Machine criticality assessment for productivity improvement

Abstract

Purpose The purpose of this paper is to increase productivity through smart maintenance planning by including productivity as one of the objectives of the maintenance organization. Therefore, the goals of the paper are to investigate existing machine criticality assessment and identify components of the criticality assessment tool to increase productivity. Design/methodology/approach An embedded multiple case study research design was adopted in this paper. Six different cases were chosen from six different production sites operated by three multi-national manufacturing companies. Data collection was carried out in the form of interviews, focus groups and archival records. More than one source of data was collected in each of the cases. The cases included different production layouts such as machining, assembly and foundry, which ensured data variety. Findings The main finding of the paper is a deeper understanding of how manufacturing companies assess machine criticality and plan maintenance activities. The empirical findings showed that there is a lack of trust regarding existing criticality assessment tools. As a result, necessary changes within the maintenance organizations in order to increase productivity were identified. These are technological advancements, i.e. a dynamic and data-driven approach and organizational changes, i.e. approaching with a systems perspective when performing maintenance prioritization. Originality/value Machine criticality assessment studies are rare, especially empirical research. The originality of this paper lies in the empirical research conducted on smart maintenance planning for productivity improvement. In addition, identifying the components for machine criticality assessment is equally important for research and industries to efficient planning of maintenance activities.

Practices of preventive maintenance planning in discrete manufacturing industry

“…In addition to working hours, process criteria, such as strain and process precision, vary across machines and over time. These varying working conditions provide an important argument for developing diversified PM plans, particularly focusing on solving the maintenance needs of critical equipment (Gopalakrishnan, et al, 2019;Hagberg and Henriksson, 2010).…”

“…There are several driving forces for implementing dynamic maintenance plans. Some authors have noted that equipment criticality is becoming more dynamic, and therefore maintenance plans need to be adjusted accordingly (Gopalakrishnan et al, 2019;Adams et al, 2016). Other circumstances include the introduction of condition-based maintenance and predictive maintenance (Aizpurua et al, 2017).…”

Digitalisation and Innovation in the Steel Industry in Poland—Selected Tools of ICT in an Analysis of Statistical Data and a Case Study

“…Autonomous data and predictive data are useful for self-learning production systems [57][58][59][60][61][62]. Smart machines in established areas improve themselves (machine learning).…”

A Knowledge-Based Digital Lifecycle-Oriented Asset Optimisation

“…To perform a detailed risk analysis, the first step is to identify critical assets to narrow the scope of consideration. There are some methods used to identify critical assets, such as FMECA (Failure Mode and Effects and Criticality Analysis), ABC classification [35], risk analysis [36], Analytical Hierarchy Process [35], fuzzy analysis, CEIM (cost-effective maintenance measure) [37], and Weibull analysis [38]. Another example of an already existent methodology is the criticality assessment adapted from Kinz et al [39], which consists of the steps of asset selection, criteria assessment, identification of critical assets and detailed analyses for cost and risk reduction [40,41].…”