Using Information-theoretic Principles to Analyze and Evaluate Complex Adaptive Supply Network Architectures

Information-theoretic principles can be applied to the study of complex adaptive supply networks (CASN). Previous modeling efforts of CASN were impeded by the complex, dynamic nature of the systems. However, information theory provides a model-free approach to the problem removing many of those barriers. Understanding how principles such as transfer entropy, excess entropy/predictive information, information storage, and separable information apply in the context of supply networks opens up new ways of studying these complex systems. Additionally, these principles provide the potential for new business analytics which give managers of CASN new insights into the system\u27s health, behavior, and eventual control strategies

Methodology for Simulation and Analysis of Complex Adaptive Supply Network Structure and Dynamics Using Information Theory

Supply networks existing today in many industries can behave as complex adaptive systems making them more difficult to analyze and assess. Being able to fully understand both the complex static and dynamic structures of a complex adaptive supply network (CASN) are key to being able to make more informed management decisions and prioritize resources and production throughout the network. Previous efforts to model and analyze CASN have been impeded by the complex, dynamic nature of the systems. However, drawing from other complex adaptive systems sciences, information theory provides a model-free methodology removing many of those barriers, especially concerning complex network structure and dynamics. With minimal information about the network nodes, transfer entropy can be used to reverse engineer the network structure while local transfer entropy can be used to analyze the network structure’s dynamics. Both simulated and real-world networks were analyzed using this methodology. Applying the methodology to CASNs allows the practitioner to capitalize on observations from the highly multidisciplinary field of information theory which provides insights into CASN’s self-organization, emergence, stability/instability, and distributed computation. This not only provides managers with a more thorough understanding of a system’s structure and dynamics for management purposes, but also opens up research opportunities into eventual strategies to monitor and manage emergence and adaption within the environment

Confidence Investigation of Discovering Organizational Network Structures Using Transfer Entropy

Transfer entropy has long been used to discover network structures and relationships based on the behavior of nodes in the system, especially for complex adaptive systems. Using the fact that organizations often behave as complex adaptive systems, transfer entropy can be applied to discover the relationships and structure within an organizational network. The organizational structures are built using a model developed by Dodd, Watts, et al, and a simulation method for complex adaptive supply networks is used to create node behavior data. The false positive rate and true positive rates are established for various organizational structures and compared to a basic tree. This study provides a baseline understanding for the accuracy that can be expected when discovering organizational networks using these techniques. It also highlights conditions in which it may be more difficult to successfully discover a network structure using transfer entropy and bounds confidence levels for practitioners of such methods

Confidence Investigation of Discovering Organizational Network Structures Using Transfer Entropy

Transfer entropy has long been used to discover network structures and relationships based on the behavior of nodes in the system, especially for complex adaptive systems. Using the fact that organizations often behave as complex adaptive systems, transfer entropy can be applied to discover the relationships and structure within an organizational network. The organizational structures are built using a model developed by Dodd, Watts, et al, and a simulation method for complex adaptive supply networks is used to create node behavior data. The false positive rate and true positive rates are established for various organizational structures and compared to a basic tree. This study provides a baseline understanding for the accuracy that can be expected when discovering organizational networks using these techniques. It also highlights conditions in which it may be more difficult to successfully discover a network structure using transfer entropy and bounds confidence levels for practitioners of such methods

An Information Theoretic Investigation Of Complex Adaptive Supply Networks With Organizational Topologies

Supply networks exist throughout society in manufacturing and knowledge-intensive industries as well as many service industries. Organizations have been noted to behave as complex adaptive systems or information supply networks with both formal and informal structures. Thoroughly understanding supply network structure and behavior are critical to managing such organizations effectively, but their properties of complex adaptive systems make them more difficult to analyze and assess, forcing researchers to rely on unrealistic data or assumptions of behavior. This research proposes an information theoretic methodology to discover such complex network structures and dynamics while overcoming the difficulties historically associated with their study. Indeed, this was the first application of an information theoretic methodology as a tool to study complex adaptive supply networks. Moreover, managing these complex networks with formal and informal structures poses additional challenges because the effects of intervention can result in even more unpredictable effects. Noting that two primary functions of organizational networks are to transfer information between nodes and store information in the network, this research quantifies the effects of increased and decreased node performance on the ability of multiple organizational network topologies to accomplish these tasks. Multiple qualitative observations from previous researchers are quantitatively analyzed using information theoretic modeling and simulation. Results show an increased ability in local teams to store information within the network as well as a decreased ability by core-periphery networks to respond to increased information rates

Efficient DSP and Circuit Architectures for Massive MIMO: State-of-the-Art and Future Directions

Massive MIMO is a compelling wireless access concept that relies on the use of an excess number of base-station antennas, relative to the number of active terminals. This technology is a main component of 5G New Radio (NR) and addresses all important requirements of future wireless standards: a great capacity increase, the support of many simultaneous users, and improvement in energy efficiency. Massive MIMO requires the simultaneous processing of signals from many antenna chains, and computational operations on large matrices. The complexity of the digital processing has been viewed as a fundamental obstacle to the feasibility of Massive MIMO in the past. Recent advances on system-algorithm-hardware co-design have led to extremely energy-efficient implementations. These exploit opportunities in deeply-scaled silicon technologies and perform partly distributed processing to cope with the bottlenecks encountered in the interconnection of many signals. For example, prototype ASIC implementations have demonstrated zero-forcing precoding in real time at a 55 mW power consumption (20 MHz bandwidth, 128 antennas, multiplexing of 8 terminals). Coarse and even error-prone digital processing in the antenna paths permits a reduction of consumption with a factor of 2 to 5. This article summarizes the fundamental technical contributions to efficient digital signal processing for Massive MIMO. The opportunities and constraints on operating on low-complexity RF and analog hardware chains are clarified. It illustrates how terminals can benefit from improved energy efficiency. The status of technology and real-life prototypes discussed. Open challenges and directions for future research are suggested.Comment: submitted to IEEE transactions on signal processin

Dagstuhl News January - December 2005

"Dagstuhl News" is a publication edited especially for the members of the Foundation "Informatikzentrum Schloss Dagstuhl" to thank them for their support. The News give a summary of the scientific work being done in Dagstuhl. Each Dagstuhl Seminar is presented by a small abstract describing the contents and scientific highlights of the seminar as well as the perspectives or challenges of the research topic

A dual perspective towards building resilience in manufacturing organizations

Modern manufacturing organizations exist in the most complex and competitive environment the world has ever known. This environment consists of demanding customers, enabling, but resource intensive Industry 4.0 technology, dynamic regulations, geopolitical perturbations, and innovative, ever-expanding global competition. Successful manufacturing organizations must excel in this environment while facing emergent disruptions generated as biproducts of complex man-made and natural systems. The research presented in this thesis provides a novel two-sided approach to the creation of resilience in the modern manufacturing organization. First, the systems engineering method is demonstrated as the qualitative framework for building literature-derived organizational resilience factors into organizational structures under a life cycle perspective. A quantitative analysis of industry expert survey data through graph theory and matrix approach is presented second to prioritize resilience factors for strategic practical implementation