Viewpoints on Robust Supply Chain Network Risk Assessment Using Dynamic Bayesian Networks

We propose a dynamic differential Bayesian networks (DDBNs) and nonparametric regression model. This model is an extended model of traditional dynamic Bayesian networks (DBNs), which can incorporate temporal information in a natural way and directly handle real-valued data obtained from microarrays without any transformation. In addition, it can cope with differential information between gene expression levels, without any loss to the traditional advantage, i.e., the capability of estimating non-linear relationships between genes. We apply DDBNs to analyze simulated data and real data, i.e., Saccharomyces cerevisiae cell cycle gene expression data. We have confirmed the effectiveness of our approach in the sense that some edges have been successfully detected only by DDBNs, not by DBNs.

Dynamic Bayesian network (DBN) theory provides a valid tool to estimate the risk of disruptions, propagating along the supply chain (SC), i.e. the ripple effect. However, in cases of data scarcity, obtaining perfect information on probability distributions required by the DBN is impractical. To overcome this difficulty, a new robust DBN approach is, for the first time, proposed in this study to analyse the worst-case oriented disruption propagation in the SC. This work considers an SC with multiple suppliers and one manufacturer over several time periods, in which only probability intervals of the suppliers' states and those of the related disruption propagations are known. The objective is to acquire the robust performance of risk estimation, measured by the worst-case probability in the disrupted state for the manufacturer. We first establish a nonlinear programming formulation to mathematically materialise the proposed robust DBN, which can be used to solve small-size problems. To overcome the computational difficulty in solving large-size problems, an efficient simulated annealing algorithm is further designed. Numerical experiments are conducted to validate its efficiency.

The supply chain is the central organizing unit in today's global industries and has gained significance as one of the 21st century manufacturing paradigms for improving organizational competitiveness. In this paper, we propose a dynamic Bayesian network to represent the cause-and-effect relationships in an industrial supply chain. Based on the Quick Scan, a systematic data analysis and synthesis methodology, a dynamic Bayesian network is employed as a more descriptive mechanism to model the causal relationships in the supply chain. Dynamic Bayesian networks can be utilized as a knowledge base of the reasoning systems where the diagnostic tasks are conducted. We finally solve this reasoning problem with stochastic simulation.

The supply chain (SC) network is prone to disturbance due to various uncertainties associated with their subsystems. The COVID-19 outbreak has exposed the global vulnerability of the supply chain network. The current pandemic has severely affected almost every SC network because its members are situated at the international level. One of the reasons for SC network failure is the deterministic assumptions of different parameters. A realistic SC network model requires the use of the uncertain value of the parameters, which can be further captured by fuzzy numbers. This paper discusses the possibilistic moment of several nonlinear types of fuzzy numbers that are important for SC network modeling. We give closed-form possibilistic moments’ expression for various types of fuzzy numbers that are very similar to the moment’s properties in probability theory and stochastic process. We then illustrate the application of proposed fuzzy numbers by solving an inventory model. This paper also provides results related to the EPQ inventory model in a fuzzy possibilistic setup.

Since the onset of Supply chain management has gained maximum recognition, with many countries elevating supply chain levels at the strategic level. This article selects AH Company’s supply chain network as the research object, analyzes the main challenges faced by the company in the supply chain aspect, and evaluates its original supply chain network. It incorporates sustainable development factors into the current global supply chain management theme, taking into account four aspects: total supply chain cost, service level, corporate sustainable development vision (carbon emissions), and supply chain risks. The “Supply Chain Guru X (2022)” software is applied to propose optimization solutions for existing problems on the basis of the original supply chain network. Based on the existing data, a comprehensive comparison of the advantages and disadvantages of various optimization schemes is implemented to select the current optimal supply chain network optimization scheme. In the process of optimizing the plan, the multi-objective optimization theory and big data processing technology were emphasized, and the corresponding technical theories were integrated into the “Supply Chain Guru X (2022)” software. By optimizing the supply chain network of AH Company, the overall operating costs of the company’s supply chain network have been significantly reduced while meeting customer needs. Concurrently, it can provide a good example for optimizing the supply chain network of similar enterprises.

