While its ultimate goal is to influence acquisition to create a smarter data environment – much the way industry has used big data to better reach its customers, create efficiencies in production and more – the office’s first major action is to create awareness of its mission through a series of Digital Twin pilots that tackle readiness problems in the aviation and surface ship communities that have not been solved with traditional approaches.
The new organisation will assist systems commanders, type commanders, fleet commanders and others in identifying problems that can be solved using data analytics as a tool; facilitating a discussion between sailors and data scientists to create an approach for solving the problem; and then finding ways to apply that new approach to other parts of the Navy where applicable.
One pilot project looks at making surface ship maintenance availabilities more efficient, particularly as the Navy tries to embrace Digital Twin solutions for maintenance, only fixing or replacing components as needed instead of on a fixed schedule.
Supply officers today focus on reactively replacing parts that are consumed from their inventory, instead of proactively predicting what parts will be needed and when, which would be more useful in a Digital Twin maintenance environment.
“We want to look at one of our top surface readiness degraders and say, can we be more predictive of the supply side there, reduce the supply chain time and get better accuracy around how we provide parts for those types of repairs?”
“You might have to replace a blade on an engine, but you don’t necessarily order the bolts with that part. So can we create the relationships so that so whenever blade is replaced usually you have to have to also get this or that component – it’s kind of like e-shopping, if you bought this you may also want this – so can we create that kind of predictive supply system that would say, you just requested to replace the blade on the engine, you might also need these other parts because we find that there’s a high rate of these types of repairs with that as well.
And then it’s over to the user. The machine and the data have presented some courses of action; the user can say no don’t need that, you already checked, or you didn’t check that, maybe you need to do that, let’s go see. And hopefully then you can kind of compress these maintenance cycles a little more and speed up the time.”
Another pilot optimises operations of the ships’ power plants through a “Digital Twin” effort, where a virtual replica of the power plant is created so simulations can be run to understand how it will perform and require maintenance under various conditions.
“We wanted the communities to show that this wasn’t just some tech kind of application, but when you combine the digital and the physical expertise together, you could get outcomes that you otherwise couldn’t get if you tried to solve it on your own.
We wanted to expose the different aspects of the Navy to the tools and techniques, the language – a big part of this is, our users, our commanders have to be able to say, ‘We think there’s a data problem,’ instead of, ‘There’s a problem and let me go solve it in a traditional way.’
It’s a little bit different to say, ‘I think I have a data problem and I think I can get at this if only I had better access to data, or had the ability to analyze it differently, or had a different toolset.”
Office is addressing a major component of the Navy and Marine Corps’ F-18 readiness challenge: “non-mission capable, supply” aircraft that cannot fly because maintainers are awaiting the delivery of the spare parts needed to fix them. Both the maintainers and the supply community have separately tried to address this problem, which has only gotten worse in recent years due to insufficient funding. But the Office is coming at the problem from a new angle.
To begin the process, maintainers at the air station sat down with supply officers and data scientists to talk about the non-mission capable supply problem in a Digital Factory event.
“What we’ve seen at these factories is, in some cases the maintenance guys are like, we can’t solve this problem, it’s really really hard; the supply guys are like, well we can help with that; and the data science guys are like, okay, well what data do you both have and how might we pull it together? And can we automate the process? Can we make it more predictive instead of reactive?
During the Digital Twin Factory event, the maintenance and the supply experts were asked to think about what data they collect, through what processes, and to what end. Data shouldn’t be collected for data’s sake and they were asked to consider how data collection is actually contributing to their mission.
The data scientists then helped them talk through what data could be shared and how, to assist each community in doing their jobs better, and to ultimately reduce the time it takes for an airplane to come into the depot, maintenance to begin, required parts to be identified, the right parts to arrive at the depot, and the airplane to be sent back out for operations.
Ultimately, the maintainers were given a new data set to work with and new processes to implement on a trial basis, and the mean time to repair aircraft is already down. The maintainers have also identified new policies, training areas and additions to their data environment that they think could benefit them going forward.
In addition to getting aircraft back to mission-capable status faster, the pilot is also sparking the right kind of conversation about using data to tackle problems, with maintainers considering how to pull data from other communities and from open-source documents such as historical weather data, as well as questioning what new data they may want to collect going forward.
Navy is starting to understand the power of data analysis to help address tough problems. It’s a bit unclear now how the office will spend its time and manpower after the first pilots wrap up, though it will likely include tackling more project ideas that come in and “We’re assisting commands in holding their own Digital Factory events to brainstorm solutions to problems. We’re going to take lessons learned from these first pilot programs and using them to change the entire data environment the Navy has built for itself.
“We want to solve a problem, we want to show the community there’s value here so they can start thinking of, well I also have this other problem that might be something. And then you could start to pull that back and say, once we start to get that predictive supply piece, do we have the right data environment in order to make that scalable outside of surface readiness to other pieces?
