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Will Edge Computing IIoT solutions be a real trend in 2019?

Is Edge Computing Analytics a real Internet of Things (IoT) trend for 2019 or is it more smoke from analysts and large technology vendors? As large hardware manufacturers like Cisco, HPE or Dell are building specific infrastructure for the edge designed to be more physically rugged and secure, we should believe there will be a lot of IoT money at the Edge.

Working during last months with Aingura IIoT, I have been aware of the difficulties of develop and implement Edge Computing Machine Leaning solutions for the manufacturing industry. The company combine years of industry experience and knowledge in automation (PLCs, SCADAS, HMIs) and electrical and mechanical engineering with a unique Edge Computing distributed system used by Data Scientist to develop Machine Learning algorithms and built IIoT applications for the manufacturing and automotive industries.

However, it is worth asking if clients are ready or interested in implementing these solutions or will continue one more year with the pilots who do not go anywhere.

Here below, some personal observations that I believe would help accelerate implementation of edge computing / machine learning solutions in the Manufacturing industry.

Get help to find the needle in the haystack

With so many companies talking about Industrial Internet or Industry 4.0, with a fragmented ecosystem of IIOT vendors and the challenges that always appear in the discussions with customers, it is normal that manufacturers only asked by free pilots.

It is not just finding the needle (IIOT best or cheaper solution) in the haystack (ecosystem), it is how this needle match with your business and technology strategies.

I know, I am selling myself, but my recommendation is you get advice from independent IIoT experts.

Avoid OT Vendors Lock-In. We need machine data availability

Powerful Edge Analytics Machine Learning applications need to exchange data with Programmable Logics Controls (PLCs) of the manufacturers. Reading the specifications, we could think that it will be easy. In fact, we can find many ways to extract data from PLCs if the manufacturers will provide info how to do it. However, most of the top PLC manufacturer’s list do not allow “easily” to extract data to third parties, neither their own customers.

It is not a question of protocols, it is a question of vendor lock-in and data availability

Customers must be brave and claim for openness and avoid lock-in for 20 years more if they want innovation in their plants.

Edge Computing and Machine Learning the last frontier to break between IT/OT

In my article “IT and OT, Friends or Foes in the Industrial Internet of Things?” I was optimistic about the quick convergence of information technology (IT) and operations technology (OT). I was wrong. If you can visit manufacturing companies´ plant floors, you will see how much work still need to be done.

Edge Analytics is a key component in the integration of IT and OT and requires the combined knowledge of OT and IT work together. But the lack of skills in both areas and the impact in the operations and business makes difficult which department should lead the Edge Analytics projects.

Manufacturing companies need a role with authority (Chief IIoT Officer or CIIoT) and resources to lead the IT/OT convergence strategy.

Do not be stopped by the dilemma of Edge: To Cloud or not To Cloud

When I wrote in 2016 “Do not let the fog hide the clouds in the Internet of Things”, the hype around Edge Computing and Machine Learning started. There was a confusion about fog computing and edge computing and how this layer will impact in the IoT architecture, specially to Cloud workloads.

Today Top cloud vendors offer IoT platforms and tools that combine Cloud and Edge application development, machine learning and analytics at the edge, governance and end to end security. In the OT side, companies like Siemens have launched MindSphere, an open, cloud-based IoT operating system based on the SAP HANA cloud platform.

Manufacturing companies should not stop develop or deploy Edge Computing – Machine Learning applications to monitor the health of their machines or to improve their asset maintenance or the quality control of their plant floor processes because they are afraid of the integration with Public or Hybrid Clouds.

Edge Computing IIoT solutions help manufacturers to improve their competitiveness without the Cloud. And when ready for Cloud or Cloud will provide additional benefits make sure your Edge IIoT solution is ready for an easy integration.

Connected Machines is the only way for new Business Models

Security probably is being the main challenge for adoption of IIoT in the manufacturing industry. Manufacturers have been reluctant to open their manufacturing facilities to the Internet because the danger of cyber-attacks.

But we are heading for an economy of platforms and services that need products and machines connected. Every factory should be able to tap into machine data remotely and make it available for Machine vendors.

This requires every Edge Computing / Machine Learning system implemented to be built with the capability to share remotely data via open and secure protocols and standards like MTConnect and OPC-UA.

Having machines connected is the first step to make machines smarter, to build smarter factories and to flourish new business models as Remote Equipment Monitoring.

Key Takeaway

The benefits of using Edge Computing / Machine Learning solutions are very attractive to manufacturers because allows minimize latency, conserve network bandwidth, operate reliably with quick decisions, collect and secure a wide range of data, and move data to the best place for processing with better analysis and insights of local data. The ROI in such IIoT Solutions is very attractive.

But they will never get these benefits if they do not step up and change your outdated attitude and start soon their IIoT journey aimed at to provide tangible and innovative business value.

Francisco Maroto - Profile <a href=""></a>