Establishment of a maturity model to assess the development of industrial AI in smart manufacturing

Chen, Wenting; Liu, Caihua; Xing, Fei; Peng, Guochao; Yang, Xi

doi:10.1108/jeim-10-2020-0397

Cited by 17 publications

(10 citation statements)

References 44 publications

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“…Hence, Felch et al suggest using literature review and explorative research methods to develop maturity models. And many studies have adopted the systematic literature review (SLR) method and the expert interview to conduct maturity models [19,23,24]. We follow the convention to develop the maturity model of AI-empowered CNDBs.…”

Section: Analysis Methods and Resultsmentioning

confidence: 99%

A maturity model for AI-empowered cloud-native databases: from the perspective of resource management

et al. 2022

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Cloud-native database systems have started to gain broad support and popularity due to more and more applications and systems moving to the cloud. Various cloud-native databases have been emerging in recent years, but their developments are still in the primary stage. At this stage, database developers are generally confused about improving the performance of the database by applying AI technologies. The maturity model can help database developers formulate the measures and clarify the improvement path during development. However, the current maturity models are unsuitable for cloud-native databases since their architecture and resource management differ from traditional databases. Hence, we propose a maturity model for AI-empowered cloud-native databases from the perspective of resource management. We employ a systematic literature review and expert interviews to conduct the maturity model. Also, we develop an assessment tool based on the maturity model to help developers assess cloud-native databases. And we provide an assessment case to prove our maturity model. The assessment case results show that the database’s development direction conforms to the maturity model. It proves the effectiveness of the maturity model.

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Section: Analysis Methods and Resultsmentioning

confidence: 99%

A maturity model for AI-empowered cloud-native databases: from the perspective of resource management

et al. 2022

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“…In addition to the development of comprehensive maturity models, models considering certain specific features of industry have also been developed. An Industry 4.0 maturity model for machine tool companies [48], an industrial artificial intelligence model [49], and a digital maturity assessment model [50] can serve as suitable examples. The authors did not intend to conduct a deep and systematic literature investigation in this area.…”

Section: Quality 40 Assessment Modelsmentioning

confidence: 99%

Quality 4.0 Maturity Assessment in Light of the Current Situation in the Czech Republic

et al. 2022

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Production companies throughout the world are currently facing challenges with the extensive transformation of their processes with regard to digitalisation and other features of Industry 4.0. Simultaneously, traditional quality management processes must be significantly changed, and the umbrella term “Quality 4.0” is used to represent this transformation. Many companies, including Czech enterprises, are still struggling with the concept of Quality 4.0, as there are a lot of uncertainties, misunderstandings, and false approaches. Additionally, a lot of questions arise about how to provide an objective and comprehensive assessment of the company’s maturity or readiness for Quality 4.0. Therefore, the main objective of this article is to present our proposal of a framework and methodology on how to assess and measure the maturity level for the implementation of Quality 4.0. This proposal will be supplemented by a presentation of the main results obtained from a special field study, which was conducted at Czech production companies with the aim of obtaining a representative set of information showing their readiness to adopt Quality 4.0 and convert existing quality management systems into the new era. The authors also identify some research gaps, including the necessity to confirm the mutual relationship between the dimensions and items of Quality 4.0 and the sustainable development of companies or society.

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“…Having proven their worth as artifacts for capability development in the context of manufacturing (e.g., Chen et al (2022), Gökalp et al (2021) and Sjödin et al…”

Section: Introductionmentioning

confidence: 99%

“…This includes, for instance, storage capacity, computational capacity, as well as sufficient interconnectivity to enable the transfer of data from cyber-physical systems to local servers or the cloud. It also includes the capacity to feed insights drawn from the cloud/server to machines and onward, to customers (Chen et al 2022). Another relevant capability in the infrastructure area is 'cybersecurity & privacy'.…”

mentioning

confidence: 99%

“…Another relevant capability in the infrastructure area is 'cybersecurity & privacy'. This involves applying technical and managerial measures to ensure confidentiality, integrity, and availability when processing large volumes of data (Ullah and Ali Babar 2019;Chen et al 2022;Guggenmos et al 2022). Finally, 'cyber-physical systems & connectivity' is an infrastructure-related capability that covers capturing and collecting data via cyber-physical systems.…”

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confidence: 99%

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Data or Business First?—Manufacturers’ Transformation Toward Data-driven Business Models

Stahl

Häckel

Leuthe

et al. 2023

Schmalenbach J Bus Res

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Driven by digital technologies, manufacturers aim to tap into data-driven business models, in which value is generated from data as a complement to physical products. However, this transformation can be complex, as different archetypes of data-driven business models require substantially different business and technical capabilities. While there are manifold contributions to research on technical capability development, an integrated and aligned perspective on both business and technology capabilities for distinct data-driven business model archetypes is needed. This perspective promises to enhance research’s understanding of this transformation and offers guidance for practitioners. As maturity models have proven to be valuable tools in capability development, we follow a design science approach to develop a maturity model for the transformation toward archetypal data-driven business models. To provide an integrated perspective on business and technology capabilities, the maturity model leverages a layered enterprise architecture model. By applying and evaluating in use at two manufacturers, we find two different transformation approaches, namely ‘data first’ and ‘business first’. The resulting insights highlight the model’s integrative perspective’s value for research to improve the understanding of this transformation. For practitioners, the maturity model allows a status quo assessment and derives fields of action to develop the capabilities required for the aspired data-driven business model.

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Establishment of a maturity model to assess the development of industrial AI in smart manufacturing

Cited by 17 publications

References 44 publications

A maturity model for AI-empowered cloud-native databases: from the perspective of resource management

A maturity model for AI-empowered cloud-native databases: from the perspective of resource management

Quality 4.0 Maturity Assessment in Light of the Current Situation in the Czech Republic

Data or Business First?—Manufacturers’ Transformation Toward Data-driven Business Models

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