Biblio

Under the new situation of China's new infrastructure and digital transformation and upgrading, large IT companies such as the United States occupy the market of key information infrastructure components in important fields such as power and energy in China, which makes the risk of key information infrastructure in China's power enterprises become more and more prominent. In the power Internet of Things environment where everything is connected, the back doors and loopholes of basic software and hardware caused by the supply chain risks of key information infrastructure have broken through the foundation of power cyber-security and information security defense, and the security risk management of power key information infrastructure cyber-security has become urgent. Therefore, this paper studies the construction of the cyber-security management framework of key information infrastructure suitable for electric power enterprises, and defines the security risk assessment norms of each link of equipment access to the network. Implement the national cyber-security requirements, promote the cyber-security risk controllable assessment service of key information infrastructure, improve the security protection level of power grid information system from the source, and promote the construction and improvement of the network and information security system of power industry.

This paper proposes a cybersecurity maturity model to assess the capabilities of medical organizations to identify their level of maturity, prioritizing privacy and personal data protection. There are problems such as data breaches, the lack of security measures in health information, and the poor capacity of organizations to handle cybersecurity threats that generate concern in the health sector as they seek to mitigate risks in cyberspace. The proposal, based upon C2M2 (Cybersecurity Capability Maturity Model), incorporates practices and controls which allow organizations to identify security gaps generated through cyberattacks on sensitive health patient data. This model seeks to integrate the best practices related to privacy and protection of personal data in the Peruvian legal framework through the Administrative Directive No. 294-MINSA and the personal data protection Act No. 29733. The model consists of 3 evaluation phases. 1. Assessment planning; 2. Execution of the evaluation; 3. Implementation of improvements. The model was validated and tested in a public sector medical organization in Lima, Peru. The preliminary results showed that the organization is at Level 1 with 14% of compliance with established controls, 34% in risk, threat and vulnerability management practices and 19% in supply chain management. These the 3 highest percentages of the 10 evaluated domains.

Due to the increasing complexity of modern hetero-geneous System-on-Chips (SoC) and the growing vulnerabilities, security risk assessment and quantification is required to measure the trustworthiness of a SoC. This paper describes a systematic approach to model the security risk of a system for malicious hardware attacks. The proposed method uses graph analysis to assess the impact of an attack and the Common Vulnerability Scoring System (CVSS) is used to quantify the security level of the system. To demonstrate the applicability of the proposed metric, we consider two open source SoC benchmarks with different architectures. The overall risk is calculated using the proposed metric by computing the exploitability and impact of attack on critical components of a SoC.

Design of smart risk assessment system for the agricultural products and the food safety inspection based on multivariate data analysis is studied in this paper. The designed quality traceability system also requires the collaboration and cooperation of various companies in the supply chain, and a unified database, including agricultural product identification system, code system and security status system, is required to record in detail the trajectory and status of agricultural products in the logistics chain. For the improvement, the multivariate data analysis is combined. Hadoop cannot be used on hardware with high price and high reliability. Even for groups with high probability of the problems, HDFS will continue to use when facing problems, and at the same time. Hence, the core model of HDFS is applied into the system. In the verification part, the analytic performance is simulated.

Vendor cybersecurity risk assessment is of critical importance to smart city infrastructure and sustainability of the autonomous mobility ecosystem. Lack of engagement in cybersecurity policies and process implementation by the tier companies providing hardware or services to OEMs within this ecosystem poses a significant risk to not only the individual companies but to the ecosystem overall. The proposed quantitative method of estimating cybersecurity risk allows vendors to have visibility to the financial risk associated with potential threats and to consequently allocate adequate resources to cybersecurity. It facilitates faster implementation of defense measures and provides a useful tool in the vendor selection process. The paper focuses on cybersecurity risk assessment as a critical part of the overall company mission to create a sustainable structure for maintaining cybersecurity health. Compound cybersecurity risk and impact on company operations as outputs of this quantitative analysis present a unique opportunity to strategically plan and make informed decisions towards acquiring a reputable position in a sustainable ecosystem. This method provides attack trees and assigns a risk factor to each vendor thus offering a competitive advantage and an insight into the supply chain risk map. This is an innovative way to look at vendor cybersecurity posture. Through a selection of unique industry specific parameters and a modular approach, this risk assessment model can be employed as a tool to navigate the supply base and prevent significant financial cost. It generates synergies within the connected vehicle ecosystem leading to a safe and sustainable economy.

