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Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions

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  • Raffaele Cioffi

    (Department of Engineering, Parthenope University, Isola C4, Centro Direzionale, 80143 Napoli NA, Italy)

  • Marta Travaglioni

    (Department of Engineering, Parthenope University, Isola C4, Centro Direzionale, 80143 Napoli NA, Italy)

  • Giuseppina Piscitelli

    (Department of Engineering, Parthenope University, Isola C4, Centro Direzionale, 80143 Napoli NA, Italy)

  • Antonella Petrillo

    (Department of Engineering, Parthenope University, Isola C4, Centro Direzionale, 80143 Napoli NA, Italy)

  • Fabio De Felice

    (Department of Civil and Mechanical Engineering, University of Cassino and Southern Lazio, Via G. Di Biasio, 43, 03043 Cassino FR, Italy)

Abstract

Adaptation and innovation are extremely important to the manufacturing industry. This development should lead to sustainable manufacturing using new technologies. To promote sustainability, smart production requires global perspectives of smart production application technology. In this regard, thanks to intensive research efforts in the field of artificial intelligence (AI), a number of AI-based techniques, such as machine learning, have already been established in the industry to achieve sustainable manufacturing. Thus, the aim of the present research was to analyze, systematically, the scientific literature relating to the application of artificial intelligence and machine learning (ML) in industry. In fact, with the introduction of the Industry 4.0, artificial intelligence and machine learning are considered the driving force of smart factory revolution. The purpose of this review was to classify the literature, including publication year, authors, scientific sector, country, institution, and keywords. The analysis was done using the Web of Science and SCOPUS database. Furthermore, UCINET and NVivo 12 software were used to complete them. A literature review on ML and AI empirical studies published in the last century was carried out to highlight the evolution of the topic before and after Industry 4.0 introduction, from 1999 to now. Eighty-two articles were reviewed and classified. A first interesting result is the greater number of works published by the USA and the increasing interest after the birth of Industry 4.0.

Suggested Citation

  • Raffaele Cioffi & Marta Travaglioni & Giuseppina Piscitelli & Antonella Petrillo & Fabio De Felice, 2020. "Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions," Sustainability, MDPI, vol. 12(2), pages 1-26, January.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:2:p:492-:d:306600
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    1. María Pérez-Ortiz & Silvia Jiménez-Fernández & Pedro A. Gutiérrez & Enrique Alexandre & César Hervás-Martínez & Sancho Salcedo-Sanz, 2016. "A Review of Classification Problems and Algorithms in Renewable Energy Applications," Energies, MDPI, vol. 9(8), pages 1-27, August.
    2. Jeffrey D. Sachs & Guido Schmidt-Traub & Mariana Mazzucato & Dirk Messner & Nebojsa Nakicenovic & Johan Rockström, 2019. "Six Transformations to achieve the Sustainable Development Goals," Nature Sustainability, Nature, vol. 2(9), pages 805-814, September.
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    10. Kinkel, Steffen & Baumgartner, Marco & Cherubini, Enrica, 2022. "Prerequisites for the adoption of AI technologies in manufacturing – Evidence from a worldwide sample of manufacturing companies," Technovation, Elsevier, vol. 110(C).
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    12. Sunghun Kim & Youngjin Park & Seungbeom Yoo & Ocktaeck Lim & Bernike Febriana Samosir, 2023. "Development of Machine Learning Algorithms for Application in Major Performance Enhancement in the Selective Catalytic Reduction (SCR) System," Sustainability, MDPI, vol. 15(9), pages 1-20, April.
    13. Henry Ekwaro-Osire & Dennis Bode & Klaus-Dieter Thoben & Jan-Hendrik Ohlendorf, 2022. "Identification of Machine Learning Relevant Energy and Resource Manufacturing Efficiency Levers," Sustainability, MDPI, vol. 14(23), pages 1-19, November.
    14. Beatrice Garske & Antonia Bau & Felix Ekardt, 2021. "Digitalization and AI in European Agriculture: A Strategy for Achieving Climate and Biodiversity Targets?," Sustainability, MDPI, vol. 13(9), pages 1-21, April.
    15. Manuel Woschank & Erwin Rauch & Helmut Zsifkovits, 2020. "A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics," Sustainability, MDPI, vol. 12(9), pages 1-23, May.
    16. Ahmed, Shamima & Alshater, Muneer M. & Ammari, Anis El & Hammami, Helmi, 2022. "Artificial intelligence and machine learning in finance: A bibliometric review," Research in International Business and Finance, Elsevier, vol. 61(C).
    17. Jingyu Li & Yangbo Chen & Yanzheng Zhu & Jun Liu, 2023. "Study of Flood Simulation in Small and Medium-Sized Basins Based on the Liuxihe Model," Sustainability, MDPI, vol. 15(14), pages 1-16, July.
    18. Rabab Triki & Mohamed Hédi Maâloul & Younès Bahou & Mohamed Kadria, 2023. "The Impact of Digitization to Ensure Competitiveness of the Ha’il Region to Achieve Sustainable Development Goals," Sustainability, MDPI, vol. 15(2), pages 1-13, January.
    19. Hao Wang & Chen Peng & Bolin Liao & Xinwei Cao & Shuai Li, 2023. "Wind Power Forecasting Based on WaveNet and Multitask Learning," Sustainability, MDPI, vol. 15(14), pages 1-22, July.
    20. Muhammad Rashid & Muhammad Attique Khan & Majed Alhaisoni & Shui-Hua Wang & Syed Rameez Naqvi & Amjad Rehman & Tanzila Saba, 2020. "A Sustainable Deep Learning Framework for Object Recognition Using Multi-Layers Deep Features Fusion and Selection," Sustainability, MDPI, vol. 12(12), pages 1-21, June.
    21. Krzysztof Rusek & Agnieszka Kleszcz & Albert Cabellos-Aparicio, 2023. "Bayesian inference of spatial and temporal relations in AI patents for EU countries," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(6), pages 3313-3335, June.

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