Modern railway systems operate a complex network of vehicles, tracks, and infrastructure that’s prime for disruption from the latest batch of AI innovations, including generative AI. That’s because AI-fueled technologies are peerless at accomplishing data-driven tasks that address this industry’s foundational reason for being, namely the safe, efficient, and on-time delivery of goods and passengers in compliance with country and regional regulations.
Paradoxically, bringing advanced AI-based tools into daily railway operations creates a human-centered workplace.
gettyIn a series of recent in-depth conversations, leaders from some of the world’s leading railways shared how traditional and generative AI technologies can help their organizations address their biggest challenges. After reading the report, I caught up with Johann Schachtner, who leads solution management for transportation asset management at SAP.
“While many railway operators are concerned that the scope and readiness of their current systems may be too complex to support AI-based capabilities, they are very interested in exploring how these advances can build resiliency for growth,” said Schachtner. “Railway operators realize that infrastructure and rolling stock breakdowns anywhere impact the entire business. In fact, echoing many of the people we recently talked with, one operator said that the cost of failure was so large that the gains realized through AI will be significant.”
Dynamic railway maintenance with AI
Railways are undergoing tremendous expansion in some regions as well as modernization of aging infrastructure in others. AI is well-suited to keep pace with the industry’s brisk growth and ambitious plans. In their candid dialogues, rail transport leaders ranked dynamic maintenance among the top places where AI can make a difference.
Railways have long used AI-based visual image recognition on diagnostic cars to spot track defects, allowing operators to identify potential problems and proactively take maintenance steps for greater efficiencies. With insights from real-time data, railways can more cost-effectively allocate and manage materials, people, and equipment within dynamic maintenance windows. AI-based data insights can help railways shift maintenance from fixed preventive to breakdown reduction through predictive and prescriptive activities.
It's early days for generative AI, but potential areas for maintenance-related advancements are emerging. Generative AI tools could quickly collect and analyze structured and unstructured data across the railway ecosystem, for example from OEM partners. Coupled with IoT-based augmented or virtual reality technologies, generative AI could surface data for technicians in the field to speed up diagnostic and preventive maintenance. Functioning as a virtual assistant, the technology could draw from a variety of relevant sources including historical equipment data and manuals to provide the fastest and safest fix for specific issues.
AI guides more informed decision-making
Industry leaders are just as interested in AI’s potential to help orchestrate project management across miles of equipment and infrastructure along railway lines. Using AI-fueled ‘what if’ scenario planning that incorporates variables from hundreds of projects, operators could adjust maintenance and inventory management plans to reduce costs and improve service levels. They could also strategically align investment decisions with evolving organizational priorities including safety, regulatory compliance, and growth.
People-centric efficiencies from AI-powered asset performance management
Paradoxically, bringing advanced AI-based tools into daily railway operations creates a human-centered workplace. These capabilities can deliver up-to-the-minute notifications, recommendations, and predictions that help people meet daily and long-term responsibilities. For example, using insights from connected data between maintenance equipment, station facilities, depots and yards, safety and security systems, and IT, people can manage and improve the lifecycle of railway assets, reducing downtime while increasing performance and value.
“Our SAP Business AI strategy is designed to provide customers with relevant, reliable, and responsible AI from real-time data that’s integrated across the business including maintenance, supply chain, HR, procurement, sales, and finance,” said Schachtner. “We’re embedding our AI copilot Joule into our cloud-based applications portfolio, which aligns with railway industry demands around enterprise asset management. AI-based tools will provide people with the updated information they need to make more informed decisions faster for business benefit.”
AI heralds positive railway disruption
Railway transport tends to grab market attention only when disruptions occur. AI technologies promise to deliver a good kind of disruption for an industry undergoing rejuvenation for the next era of economic growth.
To learn about all of the ways that industry market leaders are exploring AI, read the white paper: AI and Railways — Taking asset performance to new heights.
