# Data Mining and Augmented Reality: An Application to the Fashion Industry
## 6 Conclusions  
The implementation of Industry 4.0 tools enables the collection of huge amounts of data that can be capitalized in areas like quality management. However, the need to balance short lead times and elevate quality levels requires the adoption of appropriate approaches that should be integrated into companies' best practices, focusing on data analysis and visualization. On the one hand, ARM allows us to easily manipulate the huge amounts of unstructured data regularly collected on-field. On the other hand, AR provides intuitive ways of presenting data to operators, supporting them in making informed decisions in a timely manner. Even if these techniques have been widely used to support decision-makers, there is a lack of joint contributions in the quality field. Moreover, few solutions based on those technologies are being explored within low-tech dynamic sectors.  
To this end, in this paper, an approach to improve data analysis and visualization through the use of ARM and AR has been proposed. The innovation introduced by the present study is, therefore, not in the two techniques but rather in their joint application to a specific sector like the fashion industry.  
The introduction of the proposed approach is beneficial for what concerns strategic and operational aspects. The former aspect is taken into account through systematic monitoring of suppliers' performance, which leads to a redefinition of the sample sizes on the basis of the specific outcomes achieved, paving the way to the introduction of incentives for high-performer suppliers. The latter, instead, is achieved by providing direct support to the operators during their activities and their training. The proposed approach ensures a clearer view of the whole quality management process and allows more precise time and resource management.  
The applicability of the proposed approach has been verified through a case study, providing AR devices to expert and non-expert (or trainees) operators and letting them experience the adoption of the proposed technology in their daily activities. In order to formalize their perception, a qualitative and quantitative analysis has been carried out. Results show that, from a qualitative point of view, both categories consider the introduction of the framework as beneficial and helpful. From a quantitative perspective, instead, only for the non-expert operators has a statistically significant improvement in the duration of the inspection process been noticed. This aspect is related to the fact that trainees are guided in quickly identifying the location of the most likely defects and are given a structured procedure to carry out the inspection.  
The main limitation of the proposed application is related to the use of a device, e.g., a smartphone or tablet, that needs to be held by the operator. Future research direction will involve the development of the AR application for smart glasses in order to further facilitate the activities that will be able to be performed completely hands-free.  
Conceptualization, V.F., S.A., R.B. and F.E.C.; methodology, V.F. and S.A.; software V.F. and S.A.; validation, V.F., S.A., R.B. and F.E.C.; writing--original draft preparation V.F. and S.A; writing--review and editing, V.F., S.A., R.B. and F.E.C.; visualization V.F. and S.A.; supervision R.B. and F.E.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
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Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
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The authors declare no conflict of interest.