A Simulation Framework Dedicated to Characterizing Risks and Cascading Effects in Collaborative Networks

Collaborative networks are complex systems and consist of many factors with dependencies among them. Although the number of collaborative networks such as advanced supply chains or virtual organizations/laboratories/e-science is growing and their significance is increasing in the world, many of them are unsuccessful. In addition, very little attention has been paid to the risk analysis of collaborative networks by considering the dependencies among risk factors. So, the precise risks analysis associated with collaborative networks projects is crucial to attain a satisfactory performance. To address this, we are proposing an advanced decision support tool called “Fuzzy Cognitive Maps” (FCM) which can deal with risks of such complicated systems by considering the interrelationships between factors. FCM states the behaviour of complex systems accurately and illustrate any complex environment based on the experts’ perceptions and by graphical representations. It is able to consider uncertainties, imprecise information, the interactions between risk factors, Information scarcity, and several decision maker’s opinions. FCM is not only able to evaluate risks more precisely in collaborative networks, but also it could be applied in different decision makings problems related to collaborative networks such as partner selection and forecasting behaviors, policy analysis, modeling collaboration preparedness assessment, etc. Hence, the proposed tool would help practitioners to manage collaborative network risks and decision making problems effectively and proactively.

Analyzing the economics of food loss and waste reductions in a food supply chain

Purpose Green innovation in mega transportation infrastructure projects (MTI-GI) requires balancing technological progress and economic growth with environmental protection and sustainable development. Due to the multiple participants, temporary project-based arrangements and significant spillover effects, innovation actors form highly complex and dynamic collaborative networks under the obligation of shared responsibilities. This research aims to analyze the structural characteristics and dynamic evolution of multi-actor collaborative networks of green innovation in mega transportation infrastructure projects, thereby revealing how collaborative relationships operate as a network mechanism within megaproject-based organizations. Design/methodology/approach The research compiles relevant cases from the Zhan Tianyou Award to build a database and extracts collaboration data to construct the MTI-GI collaborative network. Descriptive statistics present the temporal and spatial characteristics of the case data. Complex network modeling, including social network analysis (SNA) and the Barrat, Barthélemy and Vespignani (BBV) model, is employed to measure the static structural features and dynamic evolutionary pathways. Findings The results indicated that the past decade (i.e. 2014–2023) was pivotal for MTI-GI development. Rail transit and railway projects are the most active in implementing green innovation, and the innovation actors are distributed mainly across relatively developed regions and relatively less developed yet strategically important areas. SNA metrics revealed that the MTI-GI collaborative network remains weakly connected overall and presents a clear scale-free pattern. This underscores the dominant role of state-owned enterprises in capability-driven and responsibility-supported innovation within the temporary, multi-level and multi-stakeholder context of megaprojects. BBV evolutionary analysis further indicated that a larger evolutionary scale, preferential attachment based on comprehensive strength and fewer cooperative ties of new entrants reinforce the scale-free properties of the collaborative network. Moreover, the MTI-GI collaboration network demonstrates its dynamic feature of enabling an adaptive balance between short-term project collaboration and long-term governance capacity. Originality/value The findings of this research enhance the understanding of green innovation network governance in megaproject-based organizations and offer more refined strategies for managing multi-actor collaboration in complex systems.

As the supply chains are growing and becoming more interdependent, the vulnerability and the chances of supply chain failure also increases. The supply chain industry is severely affected due to the COVID-19 outbreak and industry practitioners are focusing on minimizing the ripple effect of the disruption made to the economy. Considering the unprecedented situation, the research is motivated to analyse the ripple effect in a multi-echelon supply chain and investigate the performance at various nodes to understand the capability of the supply chain to withstand the disruptions at different levels. Using discrete event simulation, this study analyses the ripple effect in the copper industry by an agent-based simulation software anyLogistix, considering various key performance indexes (KPIs) to gauge the magnitude. From the results of the simulation, it is evident that the lack of safety stocks and multi-sourcing of copper facilitate as major causes for the disruptions. The simulation helps to understand the disruption levels and make the supply chain more resilient and robust for any future disruption. Further, the study proposes resilient project management solutions to recover from the cascading ripple effect in the copper supply chain. The scientific contribution of the research is to provide supply chain managers with simulation techniques to understand the ripple effect on the copper supply chain. It helps the stakeholders to understand the importance of project management tools to reduce the cascading ripple effect in a copper supply chain. Further, the findings of this study will support contemporary managers, supply chain allies, project managers, and stakeholders to formulate strategies for recovering from the supply chain disruptions caused due to natural disasters, pandemics such as COVID-19.

