Service lifecycle management for heavy equipment

Service lifecycle analytics for heavy equipment is a collection of activities aimed at helping OEMs leverage their unique product knowledge to enrich the service and maintenance value chain as well as improve current and future equipment based on feedback from the field. With service lifecycle analytics, OEMs can provide more comprehensible serial number specific service documentation, optimize service planning, orchestrate service execution, anticipate service needs and build know-how on how to improve equipment for various aspects. Equipment lifecycle management solutions will help OEMs become a partner for their customers, rather than merely a vendor. One they trust to help them continuously maximize uptime, reduce maintenance costs and improve productivity throughout the entire lifetime of their investment.

Service lifecycle management is a group of service-related activities, enabled by a subset of product lifecycle management capabilities. Product lifecycle management is an approach that enables OEMs to track each individual piece of equipment throughout its entire useful life, cradle to grave. The foundation of this approach is to deploy a common data infrastructure between all equipment lifecycle stages, including design, engineering, manufacturing, and operation. All information regarding each individual piece of equipment is kept up to date and all decisions and changes are captured and documented. Product lifecycle management is a key enabler to many applications that require closing the loop between different areas, including continuous requirements verification and validation, smart equipment capabilities, heavy equipment smart manufacturing and service lifecycle analytics.

Service lifecycle analytics features data continuity between design, engineering, manufacturing and service - facilitated by an underlying product lifecycle management system. Information can flow both upstream and downstream in the process, which is essentially the definition of having a closed loop. A bi-directional information exchange allows for input from one stage to be passed onto the next one without discontinuities or error-prone data conversions involved. In addition, that information collected along the way can be fed back to earlier product lifecycle stages, even in real time, to support initiatives for improvement to either current or future equipment.

When heavy equipment design and engineering departments stay connected to equipment in the field through a comprehensive product lifecycle management system, they can collect data on parameters that are representative for performance loss or wear via sensors and compare those with their originally intended values. This can happen either continuously during operation, or at discrete points in time at the service location. By analyzing this information, design and engineering teams can detect and interpret anomalies, anticipate service needs and initiate corrective actions, either manually or automatically, as part of a predictive maintenance strategy. By doing so, they can ensure any piece of equipment in the field always performs as intended, and is compliant with requirements.

Siemens’ service lifecycle analytics solution helps OEMs drive efficiency in service execution by providing technicians with well-illustrated manuals, part catalogues, work instructions, training materials and service kits. These resources would be specific to each piece of equipment and up to date, reflecting the as-maintained configuration. This will increase first-time-fix rates and reduce turnaround times. In addition, when linking Siemens’ service lifecycle analytics solution to their corporate ERP system, OEMs can streamline their process to supply spare parts to the service location, and thereby improve efficiency while reducing overall inventory costs. This solution also enables OEMs to plant the seeds for adopting a predictive maintenance strategy. Reactive maintenance leads to expensive repairs and unexpected downtime for customers, while preventive maintenance leads to unnecessary spending. Predictive maintenance allows OEMs to tune service to the actual need, saving time and cost while keeping equipment performance optimized at all times. OEMs can also leverage feedback from service execution in design and engineering, improving the next generation of equipment for serviceability, and reducing service-related costs long term.