Contribution of Maintenance 4.0 in Sustainable Development with an Industrial Case Study

Kihel, Yousra El; Kihel, Ali El; Bouyahrouzi, El Mahdi

doi:10.3390/su141711090

Cited by 18 publications

(7 citation statements)

References 36 publications

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“…In addition, the current approach deals with the identification of anomalies most influencing the whole loss factors of OEE, while most of the literature just focuses on the widely investigated Availability losses (El kihel et al ., 2022). In particular, focusing on all loss factors of OEE, i.e.…”

Section: Discussionmentioning

confidence: 99%

A rule-based machine learning methodology for the proactive improvement of OEE: a real case study

Lucantoni,

Antomarioni,

Ciarapica

et al. 2023

IJQRM

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PurposeThe Overall Equipment Effectiveness (OEE) is considered a standard for measuring equipment productivity in terms of efficiency. Still, Artificial Intelligence solutions are rarely used for analyzing OEE results and identifying corrective actions. Therefore, the approach proposed in this paper aims to provide a new rule-based Machine Learning (ML) framework for OEE enhancement and the selection of improvement actions.Design/methodology/approachAssociation Rules (ARs) are used as a rule-based ML method for extracting knowledge from huge data. First, the dominant loss class is identified and traditional methodologies are used with ARs for anomaly classification and prioritization. Once selected priority anomalies, a detailed analysis is conducted to investigate their influence on the OEE loss factors using ARs and Network Analysis (NA). Then, a Deming Cycle is used as a roadmap for applying the proposed methodology, testing and implementing proactive actions by monitoring the OEE variation.FindingsThe method proposed in this work has also been tested in an automotive company for framework validation and impact measuring. In particular, results highlighted that the rule-based ML methodology for OEE improvement addressed seven anomalies within a year through appropriate proactive actions: on average, each action has ensured an OEE gain of 5.4%.Originality/valueThe originality is related to the dual application of association rules in two different ways for extracting knowledge from the overall OEE. In particular, the co-occurrences of priority anomalies and their impact on asset Availability, Performance and Quality are investigated.

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Section: Discussionmentioning

confidence: 99%

A rule-based machine learning methodology for the proactive improvement of OEE: a real case study

Lucantoni,

Antomarioni,

Ciarapica

et al. 2023

IJQRM

View full text Add to dashboard Cite

show abstract

“…In order to meet this challenge, the contribution developed in this article will guide industrialists to integrate 4.0 technologies in their processes better in order to move towards maintenance that meets the needs of I4.0 [6].…”

Section: Proposed Methodologymentioning

confidence: 99%

“…In addition, I4.0 helps to improve and satisfy the emerging demand for products through intelligent process control and management [4,5]. There is a growing global competition to adopt Industry 4.0 to control the production chain and ensure the desired level of quality and productivity while respecting the axes of sustainable development: social, economic, and risk [6,7].…”

Section: Introductionmentioning

confidence: 99%

Real Time Assessment of Novel Predictive Maintenance System based on Artificial Intelligence for Rotating Machines

Mahdi¹,

Ali²,

Youssra³

et al. 2022

JESA

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Predictive maintenance (M4.0) allows more targeted and efficient use of resources, reduces unplanned downtime, and increases production and equipment performance compared to classical existing maintenance (M3.0). This paper deals with the development of a new ecosystem that adopts the new technologies of Industry 4.0 to drive real-time monitoring and diagnosis of engine defects. The proposed architecture is based on implementing a process of identifying critical components and extracting related data (speed and acceleration) based on IoT technology. A neural model (ANN) is implemented for monitoring, detecting and diagnosing engine faults with high accuracy compared to existing techniques. The effectiveness and reliability are validated through real-time test bench studies.

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“…The technologies deployed in Warehouse 4.0 generally allow for the circulation of goods, flexibility and reactivity in order to handle more flows, and improvement of the situation of employees and the working environment [61]. It is impossible to dissociate the digital transformation processes from the technologies that are at the heart of Industry 4.0.…”

Section: New Warehouse Technologiesmentioning

confidence: 99%

“…This result represents a decision aid for the company to choose the technology or technologies suitable for each process. This detailed bibliographic study on the relationship of 4.0 technologies, with the different processes of the warehouse as well as the sustainable contribution of 4.0 technologies in the supply chain, has been presented [61].…”

Section: Relationship Between the Most Used 40 Technologies In A Ware...mentioning

confidence: 99%

Digital Transition Methodology of a Warehouse in the Concept of Sustainable Development with an Industrial Case Study

Kihel

2022

Sustainability

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Logistics is one of the sectors that is evolving in parallel with Industry 4.0, which refers to the integration of new technologies, information, and agents, with the common goal of improving the efficiency and responsiveness of a logistics management system. The warehouse is an essential link in logistics management, a factor of competitiveness, and a link between the partners of the entire logistics chain. It has become essential to manage warehouses effectively and to allocate their resources efficiently. The digitalization of warehouses is currently one of the research topics of Logistics 4.0. This work presents a methodology of the digital transition of warehouse management, which consists of four main steps: the diagnosis of a warehouse to identify the different processes, the degree of involvement of the employees, a calculation of the degree of maturity to identify the new technology and means of data transfer, and the associated software for the collection of information and the methods of data processing. This digital transition methodology was applied to an industrial company. The results obtained allowed for the improvement of all the indicators measuring the performance of the warehouse on economic, social, and environmental levels.

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Contribution of Maintenance 4.0 in Sustainable Development with an Industrial Case Study

Cited by 18 publications

References 36 publications

A rule-based machine learning methodology for the proactive improvement of OEE: a real case study

A rule-based machine learning methodology for the proactive improvement of OEE: a real case study

Real Time Assessment of Novel Predictive Maintenance System based on Artificial Intelligence for Rotating Machines

Digital Transition Methodology of a Warehouse in the Concept of Sustainable Development with an Industrial Case Study

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