Modern buyer’s mindset contributes to significant waste due to a lack of clarity around product repairs and spare part replacements. Even when information is available, service manuals are often overly technical, making the repair process complex, time-consuming, and inefficient. As a result, both machine manufacturers and operators struggle with extended downtime, unnecessary part replacements, and rising costs.
To tackle this challenge, we need to bridge the gap between manufacturers and operators by enabling holistic product lifecycle visibility and holistic product data management. A knowledge graph-powered Engineering Intelligence Network provides a centralized, data-driven approach, allowing all stakeholders to seamlessly access accurate repair information, intuitive spare parts management, and real-time documentation. By structuring and linking product data, manufacturers can provide operators with on-demand insights, making repairs more efficient and reducing unnecessary waste in the Aftermarket Engineering Intelligence ecosystem.
Want to see how this works in action? Watch the demo recorded during the Google Cloud Learning Expedition at SPREAD HQ, where Frederik Günther showcases how machine manufacturers and operators can reduce downtime by leveraging the Engineering Intelligence Network for smarter spare parts management, quick access to repair documentation, and a streamlined online ordering process.