## Appendix A  
In this section we included the referenced papers with characterization of type of research and type of contribution as per the elaborated tables. We remarked that these data ended at 2018, while we included proposals sent before 2018 but were accepted in early 2019. * [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON] A survey of the advancing use and development of machine learning in smart manufacturing. _J. Manuf. Syst._**2018**, _48_, 170-179. [CrossRef] [PubMed]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON] On the use of machine learning methods to predict component reliability from data-driven industrial case studies. _Int. J. Adv. Manuf. Technol._**2018**, _94_, 2419-2433. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON] Multi-stakeholder perspectives on indicators for sustainable maintenance performance in production contexts: An exploratory study. _J. Qual. Maint. Eng._**2020**. [CrossRef]
* Duran and Duran (2019) [PERSON]; [PERSON] Prioritization of physical assets for maintenance and production sustainability. _Sustainability_**2019**, _11_, 4296. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON] Maintenance for sustainability in the industry 4.0 context: A scoping literature review. _IFAC PapersOnLine_**2018**, _51_, 903-908. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON] Predictive Quality: Towards a New Understanding of Quality Assurance Using Machine Learning Tools. In _Business Information Systems_; [PERSON], [PERSON]; Springer: Cham, Switzerland, 2018; Volume 320. * [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Deep learning for smart manufacturing: Methods and applications. _J. Manuf. Syst._**2018**, _48_, 144-156. [CrossRef]
* [PERSON] (2020) [PERSON] A new intelligent fault identification method based on transfer locality preserving projection for actual diagnosis scenario of rotating machinery. _Mech. Syst. Signal Process._**2020**, _135_, 106344. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A hybrid machine learning approach for predictive maintenance in smart factories of the future. _IFIP Adv. Inf. Commun. Technol._**2018**, _536_, 311-317. * [PERSON] (2013) [PERSON] Scalability in manufacturing systems design and operation: State-of-the-art and future developments roadmap. _CIRP Ann. Manuf. Technol._**2013**, _62_, 751-774. [CrossRef]
* [PERSON] et al. (2017) [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A classification scheme for smart manufacturing systems' performance metrics. _Smart Sustain. Manuf. Syst._**2017**, \(1\), 52. [CrossRef] [PubMed]
* [PERSON] et al. (2015) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] Machine learning for predictive maintenance: A multiple classifier approach. _IEEE Trans. Ind. Inform._**2015**, _11_, 812-820. * [PERSON] et al. (2014) [PERSON]; [PERSON]; [PERSON] Service innovation and smart analytics for Industry 4.0 and big data environment. _Procedia Ciry_**2014**, _16_, 3-8. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON] A big data driven sustainable manufacturing framework for condition-based maintenance prediction. _J. Comput. Sci._**2018**, _27_, 428-439. [CrossRef]
* [PERSON] (2019) [PERSON] On the use of predictive models for improving the quality of industrial maintenance: An analytical literature review of maintenance strategies. In Proceedings of the 2019 Federated Conference on Computer Science and Information Systems (FedCSIS), Leipzig, Germany, 1-4 September 2019; pp. 693-704. * [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]. A systematic literature review of machine learning methods applied to predictive maintenance. _Comput. Ind. Eng._**2020**, \(1\), 1-12. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] An industry 4.0-enabled low cost predictive maintenance approach for SMEs: A use case applied to a cnc turning centre.