A methodology to boost data-driven decision-making process for a modern maintenance practice

Maintenance is evolving due to the double-sided influence of Asset Management paradigm and digitalization. In this evolution, assessing maintenance management process status in terms completeness, information data completeness integration paramount boost reliable data-driven decision-making. Grounding on Design Science Research, a methodology realized favour comparison two models, reference one company-specific one, used as means evaluate status. particular, embeds model for process. Both are artefacts tested refined during action research an automotive company willing improve The application both demonstrates that facilitated planning improvement actions various time horizons foster modern practice whose decision-making more data-driven.