Resilience was estimated for both the conditions at the city level and the decentralized zone level.

Based on RNRI values, this study identifies zones having a lower or higher resilience index. The central, southern and eastern zones have lower road network resilience and western and northern zones have high road network resilience.

The proposed methodology can be used to increase road network resilience within the city under flood conditions.

The previous literature on road network resilience concentrates on the physical properties of roads after flood events. This study demonstrates the use of nonstructural measures to improve the resilience of the road network by innovatively using the AHP-MCE approach and street centrality to measure the resilience of the road network.