Going Digital 5 Things Every Executive Should Know About Optimizing Whole-Life Asset Value
In this decade Chemical Industry is facing two mega-trends: the collapse in the price of commodities and the Industrial Internet of Things (IIoT). The importance of asset performance is a compelling response to the opportunities and threats resulting from this. But how to maximize performance of existing assets?
Asset performance has risen to the top of the agenda for many executives and boards. The importance of asset performance is a compelling response to the opportunities and threats resulting from two of this decade’s mega-trends: the collapse in the price of commodities and the Industrial Internet of Things (IIoT).
The pull-back of capital investment in the resources sector has focused attention on managing operating expenditures (OPEX). For owner-operators in asset-intensive or asset-centric industries, maximizing the performance of existing assets and extending asset life are the current priorities.
At the same time, advancements in technology are adding to the capabilities of asset performance management. The Industrial Internet of Things — the use of embedded sensors to monitor performance of infrastructure assets in real time — provides the big data that sophisticated machine learning algorithms and operational analytics turn into recommendations for proactive and predictive maintenance. “Connected assets” promise significant increases in efficiency and corresponding cost savings. More and more owner-operators in both public and private sectors “Going Digital” in a determined effort to optimize their asset performance and whole-life asset value.
In fact, digital representation of the physical asset is the foundation for many initiatives that have the goal of reducing operating and maintenance expenditures. Investing in data collection and building the models is essential, as well as having the operational analytics to support accurate decision making. Here are five things that every executive and board should know:
1. Establish Ownership of the Digital Asset
In Europe’s largest infrastructure project, Crossrail, the principals often talked about building two railways: the digital railway and the physical railway. Initially, the digital asset is a means to plan and simulate the construction of the physical asset. But after commissioning and handover, the digital asset will be the source for all asset information and the effective system of record.
The digital asset is necessary in determining the efficiency of the physical asset’s future operations and requisite maintenance activities. It is received wisdom that the lifetime operations and maintenance costs, as well as business costs, far outweigh the design and construction costs of any major capital asset – sometimes by a factor of 50:1 depending on the lifespan of the asset. Clearly, access to trustworthy and actionable data in context is a prerequisite for optimizing lifetime costs and whole-life asset value.
For owner-operators working with engineering, procurement, and construction (EPC) firms on new capital projects, it is important to establish the central importance of the digital asset and the ownership, governance, and responsibilities in relation to it from the outset. This structure implies a change of culture not only in the EPC firm but also in the owner-operator’s entire supply chain and ecosystem.
As noted above, the current focus of many owner-operators is the optimization of existing asset performance where digital assets may not exist and asset information may be incomplete. For these brownfield situations, technology advancements in reality modeling enable a 3D model to be created from digital photographs taken by a drone or UAV. An engineering-ready model can also be created when combining the images with laser scans.
This is methodology is a cost-effective and expedient process of creating digital assets and has been used to good effect in the capture of a digital context by utility companies, for example. Such digital context capture enables “as-built,” “as-commissioned,” and “as-operated” models to be developed to form the baseline for adaptation or modification projects.
2. Start Collecting Asset Information System in the Design Phase
The essential component of the digital asset is the digital engineering model (or models). Digital engineering models encapsulate an integrated set of 3D models, engineering data, and documentation that builds throughout the project and asset lifecycle to capture knowledge related to that asset. Think of the digital engineering model as a “work package” that is self-describing, records its provenance, and tracks its change history. It is the “digital DNA” of the physical asset and it exists throughout the lifecycle of the asset. The potential to improve asset performance, by reducing maintenance and operations costs, is a function of the availability and quality of digital engineering models.
Based on the lessons learned from hundreds of capital projects and asset reliability programs that we have been involved in across different industry and infrastructure sectors, our recommendation is to start collecting asset information from the outset – the earlier the better. To get asset lifecycle information management right, there must be a continuous flow of information from the early stages of design through construction and handover into operations and maintenance. Do not think of handover as a one-time event but as a never-ending process of adding to the total information and knowledge about the asset.
The real game-changer is the establishment of a connected data environment that supports asset information throughout the entire asset lifecycle. A connected data environment is a platform for managing the digital asset and the associated information to support the whole life of the asset, including visualization, spatial, models, assets, and documentation. A connected data environment supports geospatial management, asset tag management, compliance and configuration management, records management, document control, and field data management.
By combining the intelligence of digital engineering models with data from a wide variety of sources including field inspections, maintenance history, sensors, and environmental data, the connected data environment is a foundation for analytics-based operational decision support. The common data environment is continually evolving, making it an ever-improving value-add.
A water utility that implemented an analytics-based operational decision support system reported energy savings of USD 3 million in one year and operations cost savings of nearly USD 1 million.
3. Do not Discard the Project Information
Owner-operators are managing handovers worth more than USD 100 billion in infrastructure capital expenditures (CAPEX) investment. The new standard in these handovers is to incorporate the project information and construction models within the asset information model. Project information has historically been locked in application-specific file formats or even paper documents and, therefore, has been unavailable beyond the construction phase.
Owner-operators have often had to recreate this siloed data to do commissioning and completions. In going digital, organizations can streamline handovers, enable faster start up, and empower operations and maintenance personnel to make better-informed decisions based on trustworthy information.
The connected data environment enables going digital by helping to ensure that project information can seamlessly move from the construction phase into operations and is available for predictive maintenance workflows that contribute to asset safety, reliability, and uptime.
One transportation agency in the Middle East achieved a 25 percent improvement in productivity and efficiency by implementing a connected data environment that helped automate and streamline the workflows between the project team and operations and maintenance on new and refurbished infrastructure assets.
4. Design With Reliability in Mind
There is stark evidence that capital projects that fail to achieve their utilization and throughput targets upon go-live never make up the difference even four and five years later. There can be considerable value leakage during handover and commissioning. The reality that production attainment does not improve over time points to the lack of involvement of reliability and maintenance engineers in the design process.
When reliability engineers are involved in the design of the asset – establishing requirements and expectations – there is a greater probability of achieving operational readiness and superior asset performance in the long run. It is important to consider whole-life asset value rather than making false economies by cutting CAPEX or project costs. Often, reliability engineering is only involved in the start-up phase, but it is clear that it pays dividends to involve engineers who understand future maintainability early in the design phase.
5. People and Process Are Just as Important as Technology
Focus on technology and the potential of artificial intelligence and machine learning algorithms is significant these days. It seems inevitable that the next generation of asset performance management will require less human intervention, particularly on site. In the not-too-distant future, robots will take on more operations and maintenance tasks. However, it is important to remind ourselves of the human factors that remain. According to research, most asset failures can be traced back to human error – whether in operations and maintenance or in design.
In a nutshell: Going Digital Helps Optimize Whole-life Asset Value
Owner-operators should be mindful that each time they accept poor information quality they are unwittingly taking on operational risk. Poor information quality results in waste and inefficiencies in the operations and maintenance phase of the asset lifecycle, which can have a significant impact on whole-life asset value.
The potential to improve asset performance, by reducing maintenance and operations costs, as well as risk, is a function of the availability and quality of digital engineering models. Ensure safe operation, superior asset performance, and reliability while saving millions along the way. Now is the time to change your mindset, the way you work, and accelerate your “going digital” journey.