Simulation analysis of supply chain risk management system based on IoT information platform

Most decision-supports methods are dedicated to the identification and characterization of risks and opportunities. The concrete exploitation of these risks and opportunities is generally depending on the ability of users to analyze multi-dimensional situations, to mobilize their experience and to foresee consequences. In this article, a new and original data science-based vision of risk and opportunity management for decision-making purpose is introduced. The main expected benefit of this vision is to enable decision makers to manage the performance trajectory of a considered system by visualizing and combining the impact of risks and opportunity.

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“…There are also some automated risk identification approaches (e.g. using IoT) as presented in (Gao et al 2020).…”

Section: Contributions From Risk Management Techniquesmentioning

confidence: 99%

Instability is the norm! A physics-based theory to navigate among risks and opportunities

Bénaben

Faugère

Montreuil

et al. 2021

Enterprise Information Systems

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“…Big Data Analytics are a set of tools, algorithms, simulations, and optimizations that can be applied to Big Data in order to analyze them and to extract unknown, hidden, valid, and useful relationships, patterns, and information [38]. This process allows organizations to gain business insights [39][40][41][42][43], to guide decision making, to be more productive, and to produce a competitive advantage for companies [44]. BDA can be classified in three areas: descriptive, predictive, and prescriptive.…”

Section: Big Data and Sustainable Supply Chainsmentioning

confidence: 99%

Using Big Data for Sustainability in Supply Chain Management

Chalmeta

Barqueros-Muñoz

2021

Sustainability

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In the literature, several frameworks have been proposed to help sustainability management in supply chains. Nevertheless, they present a number of shortcomings. With the aim of overcoming these shortcomings, this paper proposes a framework for sustainable supply chain management composed of six dimensions: methodology, organization, stakeholders, maturity model, human resources, and technology. The main innovations of the framework are that (1) it includes a methodology that acts as a guide to sustainability management and improvement in a holistic way by using a balanced scorecard for any type of supply chain and covering the whole project life cycle; (2) it combines quantitative and qualitative methods for sustainability assessment; (3) it describes the techniques and technology to be used in each task of the methodology; and (4) it identifies the past impact of SC sustainability, as well as predicting its future impact, using Big Data analytics. The practical utility, completeness, and level of detail of the framework were validated through questionnaires answered by both five academics and three professionals. In addition, the framework was applied to a case study to (1) validate its usefulness and (2) to improve it with the feedback obtained.

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“…In this paper (Gao et al 2019), IoT (Internet of Things) information technology has been widely applied to the supply chain risk management (SCRM). Firstly, the source of risks has been sorted out, the external and internal risks have been described in detail with the risk management of the supply chain system.…”

Section: Iot In Eismentioning

confidence: 99%

Cognitive computing for big data systems over internet of things for enterprise information systems

Sangaiah

Chaudhary

Tsai

et al. 2020

Enterprise Information Systems

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Simulation analysis of supply chain risk management system based on IoT information platform

Cited by 75 publications

References 37 publications

Instability is the norm! A physics-based theory to navigate among risks and opportunities

Instability is the norm! A physics-based theory to navigate among risks and opportunities

Using Big Data for Sustainability in Supply Chain Management

Cognitive computing for big data systems over internet of things for enterprise information systems

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