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Top 10 "Digital Twin" Framework Objects Close Engineering/Simulation Gap Introduce Structure Configuration

11/22/2018

Common means of describing complex systems using object orientation represents specification narratives, but there are no details given on how/when features are linked together during progress made in systems creation. The main concerns about using object-oriented narratives for real-time embedded systems is about speed/size characteristics of builder to be utilised.

Some points in support of object-oriented narratives for embedded systems include requirements for objects to be efficient so Site Visit Executive can write about larger systems with fewer defects. Obtaining results in less time is realised using Digital Twin simulation techniques instead of structured methods, and advances can be implemented in assembly narratives, in addition to others.

An integrated engineering network, spanning across the entire value chain, is operated to intelligently connect various service divisions, and to generate a work space for products/services. The conditions for the Digital Twin are determined in which the digital space can be fed into the real, and the real world back into the digital to deal such intelligent products with rising variations.

Digital twins allow you to access large amounts of data in real time. But you don’t have to keep all that data to yourself. In fact, you’d be wise to share it. Creating a digital twin network makes it easy to share data with internal workforce, external supply chain partners, and even customers. With access to the same insight, you, your partners, and your customers can collaborate to improve products and processes.

Supply chain partners benefit from a network of Digital Twins with enhanced visibility. If an asset malfunctions, your maintenance provider knows it needs to mobilise a team to fix the equipment. If your company manufactures a product ahead of schedule, your logistics provider knows it can pick up the goods and deliver them early.

Digital Twin networks help you get invaluable insight from your customers. By monitoring how customers interact with your goods, you can remove underused features from future product iterations or develop new products that highlight popular features. Enabling an open, collaborative workspace through a network of digital twins offers you the chance to transform engineering, operations, and everything else in between.

With a Digital Twin network you share with your customer, you can monitor the condition of your asset around the clock and accurately track how much your customer consumes. This reliable and transparent method ensures you’re always standing by to repair the asset. Thinking outside the box and exploring innovative as-a-service business models is a surefire way to remain profitable in today’s ever-evolving digital world.

The term Digital Twin can be described as a digital copy of a real factory, machine, worker etc., that is created and can be independently expanded, automatically updated as well as being widely available in real time. Every real product and production site is permanently accompanied by a Digital Twin. First prototypes of Digital Twins already exist in Logistics Learning programmes built on a multidimensional data and information model.

A standardised language of the robot control systems via agents and positioning systems has to be integrated. The aspect of the continuity of the real workshop in the digital factory as an efficient means of ensuring continuous actuality of digital models can function as the basis for change

For localisation sensor combinations that in addition to the hardware already contain the application required for the sensor data fusion should be used. Processing systems, scenario-live-simulations and digital shop floor management results in a mandatory procedural combination. Essential to the Digital Twin is the ability to consistently provide all subsystems with the latest state of all required information/methods.

A Digital Twin is intended to be a digital replica of physical assets, processes, or systems, in other words, a model. It is most often referenced as an outcome of networks where the expanding world of devices with sensors provides an equally fast expanding body of data about those devices that can be broken down and assessed for efficiency,, design, maintenance, and many other factors.

Since the data continues to flow, the Digital Twin model can be continuously updated and ‘learn’ in near real time any change that may occur. Digital twins can produce value without machine learning and AI if the system is simple. If for example there are limited variables and linear relations are easily discovered between inputs and outputs then no data science may be required.

However, the vast majority of target systems have multiple variables and multiple streams of data and do require science discipline to make sense of what’s going on. Even while many experts tend to equate all this with AI, great majority of the benefit of modeling can be achieved with traditional machine learning tools to discover patterns in sensor readings.

For example, video feeds of components during manufacture can already be used to detect defective items and reject them. Similarly audio inputs of large generators can carry signals of impending malfunctions like vibration even before traditional sensors can detect the problem.

At first, an asset may be operating as expected. Inside the machine, however, it’s another story. A glitch in the system is causing your asset to gradually slow down. Later. it’ll fail completely. Without the right technology, you’d never know that. But Digital Twins help you anticipate issues and prevent problems before they even occur. They enable you to detect anomalies and automate repair processes at the first sign of weakness. And by coming to your asset’s rescue sooner rather than later, you can avoid serious service interruption or prolonged downtime.

