Testing Service Infusion in Manufacturing through Machine Learning Techniques: Looking Back and Forward

Oscar Bustinza*, Ferran Vendrell-Herrero, Phil Davies, Glenn Parry

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review


Purpose – Responding to calls for deeper analysis of the conceptual foundations of service infusion in manufacturing, this paper examines the underlying assumptions that: (i) manufacturing firms incorporating services follow a pathway, moving from pure-product to pure-service offerings; and (ii) profits increase linearly with this process. We propose that these assumptions are inconsistent with the premises of behavioural and learning theories.
Design/methodology/approach – Machine learning algorithms are applied to test whether a successive process, from a basic to more advanced offering, creates optimal performance. Data was gathered through two surveys administered to US manufacturing firms in 2021 and 2023. The first included a training sample comprising 225 firms, while the second encompassed a testing sample of 105 firms.
Findings – Analysis shows that following the Base-Intermediate-Advanced services pathway is not the best predictor of optimal performance. Developing advanced services and then later adding less complex offerings supports better performance.
Practical implications – Manufacturing firms follow heterogeneous pathways in their service development journey. Non-servitised firms need to carefully consider their contextual conditions when selecting their initial service offering. Starting with a single service offering appears to be a superior strategy over providing multiple services.
Originality/value – The machine learning approach is novel to the field and captures the key conditions for manufacturers to successfully servitise. Insight is derived from adoption and implementation year dataset for 17 types of services described in previous qualitative studies. The methods proposed can be extended to assess other process-based models in related management fields (e.g., sand cone).
Original languageEnglish
Pages (from-to)127-156
Number of pages30
JournalInternational Journal of Operations and Production Management
Issue number13
Publication statusPublished - 16 May 2024

Bibliographical note

Publisher Copyright:
© 2024, Oscar F. Bustinza, Ferran Vendrell-Herrero, Philip Davies and Glenn Parry.

Structured keywords

  • MGMT Operations and Management Science


  • Servitization
  • Machine Learning
  • Operations Management
  • Service Infusion


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