Integration of Quality Management Tools into a Digital Management System

Vasiliev, Victor A.; Aleksandrova, Svetlana V.; Aleksandrov, Mark N.

doi:10.1109/itqmis53292.2021.9642853

Cited by 10 publications

(3 citation statements)

References 4 publications

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“…In cases of deviations of product properties or product quality it is necessary to compile the necessary information quickly to get fast managerial decisions and actions for the correction or improvement of the product or process [5]. Additionally Vasiliev et al [12] state that there is a need to develop the current isolated analysis of several statistical data and integrate this analysis into an integrated management system. As Industry 4.0 progresses, data science approaches have become essential for decision-making within the manufacturing environment.…”

Section: A Special Role Of Data Science and Quality Managementmentioning

confidence: 99%

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Modular Data Analytics as a Tool for Citizen Data Scientists in Quality Management

Pähler,

Syberg,

Deuse

2024

Preprint

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The vision of Industrial Data Science to link data along the entire value chain with data from the product life cycle contains great potential for holistic quality improvements. In order to be able to exploit this increasing potential, tools and evaluations as well as the qualification of experts in quality management in the field of Industrial Data Science are essential. A promising approach is the generalisation and modularisation of data analysis processes, which can be reused in Quality Management for similar analytics tasks of different data sources in this discipline. With an appropriate integration into the company’s processes and easy accessibility of the generalized analytics modules, this approach promises to reduce the complexity and difficulty of data analysis. In this paper a total of seven different exemplary analysis modules that quality management experts can use to handle, visualise and analyse new data in Field Quality Surveillance. These are provided as part of a research project via a browser-based platform that enables users to perform data analyses without the use of other software or programming knowledge. The analysis modules are a central building block for supporting Quality Management experts to act as Citizen Data Scientists. In addition, skills training and general change management for organisational restructuring must go hand in hand with the use of the modules in order to achieve valid analytics results quickly.

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Section: A Special Role Of Data Science and Quality Managementmentioning

confidence: 99%

“…In the context of the needed integration to the company's business processes (see chapter II. A. and [12]) the described analysis modules need to be integrated into the company's QM reporting systems. The analytics modules on the 'AI-Toolbox' are more appropriate for analytics in isolated use cases.…”

Section: ) Overview With a Dashboard And Evaluationmentioning

confidence: 99%

Modular Data Analytics as a Tool for Citizen Data Scientists in Quality Management

Pähler,

Syberg,

Deuse

2024

Preprint

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“…The use of these software programs in the field of education allows teachers to impart their knowledge about the design and implementation of process control through the manipulation of teaching modules made up of industry-specific actuators and transducers [13,14]. However, a limitation is that the equipment cannot be used for all types of processes, which limits the development of industrial process control [5,15].…”

Section: Introductionmentioning

confidence: 99%

Wine Production through Virtual Environments with a Focus on the Teaching–Learning Process

Tapia,

Illescas,

Santamaría

et al. 2023

Applied Sciences

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This paper focuses on the application of the hardware-in-the-loop (HIL) technique in the winemaking process. The HIL technique provides an effective methodology to test and verify the automatic control of industrial processes in 3D laboratory environments. Two parts are considered: (i) software, which consists of the virtualization of the wine process in order to generate a realistic work environment that allows the student to manipulate the system while visualizing the changes in the process; and (ii) hardware, through which the process control is implemented in ladder language in a PLC S7 1200 AC/DC/RLY (programmable logic controller). Bidirectional Ethernet TCP/IP communication is established, achieving a client–server architecture. This article highlights the main advantages of the HIL technique, such as its ability to simulate complex and extreme scenarios that would be difficult or expensive to recreate in a real environment. In addition, real-time testing of the hardware and software to implement the control system is performed, allowing for fast and accurate responses. Finally, a usability table is obtained that demonstrates the benefits of performing industrial process control work in virtual work environments, focusing the development on meaningful learning processes for engineering students.

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Modular Data Analytics as a Tool for Citizen Data Scientists in Quality Management

Pähler,

Syberg,

Deuse

2024

2024 International Conference on Electrical, Computer and Energy Technologies (ICECET

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Integration of Quality Management Tools into a Digital Management System

Cited by 10 publications

References 4 publications

Modular Data Analytics as a Tool for Citizen Data Scientists in Quality Management

Modular Data Analytics as a Tool for Citizen Data Scientists in Quality Management

Wine Production through Virtual Environments with a Focus on the Teaching–Learning Process

Modular Data Analytics as a Tool for Citizen Data Scientists in Quality Management

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