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Making Industry 4.0 and Manufacturing Infrastructure Optimization Cost Effective

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Just like big data is transforming every market and industry, the manufacturing industry is equally being morphed in significant ways by taking advantage of the massive amounts of real-time and batch data being generated by Industry 3.0 factory automation processes. Where Industry 3.0 provides the tools for factory automation, Industry 4.0 provides the tools for big data and predictive analytics as well as the compilation of important KPIs.

Cyber Physical System (CPS), Internet of Things (IoT) and Digital Twin are important concepts in Industry 4.0, because they provide access to real-time operational data and the context around the data. Use of this data ranges from machine operational status and compiling important KPIs, like OEE, MTBF, MTBA, etc., to predictive analytics and machine learning applications, such as predictive maintenance.

CPS, IoT and Digital Twin are interchangeably used in discussions about Industry 4.0 and smart manufacturing. Each refers to a representation in cyber space of a physical machine in the real world. It is worthwhile to examine what each means and how they relate to each other.

A CPS is generally defined as a combination of physical (mechanical) components, transducers (sensors and actuators), and information technology (IT) systems (network/communication systems and computation/analysis/control systems). Some definitions include the human, such as the machine operator. In other words, a CPS is a physical world system (machine only or machine plus human) that is connected to the cyber world. A CPS can be either a closed-loop or open-loop system; meaning that it may sense the real-world parameters of the physical system and control it, or it may just sense the real-world parameters and make these available for analytical purposes.

IoT is generally defined as a combination of any of the following: trackable objects (such as RFID tags), data objects (such as sensors), interactive objects (such as actuators) and smart objects (such as software components that act on sensor data for any purpose, including pre-processing, control, analytics, etc.).

A Digital Twin is a digital replica of a physical asset. The definition of a Digital Twin emphasizes the connection between the physical and the digital replica, and the data that is generated using sensors. A Digital Twin integrates transducers, artificial intelligence/machine learning, data analytics and context awareness. An example of context awareness is an intelligent thermostat, which senses who is present, so that the person’s preferences for ambient conditions can be taken into consideration.

A common situation encountered in the manufacturing industry is one where manufacturers are dealing with “data islands.” In a data island there are either no sensors at all, or some sensors are missing, or all sensor outputs are fed into a PLC with no means of communicating the sensor data in real-time or batch mode to an external computer system for processing.

The dilemma that manufacturers face is that CAPEX and OPEX costs associated with upgrading or replacing their machines is seen more often than not as prohibitive, which puts critical OEE KPIs and the ability to predict with high accuracy when to schedule equipment maintenance out of their reach. 

GEM brings an augmentative approach that does not require replacement or upgrading of existing equipment, using a scalable Digital Twin framework to on-board any machine into its big data and predictive analytics cloud platform.

In this approach manufacturers upgrade to smart manufacturing with the Industry 4.0 benefits of Predictive Maintenance, MTBF, MTBA, OEE, Availability, Performance and Quality without the cost of overhauling their factory floors.

The result is that machines are no longer “data islands”, but are fully connected and represented by their digital twins, so that full advantage can be taken from machine and factory floor big data for the real-time compilation of KPIs and predictive analytics, such as predictive maintenance.

As an example, an electronic component manufacturer operated a wafer pick-and-place machine which was controlled by a PLC with no real-time access to data from the photo sensor monitoring the status of the wafer feeder tray and the vacuum sensor in the wafer pick-and-place subsystem of the machine. Therefore the manufacturer was not able to compile comprehensive OEE data for the machine and consequently lacked valuable insight in how to improve the machine’s OEE.

By installing an external GEM PRECARE agent to monitor the signal lines of the photo and vacuum sensors, as well as pilot lamp power lines, the GEM PRECARE cloud platform started compiling comprehensive OEE, Availability, Performance, Quality, MTBA and MTBF KPIs within 24 hours from installation.

Through augmentation manufacturers are able to compile critical KPIs in real-time and deploy predictive maintenance, further optimizing OEE, without the cost of Industry 4.0 ready equipment.

Upgrading to Industry 4.0 smart manufacturing is therefore more than ever closer at hand for manufacturers with a range of options, including extending the life of their existing equipment through augmentative solutions like GEM PRECARE.

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