Industrial Internet of Things (IIoT) solving business needs

The Industrial Internet of Things (IIoT) combines digital technologies such as cloud computing, IoT and machine learning (ML) and to drive value for the business. Businesses today need to stay competitive in the global market by reducing cost of operations and cost of servicing customer.

Ubiquitous connectivity, cheap data rate, reduction in sensor price, reduced cloud commute and data storage cost, availability of massive data from operational technology, device sensors and enterprise systems, etc., are some of the key catalysts that are making IIoT a reality.

Geo political instability, trade wars, fluctuating price of commodity, etc., further adds to challenges. Customers are demanding greater experience. They are no longer loyal to one brand. Global players entering the market and startups offering better services at the cheap rates, are further beefing up the competition.

The need of the hour for businesses is:
* How do they increase the topline by identifying customer needs and offering personalized products and services with faster turnaround and consistent quality?

* How do they innovate with business model to generate new revenue stream?

* How do they adopt sustainable business practices to contribute to the environment health and safety objectives, and increase brand value?

* How do they embrace technology advancements and infuse them in the processes to achieve all of the above?

IIoT for business
Amar Jadhav, Senior Solution Architect, SAP India Pvt Ltd, said that IIoT allows acquiring sensor data from connected devices to generate new insights, and provide action to drive value. A manufacturer can use the data generated from shopfloor to monitor the equipment health and performance, product quality and cost of operation in real time.

Amar Jadhav, SAPDQI Bureau | DATAQUEST

Predictive analytics applied on these data can help pre-empt the equipment condition, and reduce the unplanned downtime and production loss. It can also help produce consistent quality of product by providing early indicators of product defect to reduce rejections, rework and warranty claims.

With better visibility into the plant availability and control on product quality, manufacturers can achieve better on-time and in-full delivery performance, and achieve higher revenue. With IIoT, manufacturers can monitor energy consumption in real-time and find opportunities for savings, as the energy cost contributes to significant portion of manufacturing cost.

With the advancement in sensor technologies, it is possible to make the product smart. With smart products, manufacturers can gain real-time insights of their products and usage pattern. Data generated in the field can be used to improve the product design and performance.

It can also help reduce the churn rate of customers by offering superior customer service around smart products. With smart products, businesses can innovate with their model by offering product as service or creating new offerings and monetizing them.

Vijay Sethi, CIO, and Head HR & Head, CSR, Hero Motocorp., said that as technology has evolved over last few years, one can see Internet of Things (IoT) evolving in a big way because of the convergence of technologies, including sensors, cloud, analytics, machine learning, AR/VR and others. Organizations have started getting benefits of IoT, including enhancing the customer delight.

However, perhaps the biggest evolution from IoT perspective that is happening today is the Industrial internet of things (IIoT). In IIoT, the usage of IoT is going beyond the consumer, and connecting machines and devices, not just to enhance productivity, improve efficiencies, but also improving the health or safety. Many of the applications are in the areas that may be high risk.

IIoT and 5G
One key development to watch out for is how the advent 5G technology will benefit IIoT and supporting technologies. According to Amar Jadhav, IIoT is changing the way businesses are run with technology advancement in sensors, Big Data, cloud communications and other communication technologies. 5G is a next generation of communications. It will have widespread impact on multiple areas, ranging from manufacturing, education, healthcare, citizen services and gaming.

Some of the most important performance currencies that 5G brings to the table are: higher reliability, higher throughput, lower latency and increased energy efficiency. In technology terms, 5G is poised to bring applications in the areas of enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable and low-latency communications (uRLLC).

The other application 5G is going to enable is edge cloud computing from within the communications network. This multi-edge cloud can bring lower round-trip latency and higher overall availability. All of these characteristics of 5G are going to bring massive value to manufacturers in the area of equipment availability, product quality, and safe and sustainable manufacturing operations.

