Quels sont les moyens les plus efficaces d’utiliser les données pour la planification de la maintenance ?

In order to have a proper coverage and Throughput focussed Maintenance, one needs an Ontology which uses the Plant Asset Hierarchy but adds additional classifiers in terms of task complexity, importance, urgency, spares availability. This Ontology should also have dynamic determinants to handle the Business Aspects of shutdown, be it product demand, inventory status, skills and talent availability etc. Such an Ontology will allow the exploitation of Opportunistic Maintenance and take up modular tasks. Adding to prescriptive and prescriptive Maintenance, dynamic opportunistic Maintenance will complete the arsenal of the Maintenance manager

The most effective ways to use data for maintenance scheduling include implementing predictive analytics (to forecast equipment failure), analyzing historical maintenance records (to identify patterns & optimize schedules), utilizing real-time monitoring (to detect issues proactively), and integrating data from various sources (to prioritize tasks based on criticality and resource availability). In ddition, reviewing and updating schedules (in regular basis) based on data insights, ensures efficient use of resources and minimizes downtime.

Descriptive Statistics: Summarize and describe the main features of a dataset using measures such as mean, median, standard deviation, and percentiles. Trend Analysis: Identify patterns or trends in data over time to understand underlying dynamics or changes. Anomaly Detection: Detect unusual or abnormal data points that deviate significantly from the norm, indicating potential issues or opportunities for further investigation. Root Cause Analysis: Identify the underlying causes of observed patterns or anomalies, helping to address underlying issues and improve decision-making. Regression Analysis: Model the relationship between one or more independent variables and a dependent variable to predict outcomes or understand the impact.

Create equipment health dashboards that display real-time sensor data, maintenance status, and performance KPIs. These visualizations give a quick overview of asset condition. Generate maintenance work order reports that list upcoming tasks, assigned resources, and completion metrics. These reports keep stakeholders informed on maintenance execution. Develop visual maintenance schedules that show planned vs. actual maintenance dates, color-coded by asset priority. These Gantt charts help spot resource conflicts and track compliance. Build interactive tools that allow users to drill down into failure trends, cost breakdowns, and scenario models. These visuals facilitate data-driven discussions and decisions.

Maintenance schedules are live documents. Many firms consider and treat it differently. Feedback from the field makes it so important to - Improvise the schedules - Better it on time of execution, - Optimize Resources utilisation, - Capture Special tools lists, - Fine-tune Method statements - Helps Skill balancing of workers - Enhance Supervisory requirements - Spare Parts and consumable management Make a pragmatic feedback loop for the organization including quality history capture. Train all workers in feedback and history requirements And finally keep schedules in a library for easy retrieval and future reference

It’s like a game called “fill the gaps”. First of all you have to be careful about your data sources. If you’re safe on that side it’s nice to do double check with reality. So if you increase some PM activity on a particular area you should follow also the results. According to that feedbacks you can maintain your tasks.

Ensure data quality through validation, cleansing, and governance processes. Reliable data is foundational for effective analysis and decisions. Foster cross-functional collaboration between maintenance, operations, and data science teams. Diverse perspectives enrich data interpretation and problem-solving. Continuously monitor model performance and data drift to refine predictive algorithms. Regularly update models with new failure modes and operating conditions. Balance data-driven insights with maintenance technician expertise. Augment human judgment with advanced analytics while leveraging hands-on equipment knowledge.