With the Dataiku Solution for Predictive Maintenance, quickly turn vast volumes of maintenance history into optimized maintenance plans. Reliability engineers and maintenance teams can move from time-based preventive approaches to data-adjusted ones.
Add your equipment, downtime events, and maintenance data, and access ready-made dashboards to understand mean time between failure, along with remaining useful life and weibull distributions across your fleet.
Leverage maintenance history from your CMMS, EAM, or other work management system through the Dataiku application. Ingest disparate data sources and create an optimized maintenance schedule based on your ideal unplanned maintenance ratio.
For each equipment, assess remaining useful life and ideal maintenance operation schedule thanks to an explainable and transparent machine learning (ML) model.
Evaluate the impact of various attributes (such as geographic location or asset age) on unplanned maintenance. Review a predictive model-enriched approach with existing maintenance data and failure history. Design ML-driven root cause analyses by asset class and scale effectively across all sites.
Based on your ideal unplanned maintenance ratio, generate an optimized maintenance plan. Understand recommendations and adjust to needs before enforcing through your regular tools.
The Dataiku Solution for Predictive Maintenance helps answer a broad range of questions like:
Use AI and ML to optimize your maintenance and service schedule, improving asset availability and controlling cost. Easily scale consistent approaches across production sites and floor shops, and deliver all required use cases to enhance all manufacturing operations.
A composite organization in the commissioned study conducted by Forrester Consulting on behalf of Dataiku saw the following benefits:
reduction in time spent on data analysis, extraction, and preparation.
reduction in time spent on model lifecycle activities (training, deployment, and monitoring).
return on investment
net present value over three years.