RFID & Analytics Driving Agility in Apparel Supply Chain
Author(s)Kumar, Anil; Ting, Peter
The apparel industry is facing significant challenges. Today’s consumers have less patience to wait, and omnichannel retailing is the new norm. This requires the entire apparel supply chain to become more agile, which means that stakeholders need to have better visibility, speed and flexibility. While supply chain digitalization helps the industry to become more agile, enabling technology like Radio Frequency Identification (RFID) has not been adopted in scale. This capstone aims to answer how RFID creates value in the apparel supply chain by improving agility. Based on our sponsor’s RFID pilot, we study the technology’s potential in its logistics & distribution and retail stages. Using process analysis, RFID data analysis, and cluster analysis, we identify relevant value drivers for different stakeholders. In the pilot’s context, we find three clusters: fastmoving omnichannel, online long tail and retail longtail, which have different supply chain characteristics. We also connect RFID data, captured at different checkpoints, with existing system data to generate business intelligence for the clusters. The result shows that RFID improves store KPIs such as daily inventory record accuracies and on-shelf availability. In addition, we analyze supply chain policies for the following value drivers: planning, inventory management, replenishment, and store management. In general, RFID provides end-to-end product visibility, which is beneficial for all stakeholders. Also, there are different levers that can be used to improve speed and flexibility for different stakeholders. Overall, the retail store gains most value from RFID initiatives. Nevertheless, significant value can be created for other stakeholders from advanced analytics and appropriate data sharing. Organizations need to leverage analytical tools and techniques to improve supply chain agility. Our findings can be useful for other apparel businesses that currently use the traditional mass manufacturing model and are seeking to improve their supply chain agility.
Retail, Machine Learning