Purpose: – Spare parts management plays a relevant role for equipment-intensive companies. An important step of such process is the spare parts classification, enabling properly managing different items by taking into account their peculiarities. The purpose of this paper is to review the state of the art of classification of spare parts for manufacturing equipment by presenting an extensive literature analysis followed by an industrial assessment, with the final aim to identify eventual discrepancies. Design/methodology/approach: – Not only is the attention put on the literature about the subject, but also on an on-field analysis, that is presented comprehending an extensive survey and two in-depth exploratory case studies. The copper mining sector was chosen being representative for the case of capital intensive plants where the cost of maintenance has relevant weight on the total operating cost. Findings: – The paper highlights the status of the scientific literature on spare parts classification by showing the current situation in the real industrial world. The paper depicts the existing barriers that leave gaps between theory and real practice for the application of an effective multi-criteria spare parts classification. Originality/value: – The paper provides a review of the theory on spare parts classification methods and criteria, as well as empirical evidences especially for what concern current situation and barriers for an effective implementation in the industrial environment. The paper should be of interest to both academics and practitioners, since it provides original insights on the discrepancies between scientific and industrial world.

This paper proposes a methodology to build a Total Cost of Ownership (TCO) model aimed to support decision-making for manufacturing asset lifecycle management. Existing challenges for TCO adoption in industry are identified through literature review and through an explorative multiple case study involving eight manufacturing companies. Based on it, a general methodology is proposed for building a novel asset-centric performance-driven TCO model. The methodology is based on an integrated modelling approach that puts together technical performance analysis and economic analysis, enabling the asset users linking their knowledge about asset performance with offers specifications by the asset providers. In this way, the TCO model becomes a decision-making tool for the asset users that can also guide the relationship with providers. An application case within a chemical production company is described, showing how challenges are addressed through the proposed methodology, within a real context.

In the last years, the selection of the most suitable simulation software to correctly face industrial problems has become vital for companies. The choice should be made among several possibilities and some companies, especially SMEs, may not have the appropriate tools. Moreover, a change of perspective is detected in industry: the technological evolution has shifted the problem from the “best-ever” solution identification, independently from the goals, to the usability of the software, in function of the company's objectives. In this context, technicians and managers act as decision-makers, not only as receivers. The present work proposes an update of the criteria used for evaluating the most appropriate simulation software among different options. Based on that, an AHP-based evaluation framework is defined to support the selection process to face the industrial problem. The proposed methodology has been tested in an industrial case and its effectiveness has been proven.

Purpose: The purpose of this work is to investigate industrial asset management (AM) in manufacturing. After depicting gaps for AM in this sector, the role of information as a key dimension is considered to realise a summary of challenges and advices for future development.Design/methodology/approach: The work is grounded on an extensive systematic literature review. Considering the eligible documents, descriptive statistics are provided and a content analysis is performed, both based on a sector-independent normative-based framework of analysis.Findings: AM principles, organisation and information are the dimensions defined to group ten areas of interest for AM in manufacturing. Information is the major concern for an effective AM implementation. Moreover, Internet of Things and big data management and analytics, as well as data modelling and ontology engineering, are the major technologies envisioned to advance the implementation of AM in manufacturing.Research limitations/implications: The identified challenges and advices for future development may serve to stimulate further research on AM in manufacturing, with special focus on information and data management. The sector-independent normative-based framework may also enable to analyse AM in different contexts of application, thus favouring cross-sectorial comparisons.Originality/value: Industries with higher operational risk, like Oil&Gas and infrastructure, are advanced in AM, while others, like some in manufacturing, are laggard in this respect. This literature review is the first of a kind addressing AM in manufacturing and depicts the state-of-the-art to pave the way for future research and development.

Spare parts are key operational assets in order to minimise unexpected equipment downtimes that may significantly impact a company’s results. The spare parts supply chain network supports the entire spare parts operations management and it is essential to achieve the planned goals. However, most of the traditional literature on spare parts management has not focused on the underlying supply chain network. Thus, this paper studies the integration of supply chain network design and control with traditional spare parts management. In particular, a generic network optimisation modelling structure is proposed, with simultaneous optimisation of warehouse locations and inventory control decisions, allowing minimising the total costs associated with the spare parts supply chain network. The generic model is specified based on three inventory control policies widely employed in the industry, which are suitable for managing a great variety of spare parts, i.e. (s, Q), (R, s, S) and (S-1, S). Furthermore, a solution approach is proposed based on Generalised Benders Decomposition. Finally, numerical results from a real-world application case in the process industry are shown and discussed.