Traditionally, industrial operational technology networks are completely wired because of zero tolerance, when it comes to network reliability and latency. There is no suitable wireless alternative. The field sensor devices are connected to the industrial gateway, distributed control system (DCS) and supervisory control and data acquisition (SCADA)systems through wired networks using the Ethernet.

With 5G mMTC, it is possible to connect industrial machines wirelessly, without compromising on data latency, network reliability and data volume. Thus, manufacturers can optimize the CAPEX required to set up the automation network at the shopfloor to start their IoT journey.

According to Vijay Sethi, 5G technology promises to reduce costs relating to the cabling infrastructure and also ensure flexibility on shop floor by supporting things like autonomous carts, automated guided vehicles (AGVs), supporting connectivity to remote sites, allowing connectivity of dispersed factories across different locations, thus increasing the flexibility in industrial applications.


IIoT and cloud computing
Cloud computing provides massive scale storage capabilities and the elasticity to manage the huge workload generated from IT (information technology) and OT (operational technology) data optimally. With the economics of scale that some of the cloud service providers have achieved, it is also economical now to subscribe to cloud services, rather than set up your own data center, to process massive amounts of data.

Amar Jadhav felt that the benefits that IIoT brings, helps generate greater RoI for businesses. Cloud services like IoT connectivity, device management, Big Data storage, machine learning algorithms, data visualization capabilities, cloud-to-cloud interoperability, cloud-to-enterprise application integration capabilities, cloud identity services for secure data communication, and user authorization, etc., are all making cloud the de facto platform for organizations to start their IIoT journey.

Hero’s Vijay Sethi said that IIoT is today used in areas like predictive maintenance, where real-time data generated from the IIoT systems can be used to predict defects in machinery. It can ensure that action can be taken before the machine breaks down, to reduce down time, asset tracking, supply chain, monitoring conditions within a factory, monitoring machine performance like vibrations, temperature and other factors.

Hero Motocorp.DQI Bureau | DATAQUEST

This is to ensure that the performance of the machine, and hence, the quality of products, is optimal. Other areas include, remote inspection and diagnostics, tracking products and machine inventory, using AR/VR for maintenance and training, management of production facilities, the use of robotics on the shopfloor, tracking goods post-production, etc.

IIoT and smart manufacturing
Smart manufacturing consists of production machines equipped with sensors connected to DCS, SCADA and data historian systems. These are providing access to huge amount of shopfloor data, as well as the usage of other technologies such as autonomous robots, material-handling equipment, etc. These technologies are bringing a lot of process automation and efficiencies to the manufacturing operations.

Amar Jadhav said that IIoT, which can leverage these data, process it by applying machine learning and advanced analytics, can bring great to value to business. Here are some benefits around production, maintenance and quality.

* Plant capacity and machine availability are known at any point of time to schedule production operations.
* Optimized production scheduling decisions by scheduling the operations to most efficient equipments.
* Increase on time and in full performance by accurately committing the delivery date based on plant availability.
* Reduce cost of production by optimizing energy consumption.

* Optimized maintenance schedule (move from time-based to usage-based and condition-based maintenance).
* Reduced equipment unplanned downtime and production loss.
* Reduced maintenance cost by avoiding over maintenance.
* Optimized spares planning based on machine health condition.
* Increase health and safety performance.


* Consistently achieve golden batch.
* Reduce rework or scrap.
* Optimize cost of quality.
* Faster root cause analysis for quality defects.

5G-TSN and IIoT
Another area of importance is how does 5G-TSN (time-sensitive networking) integration meets the networking requirements for industrial automation. SAP’s Amar Jadhav said theindustrial automation network requires low latency and flexible communications, which are provided by TSN and 5G, respectively.

Beyond that, 5G and TSN can be integrated to provide solutions to the demands of ubiquitous and seamless connectivity, with the deterministic QoS required by control applications end to end. Ultimately, integrating these key technologies provides what is needed for smart factories.

