Smarter asset management is a business imperative for urban transport businesses across the globe.
Organizations need to manage and maintain a complex portfolio of multi-disciplinary assets and adopt maintenance strategies that balance cost, risk, performance, and utilization while ensuring reliability, safety, and regulatory compliance.
One of the key priorities is unifying and standardizing processes while accommodating variations across asset classes and types. Artificial intelligence and machine learning is also gaining importance with the adoption of digitally connected smart assets, as data-driven decisions will play a vital role in embracing mature maintenance strategies like predictive maintenance and reliability-centered maintenance. Implementing geographic information systems and mobility solutions can improve productivity by facilitating contextual and collaborative operations.
To address these priorities, organizations are moving away from multiple, siloed asset management solutions to comprehensive and integrated enterprise asset management (EAM) and asset performance management (APM) platforms that can be leveraged across the asset life cycle.
The TCS Transport Asset Management solution provides a comprehensive and integrated package for management of urban transport assets. The solution leverages TCS’ decades of asset management experience in the transport industry. It is built on the IBM Maximo® suite, enabling next-generation asset management and monitoring, with intelligent predictive maintenance capabilities on a single platform.
Urban transport organizations have highly asset-centric operations and services.
They require effective management of their assets for better business outcomes and customer experience. Effective asset management and asset performance are key business differentiators and have a direct impact on operations, the ability to meet customer commitments, obtaining federal funding, and the bottom line. Maintaining a complex portfolio of spatially spread infrastructure assets, continuously on-the-move fleet, and civil and fixed structural assets, is a big challenge and is becoming increasingly complex due to aging infrastructure and fleet, increasing demand, and budget constraints.
Urban transport organizations lack integrated enterprise asset management (EAM) solutions to manage and maintain a complex portfolio of multi-disciplinary and interlinked assets. Siloed EAM solutions fail to provide comprehensive awareness of the state of assets through their life cycle. This limits the ability of stakeholders to collaborate and ensure regulatory compliance.
Organizations need to effectively leverage connected assets data to improve monitoring and management of these assets and adopt optimized maintenance strategies aimed at improving asset performance and reducing asset downtime and maintenance costs.
The TCS’ transport asset management solution establishes a unified EAM foundation using the IBM Maximo® asset management platform with industry-specific add-ons and pre-configurations.
The solution offers the following capabilities:
Unified solution to manage and monitor assets, schedules, resources, processes, and inventories along with financial and performance analytics capabilities.
Comprehensive data dictionary of industry-specific attributes, associated meta data, and domain values for data standardization.
Preconfigured asset classifications, specifications, meters, and characteristics for ease of identification, grouping, and reporting.
Network, location, asset hierarchy, and linear reference models for identifying linear and discreet assets within a transport network and for better organization of assets, search, reporting and analysis.
Tailored linear specifications and features, dynamic segmentation, and asset relationships and dynamic meters to cater to unique requirements of railway linear assets
Asset configuration templates and reference models for configuration of managed assets like trainset and train cars with deep functional and physical component hierarchies
Enables data-driven remote asset monitoring and analytics to build conditional and predictive maintenance capabilities using artificial intelligence.