Designing Intelligent Factory: Conceptual Framework and Empirical Validation

Shafiq, Syed Imran; Vélez, Gorka; Toro, Carlos; Sanín, Cesar; Szczerbicki, Edward

doi:10.1016/j.procs.2016.09.351

Cited by 13 publications

(18 citation statements)

References 10 publications

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“…Strategic value creation measurement is underrepresented in the sample studies, being addressed in only three articles as shown in Table 4. [32] Improved agility by reacting more quickly to change in orders n/a Proof of concept Niño et al [33] Reduced waste, improved return on production process n/a Proof of concept Oneto et al [34] Improved accuracy of data model n/a Proof of concept Oses et al [35] Improved prediction of energy savings n/a Proof of concept Shafiq et al [39] Maintain just-in-time maintenance, improved asset utilization, improved flexibility n/a Proof of concept Table 4 presents the 20 articles out of 42 mentioning operational value, strategic value or both. Operational value creation is measured in 20 articles.…”

Section: Figure 5 Bi Subject Distributionmentioning

confidence: 99%

“…Park [36] Success factors and expected effects of connected factory n/a n/a Rix et al [37] Framework for die casting real time monitoring Need to link information company wide Business Shafiq et al [39] Present a technical framework for an intelligent factory n/a n/a Ternoven & Heikkilä [40] Literature review on data mining and analysis in IIoT Include social networking and human interactions in DM…”

Section: Informationmentioning

confidence: 99%

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Business Intelligence in Industry 4.0: State of the art and research opportunities

Bordeleau¹,

Mosconi²,

Santa-Eulalia³

2018

Proceedings of the Annual Hawaii International Conference on System Sciences

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Data collection and analysis have been at the core of business intelligence (BI) for many years, but traditional BI must be adapted for the large volume of data coming from Industry 4.0 (I4.0) technologies. They generate large amounts of data that need to be processed and used in decision-making to generate value for the companies. Value generation of I4.0 through data analysis and integration into strategic and operational activities is still a new research topic. This study uses a systematic literature review with two objectives in mind: understanding value creation through BI in the context of I4.0 and identifying the main research contributions and gaps. Results show most studies focus on real-time applications and integration of voluminous and unstructured data. For business research, more is needed on business model transformation, methodologies to manage the technological implementation, and frameworks to guide human resources training.

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Section: Figure 5 Bi Subject Distributionmentioning

confidence: 99%

Section: Informationmentioning

confidence: 99%

Business Intelligence in Industry 4.0: State of the art and research opportunities

Bordeleau¹,

Mosconi²,

Santa-Eulalia³

2018

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…Regarding the autonomous generation of KPI on CPS technology environment, several methodologies have been proposed. Shafiq et al [28] presented a model that gathers raw data at machine level and converts it into relevant information using CPS. The collected information is analysed by the system that provides KPI visualization.…”

Section: Lean Production Enhanced By Cyber-physical Systemsmentioning

confidence: 99%

How Industry 4.0 can enhance Lean practices

Pereira¹,

Dinis‐Carvalho²,

Alves³

et al. 2019

FME Transactions

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The global industrial landscape has deeply changed over the last few years and the Industry 4.0 concept has emerged, being enabled by successive disruptive innovations and technological development that have transformed manufacturing processes. This concept is being pointed out as the fourth industrial revolution that embraces a set of new technologies that are shaping the future manufacturing vision. However, Lean Production is a widely used manufacturing approach that brings several benefits to organizations. Despite the integration between Industry 4.0 and Lean Production is being researched in the recent years, the impacts that result from the implementation of new technologies in established lean practices is not clear. The purpose of this study that consists in a systematic literature review is assessing how these emerging disruptive technologies can enhance lean practices and analyse their impacts and benefits for organizations that are moving towards this new industrial paradigm.

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“…造、大数据和物联网技术, 此外还有在生产中应用的 CPS--信息物理生产系统(CPPS) [46] 、3D打印以及大数据时代衍生出的深度学习算法 [47] "智慧工厂" [48] 提供了强有力的技术支持. 目前我国已开展了相关智能制造示范项目的研发及实施 [49] 专家系统是最早应用在智能制造中的智能技术 [50] , 主要由知识库和推理机组成, 仅能部分代替或者辅助人在制造过程中的决策作用, 缺乏灵活性和鲁棒性; 神经网络是以数据为基础的智能技术 [51] , 能够在一定程度上学习和适应变化, 可以在很少人为干预的情况对生产制造过程各类问题进行优化; Agent技术能够通过社交、学习、推理等方法感知和适应制造过程中复杂的动态环境, 具有很强的开放性、灵活性、适应性和鲁棒性 [52] .…”

Section: 此阶段随着新一代信息通信技术和人工智能技术的发展如表3所示的rfid技术、cps、云计算、云制unclassified

Research hotspots and tendency of intelligent manufacturing

Yan¹,

Bailin²

2020

Chin. Sci. Bull.

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Designing Intelligent Factory: Conceptual Framework and Empirical Validation

Cited by 13 publications

References 10 publications

Business Intelligence in Industry 4.0: State of the art and research opportunities

Business Intelligence in Industry 4.0: State of the art and research opportunities

How Industry 4.0 can enhance Lean practices

Research hotspots and tendency of intelligent manufacturing

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