5G supports two requirement categories: massive machine-type communication for a large number of connected devices/sensors, and ultra-reliable low-latency communication (URLLC) for connected control systems and critical communication. The capabilities of URLLC make 5G a suitable candidate for wireless deterministic and time-sensitive communication. This is essential for industrial automation, as it can enable the creation of real-time interactive systems, and for the integration with TSN.

TSN provides capabilities / features around traffic shaping, resource management, time synchronization and reliability. 5G URLLC capabilities provide a good match to TSN features. The two key technologies can be combined and integrated to provide deterministic connectivity end to end, such as that between the input/output (I/O) devices and their controller, which is potentially residing in an edge cloud, for industrial automation.

Integration is needed between the 5G and TSN to provide the end-to-end Ethernet connectivity to meet the smart factory requirements. Integrated time synchronization via wireless 5G and wired TSN domains provides a common reference time for the industrial end points.

According to Vijay Sethi, the IIoT is a critical piece in an organization’s Industry 4.0 journey. Here, IIoT connectivity plays an important role. For getting value from IIoT, one needs to ensure reliable, fast and secure data transfer in a timely manner. This is where 5G and TSN can play a big role.

TSN for industrial automation
That leads to the role TSN has in industrial automation.TSN is a set of standards under development by the Time-Sensitive Networking Task Group of the IEEE 802.1 working group. The standards define mechanisms for the time-sensitive transmission of data over deterministic Ethernet networks. TSN covers three essential features:

Time synchronization: All devices that are participating in real-time communication need to have a common understanding of time.

Scheduling and traffic shaping: All devices that are participating in real-time communication adhere to the same rules in processing and forwarding communication packets.

Selection: Selection of communication paths, path reservations and fault-tolerance.

According to Amar Jadhav, most of the industrial operations are performed with the combination of equipments, which are time synchronized. Even a split-second delay in the operation of any one of the equipment, can cause quality issue, machine malfunction or production line stoppage. Various control and protection mechanisms are deployed in a highly automated environment to avoid these conditions.

Similarly, if we are talking about collecting lot of data from shopfloor from different machines and sensors, the data has to be rationalized with common time dimension. This helps finding the accurate correlation between the data and find patterns from it.

All devices that are participating in real-time communication adhere to the same rules in selecting communication paths and in reserving bandwidth and time slots, possibly utilizing more than one simultaneous path to achieve fault-tolerance. All of these features are essential for the reason mentioned above.

Vijay Sethi felt that TSN, which defines a set of IEEE standards primarily developed for Ethernet, could be a big enabler, along with 5G. The main goal of TSN is to provide deterministic services over the Ethernet wired networks, thus having guaranteed packet transport with low and bounded latency, low packet delay variation, and low packet losses.

Together, 5G and TSN would give a major boost to IIoT globally, thereby, significantly enhancing the value that can be derived from an organization’s IIoT investments.

Let us also look at the key differences between SCADA (supervisory control and data access) and IIoT, andhow those can be overcome.If you look at the industrial network topology, at the very first level, there are equipment and field sensors.

These field sensors are connected to the programmable logic controller (PLC). Multiple equipment PLCs are connected to DCS. DCS is, in turn, connected to SCADA. In fact, SCADA provides the control layer over all the shopfloor equipment, which are connected.

It provides visualization of the current state of the operating condition of the various equipment installed on the shopfloor that are working in tandem for executing the production operation. Connection from the field sensors to SCADA is mostly wired connection through Ethernet protocol over LAN.

Data is only available to control room operators for analysis and decision making. As SCADA captures lots of time series data, it sends the data to the data historian system, which provides economical storage of time series data with good analytical capabilities.

Amar Jadhav said: “When we talk about IIoT, we are talking about machines connected to the Internet sending huge amount of data to cloud and leveraging cloud elasticity, data storage capabilities and compute power to process data and generate new insights. Data and insights can be provided to all the responsible stakeholders within the organization in a control environment for enabling them with data-driven decision-making capabilities to improve the operations.