Today’s Supply Chains (SC) are engulfed in a maelstrom of risks which arise mainly from uncertain, contradictory, and incomplete information. A decision-making process is required in order to detect threats, assess risks, and implements mitigation methods to address these issues. However, Neutrosophic Data Analytic Hierarchy Process (NDAHP) allows for a more realistic reflection of real-world problems while taking into account all factors that lead to effective risk assessment for Multi Criteria Decision-Making (MCDM). The purpose of this paper consists of an implementation of the NDAHP for MCDM aiming to identifying, ranking, prioritizing and analyzing risks without considering SC’ expert opinions. To that end, we proceed, first, for selecting and analyzing the most 23 relevant risk indicators that have a significant impact on the SC considering three criteria: severity, occurrence, and detection. After that, the NDAHP method is implemented and showcased, on the selected risk indicators, throw an illustrative example. Finally, we discuss the usability and effectiveness of the suggested method for the SCRM purposes.

The purpose of this research work is to develop an approach based on risk management with a view to provide managers and decision-makers with assistance and appropriate guidelines to combine Lean and Green in a successful and integrated way. Risk cannot be managed if not well-identified; hence, a classification of supply chain risks in a Lean Green context was provided. Subsequently to risk identification an approach based on Weighted Product Method (WPM) was proposed; for risk assessment and prioritization, for its ease of use, flexibility and board adaptability. The output of this analysis provides visibility about organization's position toward desired performance and underlines crucial risks to be addressed which marks the starting point of the way to performance improvement. A case study was introduced to demonstrate the applicability and relevance of the developed framework.

The assurance of the operability of surface water treatment facilities lies in many factors, but the factor with the largest impact on said assurance is the availability of the necessary chemicals. Facilities across the country vary in their processes and sources, but all require chemicals to produce potable water. The purpose of this project was to develop a risk assessment tool to determine the shortfalls and risks in the water treatment industry's chemical supply chain, which was used to produce a risk mitigation plan ensuring plant operability. To achieve this, a Fault Tree was built to address four main areas of concern: (i) market supply and demand, (ii) chemical substitutability, (iii) chemical transportation, and (iv) chemical storage process. Expert elicitation was then conducted to formulate a Failure Modes and Effects Analysis (FMEA) and develop Radar Charts, regarding the operations and management of specific plants. These tools were then employed to develop a final risk mitigation plan comprising two parts: (i) a quantitative analysis comparing and contrasting the risks of the water treatment plants under study and (ii) a qualitative recommendation for each of the plants-both culminating in a mitigation model on how to control and monitor chemical-related risks.

Logistics risk assessment in the supply chain is considered as one of the important topics that has attracted the attention of researchers in recent years; Companies that struggle to manage their logistical risks by not putting in place resilient strategies to mitigate them, may suffer from significant financial losses; The automotive industry is a vital sector for the Moroccan economy, the year 2020, the added-value of the automotive industry in Morocco is higher than that of the fertilizer (Fathi, n.d.) [1], This sector is considered the first exporter of the country. Our study will focuses on the assessment of the pure logistical risks in the moroccan automotive industry. Our main objective for this study is to assess the logistical risks which will allow us to put in place proactive and predictive resilient strategies for their mitigation.

With the rapid improvement of China's industrial production level, there are an increasing number of industrial enterprises and kinds of products. The quality and safety supervision of industrial products is an important step to ensure people's livelihood safety. The current supervision includes a number of processes, such as risk monitoring, public opinion analysis, supervision, spot check and postprocessing. The lack of effective information integration and sharing between the above processes cannot support the implementation of whole-chain regulation well. This paper proposes a whole-chain supervision method of industrial product quality and safety based on a knowledge graph, which integrates massive and complex data of the whole chain and visually displays the relationships between entities in the regulatory process. This method can effectively solve the problem of information islands and track and locate the quality problems of large-scale industrial products.

