New Delhi, April 4 -- Mumbai-based Allcargo Logistics which offers a range of logistics solutions to domestic and global markets has been leveraging artificial intelligence (AI), cloud computing, analytics and other advanced technologies to modernise its legacy systems, standardise processes and optimise operations.

Founded in 1993, the company operates in over 180 countries and is one of the largest non-vessel operating common carriers today, which operates its full container load (FCL) activities globally through its wholly-owned subsidiary, ECU Worldwide, a global leader in LCL (less than container load) consolidation, with a focus on container freight stations, international supply chain, and specialised cargo handling services. In 2020, it acquired a controlling stake in Hyderabad-based Gati Ltd (now Allcargo Gati Limited).

In an exclusive interaction with TechCircle, Kapil Mahajan, Global Chief Information & Technology Officer at Allcargo Logistics, explained that to streamline the company's massive logistics operations, the need of the hour was to forecast demand across various routes, enabling more efficient planning.

"Additionally, the focus was on implementing real-time price prediction to enhance decision-making and responsiveness in dynamic market conditions. Furthermore, establishing a reliable proof of delivery system is crucial for improving cash collection outcomes, and ensuring that transactions are completed smoothly and efficiently. To address these challenges, Allcargo opted to harness the capabilities of cloud and AI," he said.

According to Mahajan, as a major LCL logistics provider, Allcargo faced the challenge of efficiently managing its container capacity across various trade lanes. "We optimised container capacity management by predicting demand on trade lanes like China-Singapore, accounting for seasonal trends and global events."

This is where advanced AI solutions come into play. By analysing historical data, these AI models identified patterns and trends in customer demand, accounting for seasonal fluctuations and global events. This enabled the company to make more accurate capacity commitments and better predict the volume of shipments that could be consolidated to fill the containers, believes Mahajan.

AI-powered models

The AI-powered models also support dynamic pricing and spot rate offerings, allowing the company to quickly adjust rates based on real-time demand and capacity utilisation. This has helped the company recover costs and maintain profitability, even in scenarios where the committed capacity cannot be fully utilised.

"We continuously refine the AI models and integrate new data sources to enhance the accuracy of capacity forecasting and pricing strategies. Such a data-driven approach enables the company to optimise its LCL operations, reduce excess capacity, and improve overall profitability," said Mahajan.

Mahajan believes that the logistics network at Allcargo operated like an airline's route system, with multiple pickup and delivery points. The company had around 1,500 daily route departures, with some routes not being fully utilised on the return leg, especially in consumption-heavy regions like the Northeast region of the country. That said, he analysed historical data using machine learning and big data to identify opportunities to streamline the network and reduce the number of daily departures while maintaining the same overall capacity.

Optimising network efficiency

To optimise network efficiency, Allcargo uses machine learning and big data to streamline its logistics network, reducing daily departures by 8-10% without impacting service levels. These recommendations are validated by internal experts to ensure feasibility and minimise bias.

For end-customer deliveries, Allcargo implemented computer vision to automate POD verification, overcoming challenges posed by varying signatures and stamps. A neural network-based model, deployed as an edge computing solution on the company's mobile app, accurately detects and validates signatures offline, enabling instant approval and improving cash flow by 15-20%, he informed.

On Gati's massive transformation

In February, Allcargo Gati migrated its on-premises mission-critical enterprise resource planning (ERP) system to Oracle Base Database Service on Oracle Cloud Infrastructure (OCI). This migration has improved the company's system performance and operational efficiency by over 20%, said Mahajan, adding that "this massive transformation is still underway and is expected to complete within the next few months, with AI and GenAI capabilities from Oracle Cloud with Salesforce CRM".

"Also, our data lakes and business intelligence (BI) tools are cloud-based, democratising analytics access for all employees," said Mahajan.

Mahajan further informed that ECU completed a major cloud migration, consolidating six of its global data centres, ensuring data residency and projecting $10 million in savings over eight years. Going forward, the company's plans include transitioning from Citrix to web-based application access with built-in security features.

Notably, Allcargo's tech centres are primarily located in Mumbai, Gurgaon (with logistics parks and warehousing facilities located at Hyderabad, Bengaluru, Ahmedabad, Delhi (NCR) and other trade hubs), and Belgium (following the acquisition of ECU), with additional teams in Miami, Singapore, and Dubai, employing 350-400 tech personnel globally. While leveraging external partners for speed and scale, Allcargo is transitioning to in-house management for core engineering, hiring talent from companies like Accenture and IBM.

Mahajan informed that the company utilises shared services for architecture, application development, DevOps, and global IT procurement. Global security is managed centrally by the CSO. Three Centers of Excellence (COEs) - CRM, Finance, and Terminals - promote best practice sharing.

"We're building AI-powered tools that surpass competitors, particularly in the realm of Generative AI, thanks to our skilled talent," he said.

Speaking about competitor analysis, Mahajan said, "Unlike most of our competitors who run different ERP systems in each country, we have a unified tech platform globally. While some fragmented ERPs might offer local cost advantages, standardisation, innovation, and transformation benefit from our unified approach, which aligns with how larger companies operate."

Tech roadmap

On the company's tech roadmap for the next 12-18 months, Mahajan believes that the biggest transformations involve rebuilding Gati's ERP on a cloud-native platform and migrating ECU's architecture to a three-tier model.

"We're also looking to transition from paid subscriptions to open-source solutions like PostgreSQL and Linux, reducing TCO and improving scalability. This includes designing applications for containerisation and cloud-native execution to optimise cost and scalability," said Mahajan.

Mahajan is also excited about technologies such as quantum computing. Recent developments, such as Microsoft's Majorana 1, the world's first quantum processor powered by topological qubits, he believes these innovations could revolutionise data centre provisioning and enable unparalleled processing power. "We're in a pivotal era with AI, Generative AI, and quantum computing converging to potentially transform businesses and create autonomous thinking machines," he summed up.

Published by HT Digital Content Services with permission from TechCircle.