Machine criticality based maintenance prioritization

Abstract

Purpose-The purpose of this paper is to identify the productivity improvement potentials from maintenance planning practices in manufacturing companies. In particular, the paper aims at understanding the connection between machine criticality assessment and maintenance prioritization in industrial practice, as well as providing the improvement potentials. Design/methodology/approach-An explanatory mixed method research design was used in this study. Data from literature analysis, a web-based questionnaire survey, and semi-structured interviews were gathered and triangulated. Additionally, simulation experimentation was used to evaluate the productivity potential. Findings-The connection between machine criticality and maintenance prioritization is assessed in an industrial setup. The empirical findings show that maintenance prioritization is not based on machine criticality, as criticality assessment is non-factual, static, and lacks system view. It is with respect to these finding that the ways to increase system productivity and future directions are charted. Originality/value-In addition to the empirical results showing productivity improvement potentials, the paper emphasizes on the need for a systems view for solving maintenance problems, i.e. solving maintenance problems for the whole factory. This contribution is equally important for both industry and academics, as the maintenance organization needs to solve this problem with the help of the right decision support.

Data-driven machine criticality assessment – maintenance decision support for increased productivity

“…This implies that existing assessment methods do not take a system view into consideration. Industrial practices have also shown that maintenance prioritization is not based on machine criticality (Gopalakrishnan and Skoogh 2018). In addition to the above mentioned main aspects to consider, Gopalakrishnan and Skoogh (2018) have proposed the following to address develop data-driven machine criticality assessment:…”

“…Probable future scenarios for the future for maintenance organizations are to manage large volumes of data, perform data analytics, make fact-based decisions, and provide education and training, among others (Bokrantz et al 2017). The framework aligns with these probable projections to ensure that maintenance decision-making can be made on a systems-level in a way that is dynamic, fact-based and focussed on productivity (Gopalakrishnan and Skoogh 2018). Through following the principles given, the empirical research conducted in this study increases the relevance of the current problems studied and the solutions help towards narrowing the gap between maintenance theory and maintenance practice (Fraser, Hvolby, and Tseng 2015).…”

“…In this study, the authors have made important contributions to the machine criticality assessment literature. The central approach when it comes to machine criticality assessment is qualitative and approached in a static manner (Gopalakrishnan and Skoogh 2018). The central problems discussed in this paper are coming directly from industrial practice.…”

“…Many existing machine criticality assessment tools provide support only for a specific long term decision, especially, maintenance strategy selection for an individual machine (M arquez et al 2009), whereas they have the potential to support much wider maintenance management prioritization decisions including a variety of short-and long term decisions. Additionally, the existing assessment tools are highly qualitative in nature (Pelaez and Bowles 1994;Singh, Singh, and Kumar 2015) and qualitative approaches have shown to be non-factual (Gopalakrishnan and Skoogh 2018). With the development of computer technology and large amounts of data collected by the Manufacturing Execution System (MES), industries have reached a stage where data-based decisions can be made (Subramaniyan et al 2016).…”

Prioritisation of root cause analysis in production disturbance management

“…At the stage of correcting production disturbances, ABC analysis is applied to prioritisation, especially for maintenance-related disturbances (Gopalakrishnan and Skoogh, 2018). In this case, the machines in production systems are classified and prioritised according to their function and impact on productivity.…”