The Industrial Internet of Things (IIoT) is slowly transitioning from being a mere buzzword to a concrete reality. Innovation in this field is mainly led by large companies that have already started to implement IIoT ideas in various applications. Now they are able to observe the results of their investments in IIoT.
Some of the big players in the IT world believe that competition, resource constraints, and an aging work force are just a few of the key elements that are driving innovation toward IIoT. A company that decides to invest in this technology will observe a clear advantage over other competitors in a relatively short time. The availability of a new set of analytics, generated by continuous monitoring of real-time data, will enable that same company to gain a deeper insight into its production process, and also to reduce costs by reducing waste, downtime, and unnecessary maintenance.
According to a 2013 report by Aberdeen Group, 53% of U.S. manufacturers that implement IIoT have improved their business, increased their competitive edge, and reduced total costs. For 40% of U.S. manufacturers, the biggest risk factor is the failure of critical assets. Whereas the impact of certain components is shrinking, such as logistics and supplier quality, some other areas are becoming crucial for the success of a company and need more efficient analysis tools.
1. Acquire Smarter Talent and Maintenance
Product failure is still the top risk component, but far more interesting is the effect of a new factor that emerges from Aberdeen Group’s study. “Failure to acquire and retain talent is becoming a real challenge for every company that aims to be a market leader. In our opinion, this is fundamental. We are enabling our machines to be smarter, but in order to do that, companies are required to have a more technically skilled work force that understands new technologies and that keeps itself constantly up to date.”
Today’s production machines are complex manufacturing systems that are dynamic and coupled, and the environment in which they operate is highly dynamic and highly coupled. IIoT allows us to cope with this demand of highly dynamic and highly coupled systems, moving from monolithic, slow-paced systems, to fast, on-demand modular systems. One example to demonstrate this shift is the predictive maintenance (PdM) model. Until now, PdM has been implemented using mean time between failures, often called MTBF. This time-to-failure model is stochastic, generalist, and therefore not accurate. The time frame of reference in this case is monthly-based maintenance. Downtimes are planned based on a machine’s operating time and sometimes machine parts are replaced even if they were not broken and still fully functioning. With IIoT, the paradigm of maintenance shifts from predictive to reactive. Monitoring every machine using its own particular operating conditions means scheduling downtimes can happen days before a part is supposed to break. Manufacturers can react to data generated by the machine. In some cases, it is also possible to predict precisely how many operational cycles are left before a breakdown.
2. Understand the Importance of Big Data
IoT is a network of connected smart devices, able to communicate with each other, but also able to send information to a storage system, local or cloud-based, to analyze and refine data to gain better context-related knowledge. The key is in the data, and many believe that having more data is better than having better data-mining algorithms.
It is important to understand that, in general, IoT is a Big Data problem, and for this reason we need Big Data tools to analyze it. Big Data is essentially composed of three elements:
Volumes: Massive amounts of information are produced…
Velocity: … in a very fast manner and with unprecedented frequency.
Variety: Data is generated by multiple sources, containing heterogeneous information, and in the majority of cases, not structured.
In a scenario where human operators work with an IoT system, the operators are considered the only bottleneck. A 2013 ABI research report stated that by 2020, there will be 30 billion connected devices. In the same year a Morgan Stanley report stated that by 2020, there will be 75 billion devices. Intel believes that in the same year the number of devices will be 31 billion. The results coming out of market research and predictions might not coincide 100%, but the main point is there will be a massive increase of new IoT applications, a huge demand for bandwidth, connectivity to cloud solutions, and secure access to data analytics.
Considering an average of 50 billion connected devices by the year 2020, we can estimate that the necessary bandwidth to cover all of the IoT needs will be about 20 million TB per month, an enormous demand! Dealing with such large amounts of information can be challenging. The good news is there are actions to take to alleviate this burden.
The IoT mantra says that all data must be collected (really, all of it!) to be then analyzed in a different place to extrapolate a new level of knowledge that is more useful to the data owner. A possible downside to this is the creation of huge “data museums,” where raw data is just sitting in some kind of cloud storage, losing its value waiting to be used. A much better approach would be to collect all data and then generate some “structured data” by performing some simple processing right where they originated (i.e., sensors, gateways, or data collectors). This is referred to as “fog computing” or edge computing.
Looking at IIoT from an end-user perspective, we can observe how this technology has the potential to enhance production processes. IIoT is the convergence point between information technology (IT) and operating technology (OT). These are typically two very distinct departments, but IIoT forces them to interact and cooperate to understand each other’s needs. IIoT can contribute to making machines smart by adopting the newest connection protocols to allow machine-to-machine interaction, but most importantly, by giving more timely and detailed information to external operators. Sometimes, IIoT has pushed manufacturers to invest in retrofitting old equipment or even convinced them to buy new machines. For all of these reasons, IIoT aims to improve efficiency, reliability, and productivity of operations, with a noticeable reduction in cost and waste.