“SCADA and IIoT can complement each other to generate value for the business. As SCADA is already taking care of data acquisition part, the organization can use IoT connectivity to send data to cloud and generate insights to achieve the desired outcome. The edge services sitting on the SCADA server, can help process data at the edge, and apply ML capabilities at the edge to generate predictive insights. This is critical when the latency in data processing is not acceptable. Eg., monitoring in process quality.”

Li-Fi to enable IIoT for oil and gas
Light fidelity (Li-Fi) technology is a ground-breaking light-based communication technology, which makes use of light waves instead of radio wave technology to deliver data. Li-Fi mainly deals with the transmission of alphanumeric data using visible light communication.

Li-Fi communication uses light frequencies, rather than the usual radio waves, which can produce data rates faster than 10 Megabits per second, which is very much efficient than our average broadband connection of Wi-Fi.

As per a recent post published by IEEE, Li-Fi is gaining ground through the use cases that demonstrate its viability as a global wireless solution, with an initial applicability in electromagnetic interference-challenged environments, such as hospitals, petrochemical plants, and airplanes, but also in secure environments where RF is not sanctioned.

Amar Jadhav said that most of the oil and gas facilities are restricted for the use RF waves. The machines operating in the license zones are connected to the automation system using Ethernet protocol. Li-FI can play a big role in the areas of workers’ safety, tools and tackles management, critical spares track and trace management, material handling equipment monitoring, etc., which are generally outside the purview of industrial automation.

A forklift used for loading the maintenance spares and assembly in to the truck to send it to the maintenance site can be installed with the Li-FI-enabled sensors. It can continuously provide the location information of the forklift, as well as help monitor the utilization of the forklift. LED sensors can help detect collision between two forklifts, thus reducing the safety incidents.

Similarly, workers in remote sites can be provided with wearables having Li-Fi-enabled sensors. Li-FI can also help detect the presence of dangerous gas or containment in the environment where workers are working. With this information, a safety manager can track the location and health of each worker and ensure timely assistance, whenever it is required, and insure worker safety.

IIoT replacing MES- and related apps?
Next, we will also look at whether the IIoT platforms are beginning to replace MES- and related applications.

According to Amar Jadhav, the MES (Manufacturing Execution System) sits between the plant automation system and the enterprise system. It bridges the gap between the topfloor to the shopfloor. MES system helps you to orchestrate the machines on the shopfloor to achieve the desired production output. It provides the ability to automate the data capture and post the transaction in the ERP system.

For eg., the MES system can post raw material inventory data and machine process parameter data in the ERP for production order confirmation. Apart from this, the MES enables to you capture the data related to speed loss, rework, unplanned downtime, etc. This helps organizations to calculate the overall equipment effectiveness of the equipment, which is a measure of production, quality and maintenance performance. Hence, the IIoTmay not be able to replace MES completely.

MES, with its niche capabilities, is still a very critical part of operations. The pre-built MES packages available in the market avoid building something from the scratch, and enables faster deployment and quick time to value. The IIoT platform can also be used to build the MES solution wherever the complexity is not very high. IIoT can enable a lot of advanced analytical use cases like predictive maintenance, predictive quality, energy management, etc., which are over and above the traditional MES capabilities.

IIoT market size
According to Grandview Research Inc., the IIoT market was valued at USD 357.77 billion in 2018. The global IIoT market size is expected to reach USD 949.42 billion by 2025, according to a new report by Grandview Research Inc. It is projected to expand at a CAGR of 29.4% during the forecast period. As per Mordor Intelligence, the global IIoTmarket is expected to reach USD 921.09 billion by 2024, at a CAGR of 17.62% during the forecast period (2019 to 2024).

The Asia-Pacific region is expected to account for the largest share in the overall IIoT market. The huge market in this region is mainly due to the adoption of IIoT across various industries, like manufacturing, healthcare, etc.

