ASSISTANT: Learning and Robust Decision Support System for Agile Manufacturing Environments

Beldiceanu, Nicolas; Dolgui, Alexandre; Gonnermann, Clemens; Gonzalez-Castañé, Gabriel; Kousi, Niki; Meyers, Bart; Prud’homme, Julien; Thevenin, Simon; Vyhmeister, Eduardo; Östberg, Per-Olov

doi:10.1016/j.ifacol.2021.08.074

Cited by 10 publications

(3 citation statements)

References 15 publications

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“…In spite of the complication and variety in the system, and the diverse execution of sensors and systems, 5G provides real-time data, while 5G in SCM has paved the path in various organisational-technological concepts such as cloud manufacturing, digital twins, data-driven modelling, etc. Moreover, 5G provides novel potential for artificial-intelligence (AI)-based applications in manufacturing systems and SCM [23]. The IoT provides probable advantages such as automated structures, bioregulation, environmental feedback, energy supervision, and dematerialisation [24].…”

Section: Relevance and Application Of 5g In Scmmentioning

confidence: 99%

“…Therefore, it is evident from the previous discussions that 5G technology would be one of key enablers to transform supply chain management to provide competitive advantages to the firms in the era of the metaverse. The extant literature [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] (details are given in Section 2.2) shows the relevance and importance of 5G technologies for enhancing the value delivered through the supply chain. Moreover, previous work (for instance, [11][12][13][14]35,36]) described the various challenges of the successful adaptation and implementation of 5G technologies (see Section 2.1 for detailed discussions).…”

Section: Introductionmentioning

confidence: 99%

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Critical Success Factors for 5G Technology Adaptation in Supply Chains

et al. 2023

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The present age is moving through Industry 4.0 with massive technological developments. Supply chains have become digital, keeping sync with consumer demands and preferences. The recent pandemic has reinforced the need of embracing digital technologies in managing supply chains effectively. Therefore, it is necessary that supply chains adopt 5G mobile technologies. In this regard, the present study aims to discern the critical issues for the successful adaptation of 5G technologies for supply chain management (SCM) in developing countries such as India. The success factors for the adaptation of 5G in Indian supply chains are derived from the discussions made in the related past work regarding the challenges of implementing 5G technology. Then, the listed factors are finalised through initial rounds of face-to-face discussions with a focus group of five experts. Then, a q-rung-orthopair-fuzzy (qROFS)-based rating scale is used to rate the success factors. A new qROF-weighted-neutrality-average (q-ROFWNA)-based full-consistency method (FUCOM) approach for multicriteria decision-making (MCDM) problems involving group decision making is utilised to find out the critical success factors. Based on the comparative analysis of 17 success factors (grouped into four main factors), the spectrum availability, awareness of technology and usage, the development of supporting technologies and smart cities, and skill development are found to be the top five critical factors for the successful adaptation and implementation of 5G technologies in SCM. We further carry out a sensitivity analysis and validation test and observe that our model provides a reliable and stable solution.

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Section: Relevance and Application Of 5g In Scmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Critical Success Factors for 5G Technology Adaptation in Supply Chains

et al. 2023

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“…The paper is an extension of Beldiceanu et al (2021), and it successively presents the objective and the main vision of the project in section 2, a state of the art in section 3, the main scientific building blocks of the project (section 4, a discussion (section 5), and a conclusion (section 6).…”

Section: Introductionmentioning

confidence: 99%

The ASSISTANT project: AI for high level decisions in manufacturing

Castañé

Dolgui

Kousi

et al. 2022

International Journal of Production Research

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This paper outlines the main idea and approach of the H2020 ASSISTANT (LeArning and robuSt deciSIon SupporT systems for agile mANufacTuring environments) project. ASSISTANT is aimed at the investigation of AI-based tools for adaptive manufacturing environments, and focuses on the development of a set of digital twins for integration with, management of, and decision support for production planning and control. The ASSISTANT tools are based on the approach of extending generative design, an established methodology for product design, to a broader set of manufacturing decision making processes; and to make use of machine learning, optimization, and simulation techniques to produce executable models capable of ethical reasoning and data-driven decision making for manufacturing systems. Combining human control and accountable AI, the ASSISTANT toolsets span a wide range of manufacturing processes and time scales, including process planning, production planning, scheduling, and real-time control. They are designed to be adaptable and applicable in a both general and specific manufacturing environments.

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Towards a knowledge graph framework for ad hoc analysis in manufacturing

Meyers,

Vangheluwe,

Lietaert

et al. 2024

J Intell Manuf

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The development of artificial intelligence models for data driven decision making has a lot of potential for the manufacturing sector. Nevertheless, applications in industry are currently limited to the actionable insights one can discover from the available data and knowledge of a manufacturing system. We call the process to obtain such insights “ad hoc analysis”. Ad hoc analysis at system level is very complex in an industrial setting due to the inherent heterogeneity of data and existence of data silos, the lack of information and knowledge formalization, and the inability to meaningfully and efficiently reason about the data, information and knowledge. In this paper, we provide and outline a framework for ad hoc analysis in manufacturing based on knowledge graphs and influenced by the metamodelling paradigm. We derive its requirements and key elements from an analysis of several industry application cases. We show how manufacturing data, information and knowledge can be combined and made actionable using this framework. The framework supports workflows and tools for the data consumer (i.e., data scientist), and for the knowledge engineer. Furthermore, we show how the framework is integrated with existing data sources. Then, we discuss how we applied the framework to several application cases. We discuss how the framework contributes when applied, and what challenges still remain.

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