Predictive Maintenance

Reduce unplanned downtime & optimize maintenance plans for your equipment.

The goal of this adapt and apply solution is to show how Dataiku can be used to reduce inefficiencies and equipment downtime in batch process manufacturing. More details on the specifics of the solution can be found in the knowledge base. This Solution is only available on installed instances.


Business Overview

Equipment reliability is crucial for manufacturers, ensuring responsible, safe, and consistent product production. Maintenance is key to mitigate unplanned downtime and ensure safe, continuous operation. However, finding the right time for maintenance is a challenge to balance operational and cost objectives. In many industries, maintenance is either reactive or driven by excessive time-based preventative routines, both of which are costly, erode asset performance and lower operational efficiency, costing billions each year. B

Time based preventative maintenance approaches and reactive fire fighting represent two default strategies that no longer need to be the norm. Using AI and ML, manufacturers can refine their maintenance tactics by leveraging service history and equipment attributes.  Techniques including survival analysis transform static time based maintenance schedules into tailored plans that reflect the true risk of mechanical failure by asset.

With Dataiku’s Predictive Maintenance Solution, organizations quickly turn vast volumes of maintenance history into optimized maintenance plans. Thanks to common performance metrics like MTBF, MTTR and task paretos, Reliability Engineers easily explore their fleet behaviors with descriptive analytics. ML algorithms provide remaining useful life based on maintenance history and a recommended maintenance schedule per asset, allowing Service Managers to adjust strategies. Whether for internal equipment maintenance or improving customer service, Dataiku’s Predictive Maintenance solution enables organizations to promptly revisit their manufacturing strategies.


  • Leverage maintenance history data from your CMMS, EAM or other work management system.
  • Visualize descriptive insights including mean time between failure, remaining useful life and weibull distributions across your fleet.
  • Create an optimized maintenance schedule based on your business goals by selecting 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 model.
  • Understand what’s driving unplanned maintenance by evaluating the impact of various attributes (such as geo location & asset age) on unplanned maintenance operations.