Digital Twin is one of the most fundamental things we’ve done recently. A destroyer is one of our most complex weapon systems. We have now been able to virtualise that in a small package. 

So if you think forward, now you could have your certified combat system on the ship, through operational test, ready to go fight. You could have your next system in test on the ship, being tested as you're operating in the fleet. You could have your third system with a bunch of algorithms doing AI kind of things, all on one single ship, and that can fundamentally change how we approach things.

We took a virtualised complete combat system on an old destroyer, hooked it to the sensors, shot a missile and hit a target. And so, we think this is going to be a pathway not only to really revolutionize how fast we can modernise our current systems, but then keep up with things.

“If you can’t solve a problem, make the problem harder.” If we made the problem so hard that we got only a week to update systems, not a year, then I think it will drive us to some of these new concepts.

Navy thinks this Digital Twin effort could help it get to a unified combat system more quickly. Some of the pushback on that effort is that if there is a vulnerability on one ship, a unified combat system means there is a problem on every ship? How do you make sure you are keeping ahead of that threat?

It’s something we've got to address, stem-to-stern, through the enterprise, and really bring security back as one of our fundamental things we think about when we put acquisition strategies together. 

Having said that, getting the digital twin being able to upload 24 hours from compiling the combat, all give you a lot more resiliency, because you can update your systems faster as you find vulnerabilities, you can deal with them faster. 

If you can have three versions of the combat system on the ship, and one gets corrupted, we can immediately deal with that. So, it’s actually a means of resiliency, not as a vulnerability. We’ve got to think our way through it.

The other thing we're seeing digital twin on more than just combat systems, we're now building digital twins of our shipyards. So how do we improve the efficiency of how we repair ships? And looking at, “Okay, let's look at the whole shipyard. Let's model it, let's simulate it, let's figure out where there's efficiencies.” So you're going to see that continue to kind of cross all the different domains in the Navy.

Consider an aircraft mission space where a critical spindle is about to snap. Without a Digital Twin Builder, the whirring machine will give no warning of its impending malfunction, and its failure will come at a random moment. 

Diagnosis and repair will be relatively slow, constrained by data collection and the organising of human and material resources. For a fast-moving military mission, the lag can be costly.  Because of the high value, we're talking about a lot of money for every hour of down time.

Problems can be mitigated with a  Digital Twin Builder enables machines to have sensors so that the spindle in the machine is continually monitored and its performance data sent to a control room where the data is fed into a computer that acts as a digital twin of the machine – a virtual copy that accurately reflects the machine's current operating status based on real-time sensor data and physics modeling. 

The Digital Twin, detecting a slight wobble in the spindle, might adjust its physical counterpart's operating parameters to correct for the wobble. Or, if the wobble can't be corrected for, it might warn of an impending malfunction.

In the control room an operator would then be tipped off, and set off a standardised response. "The Digital Twin creates maintenance work order, determine whether you could reroute production so that delivery is not impacted, find the appropriate maintenance personnel, and tell them where to go.

Maintenance personnel could quickly repair or replace the faulty machine, guided, either by a tablet showing a customised data feed on the impacted machine, or by a set of augmented reality glasses. Digital Twin technologies like this enables workers to minimise down time while improving performance.

"If some maintenance or repair task is beyond the skill set of a worker, using our tools, someone with the requisite skills somewhere else could see exactly what the worker standing at the machine sees and guide them on what to do.

Digital Twin robots have been designed to physically assist a human operator with tasks such as moving hot or heavy objects. At the moment, information flows only from the physical robot to its digital copy, but Digital Twins will eventually be bi-directional, so the digital version can adjust the operation of its physical twin in real time, instantly reacting to the information it receives.

Typically, by the time a customer delivers feedback about product quality problems, it can be difficult to diagnose what might have caused the problem so we can go back to a particular day, look at all the dashboards, drill down, diagnose and solve the problem." 

"The goal is to assist workers by aggregating and crunching data, then creating insights from it. The control room will provide information and predictions, but the human has to make the decision." 

But eventually, AI will take on a greater share of the decision-making burden. "As the systems become more intelligent, we can move to full automation by AI. That can relieve humans to focus on things that AI can't do: relationships, supply chain or customer issues, and managing workers."

So data will flow not just inside individual smart factories, but also between them. "Machines will be able to talk to machines directly, including machines located outside the factory. Networked machines will work together to predict failures, and to respond to them. For example, "the machine in your factory could tell a machine in an offsite factory shut down, we don't need the part you're making.
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Many organisations are sometimes reluctant to test the technology on their own because they didn't want to disrupt their own facilities. It's often difficult to bring together experts in manufacturing with the wide range of analytical scientists that could provide useful input.

Digital Twin Builders provides industry with the freedom to experiment and build, then to take back new technologies and best practices to field level missions.

"The merging of physical and digital worlds disruptively affects the way in which products are manufactured, placed on the market and operated. By utilising the insights produced by digital twins, users will be well positioned to exploit the breakthrough this technology brings.

New solutions can not only simulate products and facilities during the product development phases, but also in manufacturing and—more importantly even when the products or facilities are in the hands of end-users.

As an example, by simulating the operation of a field formation, based on digital twin solutions, operators can test which flows in the most complex structure are most efficient to run under specific conditions. Through this type of simulation, it is possible to iterate and select a line of operational benefits.

Simulations use the enormous amounts of data generated by sensors in the assets, and let agents gain valuable insights that can improve a process and provide a basis for improving future similar processes.

It’s now possible to develop hybrid models that leverage machine learning with multi-physical simulation models to accurately predict why a process in a facility may fail after it has been implemented.

New technology covers a product or plant's digital twin to simulate behavior in different environments and stresses, the system is intended to predict problems before they occur. The prediction is based on information from physical sensors and physics-based analysis based on simulation models to provide results in 3D visualisation.

"The synthesis of the digital and physical asset will enable companies to capture value throughout their product lifecycle. “This solution helps equipment operators and service providers predict and improve asset performance and reliability with technical insights. A digital twin that merges technical models, manufacturing details and operational insights is unique in the industry.”

Enabler technologies like AI, big data and predictions have found their way into the real-world mission space and manufacturing domains. Existing Operational/Info Tech systems are not designed to cope with the masses of data generated by fully connected shop-floor applications. 

High-performance computing is transformative, centered on the idea of high volume and volatile streams of data and massive compute power to perform analytics, and AI  applications on top of the data, while existing systems are locally optimised technology.

Large industrial vendors have pushed the concept of the digital twin— a full integration of the physical with the virtual world. In essence, this means that all product design data is available at the time of production. 

As an example, the full design data of a car body is compared in real-time with the as-is built data in a car body shop. In addition, production-relevant data is fed back into the design process, also reflected as closed-loop engineering. This frontloading of information will allow better decisions at a very early point of the product lifecycle and generate additional value. 

With asset data available in real-time from production through data aggregation and AI predictive and prescriptive applications will generate insight to increase overall equipment efficiency and lift value from manufacturing assets in operation.

But what happens as soon as insight is generated and the status of the physical process needs to be changed to a better state? In manufacturing for discrete and process industries, the process is defined by fixed code routines and programmable parameters. It has its own world of control code languages and standards to define the behaviour of controllers, robot arms, sensors and actuators of all kinds. 

Stable for decades, Control code resides on a controller and special tools, as well as highly skilled automation engineers, who define the behavior of a specific production system. Changing the state of an existing and running production system changes the programs and parameters required to physically access the automation equipment—operational equipment needs to be re-programmed, often on every single component locally. 

To give a concrete example, let’s assume we can determine from field data, using applied machine learning that a behavior of a robotic handling process needs to be adapted. In the existing world, production needs to stop. A skilled engineer needs to physically re-teach or flash the robot controller. The new movement needs to be tested individually and in context of the adjacent production components. Finally, production can start again. This process can take minutes to hours depending on the complexity of the production system.

Current production systems are trimmed to high stability and low variability. For example, automotive production has a large number of product variants, but still has an inflexible production system. With a car model lifetime lasting several years, production managers have learned to live with this inflexibility and value stable processes. However, customer- and technology-invoked trends will increase the need of fully flexible industrial control systems. 

First, the speed of innovation increases due to improved design systems and customer demand. Second, requirements for customisation increase steadily. Third, intelligent algorithms will produce a steady stream of proposals for process improvement. 

If we assume that AI will fulfill its promises, the majority of manufacturers will be able to gain constant insight. Companies that are able to execute these insights faster will have a competitive advantage. Additionally, every new state of a production and supply chain system will be seen as a new experiment and fed back into AI systems, ultimately generating a cycle of a self-improving system.

All control code has a digital twin in the virtual world. The local instance is constantly updated from the virtual master code. A virtual model of the whole production system provides context for control code—these manufacturing planning systems are already heavily used by e.g. line builders for automotive providers.

Having a full digital twin of a production system, including control code, insights can immediately be pushed down to change the state of a production system. 

Still, human interaction will be required. Production systems will optimise themselves based on simulated and real experiment. Improvements will rapidly be propagated and labor will optimise the learning, not the system. This could also differ over time or by external influence.

With the release of edge platforms, the technology is here today to minimise the time from insight to reaction. The power of AI is and will be the core enabler to realise automation fast and at an affordable cost.

1. Field level maintenance is generally characterised by on-near system maintenance, often utilising line replaceable units & component replacement using tools and test equipment found in the field-level organisation not limited to simply "remove and replace" actions but also allows for repair of components or end items on-near system.

2. Field-level maintenance includes adjustment, alignment, service, applying approved field-level work orders, fault/failure diagnoses, battle damage assessment, repair, and recovery to always repair and return to the user include maintenance actions able to be performed by operators.

3. Crew maintenance is responsibility of using organisation formally trained operators/crews from proponent on specific system to perform maintenance on its assigned equipment, tasks consist of inspecting, servicing, lubricating, adjusting, replacing minor components and assemblies as authorised by allocation chart using basic issue items and onboard spares.

4. Operator/maintainer system specialists for example, signal, military intelligence, or a manoeuvre unit receive functional individulised training from proponent on diagnosing/troubleshoot problems focus on system performance/ integrity identify, isolate  and trace problems to on-board spares deficits correct crew training deficiencies.

5. Maintainer work orders accomplished on a component, accessory, assembly, subassembly, plugin unit, or other portion either on system or after it is removed by trained maintainer remove and replace authority indicates complete repair is possible return items to user after work order performed at this level.

6. Sustainment-level maintenance generally characterised by “off system” component repair or end item repair and return to the supply system, or by exception, back to the owning unit performed by activity function to be employed at any point in integrated logistics chain.

7. Sustainment level intent to perform commodity-oriented repairs on all supported items return to standard providing consistent/measure level of reliability execute maintenance actions support force and supply system not able to be performed at field-level maintenance unit.

8. Exceptions made to when in-house sustainment level maintenance activities may conduct maintenance and return items to using unit but also may be performed by contract agreement comprised of below depot sustainment.

9. Below depot sustainment level maintenance assign to component, accessory, assembly, subassembly, plug-in unit, or other portion generally after it is removed from system. The remove and replace authority indicates complete repair is possible at below depot level return items to supply system also applies to end item repair and return to the supply system.
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10. Depot level maintenance accomplished on end items or component, accessory, assembly, subassembly, plug-in unit, either on the system or after it is removed define remove and replace authority indicates complete repair is possible at depot level return items to supply system, or by exception directly to using unit after maintenance is performed
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Maintenance based on schedules and timetables is being replaced with a new approach aimed at getting out in front of maintenance, looking at available sensors to predict where there is going to be failure, and eliminating that before it comes to a head. Better mechanisms to streamline parts supply from industry partners will go a long way in achieving Readiness of the Force.

Extending the lifespans of existing ships using data-driven maintenance efforts is the best strategy for growing the size of the Navy. The key to maintaining ships and enabling the Navy to extend their lifespans is data analytics.

“We have ships with a number of sensors on them, measuring things like reduction gears, shafting components, turbines, generators, water jets, air conditioning plants, high packs, a number of components, and we’re actually pulling data off those ships, in data acquisition systems.

Navy is analyzing data gleaned from smaller ship component operations is being used to determine how often such components need servicing, oil changes, filter changes, other maintenance actions and replacement. The process is called condition-based maintenance CBM and it is poised to drive improvements in maintaining ships.

“That’s one of the things we’re doing to get after utilising the technology we have today to operate the ships we have today more efficiently and more effectively.

During previous attempts at incorporating CBM, there was the concern that if major efforts like refurbishing tanks were only done when needed, rather than on a predetermined timetable, the Navy could avoid spending time and money on work ahead of need. 

However, that also meant that shipyards wouldn’t have a clear work package before a ship showed up at the pier, adding uncertainty and, ultimately, more time and cost into the maintenance availability.

This time around, Navy is looking at condition-based maintenance as a way to address smaller maintenance items in such a way that data analysis points a ship crew to components that are experiencing minor performance issues or otherwise showing signs they are about to fail before the failure actually occurs.

A pilot program using enterprise remote monitoring will occur on an Arleigh Burke-class destroyer.  Data collected will be sent for analysis, and operators will learn how to use the data to understand how their systems are performing and if maintenance or repairs are needed.

Navy wants to have a system of apps used to collect data from ship components, analyze the data, share it with operators and schedule work. The systems that will be monitoring, for example the turbine; it will tell the operators when a work procedure has to be performed and it will also then tap into the work package side of the house and generate a work package that gets sent to the ship, to the work center, to do the work. And if there’s a part involved, it will be able to pull a part from the supply system.”

Testing is occurring now, but there are some obstacles the Navy has to overcome before large-scale deployment. The Navy is struggling with how to transmit data securely. “The performance of any given asset is something we want to hold close. 

So what you have to do is architect this from kind of the get-go with that kind of security.  “You can harvest that data and you could potentially discover vulnerabilities, so you have to protect that. That’s We’re bringing that security aspect into the program.”

The Navy doesn’t have enough forces to go everywhere we need to go, and we have a pretty fragile mix of ships, so that when we miss an availability coming out on time, or we don’t build something to the schedule they’re supposed to build to, there are real-world consequences to that.”

The true determining factor of whether a ship’s lifespan can be extended is flexibility of the platform. The Arleigh Burke-class is the Navy’s workhorse today because, during the past 30 years, the Navy has successfully updated its operating systems. 

Moving forward, extending the life of the ships in this class means back-fitting many of the older Flight I and Flight II with a scaled-back version of the AN/SPY-6[V] Air and Missile Defense Radar [AMDR] to keep these ships relevant to current and future mission needs.

“If you’re willing to do the maintenance on the ships, from a hull and mechanical perspective, you absolutely can keep them longer. The issue is really not can you keep them 50 years; the issue is can they maintain combat relevance. If they can maintain combat relevance, we know we can keep them longer.”

We have big concerns about declining ability of crews to take care of their own Ships. Maintenance Crews today have fewer opportunities to become proficient at ship maintenance during shore duties, meaning crews going to sea bring with them less knowledge about how the ship and its systems work.