Accelerated life testing (ALT) is a method of reducing the lifetime of components through exposure to extreme stress. This method of obtaining lifetime information involves the design of a testing experiment, that is, an accelerated test plan. In this work, we adopt a simulation‐based approach to obtaining optimal test plans for constant‐stress accelerated life tests with multiple design points. Within this simulation framework, we can easily assess a variety of test plans by modifying the number of test stresses (and their levels) and evaluating the allocation of test units. We obtain optimal test plans by utilising the differential evolution (DE) optimisation algorithm, where the inputs to the objective function are the test plan parameters, and the output is the RMSE (root mean squared error) of out‐of‐sample (extrapolated) model predictions. When the life‐stress distribution is correctly specified, we show that the optimal number of stress levels is related to the number of model parameters. In terms of test unit allocation, we show that the proportion of test units is inversely related to the stress level. Our general simulation framework provides an alternative approach to theoretical optimisation, and is particularly favourable for large/complex multipoint test plans where analytical optimisation could prove intractable. Our procedure can be applied to a broad range of experimental scenarios and serves as a useful tool to aid practitioners seeking to maximise component lifetime information through accelerated life testing.

Raytheon Missile Systems (RMS) conducts ongoing research into an extensible general-purpose missile simulation framework. Recent research has focused on the development of a generic seeker simulation framework to support effective seeker design and verification. This paper presents advances in the techniques used to integrate the scene generation capability into genSim, the extensible off-the-shelf missile simulation implementation developed at Raytheon Missile Systems. The most recent field of inquiry is the robust scene generation of synthetic infrared signatures (including both land-vehicular and maritime targets) and is an element of the network-centric Future Combat Systems (FCS) program. In response to our DoD customer's transition to a capabilities-based technology acquisition and development approach, a discussion of scene generation simulation integration is of interest because of the increased use of simulation in the validation of weapon system performance.

Green supply chain management (GSCM) integrates environmental considerations into traditional supply chain management (SCM) to enhance sustainability and reduce environmental impact. By adopting this, firms can improve efficiency, minimise waste, and achieve competitive advantages while addressing economic, social, and environmental goals. At the heart of this transformation lies the digitalisation of supply chains, which involves the consolidation of processes and data onto unified platforms, the automation of key operations, and the utilisation of cutting-edge technologies such as artificial intelligence (AI), machine learning, and the Internet of Things (IoT). These technologies empower organisations to analyse vast data, enabling them to identify, predict, and prevent issues that may compromise sustainability objectives. This chapter briefly introduces sustainability in the supply chain, and the digital revolution impacting the supply chain, and further elaborates on the impact of these trends on supply chain performance. The chapter also includes a bibliometric review of the use of digital technology on supply chain performance. The review analyses the publication trends, identifies key research areas, and assesses influential authors, institutions, and core journals about the identified topic. It also examines geographical distribution, citation networks, and collaborative networks while highlighting common methodologies and research gaps. The review reveals a growing number of publications annually, particularly in recent years, indicating increased research activity at the intersection of supply chain sustainability and digital technology. It offers evidence-based insights for policymakers and industry leaders to implement effective sustainability and technology strategies in supply chains and highlights under-researched areas, guiding future research efforts to advance the field.

There is an increasing awareness about the paramount importance of information technology within business in the context of large businesses. However, research about the investigation of the role of information technology resources in fostering collaborative communication, network governance and relationship longevity in the small and medium enterprise sector has remained scant. The primary objective of this study was to investigate the influence of information technology on collaborative communication, network governance and relationship longevity in Zimbabwe’s SME sector. Five research hypotheses were posited and sample data from 162 small and medium enterprise suppliers were collected and used to empirically test the hypotheses. The results of this study showed that information technology resources positively influenced small and medium enterprise suppliers’ collaborative communication, network governance and consequential relationship longevity with their buyers in a significant way. Overall, the current study findings provided tentative support to the proposition that information technology resources, collaborative communication and network governance should be recognised as significant antecedents for improved relationship longevity between suppliers and their buyers in the SME setting. Therefore, managers in the small and medium enterprise sector and small and medium enterprise owners need to pay attention to both collaborative communication and network governance in order to optimise information technology resource impact on their relationship longevity with their business counterparts. Limitations and future research directions were also indicated.

Critical infrastructures are a core part in modern society, supplying essential goods and services for our everyday life. Therefore, any incident compromising the operation of a critical infrastructure can directly affect the social life. Moreover, due to the increasing interconnections between critical infrastructures, any incident can have cascading effects on other infrastructures as well. In this article, we present a novel simulation framework which allows to model the interdependencies and thus also the cascading effects among critical infrastructures. This framework builds upon stochastic processes describing, on the one hand, the relations between the critical infrastructures and, on the other hand, the random and sometimes arbitrary propagation of the consequences. This existing framework is extended and implemented in OMNeT++, which allows an easy and swift implementation of the mathematical algorithms and also provides a built-in visualization of the propagation of consequences within the critical infrastructure network. The goal is to support risk and security officers within the critical infrastructure in their decisions.

