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

Rodewald, Joshua V.; Colombi, John M.; Oyama, Kyle F.; Johnson, Alan W.

doi:10.1016/j.procs.2015.09.176

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…Integrated supply chain planning with execution feedback schedules semi-autonomous fulfillment-oriented human-robot collaboration (HRC) (Bauer et al, 2008) and intelligent machine service (iMS)-teams (Hoffman and Breazeal, 2004;Herneth, 2018;Iqbal and Riek, 2019). Resulting adaptive supply networks (ASN) are digital and secure networks for autonomous collaboration and intelligent decision-making (Rodewald et al, 2015;Herneth, 2019). Supply chain services are risk-aware and proactively respond to exceptions and deviations with distributed decision management (DMN) (Object Management Group (OMG), 2019) and automated decision-making (ADM) (Newell, 1979;Fink, 2006) as distributed automated decisionmaking (DADM) processes (Herneth, 2018).…”

Section: Intra-organizational Maturitymentioning

confidence: 99%

Smart Contracts for Sustainable Supply Chain Management: Conceptual Frameworks for Supply Chain Maturity Evaluation and Smart Contract Sustainability Assessment

2021

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Current research on smart contracts focuses on technical, conceptual, and legal aspects but neglects organizational requirements and sustainability impacts. We consider this a significant research gap and explore the relationship between smart contracts and sustainability in supply chains. First, we define the concept of smart contracts in terms of supply chain management. Then, we conduct a content analysis of the literature to explore the overlapping research fields of smart contracts and sustainability in supply chains. Next, we develop a semi-structured assessment framework to model the potential environmental and social impacts induced by smart contracts on supply chains. We propose a conceptual framework for supply chain maturity by mapping the relationships between organizational development, sustainability, and technology. We identify smart contracts as a foundational technology that enables efficient and transparent governance and collaborative self-coordination of human and non-human actors. Thus, we argue that smart contracts can contribute to the economic and social development of networked value chains and Society 5.0. To stimulate interdisciplinary research on smart contracts, we conclude the article by formulating research propositions and trade-offs for smart contracts in the context of technology development, business process and supply chain management, and sustainability.

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Section: Intra-organizational Maturitymentioning

confidence: 99%

Smart Contracts for Sustainable Supply Chain Management: Conceptual Frameworks for Supply Chain Maturity Evaluation and Smart Contract Sustainability Assessment

2021

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“…Consequently, statistical measures such as Granger causality and transfer entropy are typically used to identify functional, rather than effective structure. For example, transfer entropy has been used previously to infer networks in numerous fields, e.g., computational neuroscience [ 5 , 38 ], multi-agent systems [ 8 ], financial markets [ 39 ], supply-chain networks [ 40 ], and biology [ 41 ]. However, most of these results build on the work of Schreiber [ 25 ] by assuming the system is composed of finite-order Markov chains and thus there is a dearth of work that provides formal derivations for the use of this measure for inferring effective connectivity.…”

Section: Related Workmentioning

confidence: 99%

Minimising the Kullback–Leibler Divergence for Model Selection in Distributed Nonlinear Systems

Cliff

Prokopenko

2018

Entropy

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Abstract:The Kullback-Leibler (KL) divergence is a fundamental measure of information geometry that is used in a variety of contexts in artificial intelligence. We show that, when system dynamics are given by distributed nonlinear systems, this measure can be decomposed as a function of two information-theoretic measures, transfer entropy and stochastic interaction. More specifically, these measures are applicable when selecting a candidate model for a distributed system, where individual subsystems are coupled via latent variables and observed through a filter. We represent this model as a directed acyclic graph (DAG) that characterises the unidirectional coupling between subsystems. Standard approaches to structure learning are not applicable in this framework due to the hidden variables; however, we can exploit the properties of certain dynamical systems to formulate exact methods based on differential topology. We approach the problem by using reconstruction theorems to derive an analytical expression for the KL divergence of a candidate DAG from the observed dataset. Using this result, we present a scoring function based on transfer entropy to be used as a subroutine in a structure learning algorithm. We then demonstrate its use in recovering the structure of coupled Lorenz and Rössler systems.

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“…For example, the authors could have chosen to vary the time delays among the nodes, create randomly variable (but bounded) time delays, or to ignore some number of inputs to determine production timing [38]. However, for this initial application, the deterministic approach of a 1-unit time delay was chosen to more clearly demonstrate the method without adding unnecessary noise.…”

Section: Conceptual Static Supply Network Simulationmentioning

confidence: 99%

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

Rodewald

Colombi

Oyama

et al. 2016

Entropy

Self Cite

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Abstract: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.

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

Cited by 6 publications

References 13 publications

Smart Contracts for Sustainable Supply Chain Management: Conceptual Frameworks for Supply Chain Maturity Evaluation and Smart Contract Sustainability Assessment

Smart Contracts for Sustainable Supply Chain Management: Conceptual Frameworks for Supply Chain Maturity Evaluation and Smart Contract Sustainability Assessment

Minimising the Kullback–Leibler Divergence for Model Selection in Distributed Nonlinear Systems

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

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