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Deploying an Industry 4.0 Solution In-House, Out-Sourced or Jointly

Deploying an Industry 4.0 Solution In-House, Out-Sourced or Jointly

The promise of the benefits that can be reaped from big data and predictive analytics through access to machine and operator data is compelling enough for most manufacturers to seriously direct their IT and OT departments to look into ways to enable such machine and operator data acquisition. Indeed, being able to optimize overall equipment effectiveness or OEE in terms of equipment availability, performance and quality of produced items by the equipment directly affects manufacturers’ bottom lines.

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The Future of the Paperless Factory

The Future of the Paperless Factory

Eliminating paper in manufacturing, also known as the Smart Factory or Industry 4.0, is just the first step of the digital revolution. There has been a significant transformation when we compare modern-day printed material to that of our ancestors’ early drawings of shapes, symbols, and pictures on cave walls. The next step of this advancement is more than just becoming ‘paperless.’ Transferring physical media into the digital domain, a crucial element of our digitalized Smart factory, provides a far more significant fundamental change. The future of ‘Paperless Manufacturing’ is much more than just eliminating paper from manufacturing.

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Cyber Physical System, Internet of Things and Digital Twin: Central Concepts for Industry 4.0

Cyber Physical System, Internet of Things and Digital Twin

The Cyber Physical System (CPS), Internet of Things (IoT) and Digital Twin are all central concepts in Industry 4.0, often used interchangeably in discussions about Industry 4.0 and smart manufacturing. Each refers to a representation of a piece of equipment in cyber space. Such representations are of central importance in Industry 4.0 and for smart manufacturing, since they provide access to real-time operational data of the represented equipment. Use of this data ranges from machine operational status and compiling important KPIs, like OEE, MTBF, MTBA, etc., to big data analytics and machine learning applications, such as predictive maintenance. It is therefore worthwhile to examine what each means and how they relate to each other.

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