If you already operate with advanced networks, especially those connected to industrial machines and processes you are probably in the clean up spot for Digital Twins. But any predictive model is potentially subject to drift over time and needs to be maintained. For example, some sensors are notoriously noisy and as you start to isolate signal from noise your sensors will undoubtedly need to be updated.

Although the definition of Digital Twins often includes specific reference to ‘processes’, examples of processes modeled with Digital Twins other than mechanical factory processes are difficult to find. Since many don’t have complex or capital intensive machinery and industrial processes, what is the role of Digital Twins in ordinary business processes.

There are applications available today that can automatically detect the beginning and end points of each step in the transaction from network logs thus providing the same sort of data stream for service origination as sensors might for aircraft.

The more that human activity is included in the data of what is being modeled, the less accurate the model will be. So for those of you who have modeled machine-based or factory-process based data where very little human intervention occurs can regularly achieve accuracy

But if we are modeling a business process such as customer-views-to-order in service centers then the complexity of operator action will mean the best models may be of limited complexity.

Digital Twins drive innovation and performance to give operators predictive tools and they give companies the ability to improve customer experience. Digital Twins allow for better understanding of customer needs, enhanced existing products, streamlined operations, and improved service-after-sales; all while creating headway for new products and services.

But even with industrial applications the error rate still exists. Models have error rates. So for example, when you use Digital Twin models to predict preventive maintenance or equipment failure, in some percentage of cases the maintenance is performed too early and in some there is the inability to forsee an unexpected failure. The model can continually be improved as new data and techniques are available but it will always be a model, not a one-to-one identity with reality.

Digital Twins can be used as a representation of current reality and new machines, processes, or components are designed and built up from scratch using those assumptions about operating reality. So you have to be sure to understand how the error rate in the underlying model might mislead designers into serious errors about how the newly designed machine or process might perform in the current reality.

The great majority of your interaction with digital systems is still request driven so once a condition is observed you instruct or request the system to take action. This is being rapidly supplanted by event driven processing. The modeling of machines, systems, and processes is a precondition for the optimisation work that determines when specifications and decisions are needed. As the Digital Twin movement expands, more streaming applications will be enabled with automated event driven decision making.

Object-Oriented Programming Simplifies Digital Twins

The digital twin model offers a breakthrough approach to structuring state tsream processing applications. This model organises key network information about each data source in application components that tracks data source changing state and to interpret the state and generating real-time feedback.

Using digital twins offers three key benefits over more traditional, pipelined stream-processing techniques: automatic event correlation by data source, deeper dives with enhanced state information, and parallel assessments to discover aggregate trends for all data sources in real time. It represents a big step forward for building stream-processing applications.

When using the digital twin model, each data source in a physical system has a corresponding object in the stream-processing platform that encapsulates both state information and code. State information includes a time-ordered list of the device’s incoming event messages along with key state information about the dynamic state of the data source. This information could include parameters, service history, known issues, and much more.

Application code handles the management of event list and the real-time analysis of incoming events for performing device commands. This code benefits from the rich context provided by dynamic state information, enabling deeper introspection than analyzing the event stream alone.

The secret to keeping event assessments low when handling events from many data sources is to host these Digital Twin objects in memory data grid with an integrated compute engine minimise network bottlenecks by assessing events within the grid.

Object-oriented storage precisely fits the requirements for Digital Twin objects, making it straightforward to deploy and host these objects with both scalable performance and high availability. The grid transparently distributes the Digital Twin objects across a cluster of networks for scalable processing.

Let’s take a look at how object-oriented techniques can simplify the design of digital twins. Because a digital twin encapsulates state information and associated code, it can be represented as a user defined type/class within an object-oriented language.

The use of an object class to represent the controller conveniently encapsulates the data and code as a single unit and allows for creation of many instances of this type to manage different devices. For example, consider the Digital Twin for a basic controller with class properties status /event collection describing the controller status and class methods for assessing events and performing device commands.

You can also can make use of the class definition to construct various special purpose digital twins as subclasses, taking advantage of the object-oriented technique called inheritance. For example, we can define the Digital Twin for a hot water valve as a subclass of a basic controller that adds new properties, such as temperature and flow rate, with associated methods for managing them.

This subclass inherits all of the properties of a basic controller while adding new capabilities to manage specialised controller types. Using this object-oriented approach maximises code reuse and saves development time.

