The Study of Applying a Quality Management Tool for Solving Non-Conformities in a Automotive

Luca, Liliana

doi:10.4028/www.scientific.net/amm.809-810.1257

Cited by 11 publications

(4 citation statements)

References 4 publications

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“…Therefore, the detection and reporting of NC could happen during the audit, inspection, documentation review, product testing, customer complaints, stakeholders feedback, and general observation based on experience (Luca 2015). In addition to the above, NCs can take many forms and types, which may vary depending on the industry type, so that common NCs include: failure to identify issues, failure to de ne processes, plans, and schedules adequately, process deviations and product defects, deviations from a speci cation of product characteristic, missed plan, customer and supplier return.…”

Section: Nonconformity Managementmentioning

confidence: 99%

A novel JavaScript MEAN stack approach for real-time nonconformity management in SMEs within Industry 4.0 and Quality 4.0 concepts

Đorđević¹,

Stefanović²,

Cvetić³

et al. 2021

Preprint

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Nonconformity detection and reporting are evergoing issues in quality management practice and theory. Throughout industrialisation, and consequently, in the Industry 4.0 period, manufacturing organisations strived to reduce nonconformities to the minimum level and reach zero-defect production. Nowadays, it is feasible to overcome this issue by incorporating adequate information and communication technologies and edge devices for nonconformity reporting, and nonconformity classification. These assumptions were a starting point for the research presented in this paper. This paper aims to propose a software solution for nonconformity detection and preventive and corrective actions definition that expands the utilisation of edge devices in compliance with Industry 4.0 and Quality 4.0 paradigms. Presented software solution design is based on JavaScript programming language and its ability to be implemented in all software solution's tiers through interconnected frameworks (MongoDB, Express.js, Angular, and Node.js) in a MEAN stack. The developed solution is implemented in three small and medium enterprises. Initial results show several benefits in increasing nonconformity detection and reporting, increasing employee participation in preventive and corrective actions definition and improved quality management system. The number of corrective and preventive actions was taken based on the assistance of the prediction module. All nonconformities were related to specific sections of ISO 9001:2015 standards so that quality managers and managers in the companies could have insight into the sources of the issues and the foundation for defining different managerial actions. When comparing the newly developed solution and other affordable solutions, it was determined that the new solution has a higher operating velocity if there is a significant increase in the volume of queries towards databases. The paper's main contribution reflects in the software solution's presentation intended for affordable identification and nonconformities massive workload reporting, integrated with other software modules for quality analysis, prediction, and problem-solving. In this way, it is possible to obtain horizontal scalability and richness of the proposed software solution for smart enterprises focused on World Class manufacturing and lean manufacturing. Furthermore, it is possible to achieve a shorter time for nonconformities elimination and better compliance with the ISO 9001: 2015 standard. The presented solution usage contributes to all participants involved in the organisation of production and production itself, through (1) involvement of all employees; (2) digitalisation and improvement of existing nonconformity reporting systems; (3) improvement of nonconformity perception; (4) improved awareness and evaluation of employer's contribution to nonconformity management; (5) making the technology easy to use. The research's novelty lies in presented solution utilisation for real-time nonconformity classification and proactive and corrective measures proposals through communication and prediction modules.

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Section: Nonconformity Managementmentioning

confidence: 99%

A novel JavaScript MEAN stack approach for real-time nonconformity management in SMEs within Industry 4.0 and Quality 4.0 concepts

Đorđević¹,

Stefanović²,

Cvetić³

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…Fishbone diagrams are used in security and safety applications [30][31][32][33]. Asllani et al [30] used fishbone diagrams to identify possible contributory factors of network failure/intrusions, and used six different categories to sort and relate contributory factors.…”

Section: Fishbone Diagramsmentioning

confidence: 99%

“…Zhao et al [31] used fishbone diagrams to illustrate possible contributory factors of tower crane accidents under five different categories. Luca et al [32] used fishbone diagrams to illustrate possible contributory factors of noisy functioning of an automotive flue gas system under four different categories. Zhu et al [33] used fishbone diagrams to illustrate possible contributory factors of crude oil vapors explosion in the drain under six different categories.…”

Section: Fishbone Diagramsmentioning

confidence: 99%

Combining Bayesian Networks and Fishbone Diagrams to Distinguish Between Intentional Attacks and Accidental Technical Failures

Chockalingam

Pieters

Teixeira

et al. 2019

Graphical Models for Security

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Because of modern societies' dependence on industrial control systems, adequate response to system failures is essential. In order to take appropriate measures, it is crucial for operators to be able to distinguish between intentional attacks and accidental technical failures. However, adequate decision support for this matter is lacking. In this paper, we use Bayesian Networks (BNs) to distinguish between intentional attacks and accidental technical failures, based on contributory factors and observations (or test results). To facilitate knowledge elicitation, we use extended fishbone diagrams for discussions with experts, and then translate those into the BN formalism. We demonstrate the methodology using an example in a case study from the water management domain.

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“…The quality major assurance in projects represents essentially the evaluating overall performances of the project to ensure that it meets the standards of reference [10]. The quality assurance in projects includes, Fig.…”

Section: Contentmentioning

confidence: 99%

Quality Assurance as Major Process of Quality Management in Projects

Cîrțînă

Davaris

2015

AMM

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Quality management in projects includes the processes necessary for providing the fact that the project satisfies the necessities it had been accomplished for. The quality planning function must remain present throughout the project life cycle. The quality assurance in the projects can be defined as part of quality management in projects that provide confidence that the requirements relating to the quality of the activities carried out in the project will be met. We propose a model in the work for the implementation of the quality assurance process at every stage of a project, from project idea to project results. The proposed model, called PDP/PIP (project definition process/ project implementation process) is described as an application for a project machinery industry standard tools. The outcome of the project quality assurance is a quality improvement plan. This represents a document which is specified actions designed to raise the efficiency of quality management processes in projects and quality of results of the project.

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The Study of Applying a Quality Management Tool for Solving Non-Conformities in a Automotive

Cited by 11 publications

References 4 publications

A novel JavaScript MEAN stack approach for real-time nonconformity management in SMEs within Industry 4.0 and Quality 4.0 concepts

A novel JavaScript MEAN stack approach for real-time nonconformity management in SMEs within Industry 4.0 and Quality 4.0 concepts

Combining Bayesian Networks and Fishbone Diagrams to Distinguish Between Intentional Attacks and Accidental Technical Failures

Quality Assurance as Major Process of Quality Management in Projects

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