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Process Improvement / 4.0

Technord has developed a new activity which has many names: modelling, simulation, data science, artificial intelligence, machine learning, digital twin, big data, feature engineering, decision support, analytics, BI, etc. Let's call it 'Process Improvement' (PI).

Get better control of your production site

For more than 30 years, TECHNORD has been serving clients in various industrial sectors, providing them with multi-purpose expertise and a cross-functional vision of the potential of state-of-the-art technologies.

Being a full-scale industrial integrator and therefore generating lots of data for the customer, the next logical step was to leverage this wealth of information to enable the customer to be more agile and achieve an extra percentage of performance improvement, or to solve a problem.

On the one hand, PI is fully aligned with our  MES (Manufacturing Execution System) activities and IOT (Easysense platform and partnership with IBM).

On the other hand, PI meets the objectives of continuous improvement and operational excellence.

We analyse your data, extract the information that helps to improve your operational excellence, and then we do modelling.

And with all of this, we can integrate it into your process, and connect this new tool for a prediction or for live decision support.

The skills around the data are synthesised in our Smart4Industry showcase: IOT, Cloud, MOM/MES, as well as the PI activities, namely DS, AI and Maint (respectively Data Science, Artificial Intelligence, and Maintenance).


The Internet of Things (IOT) makes it easy to generate new measures that can be transported securely, thanks to dedicated networks, local data/monitoring/cloud storage databases (see Cloud4Industry). Among the available technologies, wireless sensors running on specific batteries offer a quick and low-cost implementation with a minimal configuration (click and go). See our Easysense offer


Cloud data storage offers a complete range of services, on the basis of a monthly subscription (a new mode of consumption in the industrial context) depending on the volume to be stored, the level of confidentiality/security, the type of data, the way in which it is processed and the applications required for this purpose, etc. See our Cloud offer


Manufacturing Operations Management (MoM) translates manufacturing orders from Enterprise Resource Planning (ERP) into real-time operations and provides a ” crisp “, real-time overview of the production flow (dashboarding) that greatly simplifies performance evaluation. See our MoM offer


In this “Data Science for Industry” offer, we typically operate a register of your data and provide an initial mathematical model. These offline developments are used to analyse the performance of processes, to understand how variables influence each other and can also be the foundation for future online developments to help the control of processes in real-time.

Among other things, conceptual modelling based mainly on physical balances can be used to improve the knowledge of the process (chemical or biological reactions, energy and mass transfers, etc).


The ‘AI for Industry’ offer enriches the ‘DS4Industry’ developments by putting them in ‘live’ mode, that is, by adapting them to a real-time situation for future insertion into the process.

The model analyses live measurements, identifies the situation and makes a prediction of the future behaviour of the process.

Software sensors can also be developed that estimate variables that cannot be physically measured in real- time.

Hybrid modelling combines the advantages of statistical learning and conceptual modelling, incorporates scientific knowledge (when it’s possible), and leverages historical and ‘live’ data.

This prediction of future behaviour can be compared to the KPI targets to suggest correction instructions (open loop) or apply them automatically (closed loop to ‘advanced process control’/digital twin).


In the same philosophy as AI4Industry, specific studies can be conducted on equipment and sensor data relative to the equipment, in order to develop conditional and predictive maintenance.

Conditional maintenance: alert thresholds are defined on the basis of historical data and alarms are triggered when they are exceeded to anticipate and mitigate potential failures.

Predictive maintenance: on the basis of the available measurements at time T, the modelling will make it possible to predict whether there is a risk of failure in the following hours/days. This anticipation enables a more comfortable management of planning and intelligent maintenance.

Projects & References Technord has accumulated references in several fields

and has established a progressive roadmap for this type of innovation.

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