While a complex challenge to address, part of the solution would be ensuring that crews can begin pre-deployment training on time – without delays from ship maintenance availabilities going long – and ensuring that that training time includes an emphasis on maintaining and repairing the ship.

Years of constrained funding that have taken risk on things like tech manuals for Engineering Operational Sequencing System, Planned Maintenance System and resourcing maintenance, to where what has been the risk-taker has been compressing that training timeline.

Biggest takeaway from training period is the importance of being flexible and "adapt as-you-go" while prioritizing the task at hand. "Really, you have to have an ability to ask yourself … 'What are my priorities? What do I need to be doing at this moment with my hands?' You kind of have to figure out based on what's going on around you, what you should be focusing on.

“It’s clear what the operational fleet’s demand is for us, which is to make sure the ship can be maintained, and the sailors where possible can do that. So we are focused on training for the sailors, making sure they had the equipment, the spare parts, the technical documentation and more to help the ship’s crews conduct more work themselves.

Crews found all kinds of things to 3D print – wrenches, assembly parts, protective covers to shield expensive equipment from repeated impact, and even a cover for a laser device that was on back order for several months and was instead printed in a single day.

“If you have a cable assembly for your utilities, you can only order the entire assembly – but if you don’t need the entire assembly, you just need one little component, you could print that one component for a few dollars rather than have to order the entire assembly which may cost several thousand dollars spending on what the item is.

There is still an ongoing conversation on the division of labor between sailors on the ships’ crews and the Regional Maintenance Centers, along with the role of contractors.

In a contested operating environment with denied communications, you are not going to have the ability to phone home.”

We have a certain amount of maintenance that’s in hands of the crew, we have a certain amount of maintenance that’s in contractor hands, and over the life of the program we’d like to get more of that into sailor hands and less of it in contract hands. “That not only decreases cost but it increases ownership.”

“The key is to increase sailor ownership and decrease the reliance on contractors and original equipment manufacturers. Manning requirements do not support shifting the entire maintenance workload to the crew, as their capacity is limited. But we are committed to maximising the amount of planned maintenance that we perform by the crew.

Navy is trying to boost its crews ability to perform more maintenance work on the ship without outside assistance. While this wouldn’t make much of a dent in the looming surge in workload, it could cut down on contractor maintenance costs, and it would lead to a more self-sufficient fleet capable of operating in complex environments.

“From talking to crews, one of the frustrating things for them was, it was just kind of the way we set it up, but contractors would come aboard and do the work but the crews would have to hang all the tags … so it was kind of like, rather than having the crew do the work, we would just have the sailors do all the setup and teardown, and then the contractors would step in and do the work and the sailors would watch them do the work. It’s crazy. There’s some maintenance items that are appropriately done by the depot – reset and safety … some smaller day-to-day tasks we just had to get after that.”

It would be nice to get all the open and inspects done before the avail and put the results in the solicitation for you to do the work,” so the work package is accurate and the tank work doesn’t show up as “new work” later on, which impacts cost and schedule.

“By not getting it in the solicitation, it guarantees that you’re going to have growth and new work in the availability. You’ve got it scoped in to the solicitation to do the open and inspect, but not the results of it. What we do there is we have industry provide us hours on what they think may be needed. So it’s one of the additional parts or final parts of the avail that’s not defined.

“The Navy can go do the open and inspect work. We do multiple assist visits to the ships for areas that doesn’t include things like hot work, cutting something open to look, or something that would cause a system to come down, because the ship’s not in the avail.

“We’re conducting a very extensive conditions-based engineering reliability maintenance examination. The Navy, certainly the surface navy, in many cases by default, has done a very heavy reliance on time-based maintenance – so it’s monthly, time to change the oil, and we would do that. Well, that certainly is preventive, but is it the most cost-effective, most efficient and most effective way to do maintenance:”

“So we’re going to take a big swing at, are there ways we can certainly be more effective and efficient? When you have an optimally manned or minimally manned crew, you need to be effective with that time because you want to make sure you’re doing the right maintenance. 

If you just say, time-based, you’ve got to do all this, you might have to make some risk decisions on which maintenance to do, but it might not be the right maintenance to do and the right maintenance to forego. 

If you had sensors and systems and the ability to say this piece of equipment is more at risk – so do I go do the change oil on my port diesel engine or change the oil on my starboard diesel engine? If we had the metrics and assessment rigor that would say we might be getting ready to experience a casualty on your port engine, then we would wait to do the starboard and go do the port engine. So that’s sort of the thought process behind the conditions-based maintenance instead of the time-based maintenance.

Where you are constrained with man hours with a smaller crew, you sometimes have to make those decisions, so we’re taking a look at how we can use the assessment rigor to help drive us into making the right maintenance decisions. And then what that may allow us to do as well is examine do we have the right crew complement, numbers and by ratings, designators, skillsets. Do we have the right total numbers, and do we have the right skillsets?”

Every launch. a slew of maintenance checks have to be conducted. “All of those checks that are in the regular routine operations of the ship are what the ship crew does naturally when they’re out to sea, which is why we end up with so many man hours a year” 

“It was really about the monthly level and below checks are kind of within the capacity and the capabilities of the crew. And then those checks that went beyond the monthly scope usually were more intrusive and demanded more man hours – not always the case, but typically – and those were, in many cases, planned for those to be contractor-executed checks, because if you were doing them quarterly you could probably schedule them in conjunction with periods of time when the ship would be in port.”

As the fleet operates the ships more, crews will find more efficient ways to schedule maintenance work, trimming down on the number of hours required to do maintenance. The way to make a real dent in total maintenance, though, would be to fully implement the conditions-based maintenance model.

One ship was equipped with thousands sensors that send data off the ship on the status of various shipboard systems. Using that data to make decisions about when to perform maintenance – rather than just doing a daily, weekly or monthly check because a manual says so – would be the most efficient use of the small crew’s time

It's long days, it's busy days … it's not an easy job. "But it's rewarding. Its an organization where crew members have the ability to impact operations at a strategic level. And that's a pretty amazing thing."

1. “You can do more than anyone to justify our goals if we can get this right”

2. "We are transforming our capabilities and utilising everything available to achieve the mission.”

3. "It is about prioritising what we are going to do, it's about focusing on the long game.”

4. “I’m pleased with the changes we’re implementing. As long as we are working together, we are in the right place.”

5. “We need to understand requirements and what's our end state and know exactly where we are at.”.

6. “These sessions validate my understanding of intent and how you're responding to my intent.”

7. "I have not seen a commander march down the field like you have over the last three years.”

8. "What I think about every day is are we enabling team activities, and are we capable of sustaining our relationship? We have to ensure the right tools are available for training.”

9. "We have a tough mission and optempo is not getting lower, it's getting higher. We need to use our resources smartly.”
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10. "This was a very good rundown. You are clearly aligned with my intent. I am grateful for what you do every day. Thanks for doing it.”

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Navy must find ways to get ships and capabilities out to the fleet faster. There are  issues, such as the Ford-class aircraft carrier that continues to climb in cost, and fixing a servicewide maintenance mess caused by years of cuts and overtasking of a shrinking fleet.

Navy is focused on. first and foremost, delivery. We have got to get capability into the fleet, whether that’s new capability, new construction or sustaining the capability we have. So the big measure of success is through the fleet size. Reorganising our value proposition along that line is an important piece.

Second piece is focusing on agility. The world is changing faster and faster, threats are changing, we’re in a world competition. And so if we can’t change faster, we are not going to be relevant.

Third piece has been focusing on getting some fundamental costs out. And as we try and grow the fleet, they won’t do as much good if we can’t afford to build and operate that fleet. We want to take fundamental costs out of the system.

Congress has been vocal about the need to control the costs of the first-in-class ships. We’ve got Columbia coming up, as well as FFG(X), large surface combatant and a fleet of unmanned ships. How will Navy get their arms around that?

That’s certainly a fundamental thing to watch. First-in-class ships are tough. It comes down to a couple of different things. One is having a robust dialogue as we are building requirements with both industry and our technical experts, and moving away from transactional requirements. The frigate is an example where we’ve had a much more interactive dialogue. We've actually changed requirements based on cost and time, and so, that’s an important element. We’re doing a lot more of that.

We’re also looking hard at the contract vehicles. The contract strategy of the future is probably not one contract of one type to one supplier, but a number of contracts. A prime contractor build, a price challenge to look at new technology, a Digital Twin to evaluate it — all of those things will play into allowing us to get more credibility in our delivery programs out of the gate.

Industry seems excited about predictive analytics in the maintenance world: knowing what’s going to break before it breaks. Is there potential  for significant cost savings? That’s where the commercial world’s gone. We have the data. We haven’t integrated the data and the analytics into our processes the way we need to.

In the future we will essentially have a Digital Twin of the ship in terms of how all the systems are working, what needs maintenance, what doesn’t and how are they operating, to better condition our maintenance planning.

The biggest thing we need to improve in our maintenance work is planning, and so that when we open up a ship, we have a better idea of what needs to be done. And then a little less focus on cost and more focus on schedule, because what the fleet really is sensitive to is ships coming in on time and ships coming out on time.

Sometimes we get a little too fixated on cost, and we incur a lot of cost because we control cost by having schedule move-out, and that just ripples its way all the way through the system.

We need to set the standards for shipbuilders in the ship repair world, and then let them go so that we are not unnecessarily holding up maintenance activities as they’re ongoing. That’s what we need to deliver for the fleet.
The private shipyards that do maintenance often complain the Navy is too unpredictable. How can the Navy regain the trust of industry?

Lack of either planning or adhering to plan has been one of the core issues. This is the first year we’ve ever produced a 30-year ship maintenance plan. Now, it’s not specific for every ship at every time, but it shows the amount of work we’ve got to do and how we’ve got to build so that industry can make smart investments seeing that.

Then what we’re trying to do for the ships returning from deployment is we’re moving away from executing a contract 30 days before we needed to start the maintenance. So now we’re back to a six-month goal, so we have a plan.

We’re never going to be able to control every variable. It’s not a commercial effort where you own every route and you can control every variable to hyper-optimise. We don’t want to hyper-optimise because then you lose some resiliency. But we’re trying to find that sweet space of enough predictability so that we can be efficient.

The other thing we’re doing is setting some controls. In new construction, we have a very disciplined way to add work in. We didn’t have quite that level of discipline in repair, and so we tended to add work in late in the game.

We’re adding some discipline into that, so if we advance plan a little bit sooner and add some more discipline, then we can make a better decision on the exact impact. Because while it may not seem that large for that individual ship, it could have a huge ripple effect, and we need to be cognizant of that when we make those decisions.

Navy has been especially challenged on the submarine maintenance front, particularly with the attack boats. Is there some light at the end of the tunnel? We have accelerated by about a year hiring all the DoD folks in the shipyard to get to full strength. So the good news is we’ve got the workforce. The challenges are that they’re fairly green.

There has been some great work, at some locations but all the shipyards are getting that experience to level up. That will give us the depth going forward in a couple of years to take care of our fleet as we build back up to a 66-submarine fleet.

To help some of the surge, we’ve been sending some to the private yard. And that’s another area we’ve got to get a skilled base of repair specialists. We’re challenged a little bit on the front end, We will work our way through that.

Then there is rebuilding the shipyards for the future. Navy has about a $21 billion investment over the next 20 years. That will allow us, as the workforce matures, to then gain efficiency, about 20 to 30 percent, which will then allow us to take that increased load once we come out of the dip in submarine numbers.

Columbia-class ballistic missile submarine program is on deck as the Navy‘s #1 priority. There supplier issues. Does the Navy have a plan to stabilize the supplier base?

We have a submarine industrial base — both DoD, supplier, and at 2 private shipyards— that has done tremendous things rebuilding itself from a decade of hiatus in the ’90s. We’ve gotten up to two Virginias per year and done a very good job with that.

Submarines are very sensitive to cadence and sequence, and so the arc will be, as we add in Columbia and some of the other mods we want to do to Virginia, not to mess up that cadence and sequencing. That would cause massive disruptions.

So we supremely focused on Columbia, making sure that design is solid. The biggest threat to Columbia is Virginia, and so if we don’t keep Virginia on track, then that can cause disruption to Columbia. 

The biggest threat to Virginia is the supplier base not being able to keep up. We have an integrated enterprise that looks at all the suppliers for all of our nuclear construction — in total it is over 300 suppliers — and making sure they are up to the task. And then, where we see challenges either getting it right or having kind of single source, proactively addressing those challenges.

Congress has been a great help with us. They provided funding to go after those, and so we will continue to manage that, but it’s a big enterprise, and we have got to keep focused on it.

Years ago, we were told that the solution to the Navy's maintenance problems was the unpredictability of fleet schedules, and that putting stability back in the schedule would create more predictable maintenance schedules.
 
Removing the fluctuations and long deployments would make creating the work packages more effective, would make contracting in time easier and would help the private and public yards plan. This would drive down costs and increase operational availability.

Yes, the Optimized Fleet Response Plan was going to fix matters. Let’s trace the arc of the past five or six years of talking points to see how Big Navy has performed over five years and three Fleet Forces commanders since OFRP was rolled out.

"Our challenge right now is that we don't have any flexibility with our money, so the only thing that we can really do is go after it in the operating accounts. So for now, that money will need to come from a yet-to-be-identified "somewhere else.

"You can look at taking out some force structure and taking better care of the remainder of the fleet -- that was our intent with the seven cruisers and the two LSD dock landing ships. "That didn't go over too well.”

Well they didn't cash in ships for maintenance because leaders didn’t want to trade force structure. They often expressed, that if the Navy's missions were not going to go away, and they were just extending deployments for fewer and fewer ships, the only way to break the cycle was to grow the Navy.

Throughout the last six years since the talking points have ranged from optimistic predictions of a turn-around to repetitive promises of more predictable work schedules driving down work spans and costs.

What is the Optimized Fleet Response Plan and What Will It Accomplish? O-FRP is a full realignment of the Fleet’s maintenance, training and deployment cycles to fit in a standard 36-month rotation.

O-FRP has been developed to enhance the stability and predictability for our Sailors by aligning carrier strike group assets to a new 36-month training and deployment cycle.  Beginning in fiscal year ’15, all required maintenance, training, evaluations and a single eight-month deployment will be efficiently scheduled throughout the cycle to drive down costs and increase overall fleet readiness. 

Then the Navy started trying to create spreadsheets that would show the yards the expected workloads so they could plan better.

The Navy’s maintenance and operational communities have completed the first iterations of a surface ship master plan for maintenance and modernization work, in the hopes of balancing out peaks and valleys in shipyard workload without impacting operational needs.

Surface ship lifecycle maintenance organisation, began an availability duration analysis to understand the relationship between the work scope and how much time the yard would need. That data then fed into the workload forecasting effort at the regional maintenance centers .

Separate sand charts were created for each Regional Maintenance Center RMC, showing the workload expected for several years to come – up to 2023 in the most recent iteration. The charts show expected work, color-coded by ship class, and make clear where the peaks and valleys are.

Data is then taken to a Surface Ship Master Plan negotiation with the type commander and fleet commanders and discuss options that will help stabilize the shipyard workforce without disrupting operational needs.

