Mahdi Bashiri

The dynamic and volatile nature of fashion supply chains has drawn increasing attention from academia and the corporate sector. Fashion products, characterized by short lifecycles, impulse buying and an unpredictable demand, necessitate that fashion supply chain (FSC) partners rapidly offer on-trend products to capture the real-time demand in the shortest time window. To achieve this, FSC partners must embrace technological innovations, collaborate, and establish partnering relations, and share real-time information. Failure to do so will result in obsolete inventory and financial markdowns. This study focuses on identifying risk categories in FSC, such as Social, Environmental, Economic, Operational, Reputational, Market, Product, Disruption, Complexity, and Workforce, along with relevant mitigation strategies. A survey questionnaire was distributed to six fashion companies in the UK, employing the Fuzzy Group Analytical Hierarchy Process (FAHP) for pairwise comparisons to assess the importance of each risk category. Fuzzy Failure Modes and Effect Analysis (FFMEA) was used to analyze the impact of each risk mitigation strategy on the risk factors. This study supports the extant empirical research that resource sharing is an effective risk mitigation strategy for fashion risk management. The study participants believe that designing resilient, flexible, agile, and responsive systems with increased levels of communication and information sharing with the help of emerging innovative technologies are the more robust mitigation strategies for fashion risk management. This study has evaluated the role of emerging technologies in risk management, confirming that ICT and Artificial Intelligence are the most effective technologies for managing potential risks in the fashion industry.

This article proposes a two-stage stochastic profit-maximizing hub location problem (HLP) with uncertain demand. Additionally, the model incorporates several carbon regulations, such as carbon tax policy (CTP), carbon cap-and-trade policy (CCTP), carbon cap policy (CCP), and carbon offset policy (COP). In the proposed models, an enhanced sample average approximation (ESAA) method was used to obtain a suitable number of scenarios. To cluster similar samples, k-means clustering and self-organizing map (SOM) clustering algorithms were embedded in the ESAA. The L-shaped algorithm was employed to solve the model inside the ESAA method more efficiently. The proposed models were analyzed using the well-known Australian Post (AP) data set. Computational experiments showed that all of the carbon regulations could reduce overall carbon emissions. Among carbon policies, CCTP could achieve better economic results for the transportation sector. The results also demonstrated that the SOM clustering algorithm within the ESAA method was superior to both k-means inside ESAA and classical SAA algorithms according to the %gap and standard deviation measures. In addition, the results showed that the L-shaped algorithm performed better than the commercial solver in large-scale instances.