As large hardware manufacturers like Cisco, HPE, Dell and more are building specific, robust and secure infrastructure for the edge, it is believed that there will be a lot of money flowing in the IIoT Edge computing world.
The Development and implementation of Edge-Machine Learning solutions is a complex process and requires a combination of rich industry experience, knowledge of automation (PLCs, SCADAS, HMIs), electrical & mechanical engineering along with unique Edge Computing distributed system. This is used by Data Scientists to develop Machine Learning algorithms which can be utilised by IIoT applications in the manufacturing industry.
For organisations looking to implement these solutions, it is always a good idea to know more on adoption and ask for the continuation of a pilot project for more than a year.
Below are the top 5 things that one should follow to accelerate implementation of IIoT edge computing solutions in the Manufacturing industry –
1) Get help to find the needle in the haystack
With the fragmented ecosystem of IIoT vendors and companies talking about the Industrial Internet or Industry 4.0, the challenge that always appears in front of the customers is to ask for free pilots from the manufacturers.
It is not just finding the needle (IIoT best or cheaper solution) in the haystack (ecosystem), it is how this needle matches with your business and technology strategies.
I know, I am selling myself, but my recommendation to you is to get advice from independent IIoT experts.
2) Avoid OT Vendor Lock-In: We need machine data availability
Powerful Edge Analytics-Machine Learning applications require data exchange with the Programmable Logic Controllers (PLCs) of the manufacturers. By looking at the specifications we may think that it will be an easy task to extract the data from PLCs going through different ways or manufacturer’s help-guides. However, the problem is vendor lock-ins, most of the top PLC manufacturer’sdo not allow “easy” data access and extraction methods neither to the customers nor to any third parties.
It is not a question of protocols, it is a question of vendor lock-in and data availability.
Customers must seek and claim for open-source solutions to avoid vendor lock-in during the long run. The open source can better lead to the path of innovation in their manufacturing plants.
3) Edge Computing and Machine Learning: The last frontier to break between IT/OT
Edge Analytics is a key component in the integration of IT & OT and requires a knowledge of both to make it work. The lack of skills & knowledge in the IT and OT fields impact the business & operations and creates a dilemma on which department should lead the Edge Analytics projects.
Today, many cloud vendors offer IoT platforms and tools that combine the Cloud and the Edge application development, machine learning and analytics at the edge, governance, and end to end security. On the OT side, companies like Siemens have launched MindSphere, an open cloud-based IoT operating system based on the SAP HANA cloud platform.
Manufacturers should continue to deploy and develop Edge Computing – Machine Learning applications to monitor the health of their machines or to improve their asset maintenance or to monitor the quality control of their plant floor processes and shouldn’t stop because of the fear of the integration of their platform with the Public or Hybrid Cloud environment.
Edge Computing solutions help manufacturers to improve their competitiveness without the Clouds but make sure your Edge IIoT solution is ready for easy integration with the Clouds.
5) Connected Machines is the only way for new Business Models
Security is another major obstacle for the adoption of IIoT in the manufacturing industry. Manufacturers have been reluctant to open their manufacturing facilities to the Internet because of the risks of cyber-attacks.
In a fast-moving era where platforms and services require products and machines connected, every manufacturing 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 to be built with the capability to share data remotely via open and secure protocols/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.
The benefits of using Edge Computing / Machine Learning solutions are very attractive to the manufacturers because it offers minimal latency, conserve network bandwidth, improve operations reliability, offers quick decision-making ability, gather data, and process the collected data to gain insights. The ROI in such IIoT solutions is very attractive.
To get these benefits and to grace IIoT journey, manufacturers have to step-up and accept to receive tangible and innovative business value.