By 2016, leaders were touting the inherent flexibility of OFRP to allow overruns in the yards. “As stakeholders continue to adjust the Master OFRP Production Plan, no single community in the Navy will be able to focus on optimising their own processes, but rather they will all have to work together to optimise force generation at a macro level.”

For example, the ideal situation for the maintenance community would be to have a steady flow of ships come through public and private yards for maintenance and modernisation. However, under OFRP all the ships of a carrier strike group must be through maintenance and basic training and ready to start integrated training by an exact date. 

These opposing needs are being worked out in the OFRP Cross-Functional Team and have already led to some gives and takes: the ships may need to split up and go to different yards to avoid overloading a single shipyard. A ship that requires a longer maintenance period may need to cut into basic training and compress that schedule, while still being ready to start integrated training on schedule.

Leaders said this shows the flexibility of OFRP, and it also highlights the importance of cross-community collaboration: if the maintainers know in advance that a ship needs major repair work during its availability or will be stuck in dock longer to accommodate a system modernisation effort, they can tell the training community well in advance and work together to find a mutually acceptable solution.

Let's fast forward to today. Where are we now, and what is the Navy saying? If we started in 2013 with a $2 billion backlog, we rolled out OFRP in 2014, by 2016 we'd had it all mapped out and were giving the shipyards predictability, what's going on here in 2019?

If the Navy ever hopes to reach its goal of a 355-ship fleet, it won’t be by simply building new hulls and launching them. Instead, the admirals have long recognised they’ll have to extend the lives of dozens of ships already long in the tooth — and do so at a time when shipyard space is already stretched and less than half of its ships are able to complete scheduled maintenance on time.

“We’ve really got to get better than what we’re doing today. We’re digging out of a little bit of a maintenance backlog.” They insisted Navy was getting better at getting ships in and out of maintenance availabilities, but currently only about 30 percent of destroyers are able to leave the docks on time.

So the private yards need to build capacity if they are going to be able to dig out of the hole. Sure enough, predictability is still the watch word, but we've thrown in a new buzzy term: Dynamic Force Employment, which is all about being upredictable. 

Shipyards and companies in the private sector hire workers for specific projects, but the Navy usually issues a contract just a few months before a ship pulls in for work on a one-off contract, it doesn’t provide the shipyards an incentive to think long-term.

“Industry has got to hire more. We got to build a system that incentivises industry to have the right people there, so I think you’re going to see a real sea change in the way we’re working to acquire repair work that will give industry a longer view of the maintenance schedule.

Won’t the Dynamic Force Employment concept, which will see ships leave port, only to return early from a deployment, and then head out again at an unpredictable time cause havoc in the push for more predictability leaders had been talking about?

“Operations come first. “There are ways we can incorporate the thought process of dynamic force employment and still give industry enough predictability. But the model of predictable unpredictability may be tough to square with the push on the back end for more predictability.

“It’s something that the maintenance community is going to have to wrestle with. We’re going to have to think our way carefully though this.”

Now we have the new "Long-Range Plan for the Maintenance and Modernization of Naval Vessels." Now it's unclear what the difference is between NAVSEA's 2014/2015 master plan for ship maintenance and modernization and today's 30-year ship maintenance plan except an extra 20 years of projections. It's also unclear if NAVSEA is still using their 2014 master plan. But the branding is a very similar.

"This plan complements the 30-year Shipbuilding Plan and Shipyard Optimization Plan and establishes the framework to effectively sustain our investments in today’s fleet. It highlights the requisite development initiatives that will facilitate a more adaptable and reliable industrial base, while providing a foundation to support the workload forecasts of our industry partners.”

"Tools like this are critical to the success of the Navy and will help us build a culture of continuous evaluation of the industrial base capacity and capability; enabling us to meet the requirements of well-laid plans and adapt to any surge demand if the situation arose."

The new strategy is to get more predictability into the system by grouping maintenance availability contracts together in block buys.

"We did a re-look at our acquisition strategy. We had kind of gone into a 'compete every availability just in time' strategy, and while that was ok on an individual basis it didn't allow us to look at it as a system and didn't afford industry the opportunity to improve productivity and efficiency. 

“So we are looking at grouping availabilities together either geographically or by type of work, which would then incentivise investment and productivity improvements. Our on-time rate is improving out of both the public and the private yards"

The 30-year ship maintenance plan is promoted as being critical to getting private shipyards to invest in workers and infrastructure to meet the Navy's needs.

"When we've looked at it, industry responds to the demand signal that we put out there. We were not clear in showing that composite demand signal, so a key element of that 30-year ship maintenance plan was so we could show the entire demand signal. When we clearly articulate the demand, industry makes really good decisions on how to invest to help us deliver on that."

Navy needs to move away from awarding work packages such a short time prior to the start of work.  Navy needs to award the Surface Force work packages much earlier and not wait until such a short timeprior to the start of the maintenance availability to award the work package. Both the private shipyards and Navy need time to plan the work.

Why hasn't OFRP and the promised stability produced the improved maintenance availability performance that it aimed for since it appears the surface fleet is working through the same backlog it had six years ago?

What happened to NAVSEA's 2014/15 master plan and why did it not produce the results it intended?

What is different about the 30-year ship maintenance plan the Navy is now touting?

Is it reasonable to expect that private yards will invest in workers and infrastructure based on the latest maintenance plan for a fleet that has routinely under-performed and under-delivered on previous plans? The Navy is still putting depot maintenance items on its "unfunded priorities" list, so what message is that sending to industry?

If we are straining our maintenance industrial base beyond its capacity how are we supposed to hit the 355 plan?
How do you square "Dynamic Force Employment" and the operational drive for "unpredictability" with the need to get your ships in and out 

1. Exhaust all available means of resolution prior to submitting an Action Report

2. Inform expeditor or aircraft crew chief of Action Report requirement 

3. Ensure detail is added to Action Report to alleviate interpretation issues

4. Include tail number of aircraft if Action Report is related to an aircraft

5. Ensure correct Action Report categorisation, severity and classification 

6. Review all Action Report submissions to ensure correct priority has been assigned 

7. Ensure all info/attachments provided on Action Report are technically accurate/complete 

8. Ensure Action Report details provided by initiator explain problem completely

9. Approve Action Reports with record of submittal notification

10. Monitor Action status via customer relations management ​

To better prepare battalions for large scale combat operations, we’re working on sending more battalion commanders to combat training centers so they can be leaders in coordinating installation logistics for supporting power projection e.g. equipping Soldiers as they deploy.

Most  installations have logistics readiness centers, which provide essential logistical support to local commands to prepare, enable and support to the warfighter. 

"We are making changes include assimilating capabilities and utilising everything available to achieve the mission. 

Brigade commanders remain the primary leaders for integrating unique capabilities offered through the life cycle management commands. This action has reengaged directors to identify and update their equipment and infrastructure requirements, enabling plans for future allocations
. 
"It is about prioritising what we are going to do, it's about focusing on the long game.”

Updates focused on Strategic Readiness, and specifically, munitions readiness. This focus endeavors to operationalise strategic support focus areas in multi-domain operations in order to deliver ready, reliable and lethal munitions readiness at all levels of war. 

The campaign plan topics included munitions readiness; review of policies; non-destructive testing initiatives and requirements; and critical munitions; artificial intelligence integration; Logistics Management Program manufacturing integration and intelligence; transition to sustainment; and, supporting the force of the future. 

"We have a tough mission and optempo is not getting lower, it's getting higher. We need to use resources smartly. We are using scorecards to measure various performance metrics and reviewing all contracts to make them output based. 

"The question we ask is, 'What did you say you were going to do, and did you do it?'

We are instituting strategies to improve its supply performance of critical parts, in areas like reducing backorders and lowering procurement lead times. 

"When this thing breaks down on the battlefield, is there going to be a part or not?" "If not, we're failing. At the end of the day, our real grade is going to be on the battlefield."

We are expanding its depot-forward operations to bring key support services closer to Soldiers to ensure troops working in remote locations understood they are part of its mission.

One of several topics we have covered is updating contract management procedures aimed at increasing contractor accountability. It’s important for the services to better understand the performance outputs of contracts as they relate to costs. 

We must look deeper into the metrics associated with each contracted requirement. Are we achieving what we actually want to see? We’re not interested in buying "readiness for increased costs."

Our team has reduced the number of contracts managed through the command dramatically within the past year.  Other improvements include new procedures designed to better understand the command's initial and ongoing needs for contracting agreements. 

Other topics included a briefing on Reprogramming  Teams to protect aircraft from up-to-the-minute threats by stabilizing  and improving the fleet of critical communications systems.

It takes special tools to track all those aircraft and the related supply chains, and the Defense Advanced Research Projects Agency is now reaching out to industry for ideas on upgrading the military’s existing technology, which is managed through the Joint Logistics Enterprise. DARPA posted a solicitation for proposals for a program it’s calling LogX.

The logistics enterprise still uses legacy technology, according to the request. LogX is looking for research approaches “that enable revolutionary advances in science, devices, or systems,” the request states. Specifically, the program wants tools that can provide real-time logistics and supply chain situational awareness “at unprecedented scale and speed.”

The request goes on to describe the logistics enterprise as a triple-decker layer of networks. There is the physical supply chain, information networks that inform the physical mobilization of supplies and financial l networks. LogX will focus on improving the information network, according to the request.

Under the Multi-Domain Operations concept, Materiel Command has reorganized and reshaped to ensure readiness of the Strategic Support Area, where military might is generated, projected, and sustained during the fight. Joint Munitions Command and Material Command are preparing the joint force for large-scale combat across all war fighting domains.

Joint Munition Command's strategic support mission focuses on munitions readiness, but also supports several focus areas, which include: supply availability and equipment readiness; industrial base readiness; installation readiness; strategic power projection; and logistics information readiness.

Joint Munitions Command's mission is to provide the Joint Force with ready, reliable, and lethal munitions at the speed of war, sustaining global readiness. Munitions readiness is why JMC exists.

"JMC's strategic support begins with demand signals or ammunition requirements, which the enterprise uses to calibrate the logistics footprint needed to rapidly project power and sustain the fight .

The  Total Munitions Requirement includes all munitions required to support current operations for training, testing, and combat. Realistic and accurate requirements are imperative to ensure supply availability, funding levels, sufficient manning, installation throughput capabilities, and industrial base preparedness. 

"In preparation for the next worldwide contingency, the ammunition enterprise is focused closely on Combatant Command Operations Plan requirements and the ability of the logistics network to meet those requirements. Requirements, just like the stockpile, are a moving target. A deliberate and constant refresh of data, analysis, and collaboration is required. The newly established War Planning Division is synchronizing this effort for JMC to meet this goal.

"Efforts in Strategic Power Projection ensures our ability to rapidly deploy Joint forces forward," "JMC ensures munitions readiness by providing the ammo needed at the right place and right time during contingency operations." 

The services have identified support gaps and friction points in strategic support. To prepare the operational environment, JMC is enhancing world wide munitions prepositioning along with update support plans, and decision support tools. The end goal is a distribution network prepared to execute precision logistics, with an agile production base postured to sustain the Joint Force and deliver lethality that wins

Marines work to claw back readiness and increase pilot flight hours, it's the spare parts issue that has the service's top aviator most concerned. Aircraft maintainers are still sometimes resorting to cannibalization, or borrowing parts from working aircraft to make other planes operational.

The one thing that is holding the man down on every platform is not-mission-capable supply," By every type/model/series, it's a contributor to why that airplane might not be available for flying. "We couldn't sustain them. The requirement was still there, but we couldn't sustain it.  If the Marine Corps was a business, they are underwater right now, because we don't have enough power tools to make flight hour goal.

The biggest challenge squadrons of all type/model/series aircraft face is “not mission capable-supply,” where spare parts are not available and therefore the plane cannot be fixed and put back on the flight line. “If you don’t have the parts you need on the shelf, what does a good industrious sailor or Marine do? 

They go get it off another airplane.  That airplane’s a little more broken than that one over there, so I’m going to take it off that airplane and put it on that one. That’s several maintenance efforts, that’s very negative maintenance because I’m going to have to … go over, take a part off an airplane, install it on that airplane over there, and then eventually go back and put another part on the airplane I just took it off of. It’s crazy, but they’re doing it because they have to do it .

The Navy and Marine Corps have sought to address this problem by asking lawmakers for more money for spare parts and logistics. 

Distributed Operations concept supplements traditional sea and land basing options with “mobile forward arming and refueling points” for resupply mid-mission. A separate mobile distribution site would serve as the location for Marines on surface connectors or host nation forces to stage fuel and weapons that will be brought to the mobile forward arming and refueling points. Importantly, all these sites are considered “mobile” and are intended to maintain elements of “deception and decoy” – in keeping with the idea that the aircraft are supposed to be distributed and difficult to find and target.

The biggest challenge is spare parts and maintenance, and paying close attention to that supply logistics chain to avoid the problems plaguing the rest of Marine aviation. As for the maintainers, he said there’s a lot of excitement today about the F-35 transition and “right now we have just an exceptionally well trained F-35 fleet of mechanics.

We are attacking our current unacceptable Not Mission Capable- Supply rate, and the root causes for it. The supply chain that supports Marine aviation is fragmented, antiquated, and not optimized to enable the required state of readiness in our current fleet. This fact is clearly evidenced by the low rate of Ready Basic Aircraft RBA and unsatisfactory high Non Mission Capable Supply NMCS rates across nearly every T/M/S the Marine Corps currently operates. 

Each of the Independent Readiness Reviews conducted to date AV-8B, CH-53E, and V-22 identified systematic shortfalls in the sustainment organisations, processes, and resources of the supply chain that supports Marine Aviation. Accordingly, the focus of effort will be on continuing to aggressively attack these daunting challenges. The strategy to reduce the NMCS challenge will be focused on the areas of consumables, repairable, and manpower.”

Marines will work with the Defense Logistics Agency to improve the accuracy of bills of material and to “monitor fleet demand for consumables on long-term contracts and ensure vendors receive accurate demand forecasts,” and work with the Naval Supply Systems Command to improve depot component repair performance. Consumable forecasting is an issue. Lack of consumable material accounts for greater than 80% of non-mission capable supply.

Since the CH-53E is out of production, there can be delays in getting needed parts,. They are sometimes required to make certain parts in-house which can take a while. 

The situation became dire when across-the-board spending cuts known as sequestration hit. 

The readiness center was not able to hire replacements for artisans who retired or took jobs elsewhere, and it could not order badly needed maintenance materials or spend money to fix equipment at the center. This meant no preventive maintenance on plant equipment. That led to many machines being down for extended periods of time," This inhibited our ability to produce parts, further slowing our turnaround time. Both issues continue to impact our throughput and cost.

“And instead of just kind of looking at something in the aggregate, you’re now responsible for that airplane, getting the parts for that airplane.” The service is also looking for inefficiencies in existing contracts that can be ironed out, as well as improvements to the flow of parts – to avoid scenarios where spare parts stack up at loading docks but don’t make it to aircraft maintainers in a timely fashion. Marine maintainers weren’t spending enough time each month actually touching their airplanes since the supply chain logistics system was in such dire shape.