Cloud computing services are growing and developing at a rapid pace. The growth of cloud services is taking place in various forms that are suitable for various applications. Cloud computing as a computing service caters to the needs of various applications by providing a distributed environment for computing services. The services are useful in managing various engineering and management processes in the supply chain network (SCN). A supply chain network is encapsulated with challenges related to capital costs, operational costs, timely availability of information, management information system and overhead costs. Cloud computing services offer services that helps in minimizing the problems related to the challenges faced and thereby increases the productivity of the supply chain network. The supply chain firms can benefit from cloud services by way of reduced capital expenditure and reduced operational costs. Supply chain firms can be decentralized units which can utilize fog computing in which data and applications are distributed logically between data points and cloud. Also, emerging concepts of mist computing can be helpful wherein the system architecture pushes the processes nearer to the data source in a supply chain network. The expenditure mainly is because of the operational cost which can be minimized by suitable deployment of cloud computing service. The cloud services are available today as metered services thereby the operational cost can be reduced by regulating the usage. Cloud computing services from Microsoft, Google, and Amazon are already available at reasonable rates and many services providers are entering the market to pose more competition which is healthy for users from reduced pricing. It can be expected that with the developments in cloud services, the service charges will decrease which will be a great advantage to the supply chain network firms and other related firms. A supply chain network enabled with cloud computing services is referred to as the Cloud Supply Chain Network (CSCN). In this paper, the benefits and opportunities of cloud supply chain network along with the challenges faced by the firms are discussed. Firms while planning to adopt cloud services to enhance their supply chain network processes look for budgeting plans which can be determined using functions as discussed in this paper. The perspectives, principles and practices in cloud supply chain network are detailed. It also describes how one can consider the parameters of the characteristics of cloud computing in supply chain network for the purpose of modeling and analyzing the information flow. A framework of the design factors of cloud supply chain is explained which will enable the decision maker to derive the necessary results by suitably incorporating the factors in the analysis of cloud supply chain network.

One of the major restrictions of dynamic Bayesian networks (DBNs) is their inability to account for topological features such as shape descriptors, homeomorphy, homotopy, and invariance. The main reason for this shortcoming is explained by the fact that even if dynamic Bayesian networks encode statistical relationships; they are not embedded in a Euclidean space where mathematical structures abound. The goal is to embed DBNs into a Euclidean space such that these topological features can be exploited. This extension of DBNs to topological DBNs (TDBNs) leapfrogs the task of pattern recognition and machine learning by not only classifying objects but revealing how they are related topologically. We have applied the TDBN formalism to facial aging for person identification. Preliminary results reveal that the TDBNs outperform the traditional DBN with an accuracy margin of 8% in average.

During the past decade, great studies have been made in the development of standardized tools for supply chain modeling and network optimization Network optimization is the most basic type of modeling that can be performed with tools which helps to identity optimum paths or flow of goods in supply chain network. In this case, the network is defined by the flow of finished goods from origin to destination. Network modeling becomes more complex as the dimensions and scope of the supply chain expand Uncertainties in the supply chain usually increase the variance of profit or cost to the company, increasing the likely hood of decreased profit i.e. increase in total supply chain cost. Demand uncertainty and constraints posed by the every echelon are important factors to be considered in the supply chain design operations. This chapter specifically deals with the modeling and optimization of a three echelon supply chain network architecture using new Particle Swarm Optimization algorithm.

Nowadays, several components and systems are designed and produced based on reliability. Since the reliability criterion has an important role in purchasing and implementation of these systems. In the design of a reliable system, fault and failure analysis must be carried out in order to reduce fault probability of the system. When dependency and the relation between components of a complex system are important and should be mentioned, determination of system reliability is very difficult. In this paper, dynamic fault tree is used to evaluate the systems reliability that their behavior is varied with time. Dynamic fault tree is constructed and then it converted to dynamic bayesian network. In this paper, the principle of dynamic fault tree gates and their mapping into dynamic bayesian are explained and some new relations between events and gates for this mapping are proposed. GeNIe package is used to determine dynamic bayesian network based on stochastic sampling algorithms. Four systems (cardiac assist system, hypothetical cascaded priority-and system, inertial navigation system/ global positioning system integrated, and emergency detection system) are investigated; reliability and fault probability of these systems are calculated. Comparison of the results with those obtained by other researches shows the proposed method effectiveness for systems reliability modeling and assessment via dynamic bayesian network.

