Abstract
Aim: This research aims to explore the contemporary trends concerning the automation data management approaches, and their relevance for the enhancement of the management processes in the British fashion sector.
Design/Method: An extensive database of the UK fashion industry was used, which included general and specific characteristics of clothes, customers’ feedback, and purchasing behaviour. Cleaning the data included methods such as dealing with missing data and transforming nominal variables into numerical ones. Since the data was large, K-means clustering was applied to partition the data into relevant clusters of data. In choosing the appropriate number of clusters, Exploratory Data Analysis (EDA) using the elbow method was adopted, while the silhouette score was also used in the assessment of clustering performance.
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The fashion sector in the UK is a vital element of the country’s economy as it help to provide new opportunities and improve the framework of the country (Babu et al., 2024). As automation and machine learning take centre of attention in organisational management, organisations within this sector rely on big data to discover operative and value-creation solutions for customers (Balchandani et al., 2023). This explains why the ability to manage and analyse data has been so important given that the consumers’ tastes and preferences are ever-changing. Automation in data management enables real-time data analysis, is less prone to errors, and most importantly, frees up useful resources for value addition (PwC, 2021).
Several technological applications in automating data management for the fashion industry include predictive analytics, inventory and management systems, and customer marketing tools (Mohiuddin Babu et al., 2022). They help firms predict consumer patterns, manage the logistic activities of the supply chain, and personalise advertising techniques as per the consumer behaviour pattern (Chase, 2020; Accenture, 2021). For example, fashion retailers are able, to forecast which products will be popular in the following seasons, so overstock and stockout risks are limited. Further, the automated application of CRM enhances consumer behaviour analysis, which results in efficient marketing communication.
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Accenture. (2021). Transforming the fashion supply chain. Available at: https://www.accenture.com/content/dam/accenture/final/a-com-migration/r3-3/pdf/pdf-115/accenture-threads-that-bind.pdf (Accessed: 08 June 2024).
Babu, M.M., Rahman, M., Alam, A. and Dey, B.L., 2024. Exploring big data-driven innovation in the manufacturing sector: evidence from UK firms. Annals of Operations Research, 333(2), pp.689-716.
Balchandani, A. et al. (2023) The state of fashion 2024: Finding pockets of growth as uncertainty reigns, McKinsey & Company. Available at: https://www.mckinsey.com/industries/retail/our-insights/state-of-fashion (Accessed: 08 June 2024).
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