Recent reports by ASSOCHAM and EY reveal that the Indian IIoT market has the potential to unlock USD $11.1 billion in revenues by 2022, by reaching 2 billion connections, making it a key economic contributor that will help propel India towards a USD $1 trillion economy.

Industries adopting IIoT
Lastly, we will take a look at the industries and verticals adopting IIoT, and the current and future use cases for IIoT.

As per Amar Jadhav,industries ranging from discrete industries (automobile, industrial machines and component manufacturing) energy and natural resources (utilities, metal and mining, oil & gas), travel and transportation (3PL service providers, freight service providers), engineering construction and operation (EPC service providers, infrastructure operators) to consumer product companies (FMCG and retail) are some of the early adaptors of IIoT.

Here are some industry-wise current use cases of IIoT:

Automobile engine manufacturers test every engine that they have assembled, first using cold test bed, and then using hot and load test bed. These tests contribute to approximately 1% of the overall engine cost. Moreover, hot and load test significantly increases the cycle time and introduces the bottleneck in the production operation.

With IoT and ML, it is now possible to analyze huge amount of data generated from test bed equipment and engine test cycle across historical tests to predict whether the engine is be required to be sent to hot test or load test, reduce the cost of engine testing and reduce cycle time. Edge computing will enable faster processing of data at the edge to speed up decision making.

Electricity distribution utilities operate huge amount of assets spread across geography to provide quality and reliable supply to their customers. The failures of these assets can create stress on the network, impact revenue and increase repair expenditure. One of the critical assets in the network is the underground cable.

Any fault in the cable network can cause supply disruption. Moreover, cable fault requires paying re-instatement charges to the municipality as it is needed to dig the road and fill it again.

By leveraging cable geospatial data and attributes like length, joints, type of material , age, etc., and combine it with ERP data like type of fault, repair cost, etc., as well as bringing real-time data of amount of load carried by cable using automation system data, utilities can calculate the health score of cable and use spatial analytics to view the cable network with color coding indicating its health. This can help utilities to take proactive shutdown and replace cable stretch which has high probability of failure.

Industrial machine manufacturer
The traditional business models of an industrial machine manufacturer is to sell equipment to the operator and offer maintenance service as part of the annual maintenance contract. Operators today are looking at balancing their OPEX and CAPEX. Rather than owning equipment, they are renting the equipment. This opens up a new business model for manufacturers. Manufacturers can now offer equipment as service.

For ensuring the machine availability and output that the client is expecting, it is required to monitor the equipment remotely and ensure timely maintenance to avoid loss of revenue. Proactive maintenance planning can help manufacturer offering this new service, reduce their own operational cost.

Example of future IoT use case
Oil & Gas
One of the critical equipment used in Oil & Gas refinery or petrochemical refinery are heat exchangers. Heat exchangers transfer heat from one medium to another. The proper design, operation and maintenance of heat exchangers make the process energy efficient and minimize energy losses. Heat exchanger performance can deteriorate with time, off-design operations and other interferences such as fouling, scaling, etc.

It is necessary to assess periodically the heat exchanger performance in order to maintain them at a high efficiency level. The rupture in the tube can cause the containment loss, which may result to process the safety incident.

With the current system the challenges that the organizations face are around lack of real-time view of heat exchanger performance, the post facto analysis of performance data to trigger the energy efficiency improvement projects, increase fuel usage due to inefficient heat transfer, costly to put sensors to monitor all the input parameters, lack of simulation capabilities, etc.

Most of the equipment have their engineering data available with organization. With this data, it is possible to create 3D engineering simulation model, based on the boundary condition defined (input parameters and their ranges). It will be possible to estimate in real-time heat exchanger performance based on the engineering model and operational data (IoT data).

Parameter values will be estimated based on the simulation model, where there are no sensors are installed. With this, it would be possible to predict the heat exchanger performance data in real time, as well as get the visibility into contributing factors for deteriorating performance for planning proactive corrective preventive measures.

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