Digital transformation, the industrial internet of things (IIoT), and smart connected assets have been prominent themes over the past 12-18 months. For the most part, these have been separate focus areas in industrial organizations. In fact, however, they're closely related topics. As pilot projects take hold in a variety of industries, no one industry has the lead; many are finding small wins and value. This is creating a larger opportunity for investments in technology and transformation in 2017. This means that pilots are successful, so expansion into larger asset bases or different parts of the business will occur.
These enabling technologies and related transformational efforts are letting organizations gain competitive advantages. The early adopters of this new paradigm can expect lower overall operations costs thanks to improved asset reliability, longer asset life, and lower decommissioning and disposal costs. What is missing is a discussion of the larger picture of these projects' potential effects on the asset lifecycle and what it all means for operations and maintenance moving forward in the IIoT era.
At the heart of the discussion should be a commitment to understanding everything this new era touches related to the asset. This includes changes in the technology architecture: The asset becomes smart; the workforce becomes empowered; and applications evolve. At LNS Research, understanding the new asset lifecycle era in IIoT is a key focus and we believe emerging technology will continue to play a prominent role in asset lifecycle management.
A new way of thinking about the asset lifecycle
Traditionally the asset lifecycle has been viewed as a silo, with only operations and maintenance responsible for "plan, do, check, and act" processes from an asset's design until its death. In an IIoT era, this changes as assets evolve digitally and physically. More touchpoints occur with outside groups that hunger for data that can help manufacturers, suppliers, sales and marketing departments, and customers. This means that we need to think of the asset lifecycle as a platform. The notion of connected platforms comes the consumer world: Think of the success of Facebook, Snapchat, Instagram, What’s App, etc. These solutions ultimately became successful because of the size of connections made.
For industrial platforms to take hold, we need to go beyond IIoT thinking and bring the platform to what’s of value to connecting in the first place – the asset. It’s not just about connecting an asset, adding more sensors, and enabling predictive analytics; it’s about creating as many connections as feasible to that asset throughout its entire asset lifecycle. For social media platforms, this means people, but it's also advertisers, the analytics to direct to whom to send a message, the momentum of stories and collaboration, and getting as many people on the same platform to scale and creating value exponentially.
This means connecting assets, services, workforce, suppliers, manufacturers, sales and marketing, operations, and maintenance together on one platform, with many applications that span specific users or use cases to enterprise apps for many. Following are some of the innovations that begin to emerge in an IIoT era.
As technology has progressed, we have seen an evolution from break/fix reactive maintenance to condition-based maintenance (CBM) and, ultimately, to the holy grail today: predictive analytics. In the past, the prohibitive factor in moving from reactive to predictive maintenance was the high cost of sensors and network connectivity. Now the convergence of cloud and big data is enabling cheaper infrastructure costs, increased flexibility, and greater processing power.
There are two levels to consider when talking about becoming prescriptive. The first is the ability to understand and prescribe which maintenance activity or activities should be taken to postpone or prevent asset failure. The second is the ability to prescribe operational changes to alter the profile of the equipment to delay or prevent the failure. The first is important to become maintenance-smart, while the second enables operational excellence.
Early indicators show that organizations that adapt smart connected assets gain a competitive advantage and can be more profitable in doing so. As asset-intensive industries move from traditional analytics toward predictive and prescriptive analytics, the insights are an opportunity to provide better services. These analytics will incorporate new sources of data, such as video and geospatial data as well as new algorithms via machine learning to further push organizations to evolve into new business models and competitive offerings.