The industrial internet-of-things (IIoT) is here to stay. Many heavy industry companies in fields such as engineering, mining, oil and gas, and manufacturing are accelerating their adoption of digital transformation journeys due to recent global events.
The barriers to adopting IIoT technology has also fallen dramatically in the past decade. Historical challenges to implementing IIoT solutions included expensive components to add network connectivity, difficulty aggregating data from disparate data streams, and lack of a centralized database or dashboard.
Now businesses can have the opportunity to adapt and maintain operational excellence in volatile times through digital transformation. The current global crisis is accelerating cloud and the use of data in increasingly sophisticated ways to provide visibility and certainty into operations.
Smarter data-driven decisions
Adoption of analytics is one of the greatest drivers of digital transformation, as businesses seek greater data-driven insights. Data acts as a source of truth that helps teams focus on the critical factors that determine business resilience. There has also been a fundamental shift in mindset. Businesses are acutely aware that they must become more resilient by using technology.
Companies are using IIoT to their advantage to securely connect, and collect data from diverse remote assets, channeling information to advanced operational applications, and closing the loop by feeding key business applications. This helps to enable optimization, asset management, enhanced analytics, and modelling/simulation, thus providing and improving business efficiency.
This has been particularly true for the industrial sector, for instance, where IIoT has had a significant impact in five key areas, as below.
1. Real-time operational information is used to understand what is happening in real-time and enables the condition management of asset and operations lifecycles. For example, a dashboard displaying vibration frequency of a rotating asset such as a turbine during operation provides real-time understanding of the asset operational behavior and state.
2. Historical operational information helps to understand what has happened in the past to create intelligence around operational behavior of assets. Through operational trends, display of KPIs and dashboards, abstracted views of operational states can be created. For example, a graph may be displayed on a dashboard showing the turbine’s past vibration frequency during operation. This can be compared to the real-time vibration frequency, creating intelligence on the asset’s long-term operational trends.
3. Predictive analytics is used for what-if type modeling. Integrating up real-time and historical data enables your team to assess potential outcomes of operational states and behaviors, even accounting for tertiary variables. Deterministic or non-deterministic models can then be applied for open-loop simulation and predictive analytics. For example, you can now estimate how long a piece of equipment can run before it requires inspection or is predicted to fail.
4. Prescriptive analytics describes what’s needed to optimize asset and operations lifecycles. Scenario-based guidance is created and delivered through learning elements and closed-loop algorithms to enable your team to calibrate planning and scheduling across the entire enterprise value chain. For example, using a unified supply chain model, scenario-based calculations can be used to optimize maintenance schedules and performance, minimizing impact to your operations.
5. Enhanced safety can be achieved via combination of connected IoT devices, augmented and virtual reality technology to provide real time operating procedures and key messages to operations personnel, reducing human error for performing specific tasks. Operators are also supplied with information about the location of existing hazards by superimposing them over the operator’s location.
IoT is evolving
Industrial organizations will continue to evolve how they handle and present data at the plant level, and those who make sensible choices to ensure flexibility and expansibility will unlock unlimited potential in existing and expanding data. If your organization does not have a strategy in place for digital transformation, the all-important first step is to execute a pilot project. Here are the key steps.
1. Define an operational architecture (OA) – a key to success is think big and start small.
2. Choose a state–of–the–art user interface and data platform – What you choose today will undoubtedly evolve over time: in this case starting now and changing later is infinitely better than waiting for the next generation.
3. Tackle small projects to prove the key requirements – user interface, sharing plant and business data across the enterprise – in real world use cases. Keep in mind it is still early in the fourth industrial revolution and things will change. Consumer electronics will continue to drive change in human-machine interaction, and vendors will make choices about which technologies to embrace and leave behind.
Industrial organizations must work with their vendors to ensure they get technology to help them make the transitions needed as technology and the workforce continues to change.
Building resilience from within
Covid-19 has significantly curtailed global and local mobility, leaving the global economy to face the prospect of a sharp recession. This will put businesses under enormous strain in 2020. To help navigate the challenge, digital transformation can provide the data driven insights needed to adapt and overcome.
IIoT offers organizations a powerful framework for operational continuity. Enabling users of all levels and experience to access the critical information they need to do their jobs successfully. IIoT devices also empower workforces with the digital services they need, such as equipment utilization, condition management and more.
IIoT offers innovative ways to monitor and manage objects in the physical world, particularly as huge streams of data offer companies’ better avenues for decision making.
The results? More uptime, more efficiency and a more engaged and empowered workforce due to the access they have to a unified stream of insightful intelligence, at a time when it’s never been more important to contextualize data and information that drive actionable insights.
Digital transformation is enabling companies to enhance their capabilities, increase their reach and returns across their asset and operations value chains. The use of IIoT through real-time online monitoring and analytics has had a profound impact by improving response times to potential issues and minimizing possible damage to the environment, which has ultimately resulted in the avoidance of costly unscheduled shutdowns, while improving profits.
IIoT has made a vast difference to the efficiency of the industry and simply put, it is here to stay for the foreseeable future.
About the author
Ravi Gopinath, is chief cloud officer and chief product officer, at AVEVA. He was also appointed chief operating officer of AVEVA in March 2018, following the merger with Schneider Electric’s software business. Previously, he was executive vice president of Schneider Electric’s software business.
About AVEVA
AVEVA Group provides industrial software to transform complex industries such as oil and gas, construction, engineering, marine, and utilities. AVEVA says its software solutions and platform enable the design and management of complex industrial assets like power plants, chemical plants, water treatment facilities and food and beverage manufacturers.
