Optimization of Operational Efficiency: how Qlik Cloud and Qlik Predict Transform OEE Analysis and Forecasting in Manufacturing Companies

Technology Reply has developed an innovative solution based on Qlik Cloud and Qlik Predict to improve machine efficiency and the entire productivity in the manufacturing sector.
Thanks to the Machine Learning features’ integration built into Qlik Cloud, the solution enables automatic forecasting of Overall Equipment Effectiveness (OEE), anticipating performance drops, optimizing planning, and supporting data-driven operational decisions.

Scenario

In the manufacturing sector, productivity and machine reliability are critical factors to ensure competitiveness and operational continuity. However, the increasing complexity of processes and the vast amount of data generated by industrial systems make it difficult to promptly detect anomalies or negative trends.

In this context, the ability to forecast OEE (an indicator that combines availability, performance, and quality) becomes a strategic element for reducing downtime, improving overall efficiency, and maximizing return on investment.

Soluzione

The solution developed by Technology Reply is structured in three main phases:

Machines’ data have been acquired and harmonized on Qlik Cloud Data Integration, ensuring consistency and traceability.

Through Qlik Predict (Qlik’s AutoML engine), multivariate regression models are trained for OEE forecasting. The AutoML engine automatically performs feature selection and hyperparameter tuning to maximize predictive accuracy.

Results are available directly in Qlik dashboards, where users can compare historical performance with future forecasts and quickly identify areas of inefficiency. Additionally, integration with Insight Advisor feature allows users to query the dashboard using natural language.

Advantages

The approach of Technology Reply integrates the capabilities of Qlik Cloud with Qlik Predict's AutoML algorithms, offering numerous tangible benefits:

  • Automation of predictive analysis;

  • Accurate predictions based on environmental, operational, and process variables;

  • Automatic identification of key factors that influence production performance;

  • Interactive and dynamic visualizations that integrate descriptive and predictive insights into a single platform;

  • Scalability and cloud-native governance, with centralized data and models management.

Conclusion

The solution developed by Technology Reply represents a concrete example of how AI and AutoML can transform business intelligence into a predictive and decision-making tool. With the introduction of Qlik Predict in Qlik Cloud, manufacturing companies can shift from a reactive to a predictive approach, improving process efficiency, reducing maintenance costs, and increasing productivity.

This is a decisive step towards a Smart Factory, where data becomes the engine of innovation.