I4.0 Pilot Project on a Semiconductor Industry: Implementation and Lessons Learned

Silveira, Gabriel do N.; Viana, Rafael F.; Lima, Miromar José de; Kuhn, Henrique Cafruni; Crovato, César David Paredes; Ferreira, Sandro Binsfeld; Pesenti, Giovani; Storck, Erick; Righi, Rodrigo da Rosa

doi:10.3390/s20205752

Cited by 6 publications

(7 citation statements)

References 18 publications

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“…In contrast to the previous research perspective, publications with the perspective implementation are motivated by the need to present detailed descriptions of comprehensively realized real-world applications. Silveira et al (2020) for instance justified their work by pointing to the absence of detailed and replicable descriptions of implemented case studies. They criticized that most applications do not explain x Yuan-Fu and Min (2020)…”

Section: Resultsmentioning

confidence: 99%

“…Security deals with issues relating to the safety of data and remote control through the use of cloud technologies. The concern is that sensitive data might be exposed (N. or that remote control might be misused (Silveira et al 2020) which might impact the economic, social, and environmental pillar by theft of sensitive data, manipulation of machines which might harm workers, and sabotage of production processes which might waste resources.…”

Section: Performancementioning

confidence: 99%

“…Familiarity with software regards the advanced analytics applications concerning the software used to conduct processing tasks. A familiar software such as WEKA (Jo et al 2020) or coding environment as RStudio (Rudolph et al 2020) facilitates the work of the development team (Silveira et al 2020), and thus impacts the social pillar. Another example for the social pillar is the theme open source which is concerned with the regime under which software is usable during the processing stage (Silveira et al 2020).…”

Section: Performancementioning

confidence: 99%

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Advanced analytics applications in smart manufacturing – A systematic literature review on their perspectives, effects, and sustainability

Ullrich¹,

Buchmin²

2022

Digitization of the Work Environment for Sustainable Production

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Advanced analytics applications in smart manufacturing provide beneficiary effects such as predictive maintenance, energy optimized factories, and automized quality management of products. While the benefits and thus the sustainability effects are well investigated, the research field is characterized by many single applications with little conceptual synthesis. Therefore, this research aims to systematize the field and to identify specific sustainability themes in context of advance analytics applications. This chapter provides an overview of existing advanced analytics applications in smart manufacturing and clusters the addressed sustainability themes. Applying a systematic literature review, following the PRISMA statement, 65 different applications within four research perspective could be identified. Furthermore, ten different sustainability-related effects of the applications in smart manufacturing unfold. The content analysis resulted in 27 sustainability themes, which holistically cover all spheres of sustainability, with a focus on the social and economic spheres.

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Section: Resultsmentioning

confidence: 99%

Section: Performancementioning

confidence: 99%

Section: Performancementioning

confidence: 99%

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Advanced analytics applications in smart manufacturing – A systematic literature review on their perspectives, effects, and sustainability

Ullrich¹,

Buchmin²

2022

Digitization of the Work Environment for Sustainable Production

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“…The Industry 4.0 technologies can be used in special sectors of packaging. An Industry 4.0 pilot project proved the impact of the application of Industry 4.0 technologies on the performance of a semiconductor packaging and test company [10]. More than 70% of the articles were published in the last four years.…”

Section: Literature Reviewmentioning

confidence: 99%

Industry 4.0: challenges and opportunities in packaging logistics

Bányai

2022

IOP Conf. Ser.: Mater. Sci. Eng.

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The fourth industrial revolution opened new opportunities for the development of industrial processes, as it created the opportunity for companies to transform their conventional systems into cyber-physical systems to meet the increasingly dynamic and diversified customers’ demands. Although the primary goal of the development of these cyber-physical systems is to improve the efficiency of operations and increase the quality, but energy efficiency, environmental impact and sustainability also should be taken into consideration. Packaging is a special area of production and service, where due to the significant amount of logistics activity, the design and operation of sustainable processes with low environmental impact are emphasized. Within the frame of this article, the author provides an overview of the opportunities offered by Industry 4.0 technologies in the development of packaging processes of manufacturing and service companies and outlines the challenges to be faced in the implementation of these developments.

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“…The first paper considered in this section is [7] that studies Industry 4.0 in the semiconductor industry, where high reliability and low operating costs are critical for a business' success. In this context, this article proposes an Industry 4.0 Pilot as a compilation of lessons learned during an end-to-end development of a reference design applied to a semiconductor packaging and test company.…”

Section: Some Preliminary Findingsmentioning

confidence: 99%

IIoT/IoT and Artificial Intelligence/Machine Learning as a Process Optimization driver under industry 4.0 model

Mateo

Redchuk

2021

JCS&T

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The advance of digitalization in industry is making possible that connected products and processes help people, industrial plants and equipment to be more productive and efficient, and the results for operative processes should impact throughout the economy and the environment.Connected products and processes generate data that is being seen as a key source of competitive advantage, and the management and processing of that data is generating new challenges in the industrial environment.The article to be presented looks into the framework of the adoption of Artificial Intelligence and Machine Learning and its integration with IIoT or IoT under industry 4.0, or smart manufacturing framework. This work is focused on the discussion around Artificial Intelligence/Machine Learning and IIoT/IoT as driver for Industrial Process optimization.The paper explore some related articles that were find relevant to start the discussion, and includes a bibliometric analysis of the key topics around Artificial Intelligence/Machine Learning as a value added solution for process optimization under Industry 4.0 or Smart Manufacturing paradigm.The main findings are related to the importance that the subject has acquired since 2013 in terms of published articles, and the complexity of the approach of the issue proposed by this work in the industrial environment.

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I4.0 Pilot Project on a Semiconductor Industry: Implementation and Lessons Learned

Cited by 6 publications

References 18 publications

Advanced analytics applications in smart manufacturing – A systematic literature review on their perspectives, effects, and sustainability

Advanced analytics applications in smart manufacturing – A systematic literature review on their perspectives, effects, and sustainability

Industry 4.0: challenges and opportunities in packaging logistics

IIoT/IoT and Artificial Intelligence/Machine Learning as a Process Optimization driver under industry 4.0 model

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