The 3 stages of IoT design to make factories smart

1) Connectivity: Industrial IoT depends on pervasive and fluid connectivity between devices, sensors and software for controlling operations. In the case of factory automation, the software is known as Manufacturing Execution Software (MES).

In the past, the division between fieldbuses, plant-level networks, control networks and application layers of global industrial operations was clearly defined and separated. This was beneficial for clearly defined job descriptions between plant managers and IT architects, but was not ideal for optimization and operational scalability.

Splitting protocols resulted in difficulties when a line of CNC machines had to be connected to a company’s global control network, as it would be complex, expensive and limited in functionality. While this is gradually changing, it is still important to understand end devices, protocols and physical interfaces to achieve seamless control from the plant to virtual workstations. In short, connectivity is the first and most critical principle of industrial IoT.

2) Conversion from data to information: Today’s industrial production plants produce a lot of data. For example, in automobile manufacturing plants, up to 8,000 devices can be connected to a single network. In consumer product manufacturing, this number can exceed 12,000.

In both circumstances, motion and position sensors increasingly connect with each other, crossing the PLC barrier. After these nodes are linked, the challenge is to make all that data work. For many years, enterprise data companies using data clusters tried to bring the power of big data from the digital B2C space to industrial automation, but with limited success. This was largely due to a lack of connectivity, the complexity of production facilities and the huge amount of data produced in a typical manufacturing enterprise. That said, recent improvements in industrial network bandwidth and the use of edge computers have allowed factories to scale more easily to meet the influx of data.

This is why due diligence is required to estimate bandwidth demand and select a network provider that can meet it. In addition, the data requirements of manufacturing operations must be considered as they continue to grow. Existing operators can deploy solutions that enable real-time, localized analytics bridging the communication gap between the factory LAN, the control LAN and corporate networks. Factory operators are beginning to realize that greater connectivity between field/plant level and the enterprise can deliver significant benefits.

For example, according to a study on Industry 4.0 by McKinsey & Company, a global mining operator translated localized data into optimization measures for chemical processes that increased yield by 3.7%, equal to approximately $20 million per year.

Due to the enormous amount of data that a network of meters can produce in a day, transfer and storage are often considered too difficult to facilitate. However, with the advancement and accessibility of embedded computers and industrial wireless networks, processing and storage of “flow data” allows operations to record years of production data and alert the central control room in real time if anomalies occur. These capabilities help extend product life by identifying potential system failures before they occur. Small investments in industrial connectivity infrastructure have also pushed many process automation companies to rethink fixed depreciation models, improving long-term value.

3) Cybersecurity threat: The IT level of the industrial IoT movement is what fundamentally differentiates the industrial IoT from the IoT. In the industrial Internet, the cyber layer acts as a central hub where data from resources and sensors in the field is stored.

It is at the cyber level that custom analyses are performed and where self-learning processes can be enabled over time. In simple terms, network data is distributed among devices within a local area network (LAN), spreading bandwidth, compute load and file-transfer security across edge devices. Bottlenecks are reduced, as are potential areas of vulnerability.

By its nature, the cyber layer overturns traditional models of cybersecurity and management by moving traffic from large corporate networks to a network of perimeter devices and work-group subnets. In this model, each device contributes to the security of the wider network, and the plant manager must design with redundancy, strategic firewall placement and emergency plans in the event of network failures or intrusions.

The following IoT enablement steps will provide plant and operations managers with a due diligence process when designing a smart factory solution, resulting in a connected and secure network.