With the continuous development of world economy, the trade and connection between countries are getting closer, in which ports are playing an increasingly important role. However, due to the inherent complexity of port operational environment, ports are exposed to various types of hazards and more likely to encounter risks with high frequency and serious consequences. Therefore, proper and effective risk management of ports is particularly essential and necessary. In this research, literature from three aspects including risk assessment of port operations and service, safety management of dangerous goods, and port supply chain risk management was collected and investigated, in order to put forward the future research direction related to the risk management of port operations. The research results show that, firstly, most of the current research mainly focuses on the operational risk of traditional ports and a lot of relevant achievements have been seen. However, few scholars have studied the risk issues of smart ports which are believed to be the trend of future with the rapid development and application of high and new technologies. Thus, it is suggested that more attention should be shifted to the identification and assessment of operational risks of smart ports considering their characteristics. Secondly, although the risk evaluation systems of port operational safety have been established and widely studied, more efforts are still needed in terms of the suitability and effectiveness of the proposed indicators, especially when dangerous goods are involved. Thirdly, risk management of port supply chain is another popular topic, in which, one of the main difficulties lies on the collection of risk related statistics data due to the fact that port supply chain systems are usually huge and complex. It is inevitably that the evaluation results will lack objectivity to some extent. Therefore, it calls for more research on the risk assessment of port supply chains in a quantitative manner. In addition, resilience, as an emerging concept in the transportation field, will provide a new angle on the risk management of port supply chains.

To make supply chains sustainable and smart, companies can use information and communication technologies to manage procurement, sourcing, conversion, logistics, and customer relationship management activities. Characterized by profit, people, and planet, the supply chain processes of creating values and managing risks are expected to be digitally transformed. Once digitized, datafied, and networked, supply chains can account for substantial progress towards sustainability. Given the lack of clarity on the concepts of resilience and human rights for the supply chain, especially with the recent advancement of social media, big data, artificial intelligence, and cloud computing, the study conducts a scoping review. To identify the size, scope, and themes, it collected 180 articles from the Web of Science bibliographic database. The bibliometric findings reveal the overall conceptual and intellectual structure, and the gaps for further research and development. The concept of resilience can be enriched, for instance, by the environmental, social, and governance (ESG) concerns. The enriched notion of resilience can also be expressed in digitized, datafied, and networked forms.

As the China government already putting forward the strategic objectives to peak carbon dioxide emissions before 2030 and achieve carbon neutrality before 2060, social consensus of green low carbon has promoted the development of integrated energy services. As an emerging format, integrated energy services break the trade and technical barriers between different varieties of energy. As a carrier of integrated energy services, integrated energy service companies still have many problems in their own optimized operation. This paper studies the risk mechanism of energy chain considering the risk preference of energy service companies, and analyses the correlation between the risk preference of energy service companies and the risk of energy chain. Based on set pair theory and interval number, the paper establishes an energy chain risk assessment model to overcome the shortcomings of traditional evaluation methods, which is able to characterize risk appetite and uncertainties. Finally, the results of simulation and tests verify the effectiveness of the proposed method of the novel.

With over 80% of goods transportation in volume carried by sea, ports are key infrastructures within the logistics value chain. To address the challenges of the globalized and competitive economy, ports are digitizing at a fast pace, evolving into smart ports. Consequently, the cyber-resilience of ports is essential to prevent possible disruptions to the economic supply chain. Over the last few years, there has been a significant increase in the number of disclosed cyber-attacks on ports. In this paper, we present the capabilities of a high-end hybrid cyber range for port cyber risks awareness and training. By describing a specific port use-case and the first results achieved, we draw perspectives for the use of cyber ranges for the training of port actors in cyber crisis management.

