Integrating the Digital Twin of the manufacturing system into a decision support system for improving the order management process

Kunath, Martin; Winkler, Herwig

doi:10.1016/j.procir.2018.03.192

Cited by 188 publications

(79 citation statements)

References 19 publications

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“…An IoT infrastructure facilitates seamless traceability of individual goods along their life spans on a continuous basis [84]. Capturing data from the networked RFID tags via a cloud, digital twins form the abstract models of products or processes such as point-to-point supply chains or individual activities such as manufacturing, inventory, or transportation and logistics [81,[85][86][87][88]. These virtual instances are the descriptions of the objects or systems and can be constructed via 3D models [89,90].…”

Section: Applications Of Digital Technologiesmentioning

confidence: 99%

Evaluation of Waste Electronic Product Trade-in Strategies in Predictive Twin Disassembly Systems in the Era of Blockchain

2020

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Manufacturing and supply chain operations are on the cusp of an era with the emergence of groundbreaking technologies. Among these, the digital twin technology is characterized as a paradigm shift in managing production and supply networks since it facilitates a high degree of surveillance and a communication platform between humans, machines, and parts. Digital twins can play a critical role in facilitating faster decision making in product trade-ins by nearly eliminating the uncertainty in the conditions of returned end-of-life products. This paper demonstrates the potential effects of digital twins in trade-in policymaking through a simulated product-recovery system through blockchain technology. A discrete event simulation model is developed from the manufacturer’s viewpoint to obtain a data-driven trade-in pricing policy in a fully transparent platform. The model maps and mimics the behavior of the product-recovery activities based on predictive indicators. Following this, Taguchi’s Orthogonal Array design is implemented as a design-of-experiment study to test the system’s behavior under varying experimental conditions. A logistics regression model is applied to the simulated data to acquire optimal trade-in acquisition prices for returned end-of-life products based on the insights gained from the system.

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Section: Applications Of Digital Technologiesmentioning

confidence: 99%

Evaluation of Waste Electronic Product Trade-in Strategies in Predictive Twin Disassembly Systems in the Era of Blockchain

2020

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“…Implementation of DT still faces challenges including lack of detailed methodology and standards, difficulties in collecting and storing large amounts of data [44,70,71], developing data acquisition system, synchronisation problems, modelling of a complex system, lack of awareness, resistance of companies to adopting the technology [59], and difficulties in constructing, understanding, controlling, and simulating real-time changes in the system. High-fidelity models are required to simulate and test the product or process in a virtual environment by reducing development time and cost [26,72] The issue of high investment cost and data security is still a hindrance to many companies making DT part of their daily life [55]. There are also problems related to the lack of suitable business models and the use of digital services and goods is still new and implementation is difficult for many manufacturers [47].…”

Section: Challenges In the Implementation Of Digital Twinmentioning

confidence: 99%

Digital Twin models in industrial operations: State‐of‐the‐art and future research directions

Melesse

Pasquale

Riemma

2021

IET Collab Intel Manufact

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A Digital Twin is a virtual representation of a physical product, asset, process, system, or service that allows us to understand, predict, and optimise their performance for better business outcomes. Recently, the use of Digital Twin in industrial operations has attracted the attention of many scholars and industrial sectors. Despite this, there is still a need to identify its value in industrial operations mainly in production, predictive maintenance, and after-sales services. Similarly, the implementation of a Digital Twin still faces many challenges. In response, a systematic literature review and analysis of 41 papers published between 2016 and 11 July 2020 have been carried out to examine recently published works in the field. Future research directions in the area are also highlighted. The result reveals that, regardless of the challenges, the role of Digital Twin in the advancement of industrial operations, especially production and predictive maintenance is highly significant. However, its role in after-sales services remains limited. Insights are offered for research scholars, companies, and practitioners to understand the current state-of-the-art and challenges, and to indicate future research possibilities in the field. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…Integrating traditional DSS with AI can increase the efficiency in the decision-making process and introduce higher levels of automation, resulting in the appearance of problem-oriented intelligent DSS (IDSS) or knowledge-driven DSS. Most of the ongoing efforts in this sense are still limited to the application of rule-based expert systems, or machine-learning or genetic algorithms [ 52 ] on raw data [ 53 ]. One of the problems that affect DSSs is the need to rely on inputs collected, modelled and generated by human operators.…”

Section: Realization Aspects Of Ai-based System Elementsmentioning

confidence: 99%

An Artificial Intelligence-Based Collaboration Approach in Industrial IoT Manufacturing: Key Concepts, Architectural Extensions and Potential Applications

Trakadas

Simoens

Gkonis

et al. 2020

Sensors

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The digitization of manufacturing industry has led to leaner and more efficient production, under the Industry 4.0 concept. Nowadays, datasets collected from shop floor assets and information technology (IT) systems are used in data-driven analytics efforts to support more informed business intelligence decisions. However, these results are currently only used in isolated and dispersed parts of the production process. At the same time, full integration of artificial intelligence (AI) in all parts of manufacturing systems is currently lacking. In this context, the goal of this manuscript is to present a more holistic integration of AI by promoting collaboration. To this end, collaboration is understood as a multi-dimensional conceptual term that covers all important enablers for AI adoption in manufacturing contexts and is promoted in terms of business intelligence optimization, human-in-the-loop and secure federation across manufacturing sites. To address these challenges, the proposed architectural approach builds on three technical pillars: (1) components that extend the functionality of the existing layers in the Reference Architectural Model for Industry 4.0; (2) definition of new layers for collaboration by means of human-in-the-loop and federation; (3) security concerns with AI-powered mechanisms. In addition, system implementation aspects are discussed and potential applications in industrial environments, as well as business impacts, are presented.

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Integrating the Digital Twin of the manufacturing system into a decision support system for improving the order management process

Cited by 188 publications

References 19 publications

Evaluation of Waste Electronic Product Trade-in Strategies in Predictive Twin Disassembly Systems in the Era of Blockchain

Evaluation of Waste Electronic Product Trade-in Strategies in Predictive Twin Disassembly Systems in the Era of Blockchain

Digital Twin models in industrial operations: State‐of‐the‐art and future research directions

An Artificial Intelligence-Based Collaboration Approach in Industrial IoT Manufacturing: Key Concepts, Architectural Extensions and Potential Applications

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