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Adopting IoT technologies for measuring systems allows for a more detailed and inexpensive process monitoring. This offers SMEs possibilities to monitor, analyse, automate and optimise their production environments without spending large budgets. Moreover, wireless communication protocols allow for a seamless integration into existing production lines without interfering with the currently operational process control & management systems.

Collect & integrate:

The Manufacturing Execution System (MES) with integrated IoT platform will collect all sensor data and automatically generate a real-time overview of the overall equipment effectiveness (OEE) of production lines, unplanned downtimes, stoppage events, product changeovers, idle times, etc. This will facilitate continuous monitoring and, as a result, improve the operational efficiency and increase asset utilisation while at the same time reducing the operational cost and increasing the return on assets. The automatic production monitoring framework will also relieve operators from time-consuming and tedious manual data collection tasks and reduce errors in data logging.


The application of big data and machine learning technologies to the enormous amount of monitored production data will generate insightful analyses and predictions on asset health conditions. This will enable the clear identification of hidden bottlenecks on a line, sensitive machine parameters, unnoticed wastes, relationships among dozens of machine parameters that could not be determined by conventional means, etc. Detailed analyses will help to focus the operational efforts on the relevant processes and parameters, to fine-tune the line at its peak operational state and to maximise the production yield. This information can help operators to monitor the machine status, report anomalies for predictive machine maintenance and warn for impending machine failures, thus avoiding huge economic losses.

Optimize & act:

The empirical power consumption data of machines will contribute to improving operations on at least three levels:

  1. Advanced energy models based on power data will provide plant managers with energy consumption analyses for an entire factory;
  2. Energy-efficient production scheduling, with integrated power data[m1] , allows for job sequencing and timing;
  3. Real-time power data enable to reduce energy consumption, improve cost efficiency and reduce failure probability (real-time power consumption behaviour can reveal the health status of machines), as well as the multi-objective optimisation of specific machine configurations and parameters.

Project goals

The overall goal of ELITE is to optimise production efficiency (for SMEs) by analysing large amounts of data that become available through IoT technologies.

Efficiency optimisation will be achieved by tackling two issues, which have been identified by the industrial partners as being problematic:

  • minimising unexpected downtimes due to machine failure by predictive analytics;
  • minimising the energy consumption cost through parameter optimisation and advanced scheduling algorithms.

The result of ELITE will be a generally applicable, end-to-end solution (software & hardware) consisting of sensors, a MES (Manufacturing Execution System), an IoT platform and software modules to increase production efficiency.

The tangible goals of ELITE have been specified by domain experts (INDEFF, Soubry, Huys) based on their empirical experience:

  • More (context) data (+30%)
  • Reduced unplanned downtime (-30%)
  • Reduced energy consumption (-5%)
  • Reduced engineering time for problem solving (-70%)
  • No interference with current production process control and management systems
  • Low-cost solution that is easy to integrate 


Georges Verpoorten - 

Project partners

1/10/2017 to 30/09/2019