Industrial IoT Accelerating Digital Transformation
Karen Ravindranath, Director, WebNMS IoT
What are your thoughts on the current state of Industrial IoT landscape in India that is accelerating the increasing digital transformation?
Although India is only behind China in terms of IoT adoption industries are gearing up to embrace Industrial IoT to drive advancements in engineering and business operations. The global IoT market size is expected to grow from $170 billion in 2017 to $561 billion by 2022 and India seems to have a fairly good share of the larger picture.
As far as the understanding goes of our customer base, we are witnessing a rise in percentage of customers who have adopted IoT solution for their industrial operations. We are currently working with one of the leading vintage automobile manufacturers to monitor and manage their production line, proactively. Similarly, WebNMS is working with a couple of building energy management service providers to monitor, manage and improve energy efficiency in large commercial establishments.
For Indian businesses to be competitive in the global market and for initiatives like 'Make in India' to succeed productivity enhancement is key. This can be achieved via technology adoption, automation and digitization. It is the right time for the industries to adopt Industrial IoT initiatives to boost productivity and sustainability.
Brief us on the functionalities and benefits of customized, plug and play IoT frameworks.
Each enterprise is unique and its business processes and digitization needs are unique, there can be no ‘one size fits all’ solution in IoT. Hence customization is the key to address niche challenges faced in enterprises. Organizations have vast volumes of data generated across multiple functional units waiting to be used to improve efficiency and run a profitable business.
The industrial IoT momentum has caught-up. However, it can be stirred in the positive direction only with harmonious functioning of the IoT ecosystem in India. The IoT ecosystem includes the larger picture like device manufacturers, IoT software platform providers, solution providers and communication service providers. One factor that could contribute to ameliorate the adoption is to improve this ecosystem.
Finding the right IoT Platform is as important as finding the right solution partner. Decision makers should be on the lookout for a platform that is compatible with any hardware, provides all the necessary Industrial IoT modules, and offers the scope to scale to large and diverse deployments in the future.
An Enterprise IoT Platform like WebNMS will help solution providers quickly develop and deploy enterprise scale and secure IoT solutions tailor-made to fit each customer requirement, thereby create value in an organization by connecting to critical assets, extracting data, monitoring them real-time and rendering actionable insights for all the users, right from operations teams to top level decision makers. The core functions of the platform such as data acquisition, realtime processing data transformation, and analytics enable fast paced implementation of a full featured IoT solution. The customizability aspect of the platform makes it all the more easy and simple to create niche, plug and play solutions for various industries like Oil and Gas, facilities and buildings, manufacturing, logistics and transportation to name a few.
WebNMS IoT Platform comes pre built with domain-specific capabilities for industrial IoT, Energy Management, Asset Monitoring and Fleet Management that can help to accelerate IoT deployment for an enterprise without having to build everything from the scratch. IoT powered digital transformation can change the business landscape holistically right from operations to making strategic business innovation.
What suggestions do you have in mind for System Integrators and Channel Partners to greatly adopt IoT and also digital transformation?
SIs and Channel partners should start thinking beyond server rooms. Future digital transformation will be an amalgamation of Information Technology and Operations Technology. Since enterprises have already begun to incorporate real time monitoring, predictive analytics and insight driven operations, it is time SIs and service providers get proactive in offering existing customers value added services through IoT. This can not only help expand their existing service portfolio and rake in new revenue streams but also create opportunities for value added service for the customers. IT teams should be trained as much as the operations teams to exercise value added services through IoT, build futuristic applications and prep to handle the huge data deluge created by IoT solution.
What trends and advancements do you foresee in the present ecosystem of Industrial IoT and how are companies todaystriving to stay abreast of them?
Some of the key trends that are critical to Industrial IoT are Edge Computing, Digital Twins, Remote Asset Monitoring and Connected Business. Augmented Reality and AI are the other future driven technologies that would drive IIoT.
Edge Computing: In manufacturing, data volumes and velocity are huge. Edge computing would be a key value-add in addressing the need for real-time response, overcoming latency, optimizing bandwidth and combating connectivity issues. In critical manufacturing operations where downtime is not acceptable, edge computing helps in continuing the process even in low connectivity or low bandwidth conditions where pre-filtering and streamlining data before demanding bandwidth is smart. WebNMS Edge computing agent EdgeX is a game-changer in Industrial IoT solutions, by connecting a heterogeneous set of both brownfield and greenfield devices and machines, providing real-time alerting and analytics at the edge network, optimizing and securing data transmission to cloud.