1. Measurement System Allows Scoring Flexibility

“One size fits all” approach is prevalent at even some of the most well-recognised supply chain organisations. Better systems will allow adjustments to the performance categories and their weights to reflect the realities of different supply requirements. The best scorecards align directly with the outcomes sought from doing business with a particular supplier.

For example, an automotive original equipment manufacturer could change the way it evaluates suppliers by involving more employees in the process and giving them the power to adjust the weights used to evaluate suppliers.

Create internal “boards,” one for each of its product segments to determine weights of the various performance categories against which suppliers are evaluated. Each board consists of specialists in cost, technology, quality, and logistics who are responsible for posting supplier data regularly on a supplier portal. Suppliers are able to see the names and performance of their competitors, although the product boards have the authority to withhold names within their product groups if they so choose.

2. Internal Customers Evaluate Supplier Performance

In the information age, internal customers should be able to submit comments and ratings about a supplier's performance directly into a scorecard system. These individuals are usually in the best position to evaluate a supplier's operational performance. For example, when internal participants have looked at a supplier much of the performance score relates to something called “account management”, reflecting how well a supplier works with the company and responds to requests and concerns. Buyers actively solicit input from engineering, product management, marketing, sales, and product support before assigning a score, reflecting an extensive level of cross-functional input across the company.

A worthwhile exercise is to assemble an internal team to compare the current state of supplier measurement against an ideal future state. Any gaps that exist between the current and future states—and there could be many—will require a clear plan to bring an existing system closer to a preferred system.

Procurement teams should consider allowing suppliers to enter a Web-based portal to view any free-form comments or scores submitted by internal customers. This supports the efficient and open exchange of information, something that is widely practiced with other supply chain applications like sharing demand forecasts, for example. Most supply chain experts would agree that information-sharing across the supply chain is a good thing. So why should sharing of supplier performance data be any different?

3. Scorecards are Reviewed and Acknowledged by Suppliers' Top Managers

Key executives at each supplier should receive electronic copies of the scorecards. Most importantly, the party sending the scorecard should track acknowledgements that the scorecards were received and reviewed, along with any responses to specific queries.

Forwarding scorecards directly to executive managers supports at least two purposes. First, these executives will have access to information that their own personnel may not willingly share. More than one executive has been caught off guard because they were unaware of issues that affected customers. Second, Information will likely reach the individuals who can effect meaningful change when it is required. It makes a lot of sense to provide vital “feedback” information to those who are ultimately accountable for performance results.

4. Suppliers with More than One Location Receive Multiple Scorecards

There can be a tendency to count a supplier as a single entity, yet many suppliers provide material from multiple locations. To aggregate different locations into a single scorecard can be misleading. It also makes it harder to assign scores to specific locations.

A possible solution is to evaluate each supplier's shipping locations across a basic set of operational metrics such as cost, quality, and delivery while the supplier as a whole is evaluated by a set of higher-level metrics. Examples of such metrics include assessments of supplier innovation, responsiveness, and willingness to invest in the buyer-seller relationship. This approach keeps the unique locations grouped within the supplier's code, which helps when conducting any analyses.

5. Scorecards include Cost-Based Measures Whenever Possible

Most scorecards include price as a performance category simply because price is easy to measure. Unfortunately, price never reflects the total cost of doing business. To compensate for any disconnect between price and total cost, progressive supply chain organisations calculate metrics that reflect more than unit price.

An example of a total cost metric is the supplier performance index assumes any quality or performance infraction committed by a supplier increases the total cost of doing business with that supplier. If a supply chain manager can track these infractions and assign a cost to them, the calculated index can then be used in a scorecard to supplement a price metric. The performance index or even the adjusted price can be included in the scorecard rather than simply the price paid, although price can still be included as a scorecard metric.

6. Scorecards are Updated in Real Time

Too many scorecards still resemble a batch updating system that features periodic input of data submitted manually each month or each quarter. In a perfect world, anyone who is granted access to a scorecard system should be able to view supplier performance levels in real time. Whenever a transaction occurs, whether it involves the results of a quality audit at a receiving dock or an accounts payable transaction, data records should flow seamlessly into the scorecard database with real-time updating of supplier performance. Of all the attributes of an ideal measured system described in this article, this is the one that is rarely implemented.

For real-time updating to work, the scorecard system must be linked to other supply chain constituencies, including accounts payable, quality control, and transportation. Any system that stresses objective rather than subjective assessment, particularly in a real-time scenario should receive serious consideration. It's safe to conclude that most supply chain systems are moving toward real-time data visibility. Some purchasing organisations are beginning to rely on suppliers to self-report and submit their performance to the scorecard system on a frequent basis. A few leading companies are even beginning to solicit performance data from or about second-tier suppliers.

7. The Measurement System Separates the Critical Few from the Marginal Many

In some situations not all suppliers are being measured using scorecard systems. But should this really be a cause for concern? In an era when fewer suppliers are providing a greater share of total purchases, there has never been more need to separate the critical few from the marginal many. If a supply chain organisation is adamant about measuring most of its suppliers, then the less critical suppliers should receive a basic scorecard—perhaps even one that is categorical. At some point, depending on the level of effort required to obtain scorecard data, the cost to measure a supplier could outweigh the value of measuring that supplier. When this is the case, a logical response is to not measure, measure less frequently, or simplify the type of scorecard used.

8. The Metrics Database Allows User Flexibility in Retrieving and Displaying Data

An effective system will not only generate the scorecard itself; it will enable data to be presented in a variety of reporting formats, along with easy generation of useful reports. On-demand reports can show side-by-side supplier rankings, demonstrate performance changes by category, and highlight the suppliers that improved or deteriorated in performance over a certain period. A database that allows the slicing and dicing of raw data is an essential element of an ideal scorecard system.

9. The Measurement System Provides Early-warning Performance Alerts

Most measurement systems are reactive in that they report what has happened, not what is likely to happen. As with a statistical process control system, an ideal measurement system would be able to “look ahead” to spot troublesome trends and non-random changes in a supplier's performance before it becomes out of control. An ideal system would notify supply chain managers of potential problems before the impact of those problems is even realised. The system would have predictive capabilities.

Consider the possibility of generating early warnings when using advance shipping notices Any time a notice reveals a possible late delivery after comparing expected transit times against a due date, a material planner would receive a warning of the potential delay. Or consider real-time GPS tracking systems that could reveal that supply chain delays are occurring, with a notification sent to the appropriate personnel. It is almost always better to be proactive.

10. Suppliers Can View and Compare their Performance Online

For many years, almost every supply chain organisation refused to identify the scores and names of competing suppliers within a category or commodity group. Later, most organisations became more willing to show relative comparisons against competing suppliers The time has come to accept that scorecards present a good way to create healthy competition among suppliers. That means permitting and enabling them to access their scores online, complete with comparisons to other suppliers in the same or similar commodity groups.

Scorecard transparency is an idea whose time has come. It’s like looking at the standings of any sports league. Doesn't every team know precisely where it stands in relation to competing teams? We have built a good system into an excellent one.
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Automation has allowed supply chain operations within companies to perform tasks with minimal human intervention or interaction. Automation methods vary significantly in size, functionality, dexterity, intelligence and cost, from robotic process automation to flying vehicles with artificial intelligence.
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Traditionally, automation and robots have been deployed for executing routine and repetitive tasks, requiring complex programming for implementation, while lacking the agility to easily adjust operations. As the automation technologies have become more sophisticated, set up times are decreasing, requiring less supervision and are allowing for the smooth integration and transformation of legacy supply chain systems.

The phases within traditional supply chain systems all acted as autonomous phases that had minimal visibility into the other segments of the chain, whereas, with automation, the supply chain is streamlined from end-to-end, enabling all different piece of the chain to be managed in tandem.

“As supply chain operations continue to shift focus to direct customer needs, autonomous robots can serve the end consumer with better service and faster response times. Emerging advancements in technology such as autonomous trucks, 3D printing and warehouse automation will foster changes in how shippers, retailers and manufacturers configure their supply chains and distribution strategies. The advancements will encourage industrial users to embrace and modernise their materials handling capabilities to meet the growing market demands.

The time for companies to assess their supply chains for piloting autonomous robots is now. Depending on needs and existing capabilities within the supply chain, implementing autonomous robots—from robotic process automation to self-guiding vehicles with artificial intelligence—can provide significant improvements in productivity and efficiency, while reducing labor costs and improving customer satisfaction.

While automating tasks is a much more convenient and efficient way to manage a supply chain and does provide immense benefits, managers and leaders within an organisation may prefer to be able to track specific actions and outputs. For that reason, many automation solutions provide a comprehensive & customised dashboards that give leaders visibility into all the necessary data and processes.

Even while most industries got on board years ago, logistics has been a bit slower to implement and reap the rewards of big data. Granted, the industry already leverages big data in a multitude of ways, but it is unquestionable that it could be better utilised to advance the industry even further.  It’s necessary to have the right data in the right format at the right time available for all stakeholders. Establishing metrics facilitates this process by providing the vital data cleansing and predictive optimisation that logistics companies need to succeed.

Forecasting every aspect of the supply chain surely isn’t  the easiest task, so more, accurate data, along with a platform that visualized that data, would be useful for better executing supply chain strategy. Recording data on every aspect of the business is the number one opportunity when we think about big data in terms of supply chain management. Exploiting every piece of data from every single step of the supply chain can bring immense value to businesses. It will ensure end-to-end visibility for all parties, greater efficiency, and optimis ed digital processes. 

One way to acquire digital intelligence is by having data available from all aspects of the supply chain including manufacturing, e-commerce and retail data too will make it easier to improve processes and better plan for the future. However, the raw data itself is not enough, you have to  transform the raw data into actionable insights which can be shared with all relevant stakeholders across the supply chain in a timely manner.

You need to find platform tools to make sense of the data – and you might want to be able to integrate it into your own systems to see all the information in one place. You also need to ensure that all your systems and devices are transferring data to you in your preferred format. 

The insights that you gather may not only be useful for you, but also for your partners. At the end of the day, this type of data sharing in logistics can help to improve operational efficiency by capturing fluctuating customer demand, external factors, and the operations of your partners. It will improve transparency and help all stakeholders to streamline their processes, ultimately improving the quality of your processes, and the overall performance of your business as well. 

As you gain more control over every aspect of your business, from optimising resource consumption to improving delivery routes, the increased efficiency will allow you to speed up your operations  improve customer retention, and increase resolution of objectives. However, you certainly need to evaluate what data you can and want to share with your partners. Only some data will result in a win-win situation where the processes and solutions of both parties benefit. Tools already exist to foster business collaboration through this type of data sharing solution.

Clearly, big data itself is not enough. When you receive raw data in bulk, it’s not very useful. You must also have data processes in place to ensure the adequate storage of the data, comply with all the regulations and security, and ensure that the quality of the data is flawless, so you can validate and enrich it.

Once the data is validated you can create actionable insights for any number of purposes: improving partnerships and cooperation, managing external factors and risks, optimising routes, schedules, and deliveries, making sure you deliver everything on-time and boost customer satisfaction, and, ultimately, improving operational efficiency and becoming more profitable.

You can implement quick connections and message translations between supply chain partners and customers. Integrating with carriers, shippers,  and the systems that they use to increase speed and agility. This seamless real-time flow of 100% accurate data, provides organisations the ability to analyze and optimize all supply chain processes.

It has been a long time since the supply chain has simply been a way to produce and deliver a product. It’s probably the primary source of competitive advantage within vehicle industries. The intelligent and connected supply chain builds upon network connectivity to integrate the latest  digital technologies in a way that can transform every element of your business.

Connected supply chains enable growth through an extended digital world that unites employees, trading partners systems and things. You can boost your competitive edge with machine learning-based advanced analytics to predict outcomes, optimise and automate business operations, and take informed decisions.

Predictive maintenance is one example:  Being able to predict when a part of sub-system of a serviceable product is likely to fail is a key investment area for the supply chain. Whether that part is within the production process, within the warehousing environment or part of a connected vehicle, an intelligent and connected supply chain can automatically monitor and analyze performance to boost operating capacity and lifespan. This system can intelligently decide whether the part needs to be replaced or repaired and can automatically trigger the correct process.

Another example is Proactive Replenishment. Reducing inventory levels while improving customer experience requires the ability to automate much of the replenishment process to continuously monitoring stock levels and re-stock as levels drop or demand grows. The connected supply chain provides real-time inventory visibility. As well as stock levels, it can indicate the condition of each item to ensure the quality of items. You can automate the replenishment of parts from the supplier before they are needed in the production process.

Supply chain visibility is key. Knowing where exactly an item is, what condition it is in and when it is going to be delivered is of vital importance to all supply chain operations. While the previous generation of tags and sensors could provide some information on location and condition, it was very limited. The connected supply chain provides improved end-to-end visibility of shipments ‘from floor to store’. It enables the continuous flow of data from highly connected supply chain ‘assets’ at every stage of the process. This includes tracking and monitoring improvements in ‘last mile’ delivery.

The concept of autonomously delivering products is slowly starting to become a reality. While there are many hurdles to overcome before the point is reached where there's no human intervention in the supply chain, there are many industrial examples that indicate it's feasible and practical.

An autonomous supply chain has the capability to process a request to grab a component from its location and to autonomously deliver this component to a specified delivery point, all without human intervention. 

Key elements of such a system include the ability to: Interpret the request, Find the part's location, Load the part onto a transportation system, Identify the delivery point, Transport to the delivery point, Off load the part and provide feedback to the supply system. If all steps are automated and do not require human intervention, then the supply system is autonomous.

Is an Autonomous Supply Chain Feasible? In a real-life situation, an autonomous supply chain needs to be able to process thousands of requests, often in a very short period of time. It also has to have the ability to find different components and transport them automatically to multiple delivery points.

From this definition, it's clear there has to be a high degree of order and standardisation. Additionally, the entire process needs to be supervised by incorporating a comprehensive database that knows the location of every part and delivery point. It has to be able to compute the best route to the delivery point and to avoid congestion.

Provided these conditions can be met, an autonomous supply chain is feasible. There are many examples where such systems can be found. What is still far from feasible is an autonomous delivery system that's able to work outside of a rigidly controlled environment. 

Manufacturers have been taking steps to organise and control their manufacturing processes for a long time. These systems possess the raw intelligence needed to identify the parts required to assemble a complete article, such as a motor vehicle. In fact, most systems are sophisticated enough to allow different products to be manufactured, in any sequence, on a single line. It was a relatively small step for manufacturers to organize automated and semi-automated delivery of components from the incoming goods warehouse to the production line, as and when required and in the correct sequence.

Tools used by such systems include Part identification: Machine-readable sensors and barcodes to physically identify components, Robotic picking: Automated forklifts to locate and fetch items, Intelligent transportation: The use of autonomous vehicles as well as transportation conveyors to deliver parts to specific locations, Feedback: As components are used, automated orders raised for new components.