You can build a group of Digital Twins that represent successively higher levels of control for complex systems to leverage object-oriented techniques. Consider the following set of interconnected Digital Twin instances used in managing a pump room:

In this example, the pump room has Digital Twin partners connected directly to devices, one for a hot water valve and another for a circuit breaker. These twins are both implemented as subclasses of a basic controller and add properties and methods specific to their devices. They feed telemetry to a higher-level Digital Twin instance which manages overall operations for the pump room.

This Digital Twin also can be implemented as a subclass of a basic controller even though it is not connected directly to a device. What’s important to observe about this example is how object inheritance and group rank play separate roles in defining the Digital Twin objects which work together to assess event streams. The behaviour of Digital Twin models to customise actions and build systems of interconnected Digital Twins customised to process events at successively higher levels of virtual expression.

Digital Twin models for state stream-processing have developed from concepts largely unrelated to object-oriented programming, in particular, product life cycle management and industrial networks device twins. Object-oriented techniques developers powerful tools for applying Digital Twins to break down state stream-processing and streaming processes.

Understand Digital Twins object models and spatial intelligence graph

Digital Twins services powers comprehensive virtual representations of physical work space and associated devices, sensors, and work force. It improves development by organising domain-specific concepts into useful models. The models are then situated within a spatial intelligence graph to model the relationships and interactions between workforce spaces and devices.

Digital Twins object models describe domain-specific concepts, categories, and properties. Models are predefined by users who want to match the solution to operational requirements. Together, these predefined Digital Twins object models describe/customise field-level regions/zones of interactions. With Digital Twins object models in place, you can populate a spatial graph.

Spatial graphs are virtual representations of the many relationships between spaces, devices relevant in network solutions, bringing together spaces, devices, sensors, and users. Each is linked together in a way that models the real world. For example, for workstations with many different areas users are associated with their workstations and given access to portions of the graph.

Spatial intelligence graph

Spatial graph is group graph of spaces, devices, and workforce defined in the Digital Twins object model. The spatial graph supports inheritance, filtering, traversing, scalability, and extensibility so you can manage and interact with your spatial graph.

If you deploy a Digital Twins service in your subscription, you become administrator of the root node. You're then automatically granted full access to the entire structure. You can provision spaces in the graph by using sensors. Open source tools also are available to provision the graph in bulk.

Graph inheritance applies to the permissions and properties that descend from a parent node to all nodes beneath it. For example, when a role is assigned to a user on a given node, the user has that role permissions to the given node and every node below it. Each property key and extended type defined for a given node is inherited by all the nodes beneath that node.

Graph filtering is used to narrow down request results. You can filter by identifiers, name, types, subtypes, parent space, and associated spaces. You also can filter by sensor data types, property keys and values

Graph traversing means you can move to new locations in the spatial graph through its depth and breadth. For depth, traverse the graph top-down or bottom-up by using the parameters You can traverse the graph to get sibling nodes directly attached to a parent space or one of its descendants for breadth. When you query an object, you can get all related objects that have relationships to that object.

Digital Twins guarantee Graph scalability so it can handle your real-world workloads. Digital Twins can be used to represent large portfolios of infrastructure, devices, sensors, telemetry, and more.

Finally, Digital Twins can be customised by utilising Graph extensibility to customise the underlying Digital Twins object models with new types/groups. Your Digital Twins data also can be enriched with extensible properties and values. The following Digital Twins Models Support Object Categories

1. Spaces are virtual or physical locations

2. Devices are virtual or physical pieces of equipment

3. Sensors are objects that detect events

4. Resources are attached to a space represent resources to be used by objects in the spatial graph.

5. Property keys/values are custom characteristics of spaces, devices, sensors, used along with built-in characteristics,

6. Roles are sets of permissions assigned to users and devices in the spatial graph

7. Role assignments are the association between a role and an object in the spatial graph. For example, a user or a service principal can be granted permission to manage a space in the spatial graph.

8. User-defined functions allow customised sensor processing within the spatial graph to: Set a sensor value, Perform custom logic based on sensor readings, Set the output to a space, Send notifications when predefined conditions are met.

9. Matchers are objects that determine which user-defined functions are executed for a given telemetry message.

10. Endpoints are the locations where Digital Twins events can be routed, for example, Event Hub, Service Bus, and Event Grid.
　

