Exploring predictive maintenance applications in industry

Abstract

PurposeAsset owners and maintainers need to make timely and well-informed maintenance decisions based on the actual or predicted condition of their physical assets. However, only few companies have succeeded to implement predictive maintenance (PdM) effectively. Therefore, this paper aims to identify why only few companies were able to successfully implement PdM.Design/methodology/approachA multiple-case study including 13 cases in various industries in The Netherlands was conducted. This paper examined the choices made in practice to achieve PdM and possible dependencies between and motivations for these choices.FindingsAn implementation process for PdM appeared to comprise four elements: a trigger, data collection, maintenance technique (MT) selection and decision-making. For each of these elements, several options were available. By identifying the choices made by companies in practice and mapping them on the proposed elements, logical combinations appeared. These combinations can provide insight into the PdM implementation process and may also lead to guidance on this topic. Further, while successful companies typically combined various techniques, the mostly applied techniques were still those based on previous experiences.Research limitations/implicationsThis research calls for better methods or procedures to guide the selection and use of suitable types of PdM, directed by the firm's ambition level and the available data.Originality/valueWhile it is important for firms to make suitable choices during implementation, the literature often focusses only on developing additional techniques for PdM. This paper provides new insights into the application and selection of techniques for PdM in practice and helps practitioners reduce the often applied trial-and-error process.