Digital manufacturing and innovation

Stanko, Michael A.; Rindfleisch, Aric

doi:10.1111/jpim.12686

Cited by 8 publications

(2 citation statements)

References 85 publications

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“…From a regional perspective, digital technology can better integrate into the design, manufacturing, and operations of various industries and processes (Weigel and Manfired, 2019;Zhong et al, 2022) than traditional manufacturing enterprises. Digital technology can empower enterprises to optimize the industrial structure while maintaining economic benefits and reduce the mismatch of resources, thus reducing carbon emissions (Stanko and Rindfleisch, 2023). According to signaling theory, in market competition, as the rapid accumulation of data by manufacturing enterprises increases dramatically, digital technology, as a basic and strategic resource for obtaining development information (Song et al, 2019), can significantly improve the search scope, transmission speed, and transparency of information (Yoo et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Digital technology, green innovation, and the carbon performance of manufacturing enterprises

Li,

Ji,

Zhang

et al. 2024

Front. Environ. Sci.

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With the continuous promotion of digitalization and the global trend toward a low-carbon economy, the issue of whether enterprises can enhance their carbon performance with the assistance of digital technology has aroused widespread attention from both academia and industry. In order to explore whether digital technology can improve the carbon performance of manufacturing enterprises, this study, based on resource orchestration theory and signaling theory, utilizes data from China’s A-share manufacturing enterprises from 2012 to 2021 to empirically investigate the relationship between digital technology and the carbon performance of manufacturing firms. It also explores the mediating conduction path and boundary influencing factors between them. Its findings demonstrate that: digital technology is capable of improving carbon performance; green innovation (including green technology and green collaboration) has partially mediating effects; there is a catalytic role for environmental information disclosure in utilizing digital technology to enhance carbon performance. Building on this, we find that the impacts of digital technology, green innovation, and environmental information disclosure on carbon performance vary due to differences in the nature of industries and the strategic aggressiveness of enterprises. Specifically, the role of digital technology on carbon performance seems somewhat more pronounced among firms in the high-tech industry and those employing defensive and analytical strategies. Additionally, the effects generated by green innovation and environmental information are more pronounced in the high-tech industry and among enterprises that adopt analytical strategies. This study reveals the inherent mechanism of digital technology in enhancing the carbon performance of manufacturing enterprises, which provides empirical evidence for the development of digital technology and the improvement of carbon performance in manufacturing enterprises, thus helping promote low-carbon economic transformation.

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Section: Introductionmentioning

confidence: 99%

Digital technology, green innovation, and the carbon performance of manufacturing enterprises

Li,

Ji,

Zhang

et al. 2024

Front. Environ. Sci.

View full text Add to dashboard Cite

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“…During the last decade, digitization has established new rules of consumption and purchase and created new consumer habits, that has led to the manufacturing adaptation. Application of the Industry 4.0 model, with elements: CPS, IoT, additive manufacturing, BDA and AI/ML is shown in [70]. Research presented in [71], shows that innovation is a key factor for the spread of the Industry 4.0 model in manufacturing from the following aspects: (i) sustainability and digital manufacturing, (ii) development of knowledge for digital manufacturing, (iii) effects of digital manufacturing on customer's experience, (iv) apperance of digital twins, and (v) AI in digital manufacturing.…”

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confidence: 99%

On the Way to the Development of Smart Manufacturing Model in Practice

Djoric,

Jordanovic,

Lalic

et al. 2024

Preprint

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Digital technologies have been present in manufacturing and industry for a long time, and today they represent the basis for the application of the Industry 4.0 model in practice. The transformation marked by the Industry 4.0 model is based on the application of a large number of models of communication technologies, solutions for connecting entities (machines, parts, ...) in the factory within Internet of Things (IoT), as well as the possibility of big data analysis (BDA) throught the artificial intelligence tools (AI/ML). These technologies provide great opportunities to create more flexible and profitable manufacturing processes based on data, and using the Internet. As more and more factories and their equipment are IoT enabled, the volume of data will only grow, hence the application of digital technologies is inevitable. To truly pave the way forward to Industry 4.0 and beyond, manufacturing must evolve into cognitive manufacturing, which is the base model of smart manufacturing. This paper provides an overview of the development of the concept of digital manufacturing, from different aspects, with examples of application in real manufacturing. The goal is to create a good practice of digital manufacturing with the elements of Industry 4.0, as a basis for the development and application of the smart manufacturing (SM) model.

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Artificial intelligence enabled product–service innovation: past achievements and future directions

Naeem,

Kohtamäki,

Parida

2024

Rev Manag Sci

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This study intends to scrutinize the role of Artificial Intelligence (AI) in Product-Service Innovation (PSI). The literature on AI enabled PSI, other related innovation business models, product-service systems, and servitization has grown significantly since 2018; therefore, there is a need to structure the literature in a systematic manner and add to what has been studied thus far. Product-service innovation is used to represent the relevance of achieving innovation in business models dealing with innovation outcomes including artificial intelligence. This study used bibliographic coupling to analyze 159 articles emerging from the fields of computer sciences, engineering, social sciences, decision sciences, and management. This review depicts structures of the literature comprising five (5) clusters, namely, (1) technology adoption and transformational barriers, which depicts the barriers faced during the adoption of AI-enabled technologies and following transformation; (2) data-driven capabilities and innovation, which highlights the data-based capabilities supported through AI and innovation; (3) digitally enabled business model innovation, which explained how AI-enabled business model innovation occurs; (4) smart design changes and sustainability, which reveals the working of AI in product service environments with different design changes and transformations based on sustainability; and (5) sectorial application, which highlights industry examples. Each cluster is comprehensively analyzed based on its contents, including central themes, models, theories, and methodologies, which help to identify the gaps and support suggestions for future research directions.

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Digital manufacturing and innovation

Cited by 8 publications

References 85 publications

Digital technology, green innovation, and the carbon performance of manufacturing enterprises

Digital technology, green innovation, and the carbon performance of manufacturing enterprises

On the Way to the Development of Smart Manufacturing Model in Practice

Artificial intelligence enabled product–service innovation: past achievements and future directions

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