Most of the interest at present is on the autonomous delivery of goods from suppliers to customers. This is things like drones dropping parcels off to mobile ground troops and platoons of trucks driving autonomously on highways.

Currently, all such systems are still in development, especially in terms of mass deployment. Still, many exciting ideas are being evaluated. A drone linked to a delivery truck that flew autonomously from the delivery truck to drop off a parcel and return while the truck continued on its route was tested. Other exciting concepts include autonomous ships sailing the oceans and the use of autonomous delivery robots.

Artificial intelligence is taking up the pace when it comes to global logistics and supply chain management. As per a number of executives from the transportation industry, these fields are expected to go through a more significant transformation. The on-going developments in the areas of technologies like artificial intelligence, machine learning, and similar new technologies have the potential to bring innovation to these industries.

Artificial intelligence comes with computing techniques which helps to select large quantities of data that is collected from logistics and supply chain. You can put such methods to use, and they can be analyzed to get results which can initiate processes and complex functions.

The efficiencies of the company in the areas of network planning and predictive demand are getting improved with AI capabilities. Companies get to become more proactive by having a tool which can help with capacity planning and accurate demand forecasting. When they know what the market expects, they can quickly move the vehicles to the areas with more demand and thereby bring down the operational costs.

To avoid risks, anticipate events and come up with solutions, now techs are using data. The data helps companies to use their resources in the right way for maximum benefits, and artificial intelligence helps them with it more accurate and faster manner.

You can’t talk about artificial intelligence without mentioning robotics. Even though robotics is considered as a futuristic technology concept, the supply chain already makes use of it. They are used to track, locate and move inventory within the warehouses. Such robots come with deep learning algorithms which helps the robots make autonomous decisions regarding the different processes that are performed in the warehouse.

Apart from robots, artificial intelligence is also about big data. For the logistics companies, Big Data helps to optimise future performance and forecast accurate outlooks better than ever. When the insights of Big Data are used along with artificial intelligence, it helps to improve different areas of supply chain like transparency and route optimisation.

For AI in the logistics industry, coming up with clean data is a huge step, and they cannot implement without having such usable figures. It is not easy to measure efficiency because data comes from different sources. At the source level it is not possible to improve such data, and so algorithms are used to analyze data, enhance the quality of data, identify issues to attain transparency which can be used for business benefits.

When you are moving cargo across the world, it is always good to have a pair of eyes to monitor, and it can be best when it comes with state-of-the-art technology. Now you can see things in a new way by using computer vision which is based on artificial intelligence for the logistics.

Autonomous vehicles are the next big thing that artificial intelligence offers the supply chain. Having driverless trucks can take a while, but the logistics industry is now making use of high-tech driving to increase efficiency and safety. The significant change is expected in this industry in terms of assisted braking, lane-assist, and highway autopilot.

AI provides the supply chain with contextual intelligence which can be used by them to reduce the operating costs and manage inventory. The contextual information helps them to get back to the clients quickly.

Companies make use of AI along with machine learning to get new insights into different areas which include warehouse management, logistics and supply chain management. Some of the technologies used in these areas are AI-powered Visual Inspection to identify damage and carry out needed correction by taking photos of the cargo by using special cameras and Intelligent Robotic Sorting to sort palletized shipments, parcels and letters.

The opportunities to integrate autonomous drone logistics exist at every stage of the supply chain, where improved efficiencies, lower costs, safer work environments, and higher productivity levels are just a few of the returns organisations are seeing from their logistics drone investments.

Either your organisation prefers highly automated rules-based system to get supply chain work order request into hands of a technician virtually automatically, or a more manual system where Help Desk Dispatchers make decisions about when and who handles a particular work order.

1. Create, receive and route application-based work requests: Work request is basic communication tool for reporting supply chain problem so action can be initiated to get it fixed.

2. Obtain approvals as part of workflow if necessary: Generate workflows to mirror organisation processes for getting supply chain work done.

3. Receive supply chain alerts on critical issues in workflow: Allow for prioritising work must to be done and ability to work orders.

4. View comprehensive list of work orders in process: Provide supply chain activity feeds, grids and reporting capability to see what work has yet to be completed and how long work in backlog.

5. Highlight overdue work, or sort work orders on place, space, asset or technician basis: Offers  supply chain tools and reports so available information to keep the operations running smoothly.

6. Link related work orders: Being able to group work orders allows for more efficient assignment of supply chain work to be done.

7. Attach drawings and specs, etc.: See drawings, pages of repair manuals and other documents to speed up supply chain and maintenance process.

8. Define supply chain schedule: Schedule work to be done so field-levels can submit work requests or query requests to see when it will be done.

9. Create and update supply chain Task Schedule of pending work orders: Use task schedules to keep track of what work is being done and when.
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10. Schedule proactive  supply chain Jobs: Any work request can be made repetitive by filling out additional checks defining dates, times and frequency; add reminders
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Now Marines are saying if something is broken, don’t throw it away. “Instead, we think about how we might repair it.” For example, recent experiments have focused on field-repair of vehicle piston heads. Testing the resilience and capability of 3D print systems is part of the Marines’ experimentation, but an even bigger part is developing practice at spotting opportunities for 3D Print to keep parts in use longer that might otherwise be discarded. 

3D printing—particularly in metal—offers a promising way to obtain short-run parts in a hurry. For a business, the benefit is merely filling the order rapidly. But for the Marines, the benefit might be winning the battle by sourcing solutions to fix supply chain problems like a broken component required to return a downed piece of equipment to use. 

Manufacturing parts through a 3D printer can cut down on time and cost in comparison to ordering specialized parts, especially if there is no longer a viable supply chain available for a specific part. It also allows engineers to design and construct brand-new designs and are able to test them. This capability provides engineers the creativity to no longer be constrained to the typical methods of manufacturing, 

Is hybrid metal AM a part-making resource, a tool-making resource, a resource for repair? It depends on the need, and it depends on the perspective of the particular user thinking about this capability.

Headquarters Marine Corps is now looking at draft policy on additive manufacturing, and the feedback from the MAGTF experience is shaping that policy.

Marines involved in the additive manufacturing effort found all kinds of ways to innovate during the deployment. Within the size confines of the printer, the Marines found all kinds of things to print – wrenches, assembly parts, and protective covers to shield expensive equipment from repeated impact, and even a cover for a laser device that was on back order for seven months and was instead printed in a single day.

“If you have a cable assembly for your utilities, you can only order the entire assembly – but if you don’t need the entire assembly, you just need one little component, you could print that one component for a few dollars rather than have to order the entire assembly which may cost hundreds or even a couple thousand dollars spending on what the item is.

While DoD is interested in additive manufacturing, particularly downrange in operational scenarios where certain items might be harder to acquire, more work needs to be done by industry to certify 3D printed parts and prove that they can stand up to the stresses of military use.

Suppliers are developing “hybrid” applications, where 3D printing and traditional manufacturing techniques like forging are both used to make a qualified part. this technique is attractive for both the commercial and defense aerospace markets that need qualified parts but want to reap the benefits of 3D printing.

DoD is constantly struggling to find suppliers for parts on aging planes that the original manufacturers may no longer produce. In some cases, the companies that built the part have long exited the business, but the DoD requirements remain. Oftentimes we have parts that we can no longer get, and they’re in small numbers, so we can’t get interest in the part of an industry to build something when there’s only a small number and they have to retool or do that.

The only solution at the moment is to pay more. We often can find somebody, but at a really high price, and we’re stuck them because we don’t have any competition. We hope this will change in the near future, thanks to developments in 3D printing.

It’s hardly a new technology at this point, but things have changed for how DoD looks at the potential of 3D printing. The first is that the technology has become more widespread and adopted across the Pentagon. 

Where even a few years ago there was resistance to the idea a 3D printed part could be as reliable as a classically forged piece, there is now acceptance that parts printed via additive manufacturing can be secure and stable.

For a globally deployed force, getting something delivered in the right place at the right time can be challenging. DoD is is undertaking a variety of logistics initiatives to help ease that burden and make the force more lethal, and improve visibility of where things are and where they need to be in a decentralised way. That will give it the agility to be proactive, and enable the logistics support to be out in front of where operators need these things.

This logistics command and control will be absolutely essential to link the different capabilities to the  people to the different assets to provide the rapid response capability that a combatant commander will need. DoD can’t do this alone, but rather will require support from the industrial base, international partners and the joint force.

In terms of specific initiatives to improve service-wide logistics, additive manufacturing is playing a large role. DoD has been pursing this for some years and still has work to do, advancements in 3D printing allow the force to not worry about the supplying and ordering of parts cutting out the supply chain and reducing timelines for making parts.

The next phase for the service in terms of 3D printing is mainstreaming the process, which means transitioning from plastic parts to metal. Plastic works really well, but we don’t want to replace all aircraft parts with a piece made out of plastic.

“Can we find better ways to maintain these airplanes? Is there some new technology that can help us? Or some new repair processes?” 

The 3D printing process is one way to do that.

“We’re responsible for all the maintenance, repair and overhaul of all components that come off the aircraft."

The team has a wide variety of things to maintain, including many parts that would require an expensive bulk buy in the supply chain when they really need only one or two. Examples include mic switch knobs, crew compartment panels, sun visor brackets, and armrests. 

The unit already uses 3D printers in various sizes, but they want others with flexible technologies to support making parts that engineers haven’t necessarily thought of yet. 
The standby compass cockpit panel dashboard is something the unit is currently prototyping. 

Even with larger items, which require more durable parts and aren’t suited to 3D production, 3D prototyping streamlines the process by experimenting with designs that will be manufactured later with metal.

“You’re using the 3D printed technology in order to deliver a prototype that confirms form, fit and function … and then you can be confident of your repair solution or your new part, delivering that to the traditional, organic manufacturing unit that’s going to use multi-axis milling machines to cut metal into that part.

The in-house 3D process is much faster than going back and forth with outside suppliers for parts. “Once you get the geometry, you can print it overnight and have it the next day."

To identify the geometry of the part and print it takes only a couple days. “With diminishing sources of supply, the benefit for us is the flexibility and the agility to respond to a warfighter need.

“The benefit is speed. That’s our bottom line."

Marines have now deployed self-contained hybrid additive manufacturing facility called the “ExMan” unit, or Expeditionary Manufacturing. The ExMan represents potentially the most effective resource yet for enabling deployed personnel to be self-sufficient in providing for their own critical hardware needs, offering a model for how an established manufacturer might proceed with additive, because the Marines are following a learning curve comparable to what a machine shop might expect.

ExMan is already aiding and saving cost for Marines even though it has yet to be deployed and will provide a valuable alternative to existing supply of, for example, unmanned aerial vehicles—drones. These devices are still new, and prone to break frequently. As yet, there is no formal supply chain for needed parts, because there is no understanding yet of what replacement parts are key or how frequently they will be ordered. As a result, some needed parts have a lead time longer than one year. In response, the Marines have been keeping drones flying by making needed parts through 3D printing. 

One example of this relates to recognising the use cases. Marine team is developing and evaluating the capabilities of the ExMan. “Having hardware supplies at the point of need is always a problem. For instance, a broken steering-column pinion gear might render a Humvee inoperative, but obtaining this replacement part far forward in the field fast enough to matter might be close to impossible. To respond to problems like this, Marines have long done manufacturing in the field. Existing portable machine shops offer milling or turning capabilities. 

Marines are exploring the problem of the pinion gear. Is it now possible to solve a problem such as this one in the field? That is, is it possible to return the Humvee with this problem back to use quickly? The component is too difficult to make through machining alone, but perhaps straightforward to make through hybrid AM. For example, what about 3D printing gear teeth onto a piece of metal tubing? The “innovative mindset” consists simply of this: Recognising that many formerly prohibitive supply chain challenges are no longer prohibitive when additive is added to machining.

Yet a part like a pinion gear is too challenging for systems such as these, for multiple reasons. The part is too complex to make on a lathe or mill in an exigent setting, and carrying enough raw material to be prepared to make a part such as this would represent a problem in itself, since machining a shaft with gear teeth out of solid stock would mean cutting a lot of material away. 

Attempting this additive manufacturing at a tactical level was part of an “advancing the force” mission assigned to the MAGTF, to push the Marine Corps closer to its vision laid out in the latest Marine Corps Operating Concept. 

As the Marines look to implement the vision outlined in the Marine Corps Operating Concept, the 3D printing initiative to make parts for which the Marine Corps owns the design and can alter it as needed as mission requirements and the threat environment change – are providing a first look at what Marine operations in the next decade may look like.

“The Marines were coming back from deployment saying that we’re seeing quadcopters and things like that going over our positions, so we started to inject that into our training. 

“There’s not a lot the regiment can do on its own in order to defeat that threat, so we really worked on mitigating it – practicing good procedures, reducing our signature so we’d be less vulnerable to it. 

And then when we deployed we stepped it up a notch – because we were at so many locations across the theater and there are so many different approaches being taken by the joint force, we were exposed to almost all of them, so we tried to bring that together so we can harness that and build that knowledge for our Marines.”

Using the drones in MAGTF operating forward is the perfect setup for innovation and learning that comes from having the full range of Marine Corps assets operating under a single commander.

“It Provides a bias of problem-solving and looking for original solutions, because you have that mix of different capabilities.  Also, from an operational standpoint, “some of the best innovation comes when people have practical problems they’re trying to solve. It actually stimulates the creativity to advance the state of the art. That’s kind of what we lived every day.”

Many of the Marines involved already had experience with writing codes, using 3D printers, soldering and other skills needed for the drone assembly, so “just taking advantage of the natural talents we already had out there, we were able to pull them in and use them to our advantage. It helped retention – the Marines were very excited that we were able to do some things faster than we otherwise would have.”

The drone-printing effort contributed to several learning efforts. Hybrid Logistics Working Group had been stood up, with additive manufacturing being a prime focus for the group. 

“As we wrestled with additive manufacturing at the tactical level, whenever we came into a supply chain conflict we were able to tap into that group in order to start to address some of those questions.

“What we learned from that is, you need the push-pull, where Marines at the lower tactical level are trying to solve problems today, and you need that immediate feedback to the policy level so that they can remove barriers, because we naturally found them. 

“This is new and everybody is exploring it. Through that working group we were able to do it a lot faster.”

A 3D printer works by taking a three-dimensional image or model, and printing the part one layer at a time, upward from the bottom-most layer. The layers are fused together by some sort of adhesive agent. The printer may take hours to finish a part, depending on the level of complexity and size. 
This innovative capability enables maintainers to create one-off modifications of aircraft parts at reduced costs in terms of both time and materials to aid in the advancement of Edward’s unique test and evaluation mission.

But the dilemmas are these: Metal AM systems are generally costly, bulky relative to the size of parts they produce and difficult to use given the safety considerations necessary for handling powder metal. These factors are problematic for a machine shop, and prohibitive when it comes to the prospect of letting the Marines use metal 3D printing in the field.

And for the Marines, one more concern is that metal 3D printing ought to operate in conjunction with CNC machining within a single setup, for the sake of truly obtaining the part as fast as possible. That is, the Marines’ interest is in “hybrid” AM combining additive and subtractive operations. For all these reasons, a small CNC milling machine with an add-on metal 3D-printing head comprises the heart of a system Marines are evaluating for making repairs and replacements to hardware items in the field, far from traditional supply chains. 

