Big Data Analytics and Forecasting in Fashion
A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).
Deadline for manuscript submissions: closed (30 November 2020) | Viewed by 1689
Special Issue Editors
Interests: time series analysis and forecasting; applied statistics; quantitative methods
2. Research Institute of Energy Management and Planning (RIEMP), University of Tehran, Tehran 1417466191, Iran
Interests: big data; AI; machine learning; statistics; digital twins; digital health; advanced technology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In an age of fast fashion, high street retailers are struggling to remain competitive by maximising consumer insights through the exploitation of big data (Styles, 2019) and its potential for enhancing fashion analytics and forecasting. For an industry that has traditionally relied heavily on creativity and intuition for direction in designing, buying and merchandising, the time has come to get fashionable with (big) data (Silva et al., 2019a). In fact, Chinese e-commerce giant Alibaba used big data recently to predict fashion trends and had their collections built around these (Livni, 2019), whilst recent academic research has focused on the possibility of using Google Trends for predicting fashion consumer behaviour (Silva et al., 2019b). Whilst big data presents many opportunities for enhancing the status of sustainability, business operations and productivity within the fashion industry, it also has several challenges that hinder its wide application, from data privacy issues to a lack of data-savvy fashion graduates and slow academic research. Thus, this Special Issue will focus on the evolution of the fashion industry in an age of big data and rapid technological innovation and seek to promote and motivate more industry-relevant academic research into this subject area. Manuscripts are invited on topics that include but are not limited to the following:
- The impact of big data on the entire fashion value chain from concept to consumption;
- The influence of big data on the emergence of new business models;
- How big data impacts supply chain and marketing;
- How traditional fashion industry roles are changing through the leveraging of big data;
- Big data analytics in fashion: applications, consumer attitudes and perceptions;
- Big data opportunities and challenges in fashion;
- Time series analysis and forecasting in fashion;
- New insights on trend forecasting in an age of big data;
- The future of blockchain technology in the fashion industry.
References
Livni, E. Alibaba designers used AI to shape New York Fashion Week looks. Available online: https://qz.com/quartzy/1704770/alibaba-designers-used-ai-for-new-york-fashion-week-looks/ (accessed on 18 September 2019).
Silva, E. S., Hassani, H., and D. Ø. Madsen. Big data in fashion: Transforming the retail sector. J. Bus. Strategy 2019.DOI:10.1108/JBS-04-2019-0062.
Silva, E. S., Hassani, H., D. Ø. Madsen, and Gee, L. Googling fashion: Forecasting fashion consumer behaviour using google trends. Soc. Sci. 2019, 8, 111.
Styles, D. Data and the dress: what Christopher Wylie can teach fashion. Available online: https://www.theguardian.com/fashion/2019/aug/26/data-and-the-dress-what-christopher-wylie-can-teach-fashion (accessed on 18 September 2019).
Dr. Emmanuel Sirimal Silva
Prof. Dr. Hossein Hassani
Ms. Liz Gee
Guest Editors
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