Manufacturing industry has been looking at automation as a means of boosting quality and productivity for many years, from the days of Henry Ford inventing the production line to the increasing use of robots and the IoT.
Today’s critical industries encompass both the infrastructure and manufacturing sectors. A new suite of low-cost energy-efficient devices, accessible through WiFi provides the ability to link with Cloud-based applications such as Big Data analytics.
Some commentators refer to the new era as Industrial IoT (“IIoT”) or Industry 4.0. Adding IIoT to the mix brings a whole host of new opportunities to continue the process.
On the downside, the relative newness of Industry 4.0 brings new risks as it is deployed. Increased state surveillance, increased criminal activity and supply chain risks follow on from incomplete, missing, or defective cybersecurity included in the new device’s firmware and software. It clearly needs a stable power and communications infrastructure to operate successfully.
Having accepted all that, and that we need to have robust backup systems to keep the IoT devices running, what are the areas that will benefit from the IIoT?
A Brief Overview of IIoT
IIoT uses much of the same technologies as the broader IoT. At the end of 2019, there were around 27Billion IoT devices, and over 30Billion are expected in 2022. The IIoT market is expected to have reached $200Billion in 2021.
IIoT includes the pre-existing Systems Control and Data Acquisition (“SCADA”) and Industrial Control Systems (“ICS”) systems that are in operation in industrial control and management environments and infrastructures.
IoT brings them together with the objective of enhancing efficiencies and optimising production in manufacturing and the wider delivery of products and services. There will also be benefits in safety improvements and cost reductions. Many ERP systems can now use data supplied by IoT devices to track and analyse the real-time production process, monitor the condition of manufacturing equipment and provide input to predictive analytics.
It has also provided new network infrastructures. In the early days, all IoT transactions were sent to core systems for analysis and response. This generated large volumes of network traffic that could reduce service levels in other applications.
After some thought and research, it was realised that many trans actin were ignored by the core processes. Moving the analysis functions to the edge of the network would significantly reduce the network traffic, saving cost and improving service levels. Cloud technologies gave the opportunity to do this, and so, the concept of “Fog Computing” was born, in essence, having many semi-independent network clouds at the network edge. Transactions were processed in the local cloud, and only the summary transactions needed for overall monitoring were passed back to the core systems.
A new breed of IIoT devices has recently come to the fore, autonomous transportation. Just like driverless cars, factories can now use driverless vehicles to transport work-in-progress and finished goods between production steps and finished goods warehouses. The difference between the prior automated transport systems and the latest driverless vehicles is that the new vehicles are not limited to pre-determined routes laid out as tramlines.
What is to come rather dep[ends on the continuing development of the hyper-connected Internet environment promised by recent advances in 5G, WiFi and fibre technologies. Some countries are rolling out smart cities with ubiquitous WiFi coverage and Fibre to the Home. As infrastructure developments continue to roll out, the ability for Industry to connect factories, suppliers and customers will improve.
Large amounts of data that need to be processed by advanced analytic software will travel on these new superhighways and will need to be met by significant processing and storage capacity. The growing adoption of Cloud Computing will enhance the process.
A new factor that has emerged over the last two years, following restrictions imposed by the pandemic is the increased use of remote working, both from a mobile perspective and from the new working from home paradigm.
This will change how industry operates, particularly in the service industries, and will build on the infrastructure improvements currently underway. As an example, 5G, despite its health risks and WiFi communications will allow seamless broadband communications from areas currently underserviced or not serviced at all.
Strictly speaking, Ai is not part of the IoT, though it will leverage the benefits flowing from the adoption of IIoT in the workplace. The significant amounts of raw data generated by IIoT can be processed by an Ai engine to increase understanding of the data and the information hidden in it. For example, it is already starting to be used in the mining and petrochemical industries to analyse survey results and indicate where minerals or oil could be found.
In general terms, AI, linked with IIoT can be used in machine learning to allow individual IIoT devices to improve, alert and on occasion decide how best to operate.
IIoT Is here to stay in industry, in both the manufacturing and service sectors. The benefits that accrue from being able to process, and with AI, analyse large amounts of raw data can mean the difference between a cost-effective and a redundant process.
To be sure, there are significant cybersecurity issues to be addressed and overcome, but experience shows that is a struggle between the black hats and white hats that will continue. This time the difference is that failure can have very serious consequences.
Overall though, industry is embracing IIoT.