Every day, we see more practical examples where manufacturers have successfully embraced digitization at the heart of their manufacturing and supply chain operations to create tangible business value. In part, this is due to the emergence of new technologies and the maturing of existing technologies applied in new and innovative ways. Manual and analog control processes are inefficient at handling the complexities of the modern supply chain, so instead, we rely on gross approximations. These are based on generalized and highly aggregated KPIs and operating metrics to understand the general performance and status of those manufacturing supply chains. Unfortunately, these metrics lack any real degree of operational acuity; therefore, we must accept a high degree of variability, inefficiency, and risk across those operations as there’s no other feasible alternative.
However, the digital information collected via IoT technologies endows us with two fundamental benefits: information reduces uncertainty, and information can be put to good work. When we apply experience and context to information, we gain knowledge, and when we apply that knowledge, we gain intelligence. If we can successfully leverage digital intelligence across the entire supply chain, in near real-time, with a high degree of acuity. We can optimize those supply chains across the dimensions of cost, value, and risk. Until now, we did not have the technology available to do that economically.
On one side, we have access to low-cost digital sensors and data acquisition devices to capture that information in real-time from across the manufacturing supply chain. On the other side, we have the digital infrastructure to store, analyze, and visualize that data in real-time and make effective decisions from that data. This is completed either manually by human interpretation and action, or automatically, through artificial intelligence (AI) and machine learning (ML), using highly scalable and cost-effective, cloud-computing and software-as-a-service solutions.
This ecosystem breaks down if we cannot get data from the source to the cloud, or to other nodes within the network, reliably and in near-real-time. This is where 5G applications will play a critical role in enabling the Industry 4.0 promise to become a reality. For communications, we are still heavily reliant on more traditional networks, such as wired LANs, WiFi, and 4G, all of which have significant limitations in speed, latency, capacity, reliability, and coverage. In highly disaggregated supply chains and networks of densely populated devices and sensors, those limitations can alone impede the emergence of this successful digital transformation.
In contrast, 5G brings much greater coverage with a far greater capacity for the number of nodes in a given network segment with much greater bandwidth with very low latency. While we have seen continuous improvements in WiFi standards (for example, the most recent 802.11ac, and another 802.11ax currently being released) that greatly improve network capabilities, we have not seen the same progress with 4G. So, 5G was inevitable, or it would have been the Achilles’ heel to Industry 4.0 becoming a practical reality.
What Are the Use Cases?
Within manufacturing, there are numerous use cases. The first is where the utilization of real-time, highly granular location data from across the supply chain can be used to greatly increase efficiency and agility and reduce operational risks. Widespread adoption of 5G will provide manufacturers with the ability to monitor and track the location and velocity of materials and components throughout the entire supply chain. As a result, this technology will enable them to leverage that knowledge to improve each area of their supply chain.
Another is the greatly increased use of sensor data to capture data about a specific characteristic. That may be the temperature and humidity in an area of a production facility or vehicle, the speed of a filler head in a packaging operation, the chemical composition from a laboratory device, and so forth. This intelligence can be used to make better real-time decisions, and even predictions about performance and trends, and it enables manufacturers to continuously monitor and fine-tune processes and to respond to events before they have a material impact on their operations (such as with predictive maintenance or predictive quality).
Finally, an increasing part of the Industry 4.0 and IoT paradigm is the ability for machines to act intelligently with what is termed edge computing and to communicate directly with other machines to bring about a particular outcome—the so-called machine-to-machine (M2M) approach. Rather than the typical client/server approach, which is still foundational in cloud-computing models, this creates an interconnected web of machines, sensors, and devices that work in collaboration to optimize complex processes in real-time. 5G becomes essential to making that model work, as do the emerging new WiFi standards. Ultimately, there will likely be a valid use case when improvements in performance, efficiency, and agility can be gained by leveraging real-time access and analysis (either manually or automatically) of data to minimize cost, maximize value, and mitigate risk.
How 5G Applications Will Affect Technologies
5g applications will have a more profound impact on the success of AI and ML, as those algorithms are predicated on large amounts of data. Not just greater volumes from a single source but the breadth of data from sources throughout a causal model. In typical manufacturing operations, there are literally thousands of characteristics that impact on a process or product feature. Using intelligent algorithms to understand the correlations and to predict a future outcome relies on a high degree of data acuity from many sources fed into those algorithms in a continuous stream.
Take weather prediction as an example. To accurately predict the weather, we don’t just try to predict from a single data point, such as wind speed. We use continuous monitoring to analyse the causal relationship from many data points through a very large space. This creates the need for a lot of data from multiple sources transmitted at a very high speed with very low latency. 5G will enable data to be captured from a greater number of sources and at much higher data rates, with very low latency, which will be vital to making these intelligent algorithms more successful at fulfilling their purpose.
Written by Jason Chester, Director of Global Channel Programs at InfinityQS