In today’s episode, I will comment on scenarios where Machine Learning for Operations (MLOps) is creating areas of collaboration between Information Technology (IT) and Operational Technology (OT) organizations.
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Why do we have separate IT and OT organizations?
From a historical point of view, Information Technology (IT) typically covers centralized processes where data is available in digital format, and all the assets and processes are digital.
Typically, Information Technology’s end goal is to process information and distribute it among thought leaders to make informed decisions in compliance with security and auditing rules.
As Digital Acceleration is looking to interact with customers and partners in real-time, these solutions improve operational efficiency and Customer Loyalty.
Likewise, Operational Technology (OT) typically covers distributed processes to transform raw materials into finished goods and services. In addition, data is available in siloed solutions, and the assets may operate with a mix of analog and digital sensors, so many assets’ operational information is not always available for processing in real time.
Similarly, the end goal of OT is to support the business needs with quality and cost-effective finished goods.
Due to the nature of the business process, historically, companies had defined two parallel organizations. As we improve technology in both organizations and processes, we will find new opportunities for synergies and consolidation.
What changed with Digital Acceleration initiatives?
Digital Acceleration includes introducing new technologies in the business footprint, including the Internet of Things (IoT/IIoT), Artificial Intelligence, and Machine Learning. The organization now has an updated Digital Twin Model by adding these technologies.
In this situation, having a Digital Twin Model allows the business to review, analyze and control the whole business (IT and OT) with a similar level of granularity.
To illustrate, the world economic forum is predicting that as we continue developing effective Digital Twins Models, new opportunities for integration between IT and OT will arise.
What is the purpose of MLOps?
A new trend or use case for Digital Transformation initiatives is MLOps or Machine Learning for Operation.
Basically, this use case analyzes operational logs of IT and OT assets and explores ways to improve operational efficiency. This is possible because the customer also has an updated digital twin model, so information needed to identify early performance issues is available.
Generally speaking, MLOps is looking for ways to improve operational efficiency by learning and anticipating alarms and failures. IoTForAll has an excellent article commenting on best practices for MLOps deployments.
Other areas where MLOps suggest IT and OT synergies
There is a recent trend of digitizing all aspects of EHSQ (Environmental, Health, Safety, and Quality). In detail, these solutions cover Incident Management, Permit to Work, Management of Change, Competence Management, Chemical Management, Mechanical integrity, and Environmental reporting, among others.
Presently, as customers are migrating their EHSQ solutions from physical records or spreadsheets to complete solutions deployed on the cloud with AI and ML functionalities, they can now respond in real-time to internal and external needs, such as:
Internal benefits of EHSQ
- Minimize risks
- Identify hazards
- Capture & modify unsafe behavior
- Improved security
- Governance
External Benefits of EHSQ
- Compliance,
- Environmental reporting,
- Audit safety standards, and
- Sustainability
This is another area with great potential for MLOps and effective teaming between IT and OT organizations.
Good enough?
What are your thoughts on the subjects raised in this edition of the Digital Acceleration Newsletter?
Share them in the comments below, and if you have ideas about other topics you like to see covered in this newsletter, feel free to add those suggestions.