Representative End User Clients
Representative Automation Clients
Representative Software Clients
In the month of April, I was invited to deliver the keynote address to SAP’s oil and gas customers and executives at the International Oil & Gas and Utilities event in The Hague. My talk was essentially about informing the industry of some of the ways industrial companies are finding ways to improve operating margins using machine learning and operational analytics.
One of my key messages compared how the industry has historically deployed sensors and real-time information for control and safety purposes, and how sensors are now supporting new business outcomes - outside of existing automation architectures. Today and in the future the use of machine learning and operational analytics changes the game of operations and maintenance. In heavy process industries, automation engineering professionals have long since deployed sensors for process control and automated safety systems, but now the new paradigm of sensor deployment has created:
Automation Engineers have long understood the purpose of the plant piping and instrument diagram (P&ID) to govern field instrumentation and sensors (pressure, level, flow, temperature) installed in the process. Historically real-time sensors supported the core process flow requirements, but left out the majority of the assets associated with the indirect supply chains. An example of the indirect supply chain is a maintenance and repair process, and any processes associated with the critical plant assets (distillation columns, fired heaters, exchangers, rotating equipment, valves).
ARC research has highlighted the financial opportunity and detailed several proof points by companies that use sensors and prescriptive analytics in unique ways. In many cases, these industry leaders have been successful to shift maintenance programs from reactive to predictive providing a major step change in the improvement in operations and maintenance cost.
ARC defines the Industrial Internet of Things (IIoT) as the transformation of industrial products, operations, value chains, and aftermarket services that are enabled through the expanded use of sensors, digitization, networking, and information systems. Like other industrial manufacturers, Oil and Gas energy companies are beginning to recognize that a new set of technologies and practices are poised to disrupt the manner that facilities and plants are operated and maintained. This is leading to more efficient competitors, new and different business models and solutions.
Recommendations to owner operators in this industry looking to improve cost or margins include:
One of my key messages compared how the industry has historically deployed sensors and real-time information for control and safety purposes, and how sensors are now supporting new business outcomes - outside of existing automation architectures. Today and in the future the use of machine learning and operational analytics changes the game of operations and maintenance. In heavy process industries, automation engineering professionals have long since deployed sensors for process control and automated safety systems, but now the new paradigm of sensor deployment has created:
- New business models such as servitization. An example of servitization is an OEM that transforms a product-centric model to a service-oriented offering. By connecting an assets' instrumentation back to the OEM or manufacturing, a high level of reliability and service can be obtained.
- New maintenance practices such as a shift from reactive and break-fix maintenance to predictive maintenance and prescriptive by the addition of sensors to physical assets.
Automation Engineers have long understood the purpose of the plant piping and instrument diagram (P&ID) to govern field instrumentation and sensors (pressure, level, flow, temperature) installed in the process. Historically real-time sensors supported the core process flow requirements, but left out the majority of the assets associated with the indirect supply chains. An example of the indirect supply chain is a maintenance and repair process, and any processes associated with the critical plant assets (distillation columns, fired heaters, exchangers, rotating equipment, valves).
ARC research has highlighted the financial opportunity and detailed several proof points by companies that use sensors and prescriptive analytics in unique ways. In many cases, these industry leaders have been successful to shift maintenance programs from reactive to predictive providing a major step change in the improvement in operations and maintenance cost.
ARC defines the Industrial Internet of Things (IIoT) as the transformation of industrial products, operations, value chains, and aftermarket services that are enabled through the expanded use of sensors, digitization, networking, and information systems. Like other industrial manufacturers, Oil and Gas energy companies are beginning to recognize that a new set of technologies and practices are poised to disrupt the manner that facilities and plants are operated and maintained. This is leading to more efficient competitors, new and different business models and solutions.
Recommendations to owner operators in this industry looking to improve cost or margins include:
- Companies should seek out experts and consultants who are familiar with IIoT and other transformational operational and maintenance outcomes.
- Create the awareness in your company about IIoT benefits, and how technology and can change the way operations and maintenance perform routine tasks.
- Challenge the status quo when concerning automation architecture and sharing real-time data with your OEMs and service providers.
- Build your internal competence or consider outsourcing the application of machine learning, operational analytics.
- Leverage the suppliers who understand the application of this technology to predict asset failures and process upsets.