In order to find out the problems of students' learning in the online learning process in time and ensure that students can successfully complete the online learning course, the dynamic Bayesian network is used to predict students' online learning scores. First, the online course Bayesian network is established, and then the dynamic Bayesian network is established by combining the Bayesian network and hidden Markov according to the time slice. The maximum expectation algorithm is used for Bayesian parameter learning, and the scores are predicted according to video learning, chapter test and other influencing factors. The results show that the dynamic Bayesian network can effectively predict the online learning results, the prediction results provide teachers with decision-making, and students can carry out targeted reinforcement learning according to the prediction results in the learning process.

Firms source goods and services in complex, global supply chains. The network structures of these far‐reaching supply chains are believed to influence both firm‐level and supply chain‐level behavior. However, researchers and managers are challenged to manage extended supply chain networks because firms typically do not actively oversee their entire supply chain network. The extant research on the underlying network topologies is limited and relies on untested structural assumptions. This study addresses the latter issue by examining structural topologies in supply chain networks. We draw from the network and supply chain literature and empirically investigate the presence of topologies found in complex networks. In particular, we look for: small‐world, scale‐free, community, and hierarchical network structures. We study 21 extended (five‐tier) supply chain networks of firms representing various industries. Our analysis provides evidence for community, scale‐free, and hierarchical structures within the observed supply chain networks. The identification of network topologies is relevant to understand how to study supply chain networks and provide insights into network behavior and performance.

A Reinforcement Learning Variable Neighborhood Search for the Robust Dynamic Bayesian Network Optimization Problem under the Supply Chain Ripple Effect

Economic globalization continues to expand the scope of the supply chain network structure, while increasing its own complexity, as well as the uncertainty of the network operating environment and the fragility of the operating system. An emergency on a single node or line in the supply chain network usually affects other nodes in the supply chain and brings significant risks to the enterprise. The impact of other nodes can cause the entire supply chain network to collapse, especially if the production and operation of a single-node enterprise in the supply chain may be interrupted or malfunctioned, especially in the event of an emergency. It also threatens development greatly, affecting the production and livelihoods of enterprises in the supply chain and people's lives, and has a major negative impact on social and economic development. These emergencies continue to affect the supply chain network, and the originally fragile companies face greater risks. This paper establishes a supply chain hyper-network model considering the risk function under emergencies. When an emergency occurs, the demand in the consumer market decreases or increases due to different emergencies. Therefore, revenue sharing contracts are used to coordinate, build a supply chain network model under emergencies, and solve them to obtain a model equilibrium Solution, that is, the new equilibrium state after the occurrence of an emergency.

The importance of acquiring and sharing real-time disruption information in the supply chain for proper deployment of disruption mitigation strategies is well-known in the literature. However, studies in this direction are limited in the domain of supply chain dynamics. In this paper, we investigate the effect of sharing real-time disruption and inventory information to mitigate supplier disruption through proper order allocation between the suppliers. We consider a three-echelon manufacturing supply chain network where a manufacturer and first-tier suppliers adopt dual sourcing. At the first-tier supplier level, the supply chain network is subjected to random disruption. Using control engineering modeling and simulation, we first evaluate the value of information sharing in disruption mitigation efforts, and further, we examine the effect of various control system design configurations of the manufacturer to maximize its dynamic performance in the information shared supply chain settings. The results show that, in the case of upstream supplier disruption, information transparency on the vulnerabilities among supply chain members improves the performance. Further, it is observed that for a given control structure, the selection of decision parameters affect the dynamic performance of the supply chain with proper order allocation strategy during the disruption. The findings of this research can provide the basis for managers to make informed decisions about using mitigation strategies with their supply chain partners.

Risk assessment and management for supply chain networks: A case study