Hybrid system offers an extreme version of the experience a machine shop might have in adding metal AM to its capabilities. The metal 3D printing head mounts onto an existing CNC milling machine in parallel with the metal cutting spindle. The resulting hybrid system can 3D print features and machine those features to tolerance within the same setup. 

Heads for metal 3D printing are supplied via various methods of metal deposition, including laser melting of powder spray, arc melting of wire feed, and high-velocity cold spray of metal powder. In the ExMan, the wire-fed metal 3D-printing head mounts parallel to the spindle on a milling machine, producing a system that can build 3D features and then mill and drill them to precise tolerances within a single setup. 

The input material is solid wire instead of powder and because current is used to melt the material rather than a laser. It’s not a normal arc-welding process, which generally includes some level of porosity, but instead a lower-heat approach that enables effective deposition of metals at full density.” 

This 3D printing by deposition, meaning placing metal along a precise tool path rather than using a powder bed, means a complete 3D part can be built up onto a flat surface. It also means a 3D feature can be built just as easily onto an existing part, with the existing part used as the starting work surface. In short, hybrid manufacturing is a resource for repairing or modifying existing components every bit as much as a resource for making components from scratch.

The search for metal 3D printing capability was blocked at first because powder-bed selective laser melting systems were seen to be unsuitable for deployment and use in the field, given their cost, size and safety requirements. 

Now the challenge and the opportunity of metal AM lies in the mindset change necessary to reevaluate challenges such as this. Marines—just like shops adopting AM—need to rethink long-standing assumptions about what kinds of parts can now be fabricated quickly.

Building and fielding the drone allowed Marines the opportunity to develop an understanding of how these things are put together and manufactured locally. And then how can it be used, what are its strengths and weaknesses, what are its limitations so that the Marines  better understand what tools can be applied to counter the threats that were being used against them?

To meet objectives, we commissioned this case study to not only optimise current equipment product support Job Site operations and enhance dedication to Field-Level Troops product support services, but also provide the services with the tools, templates and real world strategies so we have capacity to sustain these improvements into the future.

We established the following Job Site scope areas, which framed the objectives of this Case Study:

1. Optimise allocation of Job Site product support resources, including oversight of routine, peak & specialty work orders

2. Design product support programmes for field level unit outreach at Job Sites, including mission-driven reporting & surveys

3. Propose product support approach for receipt of individualised Job Site service level work orders with field-level units

4. Maximise "wrench turning" produce at Job Sites, including product support programmes for continued training, incentives & performance

5. Establish core product support Job Site services, specialised services evaluation & changing conditions.

6. Enhance Job Site performance metrics, including key product support performance indicators, techniques & reporting

7. Provide framework for evaluating the Job Site costs/ benefits of expanded product support services to existing or new troop units

8. Conduct Job Site space requirements assessment, addressing barriers to efficient product support operations
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9. Optimise Job Site operations, including product support policies, procedures & performance requirements for on-hand stock parts/tools
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10. Evaluate Job Site product support work order rate-setting systems and recommend adjustments to rate setting & replacement ​

A digital model is employed in simulations and as a digital twin, but those two methods differ in how they’re used, especially when it comes to the level of detail required.

Digital models are used in a variety of applications and development workflows. They can vary in degree in how they match a physical device. Models can be used as a digital twin or in simulations in addition to models used to design a system.

Current design models are a representation of a physical entity, and it’s typically used to describe what a physical entity will look like. This can be a 2D or 3D architectural model of a building or a device such as a car. The model provides dimensions and possibly descriptions of materials that would be used in construction.

A digital twin is a model that has the latest sensor data associated with a matching physical device. A digital twin is often used in process control and product operations to help monitor or control a remote system. The model doesn’t necessarily need to exactly replicate the physical device. It may even be a 2D representation, but it’s typically combined with other models to provide a context for the information that can be presented or examined.

Most process-control systems that deliver sensor data to a control program provide at least a limited digital model of a component within the system. However, with new developments the model can now be more robust and combined with other tools like Virtual Reality. For example, a heat sensor might show what part of a device is hotter by showing that portion of a model using false coloring with red indicating higher temperatures.

Digital models used in simulations often have the same type of sensor information and controls of a digital twin, but the information is generated and manipulated as part of the simulation. The simulation may replicate what could happen in the real world, but not what’s currently happening.

A digital twin can be used as a starting point for a simulation model that extrapolates how a system would operate in the future. The degree and accuracy of these simulations can vary depending on the implementation of the simulation and what type of results are desired. 

For example, a digital twin of a gas engine could simply track material consumption, power output, and heat output, but not the actual movement of components within the engine. This level of simulation may be sufficient for checking out how a vehicle would operate when using such an engine.

On the other hand, if the desired results involve how durable a particular part would be within the engine, then the level of detail with respect to the engine would have to be greater. Likewise, simulation of an autonomous  vehicle needs to know the output and control characteristics of the engine, but not the details within the engine.

Digital twins, and simulation models may share all of these aspects, depending on their function, although often a specific tool will create and manipulate a model. For example, a drawing package may be used to create a digital model, and then a process control system would use that model as the basis for a digital twin to provide the linkage between the digital twin sensors and controls with those in the real world.

Likewise, a model used in simulation may have characteristics added so that physical simulation is possible. This might include details about the virtual materials used in the model, which in turn would enable the simulation tools to replicate how the model will react during the simulation.

Any system component may incur different models that vary in the degree they replicate the actual component, as well as how they react and what kind of information can be associated with them. 

Models may have different purposes, but they may also share common descriptions such as details about dimensions, material attributes, etc. Many models will be used by multiple applications for different purposes, from showing the status of a current system to simulating a device that has yet to be constructed.

The concept of a Digital Twin is mostly applied to the case where conclusions are drawn from a physical operating product and network platform. But in the case of virtual prototyping, the Digital Twin concept is applied to an old practice in mechanical hardware.

One approach to developing a smart connected product, one that hooks up with an network platform, is to just build it. Just put it together piecemeal. Throw some sensors on a product. Wire that to some kind of embedded system. Wire that to your antenna. Start sending data to the network platform. You and your organisation can actually learn a lot from going through that exercise. 

While that needs to be done, you will quickly run into limitations on the experiments you can conduct with physical prototypes. Swapping out a sensor isn’t easy when it’s soldered in place. There might not be room, physically, for the sensor you really need for accurate measurement. You might run into too much electromagnetic interference for the antenna you planned to use.

Working through these issues is new to some organisations as they transition traditionally mechanical products to smart connected ones. However, the problems associated with resolving issues through physical prototyping aren’t new. 

In fact, physical prototyping is an old concept when it comes to hardware. Long ago, mechanical and electrical engineers figured out that modeling and simulating a design virtually means you are more likely to get it right the first time when you get to prototyping and testing. 

The benefits of an approach utilising virtual prototyping with digital twins are many. You have fewer rounds of prototyping, saving money and time. You stay on schedule. You stay on budget.

So while virtual prototyping is new to some organisations, this approach has advantages when applied to the development of linked smart, connected products and network platforms. In fact, Digital Twins are a key enabler.
You first need to set up the digital model component of a Digital Twin .

Numerical Models use machine learning and artificial intelligence. These applications or agents either extrapolate that data and/or correlate data to existing events. Both are an effort to predict future behaviour.

1D Simulation models are a combination of flow diagrams with equations or formulas behind the blocks that simulate the performance of embedded multi-disciplinary engineering systems. These models can provide deeper insights into ongoing operation.

3D Simulation models, often in the form of multi-body dynamics, are commonly used to predict the dynamics and structural performance of products. to  help characterise requirements of ongoing operations.

For scenarios based on engineering physics or asset operation, no prototype or operating product exists. As such, there is no sensor data to feed this digital model. However, the model  can be fed historical sensor data from prior products or even from past physical tests or operational data. 

In the worst case, a set of inputs can be modeled using statistical analyses or even a higher level simulation, such as a  multi-body dynamics model. This creates a set of input data that can be fed to the digital model.

The combination of the digital model and the input is enough to get started. You run the model as a simulation,  generating data from virtual sensors, which are points of measurements from the simulation. 

In this application, that virtually generated data is used instead of physically recorded data from sensors and fed to the network platform as if it were receiving streaming data from a running product. Only, in this case, there is no physical product. There is only a virtual product that is running in a simulation.

In this scenario, you overcome many issues that you might experience when trying to physically prototype a smart, connected product. You can change anything related to the sensor configuration, including placement or type.

You are not limited by network bandwidth other than the limitation between the compute resource running the Digital Twin and the one running platform. You can change the product’s design in terms of mechanical or electrical hardware, embedded systems and more. There is a tremendous amount of flexibility with this approach.

Engineering organisations have been using the “Get It Right the First Time” principle to avoid all sorts of difficult development disruptions for decades. At this point, applying it to mechanical and electrical hardware is a no-brainer.

Digital twin is becoming increasingly relevant to systems engineering and, more specifically, to model-based system engineering. A digital twin, like a virtual prototype, is a dynamic digital representation of a physical system. However, unlike a virtual prototype, a digital twin is a virtual instance of a physical system “Twin” that is continually updated with the latter’s performance, maintenance, and condition status data throughout the physical system’s life cycle. 

Here we present an overall vision and rationale for incorporating digital twin technology into systems engineering and examine the benefits of integrating digital twins with system simulation and  networks and provide specific examples of the use and benefits of digital twin technology in different industries. We are making a strong recommendation to make digital twin technology an integral part of system engineering model experimentation testbeds.

While virtual models tend to be generic representations of a system, part, or a family of parts, the digital twin represents an instance i.e., a particular system or process. Digital twin technology has the potential to reduce the cost of system verification and testing while providing early insights into system behaviour. 

The digital twin is ultra-realistic and may consider one or more important and interdependent vehicle systems such as airframe, propulsion, avionics and thermal protection. The extreme requirements of the digital twin necessitate the integration of design of materials and innovative material processing approaches. 

The context of operation of the digital twin involves an instrumented testbed in which model-based systems engineering tools e.g., system modeling and verification tools and operational scenario simulations e.g., discrete event simulations, agent-based simulations are used to explore the behaviour of virtual prototypes in a what-if simulation mode under the control of the experiment.

Insights from the operational environment are used to modify the system models used in the virtual prototype. Data supplied by the physical system is used by the virtual prototype to instantiate a digital twin. Subsequently, the digital twin is updated on an ongoing basis, so it  mirrors the characteristics and history of the physical twin with high fidelity. 

The virtual system model can range from lightweight models to full-up models. The lightweight models reflect simplified structure e.g., simplified geometry and simplified physics e.g., reduced order models to reduce computational load especially in upfront engineering activities. These lightweight models allow simulations of complex systems and system-of-systems with fidelity in the appropriate dimensions to answer questions with minimal computation costs. 

A digital twin is first created when the physical system can start providing data to the virtual system model to create a model instance that reflects the structural, performance, maintenance, and operational condition characteristics of the physical system. The physical system can be said to be ”fit for purpose” if the digital twin’s behaviour is analyzed and can be appropriately adjusted for a variety of contingency situations. 

For example, computer simulations of the braking system of a car can be run to understand how the vehicle model would perform in different real-world scenarios. This approach is faster and cheaper than building multiple physical vehicles to test. However, computer simulations tend to be confined to current events and operational environments. In other words, they do not have the ability to predict vehicle responses to future/envisioned scenarios. Also, braking systems are networked systems, not merely a combination of mechanical and electronic subsystems. 

Being a virtual representation, a digital twin is easier to manipulate and study in a controlled testbed environment than its physical counterpart in the operational environment. This flexibility enables cost-effective exploration of system behaviours and sensitivities to various types of system malfunctions and external disruptions. 

Today any digital version of a system, component, or asset is called a digital twin. With this larger interpretation, there are several questions that arise including: 1) Does a physical system have to exist before a digital twin is created? 2) Does the physical system with onboard sensors and processor need to report performance, condition, and maintenance data to the virtual system model before the latter can be called a digital twin? 3) Does the definition of a digital twin have to change because any physical asset today can be made smart with the advanced network technology?

These questions and many more need to be answered about the digital twin and its various uses. Here we present several levels of a virtual representation. Each level has a specific purpose and scope and helps with decision making and answering questions throughout the system’s lifecycle.

Pre-Digital Twin Virtual Prototype is created during upfront engineering. It supports decision-making at the concept design and preliminary design. The virtual prototype is a virtual generic executable system model of the envisioned system that is typically created before the physical prototype is built. Its primary purpose is to mitigate technical risks and uncover issues in upfront engineering. 

Like most model-driven approaches, virtual prototyping involves a model of the system early in the design process. However, a virtual prototype is not usually used to derive the final system. This is because a virtual prototype can be a “throwaway” prototype or a “reusable” prototype. The latter can be used to derive the final system. A virtual prototype is mostly used to validate certain key decisions about the system and mitigate specific technical risks early in the design process. 

For example, a model could consist of ideal wheels with dry friction contact patch rolling on a surface. It employs a simple i.e., low fidelity model of differential gear to distribute torque equally to the wheels, and reflects properties such as inertia, mass, fixed translation and torque to realise a basic structure of vehicle with mass properties defining gravity and global coordinates system. 

The sensors measure absolute position, velocity and acceleration. The trajectory control module provides torques values to the steering and differential gear mechanism. Such low fidelity models can be used, for example, in testing, planning, and decision-making algorithms related to, for example, trajectory control in autonomous vehicles performing lane changes.

Digital Twin virtual system model is capable of incorporating performance, condition and maintenance data from the physical twin. The virtual representation, an instantiation of the generic system model, receives batch updates from the physical system that it uses to support high-level decision making in conceptual design, technology specification, preliminary design and development. Data collection from the physical sensors and computational elements in the physical twin includes both condition status data e.g., battery level, mission performance data e.g., flight hours. 

The data is reported back to the digital twin which updates its model including the maintenance schedule for the physical system. Since interaction with the physical system is bidirectional, there is sufficient  opportunity for the physical twin to use knowledge acquired from one or more digital twins to improve its performance during real time operation. 

At this level, the digital twin is used to explore the behaviour of the physical twin under various what-if scenarios. Being an executable digital representation, it is easy to manipulate when exploring the behaviour of the system in the controlled simulation environment of the testbed. Any deficiencies discovered are used to modify the physical twin with the changes reflected in the digital twin. 

For example, a model could include a passenger car with power split hybrid power train. The chassis model has single degree of freedom with mass-and speed-dependent drag properties. The braking subsystem which uses brake pedal position resulting from driver action to calculate brake torque. This affects the driveline. The driveline model consists of four wheels with front wheel drive and ideal differential. The power split device consists of ideal epicyclic gear without losses. An ideal battery with constant voltage source powers DC motor model with inductor, resistor and emf component connected to shaft hub. The engine model with flywheel consists of drive-by-wire accelerator, where the accelerator inputs are converted to output torque. Road models are used to define the inertial frame, gravity, air temperature, wind velocity, gas constant for air, and air pressure. 