This research employs an extensive multiple case studies analysis to identify the most important business models affecting supply chain configurations and related enabling technologies towards the creation of collaborative networks. The results obtained from the investigation of 24 companies of manufacturing and process industry, informed by literature, identify four ‘design principles’ of business models, i.e. Personalized production, Servitization, Decentralized and modular production, and Recycle, Re-use and Sustainability. Each model is further described and discussed at the interplay between digitalization and collaborative network practices at supply chain level, showing that adopting one or a combination of the four design principles allows to actuate some of the most important features of collaboration like Vertical integration or networking of smart production systems, Horizontal integration through global value chain networks, Through-engineering across the entire value chain, Acceleration of manufacturing and Digitalization of products and services.

Based on train operation data, Bayesian networks (BN) are used to model the cascading effects of traffic control actions and their influences (such as changes in train delays) for two and three consecutive trains. Influence factors are first determined to describe the interactions between train delays and control actions, i.e. the delay changes due to control actions over time and space. Considering the interdependence of these factors, their causal relationships are obtained, and the BN model’s connection paradigm is determined based on these causal ties. The BN structures are then proposed by combining domain knowledge and a data-driven method. The proposed models are tested on the train operation data from the Chinese high-speed railways. The results show that the proposed method exhibits a good fit for the train operation data and outperforms other conventional train operation models in terms of various evaluation metrics. Besides, the strength of train control actions’ cascading effects is investigated. It shows that section train control actions are stronger than those in stations, and both are considerably correlated with train delays and recovery times in sections and stations. Finally, a macroscopic model for control actions between several trains (more than three) is obtained based on the learned paradigm of the presented models and the cascading effects between two and three trains, demonstrating the model’s extensibility. The proposed models are intended to support traffic controllers with the estimation of future train delay change patterns, the expected control actions, and the cascading effects of control actions, and they are imperative for aiding the decision-making of controllers to manage high-speed railway traffic.

ANALYSIS OF HOLONIC MANUFACTURING SYSTEMS BASED ON NON-ORDINARY COLLABORATIVE PETRI NETS

Many patterns have been uncovered in complex systems through the application of concepts and methodologies of complex networks. Unfortunately, the validity and accuracy of the unveiled patterns are strongly dependent on the amount of unavoidable noise pervading the data, such as the presence of homonymous individuals in social networks. In the current paper, we investigate the problem of name disambiguation in collaborative networks, a task that plays a fundamental role on a myriad of scientific contexts. In special, we use an unsupervised technique which relies on a particle competition mechanism in a networked environment to detect the clusters. It has been shown that, in this kind of environment, the learning process can be improved because the network representation of data can capture topological features of the input data set. Specifically, in the proposed disambiguating model, a set of particles is randomly spawned into the nodes constituting the network. As time progresses, the particles employ a movement strategy composed of a probabilistic convex mixture of random and preferential walking policies. In the former, the walking rule exclusively depends on the topology of the network and is responsible for the exploratory behavior of the particles. In the latter, the walking rule depends both on the topology and the domination levels that the particles impose on the neighboring nodes. This type of behavior compels the particles to perform a defensive strategy, because it will force them to revisit nodes that are already dominated by them, rather than exploring rival territories. Computer simulations conducted on the networks extracted from the arXiv repository of preprint papers and also from other databases reveal the effectiveness of the model, which turned out to be more accurate than traditional clustering methods.

A multiscale simulation framework for the manufacturing facility and supply chain of autologous cell therapies

In critical activities and organizations, decision making in the face of complexity has been a growing normal. Complexity troubles humans due to cognitive limitations. Moreover, humans are merely able to understand cause-and-effect relationships that are close in time and space, not the paradigm of many complex socio-technical systems. Decision-making processes shall rely on models that help harness a problem´s associated complexity – among them the dynamics of supply chains. Models typically fall into two broad categories: mental and formal models. Supply chains are complex systems, which may exhibit complex behaviour patterns. Decisions and policies within organizational systems are the causes of many problems, among them undesirable oscillations and other problematic patterns of the parameters of interest. A system is a grouping of parts that work together for a purpose. Hence, the systems dynamics methodology is an adequate approach to deal with fuel supply chain management. A model was developed that helps manage marine gasoil supply chains in the context of the navy.