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Artificial Intelligence for the Management of Servitization 5.0

Author

Listed:
  • Bernardo Nicoletti

    (Temple University, I-00196 Rome, Italy)

  • Andrea Appolloni

    (Università di Rome Tor Vergata, I-00133 Rome, Italy)

Abstract

Purpose—The sale of physical products has been manufacturing companies’ main revenue source. A trend is known as servitization for earning revenue comes from services. With the convergence of servitization and digitization, many manufacturing organizations are undergoing digital servitization. In parallel, the digitization of industry is pushing new technological solutions to the top of the business agenda. Artificial intelligence can play a substantial role in this digital business transformation. This evolution is referred to in this paper as Servitization 5.0 and requires substantial changes. Aim—This paper explores the applications of artificial intelligence to Servitization 5.0 strategies and its role, particularly in changing organizations to EverythiA.I.ng as a Service. The paper underlines the contribution that A.I. can provide in moving to a human-centric, sustainable, and resilient servitization. Method used—The basis of the work is a literature review supported by information collected from business case studies by the authors. A follow-up study defined the models. The validity of the model was tested by collecting ten experts’ opinions who currently work within servitization contracts sessions. Findings—For manufacturing companies, selling services requires completely different business models. In this situation, it is essential to consider advanced solutions to support these new business models. Artificial Intelligence can make it possible. On the inter-organizational side, empirical evidence also points to the support of A.I. in collaborating with ecosystems to support sustainability and resilience, as requested by Industry 5.0. Original value—Regarding theoretical implications, this paper contributes to interdisciplinary research in corporate marketing and operational servitization. It is part of the growing literature that deals with the applications of artificial intelligence-based solutions in different areas of organizational management. The approach is interesting because it highlights that digital solutions require an integrated business model approach. It is necessary to implement the technological platform with appropriate processes, people, and partners (the four Ps). The outcome of this study can be generalized for industries in high-value manufacturing. Implications—As implications for management, this paper defines how to organize the structure and support for Servitization 5.0 and how to work with the external business environment to support sustainability.

Suggested Citation

  • Bernardo Nicoletti & Andrea Appolloni, 2023. "Artificial Intelligence for the Management of Servitization 5.0," Sustainability, MDPI, vol. 15(14), pages 1-13, July.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:11113-:d:1195652
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    References listed on IDEAS

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    1. Palo, Teea & Åkesson, Maria & Löfberg, Nina, 2019. "Servitization as business model contestation: A practice approach," Journal of Business Research, Elsevier, vol. 104(C), pages 486-496.
    2. Yan Chen & Zijin Wang & Jaime Ortiz, 2023. "A Sustainable Digital Ecosystem: Digital Servitization Transformation and Digital Infrastructure Support," Sustainability, MDPI, vol. 15(2), pages 1-16, January.
    3. Opresnik, David & Taisch, Marco, 2015. "The value of Big Data in servitization," International Journal of Production Economics, Elsevier, vol. 165(C), pages 174-184.
    4. Ricardo Vinuesa & Hossein Azizpour & Iolanda Leite & Madeline Balaam & Virginia Dignum & Sami Domisch & Anna Felländer & Simone Daniela Langhans & Max Tegmark & Francesco Fuso Nerini, 2020. "The role of artificial intelligence in achieving the Sustainable Development Goals," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    5. Hong Joo Lee & Hoyeon Oh, 2020. "A Study on the Deduction and Diffusion of Promising Artificial Intelligence Technology for Sustainable Industrial Development," Sustainability, MDPI, vol. 12(14), pages 1-15, July.
    6. Wolfgang Ulaga & Werner Reinartz, 2011. "Hybrid Offerings: How Manufacturing Firms Combine Goods and Services Successfully," Post-Print hal-00642039, HAL.
    7. Di Vaio, Assunta & Palladino, Rosa & Hassan, Rohail & Escobar, Octavio, 2020. "Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review," Journal of Business Research, Elsevier, vol. 121(C), pages 283-314.
    8. Li, Huashan & Pournader, Mehrdokht & Fahimnia, Behnam, 2022. "Servitization and organizational resilience of manufacturing firms: Evidence from the COVID-19 outbreak," International Journal of Production Economics, Elsevier, vol. 250(C).
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    Cited by:

    1. Lyudmila Davidenko & Nurzhanat Sherimova & Saule Kunyazova & Maral Amirova & Ansagan Beisembina, 2024. "Sustainable Economy: The Eco-Branding of an Industrial Region in Kazakhstan," Sustainability, MDPI, vol. 16(1), pages 1-16, January.
    2. Daxing Chen & Helian Xu & Guangya Zhou, 2024. "Has Artificial Intelligence Promoted Manufacturing Servitization: Evidence from Chinese Enterprises," Sustainability, MDPI, vol. 16(6), pages 1-19, March.

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