With respect to developing smart, connected products linked to network platforms, we’re in the early stages. But soon, some organisations are going to want to sidestep all those difficult development problems they’re experiencing. 

Leaders are going to realise that building and testing multiple rounds of prototypes is unacceptable. They’re going to realise that many delays completely undermine their competitive market position.

As a result, organistions are going to want to adopt more proven and standardised practices. Virtually prototyping with Digital Twins is not there yet. But given the rush toward new networks, the demand for this practice is expected to only increase.

So now we have this concept of using a Digital Twin to virtually prototype a smart, connected product linked to network platforms. What does that get you? Interestingly, it allows organisations to answer a set of serious questions.

The powerful aspect of this use of a Digital Twin is that you can answer these questions without any physical prototype or testing. Everything is digital. So you are getting smarter about the operation of this product without spending any money to build anything physical.

1. Is the right data being captured from the right physical/virtual sensors in product?

2. Do we need to use a physical sensor to capture this data, or can it be a virtual sensor?

3. Is edge processing required for the sensor data processed on product or network platform?

4. Is the mechanical design right for this product in the context of electrical systems configuration?

5. Are there changes that should be considered to improve placement of sensors?

6. Are there changes that should be made to avoid electromagnetic interference?

7. How will the connected product and network platform work to fulfill requirements?

8. What conclusions can be drawn from the data once it is in network platform?

9. What data event precursors trends must be crunched manually or machine learning be applied?
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10. What data trends are precursors to events critical to the smart, connected product?


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Creating a digital twin involves building a comprehensive digital representation of the many components of a physical object, from outer features to the networks inside. Companies develop digital twins by attaching sensors to their products, assets, or equipment.  Building digital twins will give you digital versions of materials, 2D drawings, and 3D models. More importantly, you’ll have an accurate view of how your devices are operating in real time. 

Engineers leverage simulation tools to develop innovative and high-performing products in a virtual design space using design  technology. Imagine what you could do as a designer or engineer if you could take your simulation a step further and study a digital working copy of a product under actual working conditions. It could help you rapidly optimize the design, life, and maintenance of a product. 

Companies are collecting real-time operating data from product-mounted sensors. They use the data to create an exact replica of a working product, process, or service. This exact replica, called a digital twin, is a simulated model in a virtual space that performs under real-world conditions to help companies find performance issues, schedule predictive maintenance, reduce downtime, and minimise  expenses. 

An example is visibility into real-world bearing operating temperatures and the downstream effects on tolerances right within a digital model. By studying the digital twin under actual working conditions, companies can see the product in action. Engineers can make more informed choices during the design process and use digital twins to make their simulations more accurate. 

For companies already using engineering simulation to design products, connecting the simulation to the physical product in the field is necessary to deploy the digital twin solution. For companies new to simulation, the engineering team must first build the 3D product model, optimise its performance, and replicate the real world in which the product system would operate. Re-using this data in similar product simulation scenarios for future product development saves time and money. 

A digital twin is a complete 360° digital representation of a physical asset, i.e., a pump, motor, turbine, or an entire plant. By creating digital twins of physical assets, data generated by the asset during its design and operational life is collected, visualized and analyzed, enabling unified life-cycle simulation. 

During the design phase, the digital twin allows for the analysis of processes, equipment and operations through multiple simulations for optimal safety, reliability and profitability.  At the concept phase, fast evaluation of design alternatives are analyzed and continuously iterated through variable specifications allowing integrated asset modeling of interacting but separate systems. Each iteration provides a more complete data-set aiding in agile development. 

The basic notion is that, for every physical product, there is a virtual counterpart that can perfectly mimic the physical attributes and dynamic performance of its physical twin. The virtual twin exists in a simulated environment that can be controlled in very exact ways that cannot be easily duplicated in the real world, such as speeding up time so that years of use can be simulated in a fraction of the time.

Hyper-accurate models and simulations offer engineers and product designers unmatched insights across the entire product development cycle. Still, digital twins are more than just an progression of digital models, although their goal is similar: Higher quality products and better product support at less cost and less effort. 

For decades, engineers and designers have heavily relied on design application tools to digitally capture their ideas for physical objects as parametric models. Even today, more complex tools allow for the simulation of certain characteristics such as thermal properties or stresses and strains. 

While use of simulations in product design is nothing new, they have historically relied on relatively small data sets or engineering assumptions when making predictions. Digital twins, however, have access to huge data sets thanks to sensors that monitor/measure literally every facet of a product's lifecycle and fed back into an iterative design-manufacture-observe-improve loop. 

Once a product leaves the factory and is acquired by an end-user, the digital twin can begin to feed off real-world data collected by the onboard sensors. This is where  the concept of the digital twin reaches its full potential. Sensors in the end item itself will track key performance characteristics of the device as it operates in real-world conditions. Comparing actual telemetry against the predictions of the various aspects of the digital twin model yield insights only dreamed of until now. 

A useful loop results from this level of integration of the physical and virtual. Not only can the digital twin be improved based on real-world data, but future iterations can also be improved based on better understanding of actual data from end users. In some cases, where changes can be made through an update, products that have already been deployed can also benefit from lessons learned through using a digital twin. 

Being able to get that feedback to a company is invaluable. Not having to rely on a customer to call a help desk and to have that data fed into a digital twin to influence future design iteration is even more incredible. In addition, it would be almost magical if a customer received an email from the company proactively describing steps they could perform to minimise the failures that they might be experiencing without having the customer even place a call or email in the first place. 

Beyond the obvious use of  rich datasets in maintenance prognostics, digital twins could have profound impacts on the design and engineering of subsequent product iterations. Understanding how a product is actually being used in an objective and data-driven manner will lead to faster development cycles and greatly reduce the time to detect product defects or identify useful tweaks, thus reducing waste by allowing manufacturers to make real-time improvements to the products still coming off the assembly line. This can translate to huge savings by avoiding costly rework. 

Digital twins are not limited to assessing tweaks to physical properties of a design. Digital twins can also make it easier to study the impacts that tool revisions have on performance. Various configurations and settings can be rapidly tested and assessed to determine which ones will deliver optimum performance. Updates could then be pushed out seamlessly to all the devices, leveraging the same network connection that initially sent the data used to identify improvements. 

Technology can continuously monitor, collect data, and conduct analysis. Meanwhile, humans can keep their attention on higher-level work such as exploring implications of various complex courses of actions and making informed decisions. 

Embedded platforms, with their computational horsepower, efficient sensors, and reliable communications hardware, are critical to the collection and dissemination of telemetry data. This data is necessary to make digital twins smart enough so that their function is worthwhile. Then, all that data can be pumped into databases that are rapidly analyzed. Throw in the possibilities from Artificial Intelligence systems to analyze and make improvement recommendations, and it’s possible that products could improve over time without any human intervention. 

The concepts of the digital thread and digital twin have been spearheaded by the military aircraft industry in their desire to improve the performance of future programs. They apply lessons learned through these digital technologies to current and upcoming programs. 

The data in the digital twin of an aircraft includes things like specific geometry extracted from aircraft 3D models, aerodynamic models, engineering changes cut in during the production cycle, material properties, inspection, operation and maintenance data, aerodynamic models, and any deviations from the original design specifications approved due to issues and work-arounds on the specific product unit. 

The benefits expected from having a digital twin for each product unit include ore effective assessment of a system’s current and future capabilities during its lifecycle and early discovery of system performance deficiencies by simulating results way before physical processes and product are developed  Optimisation of operability, manufacturability, inspectability, and sustainability leveraging models and simulations applied during the entire lifecycle of each tail number. Continuous refinement of designs and models through data captured and easily crossed referenced to design details. 

Product Engineers working together with Manufacturing Engineers create a 3D model linked to visuals for production process instructions. Product characteristics are linked to 3D models and extracted directly out of designs into conformance requirements.

Conformance requirements are linked to manufacturing process and inspection instructions. As-built data is delivered by Production along with product unit to customer and is made available for sustainment services to continue evolving the unit’s data during operation and maintenance services  Product design changes follow the same data flow and automatically update downstream models, references and instructions. 

The digital twin is a virtual copy of the real-world thing, or a complex system of connected things. It’s not just a 3D model—it’s a living model in 3D that sees the vehicle as part of a complex technology ecosystem of electronics, navigation, communication, collision avoidance, and so on. Engineers can analyze how a vehicle performs not just in its physical environment, under every condition imaginable, but over its entire lifecycle, from an early-stage digital prototype on screen to its last day on the road. 

Aviation engineers today can use the digital twin to pinpoint when and under what conditions, or after how many hours of flying a critical part or sensor will fail. Similarly, rocket engineers are putting digital twins to work to predict and verify performance of the lightest possible materials and payloads, long before they ever perform costly tests on the launch pad. Digital twins are being used not just to model how such physical assets perform, but—longer-term—how ever-more complex systems of assets and humans will behave together as a whole. 

Emerging technologies enabling the  digital twin include simulation tools—the heart of the digital twin—which has come a long way since the early days of 3D design. Simulation tools today can codify, replicate and virtualise the performance of physical products and systems, all based on the hard-wired laws of physics. Digital twins are essentially complex simulations of any number of components in action like aircraft jet engines during takeoff but based on true operational data generated, over long periods of time, by sensors from every critical part. 

How might digital twin value emerge in a few years with an autonomous vehicle? Engineers will construct a digital twin before they design or build the real thing—enabling dramatic cost savings and accelerating time to market. Designers will collaborate from the outset with operational teams and data analysts to begin gathering different data types to start modeling and verifying how future products will perform under every condition; how different types of drivers will interact with it; what its vulnerabilities are from a maintenance and breakdown standpoint. 

Building the digital twin might start with the physical components. Engineers can pool data on type of motor, suspension, chassis and aerodynamic body they want to tap into, and the materials they’re built from. Then they would start adding new data layers—such as operational data logs of similar models, or traffic data  to model performance in different operational scenarios. Engineers will pile up all that data and start tapping into machine intelligence tools to design and model out their ideal product—long before anything hits the assembly line. 

Then comes the promise of the digital twin over the lifecycle of the vehicle. With digital twins, engineering and operations teams can see not just what is happening at any given time, but why. They can speed up simulations in operation and productivity, pinpoint when, why and how breakdowns will occur, and reduce the costs and risks of unplanned downtime. 

Engineers are building digital twins of physical structures—dynamic, simulated models of the real thing, powered by the massive amounts of data that a single structure generates around the clock, such as physical specs, energy consumption and cost data, equipment parameters and live occupancy data pulled from elevators. The promise of all this is to use digital twins for everything from predictive maintenance and optimised facility management to streamlining workspace design based on the data flows showing how real people truly use the physical space. 

Imagine a manufacturing system that, on first glance, might function like any other modern system. While seemingly ordinary, the automated machines in this system experience significantly less downtime than you’d expect. This is because each machine is being operated alongside a virtual, real-time model that corresponds to all of the dynamics and components inside the real machine. Whenever the machine might be close to malfunctioning, the virtual model is two steps ahead, either making slight adjustments to its controllers, or notifying a maintenance staff before the issue causes downtime. 

This approach to modern, efficient machine design is quickly becoming a key technology for reducing costs and ensuring smarter products. The technology behind this process uses the digital twin, and by using modern system-level modeling tools, they are becoming an important part of the virtual commissioning process. 

For those who are just getting started, a digital twin is a dynamic, virtual representation of a corresponding physical product. These models can range widely in their purpose and fidelity, but they serve as a powerful connection to the product for diagnostics, design changes, and an important new process called virtual commissioning. Companies are using digital twins across industries, allowing them to optimise their products in ways that were previously impossible. 

Initially, digital twins were typically created once the physical product was in operation. Using vast amounts of data from sensors on the product, predictive models could be created to assist with diagnostics and improvements. By using a system-level modeling tools , the creation of a model-driven digital twin can begin alongside the design process. Meaningful digital twins can be created before a physical product is finalised, allowing for a powerful test platform to validate product performance earlier than ever. 

During virtual commissioning, engineers can import their designs directly into a simulation tool to create a high-fidelity model of their system dynamics. This model gives the engineer new abilities to get quick insight into system integrations issues, long before the expensive commissioning stage. They can import their motor parameters, define their product’s motion profiles, and have easy access to a full set of results after simulation. 

Systems are tested against a high-fidelity, functional model of the physical product, giving engineers a much clearer expectation of how their automated designs will perform after physical deployment. Every issue caught at the virtual commissioning stage then becomes one less unexpected issue that might crop up during physical commissioning. Catching even a handful of issues early can often save companies huge amounts of time and money, since fixing issues on a virtual platform is far easier than revising physical products. 

The power of model-driven digital twins doesn’t end with virtual commissioning. By having a high-fidelity model paired with the physical product, there are benefits along the entire design process – even after the commissioning process itself. Companies who have adopted model-driven practices with digital twins are using these models throughout the operation of their machines, using them for real-time diagnostics, as the inline digital twins can provide information about torques, inertias, and more. This helps ensure less downtime, as issues can be spotted early and fixed faster. Many companies also experience the cost-savings of using a digital twin, as the high-fidelity model can give them much of the information that they had previously needed to obtain from expensive physical sensors. 

As the cycle of innovation quickens, digital twins are becoming increasingly popular across industries to reduce risks and get new products to market faster. With  advanced modeling tools, model-driven digital twins are securing their place as an essential component for the modern design process. 

Part of the reason for creating a digital twin is to keep the list of features straight for each individual product. In high-volume production, that includes tracking the multiple features that change from product instance to product instance. The digital twin keeps a specific record for each product. Large auto manufacturers produce so much they want to keep the information on each vehicle as precise as possible. You don’t have all of the engineering information—that would be too overwhelming—but you have digital threads that lead to that information in case there are problems with the vehicle. Then you can follow the digital thread to the product lifecycle data.

In addition to the value of a digital twin on the manufacturing line, there is also value in capturing data when the product is in the field. The digital twin is also designed to send info back to the automaker while the vehicle is being used. You continue to communicate with the vehicle through some connectivity. You can look at the fleet or individual vehicles, so you can determine maintenance or upgrading needs and alert the customers.
 
So, who is actually utilising digital twin technology? Is it being used now? “We see examples of digital twin use in aerospace and defense. We have a customer who provides training and they actually use a pretty good digital twin in their training.” 

