AI and BD in Process Industry: A Literature Review with an Operational Perspective

Fornasiero, Rosanna; Nettleton, David; Kiebler, Lorenz; Yuso, Alicia Martínez de; Marco, Chiara Eleonora De

doi:10.1007/978-3-030-85914-5_61

Cited by 3 publications

(4 citation statements)

References 41 publications

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“…Of the industry operations, process optimization and production control, maintenance management, quality management, and supply chain management are discussed more than design and engineering, which gets the least attention. This is in line with the results of other reviews [4,6,8] The low share of design and engineering operations in the papers is possibly because they are less straightforward-requiring human creativity-and thus more difficult for ML methods than more data-based tasks, such as quality control or maintenance management.…”

Section: Discussionsupporting

confidence: 84%

“…Recent reviews have analyzed research publications about the use of ML methods in the context of manufacturing industries [1][2][3][4][5][6][7][8]. Bertolini et al categorized papers on machine learning for industrial applications published since 2000 in terms of the applied algorithm and application domain [8].…”

Section: Introductionmentioning

confidence: 99%

“…Fornasiero et al [4] reviewed the use of AI and big data in process industries. They categorize publications according to the industry sector, AI, and big data technology, as well as operations, such as process control, predictive maintenance, and supply chain.…”

Section: Introductionmentioning

confidence: 99%

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Benefits of Machine Learning in the Manufacturing Industry

Ailisto¹,

Helaakoski²,

Neuvonen³

2023

Preprint

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Artificial intelligence (AI) technologies, particularly the subfield of machine learning (ML), has been expected to bring significant benefits to all sectors of business and public services. The manufacturing industry is considered one of the domains most likely to benefit from AI tech-nologies. During the recent years, there has been a growing research and development effort on machine learning–based solutions for manufacturing industries, as shown by the growing number of research publications. However, the business, environmental or societal benefits of applying ML in manufacturing industry have not been reviewed and evaluated. This paper re-views the reported benefits of ML in the manufacturing industry by analysis of scientific publi-cations. The focus of the review is on research papers that were published from 2017 to 2021. The review pays particular attention to the industry sector, manufacturing operations, machine learning methods used, maturity of results and the benefits pursued and achieved. The benefits are divided into business benefits, environmental sustainability, and societal sustainability. The analysis shows that the maturity of ML-based solutions is still low, despite the intensive research. Consequently, future research projects should place more emphasis on the maturity of the solu-tions, which would lead to their broader adoption into operational use, thus benefiting industry and society at large. This calls for closer collaboration between the research community and in-dustry.

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Section: Discussionsupporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Benefits of Machine Learning in the Manufacturing Industry

Ailisto¹,

Helaakoski²,

Neuvonen³

2023

Preprint

View full text Add to dashboard Cite

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“…Additionally, Smyth et al [18] conduct a comprehensive review of AI utilization within the supply chain sector. Fornasiero et al [19] provide a concise overview of AI and big data applications within the process industry, encompassing sectors such as cement, chemicals, and steel production. In a related domain, Regona et al [32] conduct a survey on AI's utilization within the construction industry.…”

Section: Analysis Of Related Workmentioning

confidence: 99%

A Survey on AI-Empowered Softwarized Industrial IoT Networks

Rojas,

Carrascal,

Lopez-Pajares

et al. 2024

Electronics

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The future generation of mobile networks envision Artificial Intelligence (AI) and the Internet of Things (IoT) as key enabling technologies that will foster the emergence of sophisticated use cases, with the industrial sector being one to benefit the most. This survey reviews related works in this field, with a particular focus on the specific role of network softwarization. Furthermore, the survey delves into their context and trends, categorizing works into several types and comparing them based on their contribution to the advancement of the state of the art. Since our analysis yields a lack of integrated practical implementations and a potential desynchronization with current standards, we finalize our study with a summary of challenges and future research ideas.

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