In an age when e-commerce reliability can directly influence customer loyalty, artificial intelligence is increasingly being applied in the domain of supply chain optimization. One such model, reportedly developed within the Delivery & Fulfillment channel of a major retail IT organization, is being noted for its predictive accuracy in logistics planning.
Coming from the expert table, Arulmozhi Kasthurirengan, an experienced professional in AI-driven supply chain systems, explains: “Delivery inconsistencies have long plagued the customer journey. What we are witnessing now is a smart recalibration—AI is being harnessed to eliminate blind spots in last-mile delivery.”
The model in question, a predictive algorithm designed to forecast delivery timelines with improved precision, is already yielding notable outcomes. As per the reports, it has led to a 0.7% increase in Add To Bag (ATB) and Bag to Order (BTO) rates. Impressively, customer service call volumes have reportedly dropped by 35%—a shift experts suggest could indicate better delivery transparency and improved customer confidence.
In internal projects like Returns & Registry, the model has already established itself as foundational. Speaking on condition of anonymity, a key team member shared that “this algorithm is no longer a standalone tool—it’s become the central nervous system for our delivery intelligence framework.”
The impact of this implementation has been notable. A KPI Dashboard, created to support the model’s roll-out, is being used daily to compare predicted and actual delivery days across a wide array of variables—brands, fulfillment types, locations, and shipping methods included. These insights, reportedly available in both daily and weekly formats, have contributed to making delivery operations more data-driven science.
However, this success did not come without challenges. One of the key hurdles was aligning business calendars with delivery expectations. Weekend deliveries, previously absent due to carrier contract limitations, posed a bottleneck. To tackle this, the team coordinated directly with shipping carriers to fetch real-time delivery feeds and eventually proposed a Conditional Predicted Delivery Date (PDD) model. This new approach introduced a probabilistic range rather than a fixed date, offering flexibility and clarity in communication with customers.
Arulmozhi Kasthurirengan notes, “Flexibility is fast becoming the gold standard in fulfillment. Models that provide delivery date ranges, instead of rigid timelines, represent a fundamental shift in customer-centric logistics.”
Additionally, experts suggest that the model’s built-in ability for dynamic pattern detection, responsiveness to short-term data fluctuations, and continuous learning set it apart from traditional systems. These characteristics reportedly make it especially effective during volatile shipping periods such as holiday seasons or during inclement weather disruptions.
Despite not having published formal papers, the developer behind this initiative has reportedly gained internal recognition for both technical depth and cross-functional collaboration. “Shipping is complicated. When you bring together manufacturers, distributors, carriers, and end-users, even a small lapse in coordination can cause a domino effect,” the developer said. “That’s why this model’s real achievement is not just in prediction—but in precision and adaptability.”
Looking ahead, predictive logistics powered by AI/ML is expected to become standard practice across the retail industry. “From what I see,” Kasthurirengan adds, “predictive intelligence won’t just be an optimization layer—it will be the new operational core.”
About Arulmozhi Kasthurirengan
Arulmozhi Kasthurirengan has worked in the domain of AI/ML-driven logistics and predictive modelling within the retail IT sector. Her contributions include the design of scalable KPI dashboards, coordination across multi-party delivery networks, and the implementation of probabilistic delivery models that have helped improve order accuracy, customer satisfaction, and operational cost-efficiency. Her work supported the development of fulfilment performance and data transparency across large-scale retail environments.