1. Formalise planning development, integration, and use of models to inform enterprise, programme decision making, support engineering activities to digitally represent the system of interest 

2. Ensure models are accurate, complete, usable across disciplines to support communication, collaboration and performance and decision making across lifecycle activities 

3. Provide enduring, authoritative source of secure authentication with access/controls to establish technical baseline, product digital artifacts, and support reviews for accurate decision making 

4. Incorporate technological innovation to establish end-to-end digital engineering enterprise and foster conditions for productive step advances towards goals 

5. Enable end-to-end decision making using advance human-machine interactions

6. Establish mature supporting digital engineering activity infrastructure to perform activities with connected information networks 

7. Develop, mature, and implement technology tools to realise digital engineering goals and share best practices using models to collaborate with stakeholders 

8. Improve digital engineering knowledge base, policy, guidance, specifications, and standards and streamline contracting, procurement and business operations 

9. Lead and support digital engineering transformation efforts, vision, strategy, and implementation to establish accountability to measure and demonstrate results across programmes 

10. Build and prepare workforce to develop knowledge, competence, and skills with active participation and engagement in planning and implementing transformation efforts


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​We’ve been grinding away at this Maintenance/Modernisation business, making incremental progress. We face many challenges. We commissioned a deep and comprehensive report on Ship Readiness & how surface ship Maintenance/Modernisation efforts contributed to that.  
 
We were tasked to go in and  understand, start codifying, addressing the gaps & issues. We have made progress in our ability to understand the business from end to end. 
 
The one modification is that we started out with several main Blocks, but as we started to apply whatever heat and light we could to an organisation that includes about thousands of civilians and sailors, many more private sector Wrench-turners. There was a missing piece that we need to get at – Alignment/Oversight. 
 
Alignment/Oversight is probably the single biggest challenge before us as we consider how many different organisations that have some effect on whether we plan, implement and stabilise it right as to requirements, flowing money into the process at the right time to do proper planning and execute to a schedule/plan that we all agree to at the beginning. We are challenged in that area. 
 
We have mentioned that there is a lot of interest in that area, and NAVSEA is going to take the lead in getting that right.  
 
A Quick update on the Main  Rocks: First, Assessment, Planning a and Policy. We have a programme called “Total Ship Readiness Assessment.” This is a term pulled right out of the Submarine Playbook, which is funded to the mandate of conducting assessments for every ship in the programme. We plan for it to not be under resourced by one minute or one dime for execution 
 
The challenge is that we’re working w/ the commanders [RMC side] on this—to make sure it is aligned with the ship schedules and the “Ship Readiness Manual [SRM]” – some early learning ground for us.  
 
The programme was challenged a several years back when we slowed down funding on the Operations & Maintenance Side, putting some perturbations in to the full implementation, but, again, but we  have seen an increase in levels of  Resourced programmes.
 
We have a lot of resources or process to go out and do assessments, and as we know –assessments done right, and at the right time inform a work package that is very important.  
 
Not as much progress on sustainment programme as we would like but maybe it just took as much time to become operational, to move this one a little further down the road.   
 
This one is about grouping or aligning all the resources for life-cycle assessments of systems/equipment and how they perform in common platforms that looks across all surface ships, all systems and that we’re doing the right life-cycle assessments & activities to fight our way through obsolescence  & readiness system & how they are designed, and how we go and approve that. 
 
Our intentions are to single it all up so when we talk diesels, for instance, it’s not just amphibious diesels, that it’s diesels across all surface ships. So that’s the sustainment programme.  
 
We have made great strides in engineering requirements for maintenance, to be able to deliver on an expected service life at the right time on the platform that we envisioned at the time we engineered it at the beginning so it gets 35-40-45 years. 
 
Even if we change a platform, we now have the ability to go look hard with the right engineering rigor at exactly what it’s going to take to get 5 more years out of the ship—these are bedrock requirements.  
 
Nearly every ship in the Surface Navy is in the programme, but there are some exceptions—lack of investments early on in  LCS left us with with problems coming in on the left hand side, but by it became an easier path to get every ship that can be in the programme in the programme. 
 
Many ships have completed their first engineering maintenance period, or planning for the process of having that availability. This is one where we just need to stay the course—the leadership is solid.  
 
Again, much of the process is lifted right out of the Submarine Playbook & these requirements are flowing into the budget process. Every ship’s requirement is treated equally and most requirement for engineering maintenance that we have asked for we have gotten the budget to go & execute that.  
 
Another debate is what a year of execution looks like in the flow-down. We are succeeding in this area, meeting requirements & the budgets are moving right along with that so we can go and execute.  
 
The challenge for us--we are working very aggressively on this—is taking what we know about the requirements & aligning that with modernisation requirement that’s coming together for the same availability and telling the fleet early on exactly how many days & weeks that is going to take to accomplish all that work.
The Navy is projecting a significant increase in ship maintenance and modernisation work , and we’re concerned private industry does not have the capacity to keep up.

We’re concerned in the out-years we will not have the private yard capacity we need to handle all the force maintenance requirements. Private yards have done a good job absorbing work that has come their way during the force reset, but the prospect of a still greater maintenance workload in the out-years, with additional LCS coming online and a potentially larger force overall causes concern.

“This concern extends beyond dry dock capacity shortfalls, which still loom large. Forward Deployed Naval Force is at capacity, for example. There is simply more work than the current workforce here can accomplish. We’re pushing to expand the set of yards capable of and familiar with performing Navy work. 

At the same time, the Navy is trying to boost its sailors’ ability to perform more maintenance work on the ship without outside assistance. While this wouldn’t make much of a dent in the looming surge in workload, it could cut down on contractor maintenance costs, and it would lead to a more self-sufficient fleet capable of operating in complex environments.

In the case of LCS, the ship concept was designed around a minimal crew, with the bulk of maintenance activities being conducted by contractors. Though the crew size has been increased over time, the LCSs have come back to port about every 25 days for a contractor-led maintenance availability, which includes basic tasks such as monthly system checks

LCS crews will begin taking on more maintenance responsibilities themselves.
“It’s clear what the operational fleet’s demand is for us, which is to make sure the ship can be maintained, and the sailors where possible can do that. So we are focused on training for the sailors, and making sure they had the equipment, the spare parts, the technical documentation and more to help the ship’s crews and sailors at the Regional Maintenance Centers conduct more LCS work themselves.

“The key is to increase sailor ownership and decrease the reliance on contractors and original equipment manufacturers. The Littoral Combat Ship manning does not support shifting the entire maintenance workload to the crew, as their capacity is limited. But we’re still committed to maximising the amount of planned maintenance that we perform by the LCS crew.

One of the frustrating things for the crew was, it was just kind of the way we set it up, but contractors would come aboard and do the work but the sailors would have to hang all the tags … so it was kind of like, rather than having the sailors do the work, we would just have the sailors do all the setup and teardown, and then the contractors would step in and do the work and the sailors would watch them do the work. It’s crazy. There’s some maintenance items that are appropriately done by the depot – reset and safety … but the day-to-day weighing of CO2 bottles or, we just had to get after that.”

There’s still an ongoing conversation on the division of labor between sailors on the ships’ crews and the Regional Maintenance Centers, along with the role of contractors.
Beyond LCS, though, we have concerns about the declining ability of ships’ crews to take care of their own ships. Sailors today have fewer opportunities to become proficient at ship maintenance during shore duties, meaning sailors going to sea bring with them less knowledge about how the ship and its systems work.

In a contested operating environment with denied communications, he noted, “you are not going to have the ability to phone home.”

Navy operational and maintenance communities have to find a way to get sailors more proficient at fixing downed systems and repairing their ships while underway. While a complex challenge to address,  part of the solution would be ensuring that crews can begin pre-deployment training on time – without delays from ship maintenance availabilities going long – and ensuring that that training time includes an emphasis on maintaining and repairing the ship.

“That’s years of constrained funding that have taken risk on things like tech manuals, Engineering Operational Sequencing System, Planned Maintenance System, and resourcing maintenance, to where what has been the risk-taker has been compressing that training timeline.

“We’re in an environment now where that risk is unacceptable.”

Data is everywhere. Used effectively, data can help you spot trends, track performance and drive evidence-based decision-making. Unfortunately, not everyone can access the data they need. Here are sample report examples  you can use to help you understand your product development processes and use this information to make improvements, reduce overheads and manage the Navy Fleet like never before.

1. Leverage Data Forecasts

Well led teams can leverage data to understand what customers buy and why. Marketing teams can use automation tools to track website activity and email click-through rates, improve prospects and measure campaign effectiveness. This type of data is plentiful and can help businesses gain a deeper understanding of their target markets. They can then use these insights to forecast sales more accurately and tailor their branding and future product offerings with greater success.

While this data enhances the customer-facing side of your business, there is very little data available to improve your core functions, namely product development. Sure, you can get 3D models and 2D drawings, assembly instructions and other collateral which tell you how good your products are and how clever your engineering teams are, but you have no data, statistics or evidence that can be used to make process improvements or cost reductions.

2. Design History

Design teams, project managers and executives only really connect at design reviews, where each stakeholder can review the current status of a project, discuss problems and brainstorm ideas. If a project appears to be off track or behind schedule, more resources can be thrown at it. This may solve the immediate problem, but it doesn’t address the issue at its source. Without hard data, your only evidence is anecdotal evidence. “Why is this maintenance availability  taking so long?” may elicit a number of unwanted responses.

Enterprise Tool  is designed to address this shortfall. In addition to the complete design history captured in each Shape Update detailing who made what changes and when. Shaped Update Enterprise  Tool records project details, duration, release status, team activity, supplier access, and more. In short, any activity that touches your data is logged and presented in easy-to-read graphs, tables and charts. This gives you complete visibility into who did what and when, how engineering efforts are trending over time, and who is contributing in what ways

3.  Activity Overview

Every report in Enterprise Tool contains links to more detailed reports, so a good place to start is at the top level with the Activity Overview. Although it’s called an “overview,” there is lots of very useful information that can be drawn from here. You can see the total number of hours spent on each project, project activity on a daily basis and which Documents have been worked on the most. You can also see which engineers are actively working on which projects and use this information, along with more detailed reports, to see if you need to allocate more resources to get a project finished on time.

Activity is detailed  for every design project in your organisation. For auditing and security purposes, “Exports” is a report of great interest. Unless you assign export permissions to a specific role, team or individual, no data ever leaves Shape Update; nobody is able to share designs externally and everyone must sign in to your domain to access your data. This adds an extra level of security and a full audit trail.

4. Access the latest revision of a part 

Another huge benefit is ensuring that the correct part is being made a supplier must first sign in to your domain before they can export the data you have given them access to. Every export, just like everything else in Shape Update Enterprise Tool , is logged and presented as a detailed list of who did what and when. This enables you to check that you’ve set the right permissions and data access for the right people and that everything is above board.

Additional security is provided by the Login Locations map that logs the network address of every computer that signs in to your domain. This map shows you each user’s approximate location so you can keep tabs on where your data is being accessed from. Clicking on one of the dots tells you the number of sessions run from that location and you can get a detailed list of each user’s name and on what date they signed in. This is very useful if you are working with many outside suppliers. Any outliers can be checked, just to make sure that your data is being used as intended.

For managers, the number of pending releases could indicate either a flurry of activity in release submissions or a bottleneck in the approval process. Any delays here could have serious consequences, so this chart is invaluable in helping to keep everything running smoothly. To find out exactly what is going on, simply click on the Release Status pie chart and drill-down .

5. : Release Dashboard

The Release Dashboard lists all the Parts, Assemblies, Drawings and other project-related data that have been submitted for approval. Since each release must follow an approval workflow, the pending status indicates that a release is waiting for somebody to review and sign-off. The details of each release candidate, including the actual design data itself, can be interrogated from here to see which project it belongs to, who submitted it and how long it has been in a pending state.

A submission may be pending for any number of reasons: an approver may be on the flight line waiting for more information, or it may have simply been overlooked. You can then follow up with each worker, or override the approval process if you have the right administrative permissions.

This dashboard enables you to follow the progress of each project and ensure  the approval process runs as smoothly as possible. Monitoring the number of releases gives you a good indication of where a project is in the design cycle.

6.  Project Dashboard

Shape Update is unique in how it stores design data. Rather than using files for each part, assembly and drawing, a single Shape Update Document can contain any and all project-related data and an entire team of engineers can work on the same Shape Update Document at the same time. However, in practice, most engineers or teams of engineers will work in their own Documents and link them together in a master assembly. These Documents are then organized into Projects, which are unique to Shape Up Enterprise Tool. Projects make Document management and data access much simpler using Role-Based Access, and easier to monitor using the Project Dashboard.

The project Dashboard gives you details such as modeling time for the entire project, the most active users and the most edited Documents. You can also check permissions and role assignments, which can show you at a glance which users are able to access your project data and what they can do with it.

7. Feature Activity Metrics

One metric that is often overlooked is in the Feature Activity chart. This chart breaks down user activity into actual design activities within all the Documents in a project. As a project progresses, it is reasonable to presume that the number of drawing activities should increase and the number of design changes should fall. If this does not appear to be the case, this could indicate a serious problem – especially if a project deadline is looming.

There is enough information on this page and the subsequent detailed drill-down reports, to find out exactly which areas of a project are causing delays and get a good idea of where the problems lay before further investigation. This enables you to be proactive in your project management tasks, identify problems early and resolve them before they escalate.

8. User Dashboard

The User Dashboard details all activity associated with a particular user. This report can be used to review which projects and Documents a user is working on and which design activities are taking up most of their time.
Resource allocation becomes very important when there are many projects on the go at the same time. If some projects are more important than others or are in danger of overrunning, you can use the User Dashboard to see which projects each user is spending most of their time on. This makes it easier to understand everybody’s workloads and ask them to lend a hand where needed.

If it appears that some Documents are being worked on more than others, there may be several possible reasons. If it’s a particularly difficult design problem, that could be one reason. If there’s too much to do and not enough help, that could be another. Training may also figure into this equation, too. New users naturally take longer to achieve the same results as seasoned users, so maybe more training is required?

Finally, since everybody must sign in to your domain to work on your projects, activity by external contractors and suppliers is also recorded. This enables you to negotiate contracts based on hourly rates rather than flat rates if that suits your business better.

9.  Document Access

You can look at the Document Access report. For security review purposes and overall Document activity information, this fancy report shows you the information in a concise manner. The graph displays all the information from the table at the bottom of the report with a color-coded node for each Project, Document, and User. Each node is then connected with either a dotted line to signify assigned access permissions or a solid line for Document activity.

You can click on a node to filter by Project, Document or User, search for a specific item, and hover over a line to see modeling times and access permissions. Each node can be clicked to jump straight to any of the Project, Document or User Dashboards, show a detailed audit trail, or in the case of a Document, open it for viewing or editing. This makes it an ideal way to quickly navigate your engineering data.

10. Improving Your Business With Onshape Enterprise

Shape Update Enterprise Tool is not an add-on or a separate system, it’s all part of the same product development platform. Each report is driven by a unique network, so you can easily bookmark them in your web browser for quick access. Shape Update Enterprise Tool reports and analytics are also available via mobile app too, so you can access this data from anywhere at any time.

Having this level of detail about your design processes is totally new and may seem difficult first. You will soon start to notice patterns and trends in the data and it will become easier to recognise, helping you to be more proactive and manage your teams, your projects, and your business better. Ultimately, Shape Update Enterprise Tool will help you make informed business decisions backed by real data, enabling you to reduce costs and use resources more wisely.
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Companies that are able to unearth these nuggets of information buried deep within their design processes have a significant competitive advantage over their rivals, as they can gain important insights and react quickly to expand their business in a way that is simply not possible without detailed engineering analytics.