Unlocking digital super powers: Being responsive to change is now par for the course within industry, but how can digital transformation be accelerated?

There is a massive opportunity for digitisation in industrial sectors such as oil and gas Tools to monitor, interpret and analyse data in real-time are key Predictability and transparency are the core requirements for regional industry Using machine learning to predict downtime is leading digital tr...

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Publicado en:MEED Business Review Vol. 4; no. 11; pp. 63 - 64
Autor principal: Awad, Mohamad
Formato: Artículo
Publicado: MEED Media FZ LLC Nov2019
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Unlocking digital super powers: Being responsive to change is now par for the course within industry, but how can digital transformation be accelerated?
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        au: Awad, Mohamad
        affil: Vice-president, Mena region, Aveva
      su:
        Industrial productivity
        Artificial intelligence
        Manufacturing processes
        Innovation adoption
      sug:
        subj:
          Industrial productivity
          Artificial intelligence
          Manufacturing processes
          Innovation adoption
      ab: There is a massive opportunity for digitisation in industrial sectors such as oil and gas Tools to monitor, interpret and analyse data in real-time are key Predictability and transparency are the core requirements for regional industry Using machine learning to predict downtime is leading digital transformation in the global downstream industry Q. Why is digital transformation so important for industrial customers? Based on first principle simulation, process optimisation modelling allows businesses to anticipate opportunities and be nimble enough to leverage opportunities for profit - whether through anticipatory procurement, predictive maintenance or evaluating process changes before they are deployed. Industry 4.0 has captured imaginations because of its possibilities, but technologies such as loT, artificial intelligence and machine learning also require domain expertise and case specific innovations to be optimal.
      pubtype: Trade Publication
      doctype: Article
      src: R
    language: English
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