An IT professional with more than 17 years of experience, Mayank has worked on different ERPs, application support, network & security

The industrial internet or Industry 4.0, IIoT incorporates machine learning and big data technologies to harness the sensor data, machine-to-machine (M2M) communication and automation technologies in a manufacturing unit. Gathering process data on pressures, temperatures, flow rates, RPM, vibration and other technical data will allow smart IIoT software to make plants more efficient, safer, and more reliable

The objective behind IIoT that Smart Machines/Devices may per-forms better than human brains and consistently communicates the real-time data to embedded systems. This Data can bring lot of value additions in manufacturing plant like preventive failures, Machine Utilization, Efficiency, alert to avoid outages. These en-tire data can also be utilized thru any Business Intelligence tools to track the pattern and trend which will help to diagnose or predict the incident.

The great advantage of IIoT is predictive maintenance which enhanced the productivity of product in Field which can enable digital performance monitoring and can feed back into actual usage parameters driven product development. Similarly in Factory also connect the various machines on your shop floor to PLC / embedded systems which can drive better OEE, quality monitoring, energy analytics, predictive maintenance, logistics, connected supply chain management and overall plant efficiency.

IIoT Vs IoT
Many things are similar in IIoT & IoT like Cloud , Data Center , Sensors , Connectivity, Analytics etc but the major difference between them different use case.

For instance IoT applications connect devices across multiple verticals, including agriculture, healthcare, enterprise, consumer and utilities, as well as government and cities. IoT devices include smart appliances, fitness bands, shoes, apparels etc.

IIoT applications, on the other hand, connect machines and devices in such industries as oil and gas, utilities and manufacturing. System failures and downtime in IIoT deployments can result in high-risk situations or even life-threatening situations. IIoT applications are also more concerned with improving efficiency and improving health or safety, versus the usercentric nature of IoT applications.

IIoT Use Case
It is clear already that the IIoT is emerging as some-thing quite distinct to the IoT and will change the way a wide range of verticals do

business. The IIoT is not new. It promises to revolutionize industrial prowess by improving efficiency at existing power plants, refineries, off-shore oil platforms, pharmaceutical plants, hospitals and a lot more.

For instance Fanuc, is using sensors within its robotics, along with cloud-based data analytics, to predict the imminent failure of components in its robots. Doing so enables the plant manager to schedule maintenance at convenient times, reducing costs and averting potential downtime.

Similarly Magna Steyr, an Austrian automotive manufacturer, is taking advantage of IIoT to track its assets, including tools and vehicle parts, as well as to automatically order more stock when necessary. The company is also testing `smart packaging' that is enhanced with Bluetooth to track components in its warehouses.

The objective behind IIoT that Smart Machines/Devices may performs better than human brains and consistently communicates the real-time data to embedded systems

Siemens works with Dubai to create a blueprint for future smart cities where 137 buildings will be connected via cloud-based energy analytics platform

Trends in IIoT
Bain & Company predicted industrial IoT applications will generate more than USD 300 billion by 2020, double that of the consumer IoT segment (USD 150 billion).

According to McKinsey, IIoT will unlock USD 6.2 trillion in potential economic impact by 2025. For the electricity sector alone, the World Economic Forum estimates USD 1.3 trillion of value can be captured with IIoT.

Few another interesting trends as per IFR [International Federation of Robotics]:

• 2.6 million industrial robots are expected to deployed worldwide by 2019

• The world average for robot density is 69 units per 10,000 employees

• Asia leads the pack in robot density, with Korea being the most robot dense country in the world at 531 units per 10,000 employees

• By 2019, China will account for 40 percent of sales of industrial robots

• IIoT & Robotics market to grow by 13 percent annually.

General Electric, one of the leading IoT market players, believes that by 2030, IoT will add USD 10 to USD 15 trillion to worldwide GDP growth.

Similarly Research by Accenture outlines both the benefits and obstacles corporations see when implementing IoT solutions.

• 44 percent sight poor information and communications infrastructure, and poor access to required capital

• 42 percent said lack of government support is pre-venting them from adoption

• 18 percent felt there is insufficient science, technology, engineering, and mathematics skills in the work-force

• 46 percent expect IoT to increase employee productivity

• 46 percent expect improvements in asset optimization thanks to IoT

• 44 percent expect IoT to cut costs

Conclusion
While the future of IoT is bright, it seems there is much work to be done with evolving technologies like AI , ML , NLP , RPA e.g. and then interface with legacy application to arrive at consensus with help of Big Data. Given the size of the industry now, and projections going forward, IoT has the potential to create a lot of value for organizations that embrace it.