Smart city technologies promise reduced congestion by optimizing transportation movements. Increased connectivity, however, may increase the attack surface of a municipality's critical functions. Increased supply chain attacks (up nearly 80 % in 2019) and municipal ransomware attacks (up 60 % in 2019) motivate the need for holistic approaches to risk assessment. Therefore, we present a methodology to quantify the degree to which supply-chain movements may be observed or disrupted via compromised smart-city devices. Our data-processing pipeline uses publicly available datasets to model intermodal commodity flows within and surrounding a municipality. Using a hierarchy tree to adaptively sample spatial networks within geographic regions of interest, we bridge the gap between grid- and network-based risk assessment frameworks. Results based on fieldwork for the Jack Voltaic exercises sponsored by the Army Cyber Institute demonstrate our approach on intermodal movements through Charleston, SC and San Diego, CA.

The international Supply Chains (SC) have expanded rapidly over the decades and also consist of many entities and business partners. The increasing complexity of supply chain makes it more vulnerable to a security threat. Therefore, it is necessary to evaluate security management systems to ensure the flow of goods in SC. In this paper we used international standards to assess the security of the company's supply chain compliance with ISO 28001. Supply chain security that needs to be assessed includes all inbound logistics activities to outbound logistics. The aim of this research is to analyse the security management system by identifying security threat, consequences, and likelihood to develop adequate countermeasures for the security of the company's supply chain. Security risk assessment was done using methodology compliance with ISO 28001 which are identify scope of security assessment, conduct security assessment, list applicable threat scenario, determine consequences, determine likelihood, determine risk score, risk evaluation using risk matrix, determine counter measures, and estimation of risk matrix after countermeasures. This research conducted in one of the leather factory in Indonesia. In this research we divided security threat into five category: asset security, personnel security, information security, goods and conveyance security, and closed cargo transport units. The security assessment was conducted by considering the performance review according to ISO 28001: 2007 and the results show that there are 22 security threat scenarios in the company's supply chain. Based upon a system of priorities by risk score, countermeasures are designed to reduce the threat into acceptable level.

Business's strength arises from the strength of its supply chain. Therefore, a proper supply chain management is vital for business continuity. One of the most challenging parts of SCM is the contract negotiation, and one main aspect of the negotiation is to know the risk associated with each range of quantity agreed on. Currently Managers assess the quantity to be supplied based on a binary way of either full or 0 supply, This paper aims to assess the corresponding quantities risks of the suppliers on a multilayer basis. The proposed approach uses fuzzy logic as an artificial intelligence tool that would develop the verbal terms of managers into numbers to be dealt with. A company that produces fresh frozen vegetables and fruits in Egypt who faces the problem of getting the required quantities from the suppliers with a fulfilment rate of 33% was chosen to apply the proposed model. The model allowed the managers to have full view of risk in their supply chain effectively and decide their needed capacity as well as the negotiation terms with both suppliers and customers. Future work should be the use of more data in the fuzzy database and implement the proposed methodology in an another industry.

This article analyzes the basic concepts of supply chain finance, participating institutions, business methods, and exposure to risks. The author combined the basic content of the Internet of Things and block chain technology to carry out research. This paper studies the specific applications of the Internet of Things and block chain technology in supply chain financial risk identification, supply chain financial risk assessment, full-process logistics supervision, smart contract transaction management, corporate financial statement sorting, and risk prevention measures. The author's purpose is to improve the financial risk management level of the enterprise supply chain and promote the stable development of the enterprise economy.

An increasing number of industries around the world are adopting advance manufacturing technologies for product design, among which additive manufacturing (AM) is gaining attention among aerospace, defense, automotive and health care domains. Products with complicated designs demanding lesser weight, improved performance and conformance are manufactured by companies using AM technologies. Some noticeable examples of ducting, airflow system and vent products in the aerospace domain can be seen being made out of AM techniques. One of the benefits being mentioned is the significant reduction in the number of components going into a finished product, thereby impacting the supply chain as well. However, one of the challenges in AM process is to reduce the process variation which affects the reliability of the product. To realize the true benefits of additively manufactured products, it is imperative to ensure that the reliability of AM products is similar or better than traditionally manufactured products. Current state of art for assessing reliability of traditionally manufactured products is mature. However, the reliability assessment framework for products manufactured by advanced technologies are being studied upon. In this direction, this paper highlights a structured reliability assessment framework for additive manufactured products, which will help in identifying, analyzing and mitigating reliability risks as part of product development life cycle.

