Digital Shop Floor Management

Hartner, Raphael; Mezhuyev, Vitaliy; Tschandl, Martin; Bischof, Christian

doi:10.1145/3384544.3384611

Cited by 6 publications

(1 citation statement)

References 12 publications

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“…The development in artificial intelligence and especially ML methods is fast-moving and makes enormous progress every year [33][34][35][36][37]. While in some industrial areas, ML concepts are used productively, in others implementation is lagging.…”

Section: Discussionmentioning

confidence: 99%

Approach to provide interpretability in machine learning models for image classification

Stadlhofer,

Mezhuyev

2023

Industrial Artificial Intelligence

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One of the main reasons why machine learning (ML) methods are not yet widely used in productive business processes is the lack of confidence in the results of an ML model. To improve the situation, interpretability methods may be used, which provide insight into the internal structure of an ML model, and criteria, based on which the model makes a certain prediction. This paper aims to consider the state of the art in interpretability methods and apply the selected methods to an industrial use case. Two methods, called LIME and SHAP, were selected from the literature and next implemented in the use case for image classification using a convolutional neural network. The research methodology consists of three parts, the first is the literature analysis, followed by the practical implementation of an ML model for image classification and the subsequent application of the interpretability methods, and the third part is a multi-criteria comparison of selected LIME and SHAP methods. This work enables companies to select the most effective interpretability method according to their use case and also to increase companies’ motivation for using ML.

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Section: Discussionmentioning

confidence: 99%

Approach to provide interpretability in machine learning models for image classification

Stadlhofer,

Mezhuyev

2023

Industrial Artificial Intelligence

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Software Quality in the IOT in Health Sector and Commerce Sector

Ojo-Gonzalez,

Bonilla-Morales,

Vargas-Lombardo

2023

Communications in Computer and Information Science

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Technological integration of lean manufacturing with industry 4.0 toward lean automation: insights from the systematic review and further research directions

Saraswat,

Agrawal,

Rane

2024

BIJ

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PurposeOrganizations are continually improving their practices to improve operational performance. They already employ Lean Manufacturing techniques (LM) to reduce unnecessary waste. Industry 4.0 techniques enhance operational performance in association with LM. Despite the proven benefits of LM principles and the advancements offered by Industry 4.0 technologies, many organizations struggle to integrate these approaches effectively. This research paper explores how LM principles can be combined with Industry 4.0 technologies to provide valuable guidance for businesses looking to adopt lean automation strategies.Design/methodology/approachA systematic literature review on LM and Industry 4.0 was done to investigate the possible technical integration of both methods. Ninety-two articles are extracted systematically from the Scopus and Web of Science databases. This study states a systematic literature review, including quantitative analysis of bibliographic networks and cluster analysis, to identify emergent ideas and their further implementation.FindingsThe research findings highlight the positive impact of integrating lean production with Industry 4.0 techniques, benefiting organizations in achieving their goals. A lean automation integration framework is proposed based on the literature review and the findings.Practical implicationsThis study provides industry administrators and practitioners valuable guidance for enhancing organizational productivity. These implications can provide businesses with competitive advantages, enhance customer satisfaction, and enable them to adapt to the dynamic demands of the contemporary business environment.Originality/valueThis literature review study has substantially contributed to the technological integration of lean and Industry 4.0. The work has also identified potential emerging areas that warrant further research.

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Digital Shop Floor Management

Cited by 6 publications

References 12 publications

Approach to provide interpretability in machine learning models for image classification

Approach to provide interpretability in machine learning models for image classification

Software Quality in the IOT in Health Sector and Commerce Sector

Technological integration of lean manufacturing with industry 4.0 toward lean automation: insights from the systematic review and further research directions

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