This article is part 2 of a three-part series on private networks and sports. Part 1 can be read here.
The last thing a live sports audience will tolerate is a jolt from an operations crisis in a stadium. The commissioning of a sports stadium is fraught with the risk of mishaps and unanticipated expenses. Something as routine as a pipe bursting can snowball into a crisis as it has ripple effects in a large venue. It could lead to reputation damage if celebrities are in the audience. When it’s not a game day with the regular staff, the odds of having the right combination of the right person at the right location with the right tools and the right knowledge to rectify a problem is very low. A delayed response to a crisis of this nature could put parts of a stadium out of use for days.
Private 5G networks can support repositories of data and visuals that can help rapidly track the root cause of operations mishaps. Facility managers can find remedies by instantly retrieving the knowledge of the affected subsystems and their relationships with each other. They can also visualize the data to pinpoint the problems quickly. Digital Twins store the data on the provenance of the materials, equipment, technical processes, and engineering.
Getting a grip over a sprawling venue
Much of the knowledge of the building is in the memory of construction staff members, which is lost when they leave after a project is completed or when they move to another employer. If they stay on the project as advisors or personnel, their knowledge is lost when they retire.
Furthermore, data generated during construction and stored in repositories of independent software vendors (ISVs) are most valuable when pooled on the cloud and addresses the need for end-to-end business solutions and benefits from data analytics, including operations. Interrelated data for incident management, warranty management, and work order management has historically been available only in silos. Stadiums can take corrective action when they have all the interrelated data, including construction data. They can then simulate the costs and benefits of alternative courses of action within the limits set by the choices baked in during construction with associated subsystems. Digital Twins on private networks can rapidly do the simulation and scenario analysis.
Digital twins provide a single point of control for simulations, interactivity, and decision-making. The Internet of Things feeds data from the real world to examine alternative scenarios.
ROI as the incentive for change
Resistance to digital twins, at the outset, is a common occurrence. It wanes when its ROI benefits can be displayed and executed in real-life. For example, energy consumption and costs determine the hard ROI and a significant share of the expenses in a large venue, especially with SoFi’s dimensions. There are sixteen subsystems for lighting alone, including parking, security, restaurant, field, and stadium lighting.
Historically, maintenance teams were far removed from those involved in construction, denying them access to information for building improvements. As the size of venues increases, it becomes increasingly difficult to maintain them. With digital twins, maintenance teams can access dynamic data sources to reduce cost burdens and increase value. The linchpin of the operations model is a data platform with a single source of truth for data, asset management, and a catalog of the previous history of activities in the building. Operations are simplified with 3D models, making it easy to respond to maintenance needs.
Sports stadiums are prone to rapid erosion without routine maintenance. Beyond the initial construction costs, sports stadium managers need to consider the recurring maintenance expenses required to meet fans’ expectations for a quality experience. As predictive maintenance becomes the preferred way for the upkeep of stadiums, they also need to consider the cost of data storage and integration of multiple sources of information and its use with digital tools.
Digital Twins allowed the SoFI management to gather granular data to examine cost impacts at a microscopic level. SoFi’s architects prepared a financial model to control costs from the get-go. They asked: what would it take to make the project viable? What would be the cost of maintenance? What does data storage cost? What does all the integration cost? How would our framework address five different aspects of this: offline, promoting, simulation, orchestration, and automation? These questions were answered with ease using data.
Current maintenance methods are flawed, as false positives can lead to shutdowns that eventually prove to be costly false alarms. Real-time data gathering, on the other hand, is more likely to schedule maintenance when necessary. It started with a focus on the offline digital twin, which visualizes the schema of the stadium with the plan for its construction. Subsequently, detailed real-time data from the Internet of Things is entered into the systems in use and concludes with incorporating it into operations management. After that, it advances to prescriptive and predictive analytics as it contains more data. In the first few months of a project, so much can go awry—the digital twin gains acceptance as it helps to realize monetary benefits.
Knowledge graphs for problem-solving
Digital twins help to create knowledge graphs visualizing the relationships between subsystems and their impacts. Additionally, they mesh performance data with operational data of subsystems and their effects on the overall system. You could have human-generated data, machine-generated data, and all the static correlations. The volume and the heterogeneity of the data brought to bear needs new processing methods that need private networks with the bandwidth and speed of processing.
Over time, the accumulation of the data creates a history. The data is replicated on the cloud, where it is used for analysis, trend identification, and policy formulation. You could compare the outcomes promised by vendors with the actual results. The data could be used to negotiate future contracts to achieve better results.
“Resistance to software innovations starts to melt when we work as partners to solve problems. We uncover sources of pain and collaborate to find alternative courses of action in pre-commissioning development, commissioning, opening, operations, and maintenance. It all came back to digital twins because all the use cases fell into place and prepared the ground for problem-solving,” according to one source.
An example of digital twins’ benefits is the ability to incentivize smaller companies to provide better service. They are inclined to offer new products to differentiate themselves from larger companies like Honeywell. They are not incentivized to respond to customers’ needs for new features that fix glitches in the software for a small fee. Cloud-based operations improve the economics of product upgrades, such as warranty and incident management, within larger systems operated with digital twins, even if the additional revenue is small. Much effort is saved in using them when they are not managed as silos but from a single point of control.
Conclusion
Digital Twins set the stage for successive rounds of the reinvention of the processes for maintaining large venues. By storing the data of construction and the subsystems utilized, construction managers prepare the history of a building and the means to reevaluate processes for continual improvement. The memory of past activities is not lost, and the data is easily retrievable. It can be analyzed aided by simulations of the visual artifacts of a project. It makes it possible to evaluate several scenarios. The data processing, analytics, and high-speed responses need 5G networks custom designed for the purpose and have the bandwidth to serve the needs of increasingly diverse applications.
