Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment

Lee, Jay; Kao, Hung An; Yang, Shanhu

doi:10.1016/j.procir.2014.02.001

Cited by 1,503 publications

(791 citation statements)

References 9 publications

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“…Opportunities for further development and direction and visions related to Industry 4 are introduced by (Pfeiffer 2017). The main ideas of Industry 4.0 were originally published by Kagermann based on cyber-physical system-enabled manufacturing and service innovation during the Hannover Fair event in 2011 that resulted from an initiative regarding high-tech strategy for 2020 (Lee et al 2014;European Commission 2018;Zhou et al 2015) and informed the Industry Manifesto 4.0 released in 2013 by the Acatech Academy of National Science and Technology (Druckversion 2018).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Consequences of Industry 4.0 in Business and Economics

et al. 2018

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Abstract:The introduction of information technology into all aspects of our lives has brought forth qualitative and quantitative changes on such a large scale that this process has come to be known as the Fourth Industrial Revolution, or Industry 4.0. The aim of this paper is to fill in the gaps and provide an overview of studies dealing with Industry 4.0 from the business and economic perspectives. A scoping review is performed regarding business, microeconomic and macroeconomic economic problems. Four investigators performed a literature search of the Web of Science, Scopus, and Science Direct. The selected period spanned from 2014 to 2018, and the following keywords were used for the search: Industry 4.0, economics, economic development, production economics, and financial sector. A total of 2275 results were returned. In all, 67 full papers were screened. Results obtained from the relevant studies were, furthermore, divided into the following categories: work and skills development; economy growth and macroeconomic aspect; sustainability; intelligent manufacturing; policy; and change in business processes. Findings show that the aspects of work and skills development, smart technology adoption, intelligent manufacturing, and digitalization are very well described. The government and its policies usually play the role of a needed supportive element. Usually studies lack a coherent view of the topic in question and solve partial questions.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Consequences of Industry 4.0 in Business and Economics

et al. 2018

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“…Context is vital to the interpretation of data either because assets within a fleet have their own structural idiosyncrasies (Houghton and Patel 2015;Kefalidou et al 2015), or because similar assets are used in different ways and face different demands (Lee et al 2014). Therefore, despite the potential for remote monitoring to reduce the numbers of on-site maintenance teams, a reduced local presence is still vital, not only to carry out repairs but also because data sensing is not complete (Jonsson et al 2010).…”

Section: User Factorsmentioning

confidence: 99%

“…Domains such as pharmaceutical production (McDonnell et al 2015), vehicle operations (Figueredo et al 2015;Chowdhury and Akram 2013), manufacturing (Lee et al 2014) and rail (Dadashi et al 2014) are turning to data-driven tools for management and maintenance of key assets. Typically, asset management and remote condition monitoring technologies report data from assets, often from multiple sensor points, and build a picture, over time, of asset performance.…”

Section: Introductionmentioning

confidence: 99%

“…It is also enabling an increasing movement towards the servitisation of maintenance. Original equipment manufacturers (OEMs) no longer just manufacture equipment-they have an ongoing service relationship with their customer, in part enabled through remote monitoring of asset performance (Lee et al 2014). Hence, predicate maintenance technologies are allowing new forms of e-business to emerge (Smith 2013).…”

Section: Introductionmentioning

confidence: 99%

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A cross-sector analysis of human and organisational factors in the deployment of data-driven predictive maintenance

Golightly

Kefalidou

Sharples

2017

Inf Syst E-Bus Manage

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Domains such as utilities, power generation, manufacturing and transport are increasingly turning to data-driven tools for management and maintenance of key assets. Whole ecosystems of sensors and analytical tools can provide complex, predictive views of network asset performance. Much research in this area has looked at the technology to provide both sensing and analysis tools. The reality in the field, however, is that the deployment of these technologies can be problematic due to user issues, such as interpretation of data or embedding within processes, and organisational issues, such as business change to gain value from asset analysis. 13 experts from the field of remote condition monitoring, asset management and predictive analytics across multiple sectors were interviewed to ascertain their experience of supplying data-driven applications. The results of these interviews are summarised as a framework based on a predictive maintenance project lifecycle covering project motivations and conception, design and development, and operation. These results identified critical themes for success around having a target-or decision-led, rather than data-led, approach to design; long-term resourcing of the deployment; the complexity of supply chains to provide data-driven solutions and the need to maintain knowledge across the supply chain; the importance of fostering technical competency in end-user organisations; and the importance of a maintenance-driven strategy in the deployment of data-driven asset management. Emerging from these themes are recommendations related to culture, delivery process, resourcing, supply chain collaboration and industry-wide cooperation.

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“…Recently, many manufactures have been influenced by Industry 4.0. They are not only optimizing the manufacturing processes, but also effectively controlling industrial pollution with the assistance of data maintenance and monitoring [5,6].…”

Section: Introductionmentioning

confidence: 99%

Green Production Planning and Control for the Textile Industry by Using Mathematical Programming and Industry 4.0 Techniques

Tsai

2018

Energies

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Abstract:The textile industry is one of the world's major sources of industrial pollution, and related environmental issues are becoming an ever greater concern. This paper considers the environmental issues of carbon emissions, energy recycling, and waste reuse, and uses a mathematical programming model with Activity-Based Costing (ABC) and the Theory of Constraints (TOC) to achieve profit maximization. This paper discusses the combination of mathematical programming and Industry 4.0 techniques to achieve the purpose of green production planning and control for the textile industry in the new era. The mathematical programming model is used to determine the optimal product mix under various production constraints, while Industry 4.0 techniques are used to control the production progress to achieve the planning targets. With the help of an Industry 4.0 real-time sensor and detection system, it can achieve the purposes of recycling waste, reducing carbon emission, saving energy and cost, and finally achieving a maximization of profit. The main contributions of this research are using mathematical programming approach to formulate the decision model with ABC cost data and TOC constraints for the textile companies and clarifying the relation between mathematical programming models and Industry 4.0 techniques. Managers in the textile companies can apply this decision model to achieve the optimal product-mix under various constraints and to evaluate the effect on profit of carbon emissions, energy recycling, waste reuse, and material quantity discount.

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Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment

Cited by 1,503 publications

References 9 publications

Consequences of Industry 4.0 in Business and Economics

Consequences of Industry 4.0 in Business and Economics

A cross-sector analysis of human and organisational factors in the deployment of data-driven predictive maintenance

Green Production Planning and Control for the Textile Industry by Using Mathematical Programming and Industry 4.0 Techniques

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