“And then start walking the organisations through that and where they need to partner, and how to have the right contracts in place and the right acquisition strategy to go after those types of data environments. And do we have the right skillsets inside our workforce to be able to manage the data once we start collecting it and analyzing it?”
“Part of it is about the technology and the introduction of new technology, but the really big return, that ultra-boost you get, is out of redesigning your processes and streamlining them, making them simpler and making them focus on what the user really needs. And that’s where you get a big productivity kick.
The digital twin for an asset is unique. Though the assets may have common functionality, they differ in configuration and operating conditions. So it would a big mistake to believe that similar digital twins can be created for assets with similar applications.
Big Bang approach: In the long run, organizations can envision building a digital twin for an entire factory floor. But to reach that end goal, organizations cannot look for a Big Bang approach and start investing in building the digital factory at one go. This approach would be detrimental to the organization.
A better approach would be to identify the criticality of assets and also their data dependency needs for building a digital twin. Based on these two factors, the assets can be combined into groups. Organizations can then follow a phased approach for building digital twins for these groups of assets to reach the end goal of a digital factory.
Sourcing quality data: Many organizations collect operational data via field logbooks and then update the local information management systems — which in turn become the input sources for enterprise management systems. Quality of data thus sourced gets affected by factors like data entry error, data duplication from multiple local systems, etc.
Organizations need to ensure that standardized practices are followed to minimize data entry errors by using standardized data collection templates, collecting more field samples, etc. Organizations can employ data de-duplication techniques to ensure duplication errors are minimized or eliminated entirely.
Lack of common device communication standards: As part the Digital Twin initiative, organizations have been investing in network devices to gather process data from across the enterprise. Most of these devices suffer from not being configured to speak in a single language, as currently there is no universally accepted communications standard. So these devices have challenges in understanding and communicating with each other.
Navy is facing shortages of fully qualified technical personnel capable of diagnosing and addressing issues while training the next generation of maintainers prior to touching physical systems. In some instances, new systems are brought on-line for which no expertise exists.
Digital Twin Office wants to develop a system that enables diagnosis and efficient repair through advanced modeling and provide much needed technology direction for maintenance training applied to network enabled equipment.
The primary aim of Digital Twin Office is to develop a cross-platform maintenance training system using advanced modeling techniques to facilitate the understanding of complex systems and afford powerful analytical tools to enable more efficient repairs.
Crew typically attend training centers and receive most of their rate-specific training up front, which can last several years. However, by the time crew members reach their first duty assignment their skills may have atrophied or the technology they trained on has become outdated.
So Navy wants to provide “Ready, Relevant Training” to the Fleet, which will provide a career-long learning continuum where training is delivered at multiple points throughout a career by modern delivery methods to enable faster learning and better knowledge retention.
One manner in which training construct can be delivered to each crew member is through modernization of training systems to accelerate learning, minimize atrophy, and provide on-the-job performance support that improves individual performance, and enhances mission readiness to significantly reduce the cost and time for getting the training to the Fleet, increasing agility in the Navy’s rapidly changing world.
Specifically, the goal is to provide training content accessible anytime from anywhere, and that content is updated and delivered to the Fleet faster. There will be modern content delivery at the point of need for convenient access to training content and support.
Navy wants to Develop a system architecture and demonstrate the feasibility of specific examples and implementations of Digital Twin technologies applied to Navy and/or Marine Corps maintenance training. Specifically, develop an approach whereby the Digital Twin technology can be used to create content to effectively train multiple expertise levels e.g., novice through expert.
The services are developing a Digital Twin prototype to conduct a proof-of-concept technical feasibility demonstration, and develop a Digital Twin technology infrastructure that amplifies maintenance training. Incorporate into the network system technologies to develop predictive algorithms for machine breakdown/failure and recommendations for maintenance to remediate the failure modes most effectively.
Specifically demonstrate how the Digital Twin solution i.e., data, interactive 3D models, process visualizations can be used to train multiple expertise levels.
Transition the Digital Twin technology to an operational environment. Develop a plan to transition Digital Twin technology and its associated guidelines and principles to provide much needed technology direction for maintenance training applied to network enabled equipment. In addition to the Navy and Marine Corps market, the technology could have broad applicability across DoD maintenance as well as in manufacturing maintenance, heavy equipment maintenance, and the associated training packages.
1. Allocate resources to include potential mobilised operations
2. Customise depot complex to meet requirements not performed by industry
3. Consolidate workloads to capitalise on similar/common capabilities
4. Distribute workloads to activity with capacity to perform
5. Establish Technical Interfaces between Services to share assignments
6. Identify components of Service plans to match resources with requirements
7. Consider commercial and in-house size/capability constraints
8. Fund Depot operations, construction & modernisation activities
9. Implement uniform cost accounting and information systems
10. Accomplish product support goals of administrative