Throughout the forecast period of 2018-2025, the Industrial IoT is expected to grow at the CAGR of approximately 7 percent as per MarketsandMarkets. Out of all the sectors in which the IIoT is being deployed, the manufacturing sector holds the largest share. Before discussing this technology, let us understand the basics.
In simple terms, Industrial IoT is the utilization of the Internet of Things (IoT) technology to enhance manufacturing and related processes by using sensors, actuators, and automation systems. The data generated through these components are used to make better business decisions and improve efficiency. Let’s now have a look at different aspects of this technology.
Different firms have conducted surveys at industrial levels to analyze what the companies feel about this technology. According to a survey by the German wing of PricewaterhouseCoopers, 91 percent of respondents are already investing in digital factories. Around 75 percent of respondents have made an investment in digital factories specifically to serve customers better.
In another survey by Accenture, 46% of respondents believe IoT will enhance employee productivity. Around 44 percent of respondents expect that this technology will help in cutting costs.
To understand the current scenario, let’s understand different trends revolving around Industrial IoT:
In simple terms, virtual replicas of physical devices that are utilized by IT experts and data scientists before developing actual devices are known as digital twins. By using data and graphics modeling, digital twin experts can create models of any equipment virtually.
Though Digital Twins technology is still in its infancy, it is expected to become a mainstream trend soon. In the manufacturing sector, the digital twin approach is reducing costs substantially and is also minimizing downtime due to equipment failure. Some renowned names that have started offering solutions related to this technology include Microsoft Azure, GE Digital, and Apache Kafka.
Before transferring the data to the cloud, various organizations prefer processing and storing it locally using a network of microdata IoT centers. This process is referred to as edge computing. Its adoption is crucial, in the present scenario, as the connected systems are prone to cyber-attacks. The cyber attackers won’t get a chance to tamper with critical data if it’s being processed locally.
To process the massive data, the manufacturing units spend a lot on data storage, bandwidth, and computer science. Edge computing significantly reduces the size of data, which not only reduces the overall cost but also ensures unnecessary data chunks get removed.
Collaborative robots or cobots are becoming increasingly popular in industries due to multiple reasons. Unlike their predecessors (the bulky robots), cobots are easier to handle and smaller. One example of a cobot is a small, robotic arm that detects and removes defective objects.
A sub-trend related to this industrial practice is renting out of these collaborative robots by hours. It complies with all design principles, namely interoperability, technical assistance, decentralized decisions, and information transparency. The presence of proximity sensors in cobots ensures they do not pose any threat to human workers deployed in the facility.
Using machine learning and Artificial Intelligence, IoT-enabled predictive maintenance helps manufacturers in predicting the right time to replace the equipment before an occurrence of failure. Various manufacturing units are implementing this technology to stave off unscheduled downtime and expensive machinery failure. Early corrective measures beget better product quality, which translates into improved customer satisfaction. For its successful implementation, each predictive maintenance system must have these prediction tools and techniques:
By deploying predictive maintenance service, Rolls Royce supported its customers to reduce flight delays that occurred due to engine maintenance issues. The company utilized the Azure-based IoT solutions for this purpose. Other biggies that have entered this domain include IBM, Siemens, Hitachi, and GE.
There is a long list of advantages of IIoT due to which it has become one of the most sought-after technologies in manufacturing. We have discussed the major advantages below:
Using IoT services, manufacturers are able to identify the cost of inefficient production equipment. This enables them to decide whether the equipment needs to be repaired or replaced. The use of sensors also helps in day-to-day operations. For example, companies identify the data to conduct meetings more efficiently.
This is done by minimizing the disrupting impact of meetings on workers. Through predictive maintenance, you can substantially reduce the downtime of machinery. All these factors are conducive to better productivity.
Top management can track the performance of their workers using data generated from machines and portable computer systems. The dashboard view of productivity in real-time helps in mapping the results with KPAs, thereby making it easier to achieve operational efficiency. The sensors present in warehouses track the inventory and automatically generate refilling and reordering requests.
The front-line managers also receive real-time data on assembly lines that help in analyzing the reasons behind the inefficiencies of equipment. Repair and replacement of parts to restore optimal performance is key to maximum efficiency.
Faster emergency response is one area where industrial IoT has a major role. In case of an accident, the alerts by safety systems ensure immediate dispatch of medical aid. If the danger due to the high level of gas is detected, workers get notified immediately. The possibility of risky machine failures is another dangerous situation. With the help of IIoT users are notified much before.
The role of IIoT becomes more prominent in mining industries. Various dangerous tasks in mines don’t require human involvement. The chances of accidents can also be detected, which prevents potential accidents.
The industries across the globe are already relying on big data to make informed decisions across different departments. The data generated from sensors present in the manufacturing units can further speed up the process. Scaling up of operations and expansion to new markets can also be achieved in minimum time.
One of the common challenges of logistics and supply chain are late deliveries due to traffic delays. Tracking of vehicles, along with maintenance alerts, help in overcoming such challenges. Another challenge tackled by industrial IoT is that of perishable items. Each year, around 33 percent of food gets perished during transportation.
