Requirements and Needs—A Foundation for Reducing Maintenance-Related Waste

Bengtsson, Marcus; Salonen, Antti

doi:10.1007/978-3-319-27064-7_10

Cited by 4 publications

(7 citation statements)

References 11 publications

(7 reference statements)

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“…The four most important of these variables are performance in maintenance, manufacturing, environmental and safety. Briefly, maintenance performance relates to the internal efficiency of the maintenance function (Bokrantz et al , 2020b; Bengtsson and Salonen, 2016). This includes the failure behaviour of equipment, reactive and preventative actions and cost-effectiveness and is typically reflected in indicators such as maintenance cost, time between failures and repair lead time.…”

Section: Theorymentioning

confidence: 99%

Performance indicators for measuring the effects of Smart Maintenance

Lundgren

Bokrantz

Skoogh

2020

IJPPM

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PurposeThe purpose of this study is to ensure productive, robust and sustainable production systems and realise digitalised manufacturing trough implementation of Smart Maintenance – “an organizational design for managing maintenance of manufacturing plants in environments with pervasive digital technologies”. This paper aims to support industry practitioners in selecting performance indicators (PIs) to measure the effects of Smart Maintenance, and thus facilitate its implementation.Design/methodology/approachIntercoder reliability and negotiated agreement were used to analyse 170 maintenance PIs. The PIs were structurally categorised according to the anticipated effects of Smart Maintenance.FindingsCompanies need to revise their set of PIs when changing manufacturing and/or maintenance strategy (e.g. reshape the maintenance organisation towards Smart Maintenance). This paper suggests 13 categories of PIs to facilitate the selection of PIs for Smart Maintenance. The categories are based on 170 PIs, which were analysed according to the anticipated effects of Smart Maintenance.Practical implicationsThe 13 suggested categories bring clarity to the measuring potential of the PIs and their relation to the Smart Maintenance concept. Thereby, this paper serves as a guide for industry practitioners to select PIs for measuring the effects of Smart Maintenance.Originality/valueThis is the first study evaluating how maintenance PIs measure the anticipated effects of maintenance in digitalised manufacturing. The methods intercoder reliability and negotiated agreement were used to ensure the trustworthiness of the categorisation of PIs. Such methods are rare in maintenance research.

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Section: Theorymentioning

confidence: 99%

Performance indicators for measuring the effects of Smart Maintenance

Lundgren

Bokrantz

Skoogh

2020

IJPPM

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“…It is important to realize is that this is an analysis of failing components, so a conclusion is that most components, suitable for predetermined preventive maintenance are getting that already. What is important to mention is that some of these components probably are over maintained, causing waste of time and money [8] The B-category stood for 41% of the breakdowns. This kind of errors are not possible to prevent through technical maintenance activities.…”

Section: Failure Characteristics In Context Of Maintenance Typesmentioning

confidence: 99%

“…This indicates that 78% could be prevented from recurring through root cause failure analysis. While 56% could have been prevented or predicted with CBM, this shows how vital RCFA and improvement processes still are to reach both effectiveness and efficiency [8]. As such, it is necessary to work on both basic maintenance actions, such as for instance RCFA and predetermined maintenance, as well as more of high technological advancement [27].…”

Section: Failure Characteristics In Context Of Maintenance Typesmentioning

confidence: 99%

“…This definition aligns with the study by Salonen and Tabikh [6], stating that the cost of an unplanned stop is about five times the cost of a planned stop. But as Salonen and Deleryd [7] and Bengtsson and Salonen [8] points out, it is also essential to avoid more planned PM than necessary. It is therefore important to find out any excessive PM in order to improve the efficiency of the maintenance program [8].…”

Section: Introductionmentioning

confidence: 99%

“…But as Salonen and Deleryd [7] and Bengtsson and Salonen [8] points out, it is also essential to avoid more planned PM than necessary. It is therefore important to find out any excessive PM in order to improve the efficiency of the maintenance program [8]. This clearly gives an incentive for analyzing unplanned stops and trying to replace them with planned maintenance actions.…”

Section: Introductionmentioning

confidence: 99%

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The Possibilities of Improving Maintenance Through CMMS Data Analysis

Salonen

Bengtsson

Fridholm³

2020

Advances in Transdisciplinary Engineering

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Maintenance of production equipment is one of the most critical support actions in manufacturing companies for staying competitive. More recently, with the introduction of Industry 4.0, academia, as well as industry, put a lot of effort into condition monitoring in order to implement predictive maintenance. Most stakeholders agree that maintenance need to be more data-driven. However, in order to draw true advantage of data-driven decisions, it is necessary for manufacturing companies to have implemented basic maintenance to a high standard in order to reduce for example: recurring failures, human errors, unsafe machines, etc. The real-time data can then be used to improve efficiency of maintenance tasks and schedule that adds value to the processes. In manufacturing industry, maintenance actions are commonly administered in a Computerized Maintenance Management System, CMMS, still, rather few companies analyze their maintenance records. Behind these data there is often a treasure of improvement opportunities that could be used to improve basic maintenance. The purpose of this paper is to explore how historical data from a CMMS can be used in order to improve maintenance effectiveness and efficiency of activities. In order to exemplify the possibilities of analyzing CMMS records, a case study has been performed in a plant, manufacturing driveline components for heavy construction vehicles. The study shows that one major obstacle for utilizing the CMMS data is poor description of faults and failures when it comes to work order requests, mostly performed by operators and assemblers, as well as work order reporting, mostly performed by repairmen and maintenance technicians. However, by thorough analysis of well described corrective maintenance, it is possible for industry to understand the nature of the occurring breakdowns and thus, refine the preventive maintenance program in order to further increase the dependability of the production system.

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