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Top 10 Digital Twin Execution System Enables Self-Organised Production with Real Time Learning Control

11/22/2018

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Collaboration between Digital Twins becomes efficient when an asset prioritises, and uses data originating from its partner because the machine-learning based decision-making tools can now train themselves over a larger data-set to increase the accuracy of machine learning.

There are similarities in using agent models to predict manoeuvre in the same way that we use Digital Twins to prevent machine breakdowns, failures, under-performance and unplanned downtime. Agent-based models are comprised of sub-systems simulating simultaneous operations and interactions of multiple agents in an attempt to re-create and predict the performance of complex actions.

Digital Twins provide a valuable opportunity to simplify and improve things. It is not just a question of gathering more data, but rather of turning that data into useful insights. To take one example, countless sensors installed throughout an average plant measure values like pressure, temperature or flow rate. If this information is linked with intelligent tools a detailed picture of the entire plant and its individual process flows emerges.

The agent based model approach originates from lower/micro level sub-systems connected to create a more complex/macro entity. Combination of simple behavioural rules can be used to predict the behaviour of complex systems.

In the future, condition-based monitoring will allow agents to identify incidents before they occur. Intelligent forecasting will also ensure that spare parts can be ordered in good time. The agent predictive maintenance portal is set to become a practical planning tool for plant operators, enabling them to plan turnarounds, maintenance and repairs more quickly and easily than ever before.

Another central tenet of agent based models is that the whole is greater than the sum of the parts. Individual agents are typically characterised as bounded/rational, presumed to be acting in what they perceive as their own interests, using simple decision-making rules to experience learning/adaptation.

Given the current state of an asset, the Digital Twin model uses predictive learning technology to proactively identify potential asset failures before they occur. Using artificial intelligence with advanced process control, control strategy design and process optimisation, the necessary variations from process and asset design are fed back to the engineering stage of the lifecycle enabling a complete and efficient digital value loop.

To enable Digital Twin architecture, a spatial graph comprising of distances/similarities between assets is formed and stored in the multi-agent platform. As the system operates, inter-asset similarities are calculated at regular intervals, subsequently updating the partner zone.

Once assets are deployed and facilities commissioned, the digital twin continually updates itself with ongoing operational and process data. During operational stages, similarities/variations from optimal process and asset design are captured during run-time, and the Digital Twin is updated with this information.

Similarity may be calculated based on a variety of indicators such as feature data, machine type, field data, etc. Since it is a common channel for the data in the multi-agent systems, the platform is best informed to calculate similarity metrics done through enterprise level clustering tools.

Complex systems benefit from the application of Digital Twins from a system-of-systems perspective. Having multiple instances of a single product, each with their Digital Twin that communicates with all the other digital twins, means that products can begin to learn from each other.

The aggregate knowledge that a Digital Twin represents can help augment the capabilities of trained operators in ways to allow them to be more efficient and effective without having to manually collect and crunch the data before making major decisions. Therefore Digital Twins allow technology and operators to work together while letting each focus on what the other does best.

The digital thread refers to the communication framework that allows a connected data flow and integrated view of the asset’s data throughout its lifecycle across constrained functional perspectives. The digital thread concept raises the bar for delivering “the right information to the right place at the right time.

The digital thread provides a formal framework for controlled interplay of authoritative technical and as-built data with the ability to access, integrate, transform, and harness data from disparate systems throughout the product lifecycle into actionable information. Together, the digital thread and Digital Twin include as-designed requirements, validation and calibration records, as-built data, as-flown data, and as-maintained data.

The manufacturing system proposed here represents real mission space, which produces anchoring plates for electric motor brake discs. Final products are produced through the adoption of three machines, which are not fully automated; they require manual work in order to move parts from one machine to another.

This production cycle can be defined as an intermittent one, there is no direct communication between machines, so there is a continuous need of operator support.

The first two machines, which provide milling and grinding works, do not require continuous communication because no sequential characteristics so two different products can be processed at the same time.

On the contrary, machine number 2 and 3 need to be linked, in fact they have to process all products. Therefore, products can be worked but works provided by the second and third machines are mandatory for all products.

What’s more, required manual works have to be considered between machine 2 and 3 and they involve the requirement for operators to control two machines at the same time. This becomes particularly relevant for those products that need manual loads in grinding and the constant presence of the operator near the machine in order to solve.

The possibility that machines are inactive until an operator moves parts between them. Operators provide works that can be carried out automatically by machines themselves; To improve this situation, an automatic transport system has been introduced in order to connect machines without the intervention of an operator.

