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Large scale deep learning project for top logistics company
Woman holding package
Estimated Package Arrival Time
As a Senior AI Consultant at Infosys Consulting, I had the privilege to collaborate with a globally leading logistics company, addressing one of their most pressing challenges: predicting package arrival times. Misestimations had been causing considerable strain on customer relations and internal operations, culminating in financial burdens. Drawing on state-of-the-art AI techniques and leveraging an event-driven architecture, I devised a solution that processed package scans in near real-time, transforming the company's predictive accuracy and operational efficiency.
The essence of this project revolved around the synthesis of high-end technology and practical needs. By employing deep neural networks and sophisticated recalibration techniques, I not only enhanced the predictions but also provided actionable insights for the client. This endeavor, handling over 300 million events per day, culminated in bolstered customer trust and significant operational savings.
Revolutionized package delivery predictions, handling over 300 million daily events.
Seamlessly integrated advanced technologies on Microsoft Azure, including MLFlow, Kubernetes, Azure Functions, and Event Hubs.
Proactively identified potential late packages, enabling timely interventions.
Estimated package arrival time case study
Project overview
The core challenge posed by the logistics company was clear: their package delivery prediction system was faltering, leading to a cascade of customer dissatisfaction and resource drain in customer support. The implications were not merely operational but were affecting the brand's reputation and bottom line. In response, we conceptualized an AI-driven solution that would rely on real-time package scan events, thus providing near-instantaneous predictions about delivery times.
The goal was twofold. First, to increase the accuracy of the prediction, and second, to empower the client with actionable insights based on the confidence of these predictions. By focusing on these objectives, the project aimed to rebuild customer trust and streamline internal operations.
Key achievements
Our accomplishments in this initiative were a blend of technical prowess and real-world impact.
Orchestrated a state-of-the-art streaming event-driven architecture, transforming package scans into real-time data insights.
Developed a deep neural network model which, paired with the temperature scaling recalibration technique, not only predicted arrival times but also gauged confidence levels.
Reduced operational strain on customer service, leading to significant financial savings.
Project initiation
Recognizing the multifaceted repercussions of inaccurate delivery predictions, we initiated the project by first understanding the extent of the challenge. Collaborating closely with stakeholders, we delineated the specific pain points and envisioned an AI-integrated solution to address them.
User requirements gathering
Code
Project progress
Our initial step centered on the development of the event-driven architecture. This was an intricate process, aiming to convert each package scan across the extensive logistics network into an actionable event. By doing so, we ensured a continuous influx of real-time data, offering an evolving and dynamic perspective on package movements throughout the entire delivery chain.
Following the architectural setup, the focus shifted to the predictive core of the solution: the deep neural network. This AI model was meticulously designed to predict package arrival times based on the myriad of data provided by the event-driven system. The integration of the temperature scaling recalibration technique further refined our solution. It not only bolstered the model's prediction capabilities but also introduced a measure of reliability, allowing the model to indicate the confidence of its own predictions.
Project outcome
The system's deployment heralded a new era for the logistics company. With heightened predictive accuracy, customer satisfaction surged, while the demand on customer service drastically declined. This translated to tangible savings, both in terms of finances and manpower. Internally, the system's insights also allowed for proactive measures, further optimizing the delivery process.
Profit outcomes
Client feedback
"The solution developed by the Infosys Consulting team stands as a testament to what cutting-edge AI can achieve in the real world. We've seen a significant uptick in customer trust and operational efficiency, and the financial savings speak for themselves. Truly transformative work!" - Client, Data Manager
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