Conquering the last mile in logistics
Online commerce purchases have been digitised to such an extent that customers anticipate a flawless experience. However, challenges persist in the background operations of packing, shipping, and delivery. Issues such as inaccurate address details, lack of digitization in certain processes, and reliance on manual data and checks contribute to errors, elevated costs, and delays.
To address these challenges head-on, Shipsy embarked on a mission to revolutionise the last mile of delivery, leveraging the transformative power of AI.
Re-routing with AI
In the realm of logistics management, the absence of on-ground tools contributes to delayed issue identification and stakeholder notification, resulting in prolonged decision-making. The deficiency in system support for corrective and preventive actions further compounds these issues. This challenge is exacerbated by the sheer scale at which the logistics industry operates, encompassing a vast workforce and a broad customer base.
Shipsy AI, powered by Azure OpenAI APIs, extracted relevant information from images (both printed and handwritten) and segments, and cleansed the information. The system was also able to identify anomalies and flag potential frauds. Moreover, the system demonstrates a remarkable ability to interpret improper addresses, providing valuable inputs to the routing engine.
Leveraging Azure OpenAI, helped Shipsy address a spectrum of challenges ranging from minimizing routing errors to enhancing shipment processing efficiency and ultimately reducing overall operational costs.
Soham Chokshi
CEO
Shipsy
Shipsy’s Shipping AI, fuelled by Azure OpenAI, boosts productivity and cuts down on fraud, making shipping smarter and more secure.
63%
reduction in cost
4x
increased shipment processing throughput
34%
reduction in routing errors
Shipsy’s AI-powered platform empowers global businesses to optimize, automate, track and simplify end-to-end logistics and supply chain operations.
More details at https://shipsy.io
Square Yards