Featured Application: The current paper proposes an approach to improve data analysis and visualization through Association Rule Mining and Augmented Reality. The applicability of the approach has been verified through a case study, providing Augmented Reality devices to expert and non-expert (or trainees) operators and letting them experience the adoption of the proposed technology in their daily quality management activities. The wider implementation of Industry 4.0 technologies in several sectors is increasing the amount of data regularly collected by companies. Those unstructured data need to be quickly elaborated to make on-time decisions, and the information extracted needs to be clearly visualized to speed up operations. This is strongly perceived in the quality field, where effective management of the trade-off between increasing quality controls to intercept product defects and decreasing them to reduce the delivery time represents a competitive challenge. A framework to improve data analysis and visualization in quality management is proposed, and its applicability is demonstrated with a case study in the fashion industry. A questionnaire assesses its on-field usability. The main findings refer to overcoming the lack in the literature of a decision support framework based on the joint application of association rules mining and augmented reality. The successful implementation in a real scenario has a twofold aim: on the one hand, sample sizes are strategically revised according to the supplier performance per product category and material; on the other hand, the daily quality controls are speeded up through accurate suggestions about the most occurrent defect and location per product characteristics, integrated with extra tips only for trainees.

Data Mining and Augmented Reality: An Application to the Fashion Industry / Fani V.; Antomarioni S.; Bandinelli R.; Ciarapica F.E.. - In: APPLIED SCIENCES. - ISSN 2076-3417. - STAMPA. - 13:(2023), pp. 2317.1-2317.18. [10.3390/app13042317]

Data Mining and Augmented Reality: An Application to the Fashion Industry

Fani V.;Bandinelli R.;
2023

Abstract

Featured Application: The current paper proposes an approach to improve data analysis and visualization through Association Rule Mining and Augmented Reality. The applicability of the approach has been verified through a case study, providing Augmented Reality devices to expert and non-expert (or trainees) operators and letting them experience the adoption of the proposed technology in their daily quality management activities. The wider implementation of Industry 4.0 technologies in several sectors is increasing the amount of data regularly collected by companies. Those unstructured data need to be quickly elaborated to make on-time decisions, and the information extracted needs to be clearly visualized to speed up operations. This is strongly perceived in the quality field, where effective management of the trade-off between increasing quality controls to intercept product defects and decreasing them to reduce the delivery time represents a competitive challenge. A framework to improve data analysis and visualization in quality management is proposed, and its applicability is demonstrated with a case study in the fashion industry. A questionnaire assesses its on-field usability. The main findings refer to overcoming the lack in the literature of a decision support framework based on the joint application of association rules mining and augmented reality. The successful implementation in a real scenario has a twofold aim: on the one hand, sample sizes are strategically revised according to the supplier performance per product category and material; on the other hand, the daily quality controls are speeded up through accurate suggestions about the most occurrent defect and location per product characteristics, integrated with extra tips only for trainees.
2023
13
1
18
Fani V.; Antomarioni S.; Bandinelli R.; Ciarapica F.E.
File in questo prodotto:
File Dimensione Formato  
applsci-13-02317-v2.pdf

accesso aperto

Tipologia: Pdf editoriale (Version of record)
Licenza: Open Access
Dimensione 1.87 MB
Formato Adobe PDF
1.87 MB Adobe PDF

I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1330046
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 2
social impact