Managing Deadstock in the Fashion and Apparel Industry: An Exploration of Causes, Solutions and Technological Interventions

Abstract

The fashion and apparel (FA) industry faces a critical challenge in the form of deadstock. There are various causes for deadstock, and some may be traced to supply chain and inventory management inefficiency. This research paper aims to understand the issue of deadstock, its root causes, its implications for business and strategies to mitigate it. This paper also aims to investigate the potential of adopting artificial intelligence (AI), machine learning (ML) and data analytics to reduce the issue of deadstock. This research paper uses a systematic bibliographic literature review of the relevant literature in this subject area. In conclusion, this research paper aims to comprehensively study deadstock in the FA industry. It highlights the potential of using AI, ML and data analytics in supply chain management (SCM) to increase efficiency and mitigate the deadstock issue. There is, however, a lack of sufficient research papers that focus on deadstock in the FA industry and strategies and technological interventions to overcome it. Therefore, this research paper aims to serve as an overview that can be beneficial for researchers, business stakeholders and process engineers in the FA industry to create deployable technology-based solutions to address the problem of deadstock.

Keywords

• deadstock
• fashion and apparel industry
• supply chain management