Decision Making in Lean Smart Maintenance: Criticality Analysis as a Support Tool

Passath, Theresa; Mertens, Katharina

doi:10.1016/j.ifacol.2019.10.058

Cited by 8 publications

(12 citation statements)

References 6 publications

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“…Marcello et al [51] carried out a construction of an ensemblelearning model that combines prediction results from multiple algorithms, using big data analytics, to estimate failure rates of equipment subject to distinct operating conditions (reached an accuracy value of 96.15%). On the other side, Passath et al [52] created a standard criticality analysis as a foundation of an agile, smart, and value-oriented asset management system to dynamically adjust the maintenance strategy. It was concluded that the more complex and disparate the assets are, the more essential it was to have a guideline to dynamically adapt the maintenance approach due to the environmental variations as well as production circumstances.…”

Section: Rq4: How Are the Papers Distributed In The Geographical Context?mentioning

confidence: 99%

Stories Told by Publications about the Relationship between Industry 4.0 and Lean: Systematic Literature Review and Future Research Agenda

Salvadorinho

Teixeira

2021

Publications

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Industry 4.0 and its application in the business has been the focus of attention by the academy, for its ability to establish principles of flexibility and connectivity along the shop floor. Meantime, because of a lean wave in the 1990’s, most of the western industry adopted principles, techniques and tools of lean production, whose results were quickly captured, guaranteed its adoption worldwide. Thus, with a view to turning traditional manufacturing companies into smart companies, it is essential to preserve the existing system and find ways for the two concepts (Lean and I4.0) to come together. This study was conducted based on a systematic literature review, using the Scopus database. The PRISMA process was the method used to select the articles. Finally, to analyze and discuss the results, the bibliometric analysis of the articles and content analysis were adopted. The results point to a greater impact of I4.0 technologies on lean, since it was perceived that I4.0 technologies give to lean tools a more dynamic way of working, accelerating information sharing processes and improving production manager’s and operator’s decision making. In the perspective of lean’s contribution to I4.0; however, there is little practical and theoretical application, thus the actual contribution is still somewhat blurred.

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Section: Rq4: How Are the Papers Distributed In The Geographical Context?mentioning

confidence: 99%

Stories Told by Publications about the Relationship between Industry 4.0 and Lean: Systematic Literature Review and Future Research Agenda

Salvadorinho

Teixeira

2021

Publications

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“…An experience-based assessment methodology causes human errors and long decision paths due to uncertainty of the people involved. The more important it is to reduce the human influence and to carry out the assessment automatically, based on quantitative criteria [42].…”

Section: Asset Valuation Under Consideration Of Life Cycle Costingmentioning

confidence: 99%

A Knowledge-Based Digital Lifecycle-Oriented Asset Optimisation

Passath

Huber

Biedermann

et al. 2021

Teh. glas. (Online)

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The digitalisation of the value chain promotes sophisticated virtual product models known as digital twins (DT) in all asset-life-cycle (ALC) phases. These models. however, fail on representing the entire phases of asset-life-cycle (ALC), and do not allow continuous life-cycle-costing (LCC). Hence, energy efficiency and resource optimisation across the entire circular value chain is neglected. This paper demonstrates how ALC optimisation can be achieved by incorporating all product life-cycle phases through the use of a RAMS²-toolbox and the generation of a knowledge-based DT. The benefits of the developed model are demonstrated in a simulation, considering RAMS2 (Reliability, Availability, Maintainability, Safety and Sustainability) and the linking of heterogeneous data, with the help of a dynamic Bayesian network (DBN).

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“…The review spotlights the lack of structured approaches, methods and tools to support decision-making in maintenance (Liang, 2020;Passath and Mertens, 2019) by integrating past knowledge with information extracted from data (Brundage et al, 2019;Singgih et al, 2019) and jointly considering the machine and maintenance perspectives. Based on these findings, this research contribution proposes a framework addressing the gaps identified above from the perspective of the (1) phases composing the process, (2) the actors involved and (3) methods and tools supporting decision-making.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Nowadays, service (and maintenance) delivery still needs to fully take advantage of the smart characteristics of the product and its capability to collect and share data (Pirola et al, 2020). Authors like Brundage et al (2019), Gopalakrishnan et al (2015), Passath and Mertens (2019) clarified that few companies use structured decision-making processes to support maintenance service delivery, mainly because of the lack of reliable data from the field, which affects the final delivery. Other authors (Alexopoulos et al, 2018;Roy et al, 2013) stress the need to use such instruments to improve maintenance service provision and to invest in proper data management and sharing to properly use the feedback collected during maintenance delivery as input for PSS design.…”

Section: Introductionmentioning

confidence: 99%

“…As suggested by Ardolino et al (2017), digital technologies might support service delivery improvement in PSS, but only if proper data collection and processing strategies are introduced (Mahlam€ aki et al, 2016). The lack of structured decision-making processes and tools discussed by Brundage et al (2019) and Passath and Mertens (2019) explains why employees usually capitalize on their experience when making decisions, rather than using data-based decision support tools (Gopalakrishnan et al, 2015). Brundage et al (2019) further pinpointed data quality among the main causes of wrong decisions made in maintenance service delivery processes, stressing the need to develop collection, analysis and visualization methods and tools in support of maintenance decision-making.…”

Section: Introductionmentioning

confidence: 99%

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Data-based decision-making in maintenance service delivery: the D3M framework

Sala

Bertoni

Pirola

et al. 2021

JMTM

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PurposeThis paper aims to present a dual-perspective framework for maintenance service delivery that should be used by manufacturing companies to structure and manage their maintenance service delivery process, using aggregated historical and real-time data to improve operational decision-making. The framework, built for continuous improvement, allows the exploitation of maintenance data to improve the knowledge of service processes and machines.Design/methodology/approachThe Dual-perspective, data-based decision-making process for maintenance delivery (D3M) framework development and test followed a qualitative approach based on literature reviews and semi-structured interviews. The pool of companies interviewed was expanded from the development to the test stage to increase its applicability and present additional perspectives.FindingsThe interviews confirmed that manufacturing companies are interested in exploiting the data generated in the use phase to improve operational decision-making in maintenance service delivery. Feedback to improve the framework methods and tools was collected, as well as suggestions for the introduction of new ones according to the companies' necessities.Originality/valueThe paper presents a novel framework addressing the data-based decision-making process for maintenance service delivery. The D3M framework can be used by manufacturing companies to structure their maintenance service delivery process and improve their knowledge of machines and service processes.

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Decision Making in Lean Smart Maintenance: Criticality Analysis as a Support Tool

Cited by 8 publications

References 6 publications

Stories Told by Publications about the Relationship between Industry 4.0 and Lean: Systematic Literature Review and Future Research Agenda

Stories Told by Publications about the Relationship between Industry 4.0 and Lean: Systematic Literature Review and Future Research Agenda

A Knowledge-Based Digital Lifecycle-Oriented Asset Optimisation

Data-based decision-making in maintenance service delivery: the D3M framework

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