Implementation of a novel method for managing durability data: steps towards data-driven approaches in new product development

Abstract

As product development processes grow increasingly complex and globally distributed, the structured management of engineering data has become critical for ensuring accuracy, efficiency, and informed decision-making. This doctoral research, conducted in partnership with a multinational manufacturer of heavy commercial vehicles, addresses persistent challenges in the integration, reuse, and traceability of durability data — particularly in scenarios where virtual models must be representative, where comparative insights must be drawn from large datasets, and where test procedures must be aligned with real-world usage. Building upon a logical model for experimental durability data, this study expanded and operationalized a comprehensive method for managing both experimental and simulated datasets across geographically dispersed engineering teams. Guided by the Design Science Research Methodology (DSRM), the solution was designed, implemented, and rigorously evaluated within a real-world industrial environment. Key contributions include extending the original model to new data sources such as simulations and laboratory tests; defining a structured, scalable method for managing ingestion, validation, processing, and access; and implementing harmonized metadata and post-processing routines to support cross-context comparison and engineering analysis. The method also incorporates a framework for validating simulations against physical test data and verifying test protocols based on actual product usage. Evaluation workshops with engineering stakeholders confirmed the method’s ease of use, efficiency, and organizational fit, while also identifying opportunities for broader integration and future enhancements. Furthermore, the system enabled an applied study using Monte Carlo simulations to correlate proving ground durability protocols with real-world road segment data — demonstrating its capability to support advanced, data-driven engineering investigations. By delivering a robust, modular, and transferable approach to durability data management, this research advances both academic knowledge and industrial practice. It highlights how structured data governance, combined with tailored methodological rigor, can transform underutilized engineering data into a strategic asset that drives innovation and process improvement across the Product Development Process (PDP).