Digital Twins: Digital Twins will be a crucial feature required for monitoring and managing remote assets in the manufacturing industry. Individual assets getting connected has its benefits, however a larger context is set when these assets create a digital world of connected devices where each device/node is not independent but works in tandem with other devices and factors.
Even the connected vehicles is one such arena where the changes in one asset could impact the other assets around it. Digital twins will capture a digital replica of asset data on the cloud and expand to emulate their behaviour and provide insight into real-world problems and simulate or emulate real-world solutions. They will help improve the efficiency of real world assets. . Machine learning and AI will then be able to make sense of this cohesive data and evolve interactive models that can predict failures and enable proactive measures.
Artificial Intelligence & Machine Machine: Machine Learning and Artificial Intelligence have become an integral part of shaping up IIoT deployments in the most insightful and futuristic manner. Applying ML to the IoT data from assets in manufacturing industry can help in analytical modelling of the information to study patterns, improve asset health, keep track of predictive occurrences for AI and even help making asset investment decisions. Using data from ML, Artificial Intelligence can take the digital implementations in factories to the next level. For instance, AI can predict equipment failure in advance, calculate downtime in case of failure and suggest alternatives for customer and production teams in case of unwarranted deviance. This can greatly help with continued production and customer satisfaction. Technology leaders should plan their IoT implementations keeping in mind the impact and scale of all the other allied technologies.
Connected Business:Data from the manufacturing besides improving operations, can blend with other business applications like ERP, CRM, and HRMS to establish harmonious business operations across the enterprise. Data from production and assembly can combine with ERP and CRM to ensure a continuous logical flow of enterprise wide business operations enriched and optimized using IoT data. This advanced process of combining data from multiple silos and blending them into a singular unit can improve efficiency, strengthen transparency across operations and create a strategic business impact for the business.
What trends and advancements do you foresee in the present ecosystem of Industrial IoT and how are companies todaystriving to stay abreast of them?
Some of the key trends that are critical to Industrial IoT are Edge Computing, Digital Twins, Remote Asset Monitoring and Connected Business. Augmented Reality and AI are the other future driven technologies that would drive IIoT.
Edge Computing: In manufacturing, data volumes and velocity are huge. Edge computing would be a key value-add in addressing the need for real-time response, overcoming latency, optimizing bandwidth and combating connectivity issues. In critical manufacturing operations where downtime is not acceptable, edge computing helps in continuing the process even in low connectivity or low bandwidth conditions where pre-filtering and streamlining data before demanding bandwidth is smart. WebNMS Edge computing agent EdgeX is a game-changer in Industrial IoT solutions, by connecting a heterogeneous set of both brownfield and greenfield devices and machines, providing real-time alerting and analytics at the edge network, optimizing and securing data transmission to cloud.
Digital Twins: Digital Twins will be a crucial feature required for monitoring and managing remote assets in the manufacturing industry. Individual assets getting connected has its benefits, however a larger context is set when these assets create a digital world of connected devices where each device/node is not independent but works in tandem with other devices and factors.
Technology leaders should plan their IoT implementations keeping in mind the impact and scale of all the other allied technologies
Even the connected vehicles is one such arena where the changes in one asset could impact the other assets around it. Digital twins will capture a digital replica of asset data on the cloud and expand to emulate their behaviour and provide insight into real-world problems and simulate or emulate real-world solutions. They will help improve the efficiency of real world assets. . Machine learning and AI will then be able to make sense of this cohesive data and evolve interactive models that can predict failures and enable proactive measures.
Artificial Intelligence & Machine Machine: Machine Learning and Artificial Intelligence have become an integral part of shaping up IIoT deployments in the most insightful and futuristic manner. Applying ML to the IoT data from assets in manufacturing industry can help in analytical modelling of the information to study patterns, improve asset health, keep track of predictive occurrences for AI and even help making asset investment decisions. Using data from ML, Artificial Intelligence can take the digital implementations in factories to the next level. For instance, AI can predict equipment failure in advance, calculate downtime in case of failure and suggest alternatives for customer and production teams in case of unwarranted deviance. This can greatly help with continued production and customer satisfaction. Technology leaders should plan their IoT implementations keeping in mind the impact and scale of all the other allied technologies.
Connected Business:Data from the manufacturing besides improving operations, can blend with other business applications like ERP, CRM, and HRMS to establish harmonious business operations across the enterprise. Data from production and assembly can combine with ERP and CRM to ensure a continuous logical flow of enterprise wide business operations enriched and optimized using IoT data. This advanced process of combining data from multiple silos and blending them into a singular unit can improve efficiency, strengthen transparency across operations and create a strategic business impact for the business.