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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).

Projects & References Technord has accumulated references in several fields

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

Artificial Intelligence for you ?

Being a full industrial integrator and therefore generating a lot of data for the customer, TECHNORD’s mission is to exploit this wealth of information to enable the customer to be more agile and get an extra percentage of yield improvement, or to solve a problem.

On the one hand, PI is fully aligned with our activities MES (Manufacturing Execution System) 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, we extract information that will help improve your operational excellence, we do modeling.

And we can integrate all this into your process, and connect this new tool for real-time prediction or decision support.

3 pillars for an IA project

What our your needs?

Data processing and modeling must be oriented to achieve an ambition or solve a problem. Potential topics must therefore be identified and prioritised.

Which data will be used?

The essence of artificial intelligence projects is the exploitation of data. A minimum of governance is needed to access it, to judge its quality and the necessary volume.

How to support your process improvement?

Last but not least: as with every innovation project, a decision-maker is needed to motivate the troops. The motivation and suggestions of the people involved will help develop the tool that will help them in the future!

This iterative process is effectively a simulation dialogue: data enables the simulation, the simulation stimulates ideas, then new data and ideas enable the simulation to be improved, etc.

Steps to a successful AI 4.0 project?

Start small, then expand your AI project

While good data governance and control are essential, starting with low-cost data exploitation can deliver swift results. Prioritizing efficiency over perfection will help to understand the innovation’s impact better.

We employ an incremental approach, which enables us to gather initial results and develop an agile roadmap for your business.

Take a few days to define your AI project in 3 steps

1. Conduct a feasibility study with guidance from our experts

Defining the project’s needs:

  • What is the subject?
  • Which individuals will be impacted by this project?

Defining the scope:

  • Who are the contact persons?
  • What types of data will be made available?
  • Should the provided data be supplemented?
  • Is data historization adequate?
  • What are the process-specific characteristics: its dynamics, constraints, mode of operation?
  • What type of modeling would be appropriate?

Anticipating and evaluating the project’s outcomes:

  • What return on investment (ROI) should be expected?
  • Which key performance indicators (KPIs) should be monitored?

2. Create an initial offline model

Then, we conduct a detailed analysis of historical data and engage with the client, resulting in the creation of an initial offline model. We maintain an ongoing discussion about the project’s results and its overall scope as it unfolds.

Reflect on the following questions with the model’s results in mind :

  • How can decision-making be improved?
  • What innovative work methods can you derive from the forecasts?
  • What can be automated?

3. Definie your AI Project scope with our expertise

Lets translate the results to your needs into equations! The next steps depend on the initial results and your final goal:

  • Continue to develop modeling in ‘offline’ mode, on more data or for an extended operating mode;
  • Switch to real-time control: Use historic data and the simulator to build a controller. It will analyze the measurements in real-time to recognize the operating situation and provide the appropriate prediction;
  • Suggest operating instructions to the operator to achieve the production target in terms of volume, quality, and emissions (open loop);
  • Automatically influence process control, without operator validation. It is therefore a closed-loop operation, synonymous with Digital Twin in a ‘4.0’ context, or more simply Advanced Process Control.

A solution tailored to your activity

Depending on your position in the company, you may encounter diverse challenges and restrictions.

Our experts offer solutions for Production, R&D, Supply Chain and Maintenance.

  • Production

  • R&D

  • Supply chain

  • Maintenance

Differentiation of TECHNORD

  • TECHNORD’s AI team has a strong scientific and mathematical background. This allows us to apply a wide range of tools and concepts that we can detail in a presentation:
  • We promote an unconditional transfer of the intellectual property of the projects to our clients. The tool is therefore fully owned by them.
  • TECHNORD is an experienced integrator, with teams in most of the process layers :
  • Our innovation strategy is well established with references that we will present to you.

Do you have a question about our expertise in Data Centre/AI ?

Our team gives you advice and assists you in the choice of the best solutions?

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    We are certified at the highest level by the world’s leading manufacturers, thanks to our expertise and our involvement in countless projects in various and demanding industries.

    See also Our expertise in Process Improvement

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