The modern electronics supply chain is a globalized marketplace with the increasing threat of counterfeit integrated circuits (ICs) being installed into mission critical systems. A number of methods for detecting counterfeit ICs exist; however, effective test and evaluation (T&E) methods to assess the confidence of detecting recycled ICs are needed. Additionally, methods for the trustworthy tracking of recycled ICs in the supply chain are also needed. In this work, we propose a novel methodology to address the detection and tracking of recycled ICs at each stage of the electronics supply chain. We present a case study demonstrating our assessment model to calculate the confidence levels of authentic and recycled ICs, and to confidently track these types of ICs throughout the electronics supply chain.

Securing the supply chain of information and communications technology (ICT) has recently emerged as a critical concern for national security and integrity. With the proliferation of Internet of Things (IoT) devices and their increasing role in controlling real world infrastructure, there is a need to analyze risks in networked systems beyond established security analyses. Existing methods in literature typically leverage attack and fault trees to analyze malicious activity and its impact. In this paper, we develop RIoTS, a security risk assessment framework borrowing from system reliability theory to incorporate the supply chain. We also analyze the impact of grouping within suppliers that may pose hidden risks to the systems from malicious supply chain actors. The results show that the proposed analysis is able to reveal hidden threats posed to the IoT ecosystem from potential supplier collusion.

Under the background of "Internet +", in order to solve the problem of deeply mining credit risk behind online supply chain financial big data, this paper proposes an online supply chain financial credit risk assessment method based on deep belief network (DBN). First, a deep belief network evaluation model composed of Restricted Boltzmann Machine (RBM) and classifier SOFTMAX is established, and the performance evaluation test of three kinds of data sets is carried out by using this model. Using factor analysis to select 8 indicators from 21 indicators, and then input them into RBM for conversion to form a more scientific evaluation index, and finally input them into SOFTMAX for evaluation. This method of online supply chain financial credit risk assessment based on DBN is applied to an example for verification. The results show that the evaluation accuracy of this method is 96.04%, which has higher evaluation accuracy and better rationality compared with SVM method and Logistic method.

The rise of supply chain finance has provided effective assistance to SMEs with financing difficulties. This study mainly explores the financial risk evaluation of supply chain under the leadership of 3PL. According to the risk identification, 27 comprehensive rating indicators were established, and then the model under the BP neural network was constructed through empirical data. The actual verification results show that the model performs very well in risk assessment which helps 3PL companies to better evaluate the business risks of supply chain finance, so as to take more effective risk management measures.

In the recent years, sustainability has becoming an important topic in agro-food supply chain. Moreover, these supply chains are more vulnerable due to different interrelated risks from man-made and natural disasters. However, most of the previous studies consider less about interrelation in assessing sustainability risks. The purpose of this research is to develop a framework to assess supply chain sustainability risks by rnking environmental risks, economic risks, social risks and operational risks. To solve this problem, the proposed methodology is an integrated rough decision- making and trial evaluation laboratory (DEMA℡) method that consider the interrelationship between different risks and the group preference diversity. In order to evaluate the applicability of the proposed method, a real-world case study of Tunisian agro-food company is presented. The results show that the most important risks are corruption, inflation and uncertainty in supply and demand.

Based on the analysis of cluster supply chain risk characteristics, starting from the analysis of technical risk dimensions, information risk dimensions, human risk dimensions, and capital risk dimensions, a cluster supply chain risk severity assessment index system is designed. The fuzzy C-means clustering algorithm based on data flow is used to cluster each supply chain, analyze the risk severity of the supply chain, and evaluate the decision of the supply chain risk severity level based on the cluster weights and cluster center range. Based on the analytic hierarchy process, the risk severity of the entire clustered supply chain is made an early warning decision, and the clustered supply chain risk severity early warning level is obtained. The results of simulation experiments verify the feasibility of the decision method for cluster supply chain risk severity, and improve the theoretical support for cluster supply chain risk severity prediction.