This fact by the UN’s Food and Agricultural Organization hints at the need for technology to minimize the wastage. Connected sensing technology that enables the exchange of real-time data with the stakeholders is the right solution.
Numerous organizations are now creating marketing strategies by utilizing the insights derived from IoT-enabled devices. The usage of a particular product helps in determining customer preferences. The companies can use the information to manufacture additional products that complement the main flagship devices.
This amplifies the sales, and thereby the bottom line. Serving the right set of customers is also made possible through data generated by devices that are being used by the customers. Often, the manufacturers share the data with resellers and other involved parties to better serve the customers by offering discounts or bundled packages.
Various organizations are specifically designing the systems to help reduce downtime of equipment by up to 100 percent or more. Fanuc is one such example of a downtime system designer that has integrated cloud-based analytics into sensors present in its robotics system.
It is due to this system that the company received Supplier of the Year Innovation Award from GE. Another prominent example is that of Intel. The company has decreased downtime due to FFU (equipment for cleaning and filtering the air in industrial machines) failures by 300% when compared to manual inspection.
Consumption of resources in a manufacturing unit such as energy, water, and fuel is efficiently tracked through smart meters. It helps plant managers identify how much the resources were utilized. By effective management and planning, the usage of the resources could be optimized. Energy theft could also be reduced substantially through this approach.
The importance of such systems magnifies in manufacturing units with IIoT technology, as the power consumption increases.
By installing sensors in products and outer packaging, it gets easier to determine how the customers handle the products. The impact of road conditions, temperature, and related environmental factors on packaging can be also identified. Using this data, the manufacturers can design robust packaging to keep the product effectively shielded.
Simon AI is a platform by Kontact.io that uses sensor technology and real-time location systems (RTLS) to prevent unauthorized access in the manufacturing units. Another example is the DeWalt division of Stanley Black and Decker. This division has designed a connected battery service that shuts down tools in case someone tries to remove it from a defined region.
Such examples show that cases of thefts and illegal activities in production units could be minimized significantly.
Plenty of challenges related to industrial IoT prevent it from its widespread implementation across the globe. Some challenges include:
One of the biggest fears of companies planning to implement IIoT is security. The report by Gartner that by 2020, IoT will be related to 25% attacks in industries reinforces this fear. By taking control of an actuator, an attacker can deviate a robot from its lane. It might injure the operators and damage the assembly lines. A hacker can also take control of devices like a smart meter to attack other connected devices.
Energy Management Systems (EMS) are prone to such ransomware attacks. Through a type of attack known as phishing, the entire hardware could be damaged. The attackers use this way to damage critical equipment in electrical substations, wastewater treatment plants, or a factory floor. The issues mainly arise due to lack of awareness related to IoT security, the complexity of the system, and difficulties in monitoring and management.
The colossal amount of data generated by interconnected devices in production facilities raises the challenge of storage. The companies lack the resources to store such a large amount of data. Nor it is viable to rely on standard multi-layer architectures, SQL databases and other conventional IT paradigms for this purpose.
There is a huge possibility that data obtained through sensors goes unused. This is why the cloud appears to be a feasible option for several organizations. But, security threats surface, as the units store their data over the cloud. The time is taken for transferring also slows down the analytical processes. To counter such challenges, edge computing is being adopted.
For several firms, integration of operational technology (OT) and informational technology (IT) appears as a challenge. While doing so, the chances of data loss and attacks are high. Due to this issue, small and medium enterprises find it impossible to install IIoT to their operations.
There is also a lack of management system to scale a plethora of devices across the globe, based upon the applications. Again, cloud and edge computing are solutions to these challenges. Still, more economical and secure methods need to be engineered.
There are several ways through which AI and Machine Learning (ML) can enhance the outcomes of Industrial IoT. The prediction maintenance could be improved that results in minimized wastage of resources and lower labor costs. The blend of these technologies also results in a faster response to changing market trends.
By using AI during the design phase, the designers and engineers analyze all possible configurations of a design/solution. While doing so, they can input production methods, materials types, budget limitations and time constraints to get precise results.
Maintaining the optimal level of quality gets challenging for manufacturers with the products becoming more complicated. Through AI algorithms, quality assurance teams receive alerts on minor issues that result in quality drops. In fact, there is a term for the use of AI to resolve quality issues: Quality 4.0.
Artificial Intelligence is also applicable to the supply chain of manufacturing processes. By using demand patterns on the basis of macroeconomic behavior, socioeconomic factors, location, time, and other factors, an AI algorithm can develop estimations of market demand. This translates into optimized staffing, inventory control, raw materials, and energy consumption.
As per Statista, the number of connected devices in manufacturing industries would cross 75 million by 2025. Talking about technological advancements, the machines in the future would be able to tell if it’s pulling more current or if there is an increase in vibration. The convergence of AI, AR and IoT are expected to become mainstream.
While IoT will send data and AI will make decisions, AR (augmented reality) would be used to fix the issues. Let’s hope that each advancement is a quantum leap in terms of the industrial revolution.