Decision Support Systems in the Context of Cyber-Physical Systems

Freier, Pascal; Schümann, Matthias

doi:10.3127/ajis.v25i0.2849

Cited by 4 publications

(4 citation statements)

References 25 publications

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“…The outcomes of our systematic review develop on empirical research [1][2][3][4][5][6][7][8][9][10][11]39,40,44,[121][122][123][124][136][137][138][139][140][144][145][146][147][148][157][158][159][160] contending that sustainable cyber-physical production systems developed on functional and behavioral patterns can address the inconveniences of disruptive Industry 4.0-related production environments. Wireless sensor technology monitor manufacturing assets and networked production or logistics business operations in real time.…”

Section: Discussionmentioning

confidence: 99%

“…CPPSs provide the technological basis for the digitalization and decentralization of manufacturing processes [157][158][159][160] and their integration across plant networks. Industry 4.0 offers increased adjustability, facilitating inexpensive product and service customization, and enabling lot-size one manufacturing, deep learning-assisted smart process planning, cognitive automation, and data-driven predictive maintenance.…”

Section: Deep Learning-assisted Smart Process Planning Internet Of Things-based Real-time Production Logistics and Sustainable Industrialmentioning

confidence: 99%

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Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems

et al. 2021

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With growing evidence of deep learning-assisted smart process planning, there is an essential demand for comprehending whether cyber-physical production systems (CPPSs) are adequate in managing complexity and flexibility, configuring the smart factory. In this research, prior findings were cumulated indicating that the interoperability between Internet of Things-based real-time production logistics and cyber-physical process monitoring systems can decide upon the progression of operations advancing a system to the intended state in CPPSs. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout March and August 2021, with search terms including “cyber-physical production systems”, “cyber-physical manufacturing systems”, “smart process manufacturing”, “smart industrial manufacturing processes”, “networked manufacturing systems”, “industrial cyber-physical systems,” “smart industrial production processes”, and “sustainable Internet of Things-based manufacturing systems”. As we analyzed research published between 2017 and 2021, only 489 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, we decided on 164, chiefly empirical, sources. Subsequent analyses should develop on real-time sensor networks, so as to configure the importance of artificial intelligence-driven big data analytics by use of cyber-physical production networks.

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Section: Discussionmentioning

confidence: 99%

Section: Deep Learning-assisted Smart Process Planning Internet Of Things-based Real-time Production Logistics and Sustainable Industrialmentioning

confidence: 99%

Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems

et al. 2021

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“…Hence, it can provide services for applications such as production prediction, quality tracing, energy prediction, advanced scheduling, data statistics and analysis. Also, it can support scientific and intelligent decision-making of integrated enterprise operation [29], [30].…”

Section: Methodology a Design Of Five-layer Architecture For Intellig...mentioning

confidence: 99%

Design of an Optimal Scheduling Control System for Smart Manufacturing Processes in Tobacco Industry

Liu

Wang

et al. 2023

IEEE Access

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The whole process of tobacco production is composed of many components, in which their operation and administration are currently independent. It is required to deploy smart manufacturing workflow for the whole production process, in order to realize centralized effective global scheduling. This requires an advanced administration control platform that has strong abilities of multisource data integration and automatic decision support. To bridge such research gap, this paper designs an optimal scheduling control system for smart manufacturing processes of tobacco industry. First of all, this work discusses major characteristics of future-generation production control patterns in intelligent tobacco factories (ITF). Then, a five-layer architecture for optimal scheduling control of ITF is proposed, which contains Internetof-Things layer, centralized control layer, model layer, platform layer and operation layer. In addition, a production scheduling optimization strategy is also developed for the proposed system to serve as the software algorithm that drives the running of whole smart manufacturing processes. Finally, this paper presents a comparative analysis of the proposed system's transformation in a cigarette factory. Naturally, the effectiveness of the proposed production optimization scheduling strategy is verified through simulation.

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“…The most decisive organizational factor in DSS design. Because organizational structure, processes and culture greatly determine decision-making (Freier and Schumann, 2021; Miah et al , 2020). Therefore, this research requires the identification of KM processes in the organization.…”

Section: Relevant Theoriesmentioning

confidence: 99%

Decision support system for fish quarantine measures in Indonesia

Hidayat

Satuti

Sensuse

et al. 2022

VJIKMS

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Purpose Fish quarantine is a measure to prevent the entry and spread of quarantine fish pests and diseases abroad and from one area to another within Indonesia's territory. Based on these backgrounds, this study aims to identify the knowledge, knowledge management (KM) processes and knowledge management system (KMS) priority needs for quarantine fish and other fishery products measures (QMFFP) and then develop a classification model and web-based decision support system (DSS) for QMFFP decisions. Design/methodology/approach This research methodology uses combination approaches, namely, contingency factor analysis (CFA), the cross-industry standard process for data mining (CRISP-DM) and knowledge management system development life cycle (KMSDLC). The CFA for KM solution design is performed by identifying KM processes and KMS priorities. The CRISP-DM for decision classification model is done by using a decision tree algorithm. The KMSDLC is used to develop a web-based DSS. Findings The highest priority requirements of KM technology for QMFFP are data mining and DSS with predictive features. The main finding of this study is to show that web-based DSS (functions and outputs) can support and accelerate QMFFP decisions by regulations and field practice needs. The DSS was developed using the CTree algorithm model, which has six main attributes and eight rules. Originality/value This study proposes a novel comprehensive framework for developing DSS (combination of CFA, CRISP-DM and KMSDLC), a novel classification model resulting from comparing two decision tree algorithms and a novel web-based DSS for QMFFP.

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Decision Support Systems in the Context of Cyber-Physical Systems

Cited by 4 publications

References 25 publications

Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems

Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems

Design of an Optimal Scheduling Control System for Smart Manufacturing Processes in Tobacco Industry

Decision support system for fish quarantine measures in Indonesia

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