Process modelling in the extractive industry
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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).
In this section
and has established a progressive roadmap for this type of innovation.
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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.
In concrete terms, an Artificial Intelligence (AI) project is based on 3 pillars:
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.
Take the initiative and start your innovation process with a feasibility study lasting a few days. The conclusions of this analysis will provide promising leads, to be validated in a calculation and modeling stage.
Good governance and control of the data is certainly an essential step, but an initiative to exploit your data, at a lower cost, will provide rapid feedback. It is better to be efficient and imperfect than to want to be perfect and never ready. This will be a valuable contribution to better understand the impact of innovation.
In this context, our step-by-step developments will enable to obtain first results and draw up an agile roadmap:
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