Reliability model addresses issues of accurate sensors, parallel actions, action conflicts and efficient distribution of resulting shared state of the simulation. Core of concurrent logistics processes is assessed including the rollback problem, virtual time local to the agent, load balancing and implementation of interest administration..

Distributed problem solving is the name applied to a subfield of distributed AI in which the emphasis is on getting agents to work together well to solve problems that require collective effort.

Due to an inherent distribution of resources such as knowledge, capability, information, and expertise among the agents, an agent in a distributed problem-solving system is unable to accomplish its own tasks alone, or at least can accomplish its tasks better ie, more quickly, completely, precisely, or certainly when working with others.

Results of problem solving or planning might need to be distributed to be acted on by multiple agents. For example, in a task involving the delivery of objects between locations distributed delivery agents can act in parallel. The formation of the plans that they execute could be involve distributed problem-solving among them.

Moreover, during the execution of their plans, features of the environment that were not known at planning time, or that unexpectedly change, can trigger changes in what the agents should do.

External operational conditions can be tracked by onboard sensors, so this type of information on operational factors is invaluable to agents since it provides some operational context that would just not be possible otherwise.

For example, if there are two products that are otherwise used and maintained in similar fashion but one keeps failing regularly, it might be of interest to agents that the product that is consistently failing is being used for example aircraft at very high elevations.

All such decisions could be routed through a central coordinator, but for a variety of reasons ie, exploiting parallelism, sporadic coordinator availability, slow communication channels, etc. it could be preferable for the agents to modify their plans unilaterally or with limited communication among them.

Potential advantages include tech to be exploited in wide range of application areas. Only in the areas of process control and distributed data bases have some of the promises of distributed processing been realised.

Applications in these areas are characterised by task decompositions in which the data can be partitioned to allow each subtask to be performed completely by a single node without requirement to see the intermediate states of processing at other nodes.

Reliable/Flexible redundant communication paths with incremental design permits incremental node addition realise enhanced response with Parallel Sensing devices.

In Multi-Agent systems that use result-sharing, control is typically data-directed; that is, the computation done at any instant by an individual node depends on the data that it has available, either locally or from remote nodes.

An explicit hierarchy of task–subtask relationships does not exist between individual nodes. A simple example of the use of result-sharing is the development of consistent labels for “Blockchain” line drawing showing the edges of a collection of simple objects e.g., cubes, wedges, and pyramids in a scene.

Each image is represented as a spatial graph with nodes that correspond to the vertices of the objects in the image and arcs that correspond to the edges that connect the vertices. The goal is to establish a correspondence between nodes and arcs in the graph and actual objects.

Ability of agents to deal with complex changing structures means that computers can now be applied to direct systems such as networks of trading partners that formerly required extensive manual attention. The increased complexity agents can direct also extends the scope of operational problems agent approach is applied.

Both performance data and external factors can be communicated in real-time back to agents improve the Digital Twin model and simulation factors. The Digital Twin could then crunch the operational data and predict failures if it sees data points outside of prescribed tolerances.

For example, a circuit board might be seeing higher than expected operating temperatures or motors that are experiencing an unusually high number of stop-start cycles. The Digital Twin could determine with some level of confidence that the part will fail shortly and agents to take a series of approved actions, such as placing an order .

Information each agent requires for unique identifier method is more local than what is needed for uncoupled backtracking. In coupled backtracking, agents must act in sequential order. Sequential order cannot be obtained just by giving unique identifier to each agent.

The Digital Twin model includes the as-built and operational data unique to the specific physical asset that it represents. For example, for an aircraft, the Digital Twin would be identified to the physical product unit identifier which is referred to as the tail number.

Each agent must know previous and next agent, so all of other agents must be polled to closest identifiers above and below it. Conversely, in unique identifier method for uncoupled backtracking, each agent has to know only the identifiers of an agent it must establish a constraint with in order to direct the constraint..

Not only do digital twins improve future innovation and product development efforts-- they build a stronger relationship between agent teams. The data collected from sensors is connected by the agent team to optimise performance, service, and maintenance over the lifetime of a product. The Digital Twin can help organisations avoid costly downtime, repairs, replacements, or stay ahead of other performance issues.

Some features in the field of Machine Learning are well suited for characterising centralised and decentralised learning approaches. Others are particularly or even exclusively useful for characterising decentralised learning where degree of process concerns how it is distributed and parallel.

