Co-Author: Ram Agarwal
IIoT: Revolution or Evolution
Japan is a prime example of a nation having very few natural resources. It buys most of the raw materials it needs. However, the Japanese have developed one of the wealthiest economies in the world by transforming the raw materials they purchase and add value to them through manufacturing. It can be inferred that manufacturing practices are the major aspect of the country’s economic growth.
The philosophy of IIoT is to encourage work towards developing more sophisticated technologies to automate and computerize the manufacturing practices. This trend is also captured by the phrase “Industry 4.0” or “4th Industrial Revolution” or IIoT.
The write-up describes important aspects of IoT in manufacturing and the fourth revolution of manufacturing industry (Industry 4.0). It is about how IIoT could help to improve productivity, reduce operating costs, and enhance worker safety in accordance to meeting organizational goals.
Fourth Industrial Revolution aka Industry 4.0
The IIoT is part of a larger concept known as the Internet of Things (IoT) in manufacturing. To understand it better, we need to know what the other industrial revolutions were:
First industrial revolution: Introduction of steam. It encouraged the possibility of generating energy artificially using steam.
Second industrial revolution: Introduction of electricity. In this, the machines were decentralized and each had motors.
Third industrial revolution: Introduction of computers. Computer-controlled machines had a higher precision.
Fourth industrial revolution: This is about interconnecting machines. The computer-controlled machines communicate with each other wirelessly for greater flexibility and resource-friendly mass production.
Putting IIoT into perspective
An IoT technology strategy relies on consolidating data from different systems in the cloud and applying analytics with an objective to improve decision-making.
Predictive analytics solutions can, thus, transform manual and reactive manufacturing processes to automatic and proactive ones.
The approach should be baselined with-
- Connecting assets and devices on the shop floor
- Smart and secure consolidation of operational data.
- Predictive analytics and inventory tracking.
Getting Started with IIoT
Implementing an IIoT use case in the manufacturing industry must start with the below activities to attain a return on investment:
- Identify the key assets, processes, and possibilities in the manufacturing industry to record real-time equipment data.
- Create a ‘system of systems’ that accurately captures, analyzes, and transmits data. Market platforms like PTC Thingworx can also be used.
- Establish communication to load data on the platform transmitted from disparate systems to enhance predictive maintenance.
- Consider cloud-based applications to add value as APC (advanced process control), material tracking, predictive maintenance, and planning and scheduling.
- Perform a POC and measure the ROI in a given time. Identify other use cases or implement the solution at an enterprise level.
Key Use Cases of IIoT
- Smart Connected Factory Floor
IoT technology can transmit operational data to the first hardware producers and to handle engineers. This would empower plant administrators to remotely deal with the plant units and exploit process robotization and streamlining. Measuring OEE (Overall Equipment Effectiveness), throughput and other production KPI’s becomes easy.
- Predictive Maintenance
Persistently observing pre-characterized parameters of the hardware empowers the value that may show equipment malfunction. Predictive analytics helps in keeping a noteworthy failure down the line. Predictive analytics guarantees life span and smooth running of the machines, incorporating operational objectives.
- Inventory and Material Tracking
Easily locate and monitor key inventory (e.g., raw materials, final products, parts, and containers) to optimize logistics, maintain inventory levels, prevent quality issues, and detect theft.
- Single-Screen Operator View
Combine, analyze, and deliver bits of knowledge from dissimilar resources, administrators, and undertaking frameworks into consolidated ongoing visibility of KPIs for expanded operational execution and enhanced decision-making capabilities.