Today, the world is more connected than ever. Technology is linking not only people but also machines and devices on a global scale. In this constantly connected world, businesses are increasingly turning to machine-to-machine communications (M2M) and analytics, as clean data becomes a pre-requisite for companies to optimise productivity. According to a recent FICCI-EY report, M2M connections in India are likely to reach 429 million by 2021. Clearly, more businesses are turning to this technology than ever before-leveraging data to gain awareness into workflows and production processes.
Why M2M is the way forward for smart manufacturing
M2M focuses on smart manufacturing by developing intelligent and interconnected machines that are part of the big ‘Internet of Things’ (IoT) network. Such machines collect data in real-time and directly trigger workflows for other machines, systems, or users-sending messages and alerts. By making a switch to M2M, manufacturers can reduce costs, optimise and integrate processes, and get comprehensive insights into their manufacturing processes.
For example, consider a product with high demand, limited supply and several sub-processes that is going into the assembly line. In this case, it is possible that some machines will take up a greater load than others. To avoid this, manufacturers could use M2M technology to collect and transmit data about processes, thereby improving co-ordination between machines through faster identification of faults and permitting transparency around the status of various processes. Basically, M2M allows manufacturers to establish communication between manufacturing lines, identifying the machines that are taking a higher load and moving traffic to another line.
Imagine a situation where there are 50 turbines in a wind farm and the farm has a contract to generate a certain amount of energy. If one of the turbines is down, an M2M protocol suggests to the manufacturer how the remaining machines can be used to balance the generation of two megawatts of energy. This “connected product” approach is possible using wireless and wired systems that are communicating with each other.
In this way, M2M is already transforming manufacturing processes across industries. Let us look at how organisations can leverage this technology to magnify business value.
Ensuring data consistency and security
Data consistency is a big concern, and the main obstacle to achieving it is the connectivity from an edge (remote) location. The other major area is security, and in this case, several questions arise: Who is looking in while data is passing from one machine to other? What can they derive out of this data? What is the guarantee that no one has tampered with the data set between a manufacturer’s communications with the server?
For maintaining consistency, there are numerous checks. One is the amount of data being shared, and the other involves revalidation of data that has been sent. On the security front, there is a lot more specificity that is created to make sure there is less risk.
Combining human learning with machine learning
We are making inroads into the artificial intelligence (AI) space with neuroscience, reading human behavior as well as machine behavior. The emphasis on both human learning and machine learning is due to human subjectivity. It is defined as the gap between the machine’s output and the queries conveyed by humans.
For example, when a manufacturer has young technicians who need to be trained to replace retiring employees, there is a sudden dip in the technical skills required to complete the job. A combination of M2M and augmented reality and virtual reality (AR/VR) will enable the manufacturer to reduce this gap using both machine and human learning. On one hand, M2M will eliminate human intervention by replicating human experiences of ways of working when it defines automated workflows. On the other hand, AR and VR will help in training by creating a real-life machine structure experience that makes the trainees feel like they are working on actual machines, and this experience builds them into experts. The whole process, in turn, results in machine data getting augmented through human experience.
Approaching data strategically
Analytics are of two types in general, edge analytics and enterprise analytics. Edge deals only with the IoT and M2M output, whereas the enterprise deals with the edge and other business systems like enterprise resource planning (ERP), product lifecycle management software (PLM), Manufacturing Execution System (MES), customer relationship management (CRM) and supply management. Another important aspect is the contextual understanding of who will use the analytics and at what level analytics will be shown. The latter is how the manufacturer will drive user behavior to the next best action using analytics.
For instance, if a manufacturer knows the current performance of a turbine and if it is down for the next five hours, the system’s component gets integrated into the enterprise analytics through a system integrator. The system integrator not only gives the flavor of the edge analytics but also provides significant domain and deep learning experience for smooth integration of various components in other applications. Therefore, analytics plays a huge role at edge and enterprise levels to derive the next best action to facilitate a business.
Recognising that user and customer experience are the best business value proposition
Both IoT and M2M deliver significant improvements to user experience and customer experience. Therefore, adapting to these technologies should be made easy. On the value side, there is an improvement in on-time delivery (OTD) as well. Manufacturers can now take products to market faster with very little down time because the M2M capability allows for communication to be real-time—both forecasting failure and proactively suggesting countermeasures.
Together, I believe that these new capabilities will carry enterprises into the digital age, seamlessly transforming organisations and compounding business value.