What is the typical life of some of the high value assets that operate in railway network? What differentiates railway organizations that manage assets with adequate information from others who do not? How data driven decisions helps the entire organization and where should we start?

A railway organization’s capabilities may include locomotive & coach manufacturing, service operations, information systems, building railway network and infrastructure. To understand the size of operations, for example Indian railway manages over 12000 locomotives both electric and diesel, 3 lakh wagons around 80000 coaches across India’s 68000 km network.

For rolling stock (Locomotives, Coaches, Wagons, etc.) the typical design life could be around 20 years or more. Ability to manage such assets with the right amount of information is not only a key for efficient operations but also for safety and optimal customer experience. Designing systems that captures vital information at various stages of assets would be a key differentiator on how the asset performs in the subsequent stages.

We can explore a few stages of asset life say for example of a locomotive or train set and see how information can play this differentiating role.

Building a locomotive can involve various stages like defining system level specifications, design, developing purchase specifications (in case components are purchased from suppliers), manufacturing and assembling components, testing and certifying its readiness for operations. In all the stages there are attributes of components, assembly and the product that defines the configuration of how a locomotive was built. Such information can be managed in Product life cycle management systems which initiate a digital thread with locomotive configuration, critical attributes and Product manufacturing instructions during the design stages.

As engineers design components, assemblies that form the locomotive, many leverage advanced 3D modeling tools that can go through many iterations. The designed components can go through several iterations of simulations and validations. A product life cycle management system manages change, maintains product configuration and traceability through simulation and validations. At times when field issues are reported, the data collected during simulation and validations can be leveraged for fast-tracking root cause analysis.

As the locomotive enters manufacturing stage the information transforms to suit manufacturing, purchasing and for product validations. The bill of materials is aligned to support manufacturing stages and assembly. For the components that are sourced from suppliers, specifications and key instructions can be made digitally accessible. The suppliers may not only supply components but also share the 3D model if it is an off-the-shelf component. Based on the volume and criticality businesses can setup a B2B 2-way exchange of information with suppliers. Such information can help to integrate externally sourced items into the design environment and pave the way for better design reviews and earlier detection of manufacturing or assembly issues.

Once the locomotive is accepted by customer it is ready to be placed in service where it spends maximum part of its life. From an economics standpoint the more an asset is used with lesser quality issues, higher are the margins. The product life cycle management systems can be leveraged to access information on the serviceable components, compatibility, right technical service instructions to complete service operations and record the kind of failures observed if any.

Many engineering teams look to get feedback from operations on how the product is performing in the real-life environment to improve the product and validate assumptions made during design stage. Onboard sensors and computing platforms enable collection of vital sensor data that can be useful either for diagnosis of an issue or preventing a failure in advance. Investing on sensors, live monitoring of data, leveraging machine learning and Artificial Intelligence to predict a failure and effective emergency response can improve a locomotive availability and reliability. Service operations teams also depend on configuration, service history and failure information to target any particular service scope, component replacements and tracking warranty costs.

The amount and type of data that travels the digital thread can be chosen based on business value as there will be cost associated with management of these information and ensuring they are propagated to the right consumers with quality. For example we can adopt a model based approach where the producers and consumers of the product information leverage 3D model based content as the main source of information. The 3D content also can carry product and manufacturing information like dimensions, tolerance, material etc. Such information can be directly interfaced to support computer aided manufacturing (CAM) with Computer Numeric controlled (CNC) machines to generate programming instructions for machining a part. The same 3D content can be used to test components for physical dimensions and tolerances using coordinate measuring machines (CMM). Manufacturing and Service operations team can leverage the same 3D content to author illustrative work instructions for training and guiding their workforce through right steps.

It is indeed a challenge for any large railway organization to run such a distributed operation across wide geographies!! Also evolving business models like increasing public private partnership or third-party operations creates a compelling case for such digital exchange of information, connecting the thread that can help to transition to evolving business models without compromising the efficiency of the organization.