A leading project by: BONDALTI (Portugal) – Subcategory: Digitalisation & Innovation
In today鈥檚 rapidly changing landscape, where technology plays a pivotal role, leveraging new tools is essential to maximise process effectiveness and efficiency, enabling industries to remain competitive. The rapid pace of technological advancement means that industries must continually adapt to stay ahead. These new tools and technologies are not merely optional; they are critical for enhancing productivity, optimising operations, reducing costs and consequently become more sustainable reducing the carbon footprint associated with energy consumption and other resource use.
For instance, advanced data analytics, artificial intelligence, and machine learning are transforming the way industries operate. These technologies allow the collection and analysis of vast amounts of data, providing insights that were previously unattainable. By harnessing these insights, industries can predict equipment failures before they occur, optimise maintenance schedules, and improve overall operational efficiency.
In the past, existing data analysis methods were time-consuming, requiring significant effort to analyse operational data after failures occurred.
Pilot Project and Expansion
In 2022, Bondalti began using a new tool that combines Big Data models and artificial intelligence to predict when an asset will show signs of failure. This prediction helps the company prepare for maintenance work, positively impacting areas such as cost reduction, minimising production losses, and addressing safety issues.
Using a digital platform model of their facilities, where thousands of variables are constantly analysed, the team obtains real-time diagnoses of equipment exhibiting anomalous behavior, generating alerts for efficient management. All this information, including historical data, is recorded in a dashboard for future analysis.
The project initially validated the software鈥檚 potential for future expansion to other units. Selected units were chosen based on their diverse process characteristics and challenges, aimed at assessing the effectiveness of the predictive analytics tool.
Based on the experience of the multidisciplinary team involved in the project, the methodology employed is comprehensive and applicable across various process types. It improves diagnostic capabilities, albeit requiring technical support.
The tool allows the detection of failures in equipment that was in anomalous conditions. Over the nine-month pilot project, 1350 variables were modelled and analyzed, resulting in 24 relevant events, with the majority requiring maintenance intervention.
Benefits and Future Prospects of Predictive Analytics
Due to these solid results, the project has gradually expanded to other process areas at Bondalti, with the goal of integrating all units into the predictive analytics system by the end of 2025.
This expansion reflects the company鈥檚 commitment to leveraging cutting-edge technology to enhance operational efficiency and reliability across all its processes. Implementing these advanced technologies requires a multidisciplinary approach, with close collaboration between engineers, data scientists, and industry experts. This collaborative effort ensures effective integration into existing processes, considering all aspects from data collection and analysis to practical application. Additionally, it ensures that the workforce is adequately trained to utilise the new tools and systems, fostering a culture of continuous improvement and innovation.
The implemented tool has facilitated enhanced planning for equipment interventions, sometimes even anticipating them. It has also helped identify operational errors. By leveraging predictive analytics, the company can now address potential issues before they escalate, leading to cost savings and smoother operations. Finally, with this tool, the team can free up time for other tasks with added value.
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