The Use of Artificial Intelligence Methods to Assess the Effectiveness of Lean Maintenance Concept Implementation in Manufacturing Enterprises

Antosz, Katarzyna; Paśko, Łukasz; Gola, Arkadiusz

doi:10.3390/app10217922

Cited by 43 publications

(16 citation statements)

References 42 publications

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“…The effects of VR interactive training on the teaching process, modelling, and simulation are beneficial [25][26][27][28][29][30]. However, like with many other applications, this positive effect is dependent on correct execution.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

3D Interactive Learning Environment as a Tool for Knowledge Transfer and Retention

et al. 2021

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The article deals with the design of virtual reality (VR) interactive training as a teaching method and its effect on knowledge transfer and retention of students. The first part presents the methodology of the VR interactive training design. The second part utilizes the created interactive training for a case study to evaluate its effect on the teaching process and to examine the potential of VR interactive training as a sustainable teaching method. The study took place at the Department of Industrial Engineering, Faculty of Mechanical Engineering, University of Žilina. Volunteers were divided into two groups. The first group used VR interactive training as a teaching method, while the second group used the conventional method. Both groups then underwent tests. The main goal was to evaluate the effect of the VR interactive training on the teaching process in comparison to the conventional method while trying to identify the key elements of the VR interactive training design and its influence on knowledge transfer and retention in a sustainable learning environment. At the start of the case study, four hypotheses were formed, questioning the effect of interactive training on knowledge transfer and retention in the long and short term, and its overall influence on the teaching process. Obtained data were then used to evaluate these hypotheses.

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Section: Conclusion and Discussionmentioning

confidence: 99%

3D Interactive Learning Environment as a Tool for Knowledge Transfer and Retention

et al. 2021

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“…Hsu and Wang [67] used a decision tree to classify size patterns of the human body for clothing and calculated the amount of fabric needed for garment patterns based on a database of measured data. Applying decision trees, Antosz et al [68] improved the maintenance approach of manufacturing companies. Yan and Lee [69] used logistic regression for the real-time monitoring of the health status of industrial components.…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

et al. 2021

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Sustainability improvements in industrial production are essential for tackling climate change and the resulting ecological crisis. In this context, resource efficiency can directly lead to significant advancements in the ecological performance of manufacturing companies. The application of Artificial Intelligence (AI) also plays an increasingly important role. However, the potential influence of AI applications on resource efficiency has not been investigated. Against this background, this article provides an overview of thecurrent AI applications and how they affect resource efficiency. In line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this paper identifies, categorizes, and analyzes seventy papers with a focus on AI tasks, AI methods, business units, and their influence on resource efficiency. Only a minority of papers was found to address resource efficiency as an explicit objective. Subsequently, typical use cases of the identified AI applications are described with a focus on predictive maintenance, production planning, fault detection and predictive quality, as well as the increase in energy efficiency. In general, more research is needed that explicitly considers sustainability in the development and use phase of AI solutions, including Green AI. This paper contributes to research in this field by systematically examining papers and revealing research deficits. Additionally, practitioners are offered the first indications of AI applications increasing resource efficiency.

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“…Much like Lean Management concepts have helped manufacturing, AI offers to become the next developmental process in efficiency improvement. Already, 92% of top manufacturing managers think that 'Smart Factory' modern technologies like artificial Intelligence would help them improve efficiency and encourage their employees to work intelligently (Antosz et al 2020 ). These smart technologies in smart Factories leads to become a crucial part in various smart city plans like developing smart Industries, smart drainage systems, smart traffic control systems etc.…”

Section: Methodology and Scopementioning

confidence: 99%

Integrated artificial intelligence effect on crisis management and lean production: structural equation modelling frame work

Ahmed

Mahalakshmi

ArulRajan

et al. 2022

Int J Syst Assur Eng Manag

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It is a goal that manufacturing companies strive towards on a regular basis, and it involves enhancing the efficiency and productivity of maintenance operations. It is especially vital to avoid unforeseen breakdowns, which may result in costly charges and production losses if they do not occur in advance. While the execution of an acceptable management plan affects maintenance productivity, it also affects the adoption of proper procedures and tools to help in the assessment processes in this field. This difficulty, among other things, affects a company's capacity to achieve high performance with the equipment it employs, as well as the judgement process and the design of the firm's maintenance plan. In order to achieve this goal, the aim of this paper is to exemplify how intelligent systems can be used to enhance judgement techniques in the implementation of the lean maintenance perspective, allowing for an advancement in the functional capabilities of the industry's technological infrastructure. The reseachers employed artificial intelligence technologies to look for connections between specific operations carried out as part of the deployment of lean maintenance and the findings achieved. The raw set notion, which was used in this situation, was used to determine whether or not the lean maintenance method was being used in this study. The crisis management process carries with it some of the most complex data technology concerns ever encountered. It necessitates, among other items, active information gathering and information transfer efforts, that are used for a range of functions, such as decreasing uncertainty, attempting to measure and manage consequences, and attempting to manage resources in a way that goes beyond what is generally possible to deal with daily problems. It also needs the employment of artificial intelligence technology, among other things, to increase crisis awareness.

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The Use of Artificial Intelligence Methods to Assess the Effectiveness of Lean Maintenance Concept Implementation in Manufacturing Enterprises

Cited by 43 publications

References 42 publications

3D Interactive Learning Environment as a Tool for Knowledge Transfer and Retention

3D Interactive Learning Environment as a Tool for Knowledge Transfer and Retention

Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

Integrated artificial intelligence effect on crisis management and lean production: structural equation modelling frame work

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