Practices of preventive maintenance planning in discrete manufacturing industry

Abstract

PurposeThe purpose of this study was to assess the readiness of the Swedish manufacturing industry to implement dynamic, data-driven preventive maintenance (PM) by identifying the gap between the state of the art and the state of practice.Design/methodology/approachAn embedded multiple case study was performed in which some of the largest companies in the discrete manufacturing industry, that is, mechanical engineering, were surveyed regarding the design of their PM programmes.FindingsThe studied manufacturing companies make limited use of the existing scientific state of the art when designing their PM programmes. They seem to be aware of the possibilities for improvement, but they also see obstacles to changing their practices according to future requirements.Practical implicationsThe results of this study will benefit both industry professionals and academicians, setting the initial stage for the development of data-driven, diversified and dynamic PM programmes.Originality/ValueFirst and foremost, this study maps the current state and practice in PM planning among some of the larger automotive manufacturing industries in Sweden. This work reveals a gap between the state of the art and the state of practice in the design of PM programmes. Insights regarding this gap show large improvement potentials which may prove important for academics as well as practitioners.

Maintenance practices and overall equipment effectiveness: testing the moderating effect of training

“…Predictive practices seek to identify indications that point to the need for maintenance (such as noise or corrosion). These indications are then used to determine the need for an intervention (Swanson, 2001;Bekar et al, 2015;Bahreini et al, 2019;Salonen and Gopalakrishnan, 2020). Thus, maintenance planning, data analysis and software use are related to a greater use of preventive and predictive practices and a lesser use of corrective practices (Reis et al, 2013).…”

“…The availability and reliability levels of production equipment have a strong relationship with reduced production waste (Swanson, 2001;Bekar et al, 2015), as well as with reduced investments considered unnecessary to maintenance (Wang et al, 2007). Companies can waste resources either by over-maintaining their equipment to achieve unnecessary availability at a high cost, or by under-maintaining those causing expensive breakdowns that can lead to production loss and expensive repair work (Salonen and Gopalakrishnan, 2020).…”

“…However, efficiency may vary within the same firm when the productive plants are analysed individually. Companies have different levels of maturity in maintenance (Salonen and Gopalakrishnan, 2020); thus, the same practice leads to different results in terms of efficiency. Ultimately, this can also be explained by firms that have different levels of resources and uniquely aggregate these resources, creating their own capabilities (Hitt et al, 2015).…”

“…Human resources are necessary to operate an effective maintenance program (Sheikhalishahi et al, 2016;Bahreini et al, 2019;Cheah et al, 2020). According to Salonen and Gopalakrishnan (2020), the main obstacle to applying maintenance planning is the conduct of resources, that is, the maintenance manpower. The lack of a structured plan can result in the waste of time, ineffective use of resources, and ultimately, improvement failures (Cheah et al, 2020).…”

Learning methods for remaining useful life prediction in a turbofan engine

“…Predictive maintenance performs maintenance schedule based on the prediction and analysis of the state of equipment in general. This maintenance can save a lot of time, cost, and energy as it avoids the need to carry out unnecessary maintenance activities periodically [22]. The prediction can be made by analyzing the measurement data from the sensors used and then a model is developed and used to predict the machine failure before it happens [15].…”