Convergence of Automation, IoT & AI-ML Helping CXOs to Accelerate Digital Transformation
Automation of production, whether a discrete, continuous or hybrid process, has been evolving since Industry 3.0. Starting with electronic and software-based control of machines & processes, application of robotics, PLC (programmable logic controller), SCADA, DCS (distributed control systems) and MES (manufacturing execution system) have come a long way, what we call as OT (operational technologies) and automation.
Industry 4.0 with rapid development in OT and digital technologies is providing opportunity for convergence of automation, Industrial IoT (Internet of Things), artificial intelligence (AI) and machine learning (ML) to take enterprise performance to the next level. However, CXOs face the question of how and where to get started in digital transformation. Enterprises which have already started the transformation have the challenges of how to accelerate, especially under dynamic condition of economy, market and demand. Integrating enterprise IT and OT also comes as a challenge.
Traditionally, IT and OT systems have been working in separation, primarily due to large difference in requirements and operational performance in them. OT system runs with machines, sensors, controllers and actuators, dealing with data, processing, algorithms and control in real-time. It is expected to be available without failure or malfunction, sometime running for months together before allowing any restart, shutdown or break. IT system on the other hand can run with restart and shutdown from time-to-time as required for patch, antivirus update, application migration, and others. From cybersecurity point of view, availability is of top priority in OT system with AIC (Availability of system, Integrity of data and confidentially of information in the order of priority). In IT system, confidentiality has top priority with CIA (confidentiality, integrity and availability in order of priority). This often has been a bone of contention between IT and OT.
Rapid development and penetration of IoT and digital technologies with added benefits has necessitated integration among IT, OT and IoT as a key enabler for higher level of benefits. The integrated
system provides huge amount of data which can be analyzed to discover inefficiencies and improvements in the underlying processes in real-time, be it production, enterprise functions or market transactions. This can be achieved by machine learning (ML) algorithms used by human experts and machines to learn from the data on certain tasks to maximize the performance. For example, it allows system to learn new things about health of an asset, predict its malfunction or failure. Artificial Intelligence (AI) algorithm can use this information to prescribe preventive and corrective actions for the asset based on its process model, design and operating conditions.
With the system in operation, continuously monitor in real-time the assets, systems and operations in terms of their KPIs and how they are improving
With diverse set of technologies such as IT, OT Automation, IoT, AI-ML and complexities of integration, the challenge for a CXO is what to target and where to start from. Especially when economy and business have become dynamic with uncertainty and volatility, any investment of resources and money needs to be justified with short-term gains. This can be addressed with an agile and iterative approach based on key performance indicators (KPIs). CXOs need to create a collaborative team consisting of technology and functional experts from OT, automation, IT, IoT and digitalization.
The first step is to identify key performance parameters (and KPIs) of assets, system and processes to improve based on business strategy. For example, KPIs can be productivity, quality, energy, asset health, reliability, health, safety, environment and security (physical and cyber). Next important step is to carry-out as-is assessment of the KPIs in the plant and processes. There are tools and templates available which can be used for the same. Having done that and knowing the as-is level of the KPIs, set target improvement level for each of the KPIs which would give objective and financially measurable improvements. Then with the help of technology and application subject matter experts, identify platform and solutions from OT, automation, IT, IoT and digitalization. Implement those solutions which not only improve the performance, but also measure the corresponding KPIs in real-time.
With the system in operation, continuously monitor in real-time the assets, systems and operations in terms of their KPIs and how they are improving. As required, carry-out course correction in the system and solution to prove the value of the planned improvements. ML applications can be deployed here to discover problems, improvement potentials and predict issues. Some actions can be automated using AI applications for real-time correction and improvement. Allowing time for first iteration to stabilize in terms of the whole system operating providing PoV (proof-of-value), it is time to start the next iteration with defining the next level of KPIs and repeat the above steps of Define-Assess-Solve-Monitor-Prove.
In the situations when bigger investments with longer-term benefits will be difficult to justify, each value iteration can be of few weeks or months duration to get the monetized value of the KPIs and justification for investments. This makes the digitalization a journey not a one-time destination. An agile framework and integration of OT, Automation, IoT and AI-ML would help CXOs to accelerate enterprise digital transformation in a collaborative way, achieving performance and financial targets, milestone after milestone.