By merging the analytical capabilities of Business Intelligence (BI) with the practical functionalities of Computerized Maintenance Management Systems (CMMS), organizations can optimize operational efficiency, anticipate failures, and guarantee optimum utilization of production resources and teams. Let’s dive into the world of BI integrated with CMMS, where the power of data transforms maintenance into a performance lever.
The challenges of predictive maintenance
In the complex landscape of Industry 4.0, preventive maintenance is evolving rapidly with the arrival of advanced technologies.
However, it faces many challenges.
The first is the massive collection of data from IoT sensors and connected machines. Time-consuming, this collection often results in an overwhelming volume of information to be processed (failure rate, recurrence of failure, machine efficiency rate…).
Secondly, the lack of systems interoperability is a major obstacle. Companies’ heterogeneous infrastructures often make it difficult to integrate predictive maintenance technologies.
Finally, the need to guarantee the reliability of forecasts remains a crucial issue. They need to be constantly fine-tuned to adapt to changing operational conditions. This requires continuous attention to avoid false alarms and optimize asset performance. By successfully meeting these three challenges, companies can turn predictive maintenance into a competitive lever.
Synergies to anticipate
In an industrial 4.0 environment, the integration of BI and CMMS propels operational efficiency to new heights. This alliance offers increased visibility of asset performance, thanks to analyses performed on the data collected. As a result, companies can go beyond reactive maintenance, anticipating potential failures and optimizing maintenance schedules. This proactive approach reduces unplanned downtime, improving equipment availability.
In addition, BI promotes a global understanding of maintenance data. The dashboards and analyses generated feed into strategic planning, enabling organizations to forecast future investments.
Thanks to the union of BI and CMMS, companies can go beyond simple asset tracking. This combination contributes to a global digital transformation, where data-driven decision-making drives operational performance.
Decision-making and asset management
BI-CMMS integration provides a global view of maintenance operations, via a set of precise indicators. These make it easier to understand unplanned downtime and resource utilization. Better still, they help build improvement plans and track progress. Advanced analyses enable you to anticipate human and material resource requirements, while forecasting future costs.
What’s more, the increased transparency of maintenance data provided by BI supports decision-making. Managers can assess asset performance, identify trends and optimize processes, thereby boosting profitability.
In addition, over the long term, BI helps to analyze historical data in order to identify recurring breakdowns and anomalies. Ultimately, it is capable of sounding the alarm when maintenance operations are abnormal.
By interpreting and analyzing the indicators and tables provided by BI, employees can make informed decisions. Reaching this stage – of decision-making on the basis of reliable indicators – is therefore an important step in the deployment of Industrie 4.0.
In the future, companies will be able to go even further: they will be able to integrate AI (Artificial Intelligence) to automate the analysis of huge quantities of data from their production tools and IS (Information System), enabling them to fine-tune their maintenance operations and investments with even greater precision!
Expert interview – Guilhèm Ayral, Pre-sales Consultant at Siveco Group.
