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Foundation and problematics of turning asset-intensive industries to AI

Business asset integration rooted in the beginning 20th century. Necessity of complex problem-orienteered analysis became obvious during industrialization not only in Western countries, but also in pre-USSR Russia (during railways network developing), USSR’s, Japan (industrialization, started with Meiji Ishin).

But only a decade ago World’s management and technologies have achieved that minimal level, which can introduce us possibility of fully integrated asset, able to provide us with most actual and consistent data for deep analysis, planning and forecasting, fully controllable business enhancement evaluation.

Speaking about oil-n-gas majors and super-majors, almost all companies nowadays are pushing their programs of smart/digital/connected assets (like oilfields and plants).

Seems, earlier all work carried out spontaneously, but nowadays we can clearly see some obvious directions of integration:

  1. Integrate IT-complexes into one eco-system.
    It’s not a trivial, but still routine IT-task for CIOs.
  2. Docking of business processes.
    It’s quite often, when left hand doesn’t know what the right hand is doing. Sometimes logistics, production, mining and marketing departments are working separately, which is not very good for losses minimization and over all enterprise efficiency.
  3. Business-integration in most general understanding
    Integration is not only intra-enterprise trend but also more and more information conglomerates being formed to streamline collaboration between vendors, suppliers and in some cases customers.
  4. Finally, current’s decade trend, which concatenates all three previous into sophisticated frame – business-transformation with AI, supported by IIoT and proper methodology.

Nowadays World have hundreds of all ancient (but still in operation) software implemented, and nearly ~1mln heavy machines under “asset-intensive” usage. Most of them incompatible with Industry 4.0. Not less, or even more, of lighter machines (for example, trucks and machine tools) could be covered by AI (mostly PdM) solutions, once it will become ready to massive usage.

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