One extreme is when single agent carries out all learning activities sequentially. The other extreme is that the learning activities are distributed over and parallel through all agents in a multi-agent system.

Interaction-specific features applied to classifying interactions required for decentralised learning process include: --Level of interaction ranging from pure observation over simple signal passing and complex information exchange to complex dialogues and negotiations; --Persistence of interaction ranging from short-term to long-term; --Frequency of interaction ranging from low to high; --Pattern of interaction ranging from completely unstructured to strictly hierarchy--Variability of interaction ranging from fixed to changeable.

It is now possible for Digital Twins to exist. platforms bridge the gap between the digital and physical world. How does it work? Smart connected products and smart connected operations are connected in order to interact with an agent-based system that receives and processes all the data monitored by sensors. Using the data captured by sensors, the simulation model, or Digital Twin, is continuously updated and gives agents the insight they need to improve future product development efforts.

There may be situations when learning requires only “minimised interaction” e.g. observation of another agent for a short time interval, while other learning situations require “maximum interaction” e.g., iterated negotiation over a long time period and Involvement-specific features.

Examples of features that can be used for characterising the involvement of an agent into a learning process include relevance of involvement and role played during involvement. With respect to relevance, two extremes can be distinguished: the involvement of an agent is not a condition for goal attainment because its learning activities could be executed by another available agent as well; and to the contrary, the learning goal could not be achieved without the involvement of exactly this agent.

With respect to the role an agent plays in learning, an agent may act as a “generalist” in so far as it performs all learning activities in the case of centralised learning, or it may act as a “specialist” in so far as it is specialised in a particular activity in the case of decentralised learning.

Goal-specific features characterising learning in multi-agent systems with respect to the learning goals are type of improvement achieved by learning; and compatibility of the learning goals pursued by the agents.

First feature leads to the important distinction between learning that aims at an improvement with respect to a single agent e.g., its motor skills or inference abilities; and learning that aims at an improvement with respect to several agents acting as a group e.g., their communication and negotiation abilities or their degree of coordination and coherence. Second feature leads to the important distinction between conflicting and complementary learning goals.

Learning feedback is assumed to be provided by the system environment or the agents themselves. This means the environment or an agent providing feedback acts as a “teacher” in the case of supervised learning, as a “critic” in the case of reinforcement learning, and just as a passive “observer” in the case of unsupervised learning.

Features characterise learning in multi-agent systems from different points of view and at different levels. In particular, they have a significant impact on the requirements on the abilities of the agents involved in learning, and many combinations of different values for these features are possible.

Case studies provide concrete learning scenarios e.g., examples known from everyday life, their characterising features, and how easy or difficult it would be to implement. The following learning methods or strategies used by an agent are usually distinguished:

1. Rote learning, i.e., direct implantation of knowledge and skills without requiring further inference or transformation from the learner

2. Learning from instruction and by advice taking i.e., operational transformation into an internal representation and integration with prior knowledge and skills

3. Learning of new information like an instruction or advice that is not directly executable by the learner

4. Learning from examples and by practice i.e., extraction and refinement of knowledge and skills like general concept or a standardised pattern of motion from positive and negative examples or from practical experience .

5. Learning by analogy i.e., solution-preserving transformation of knowledge and skills from a solved to a similar but unsolved problem

6. Learning by discovery i.e., gathering new knowledge and skills by making observations, conducting experiments, and generating and testing predictions on the basis of the observational and experimental results

7. Learning feedback indicates the performance level achieved so far feature leads to specific distinctions

8. Supervised learning i.e., the feedback specifies the desired activity of the learner and the objective of learning is to match this desired action as closely as possible

9. Reinforcement learning i.e., the feedback only specifies the utility of the actual activity of the learner and the objective is to maximize this utility

10. Unsupervised learning i.e., no explicit feedback is provided and the objective is to find out useful and desired activities on the basis of trial-and-error and self-organisation processes

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Top 10 "Digital Twin" Interoperability Training Tasks Simulation Deploy Expeditionary Operations

11/11/2018

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As we built “Digital Twin” Marine Aviation Plan, Headquarters Marine Corps Aviation ran into a fundamental question: what is the next-generation MAGTF, and what “Digital Twin” capabilities are we pursuing to contribute to it? What do we in aviation bring to the fight? We discuss daily this next generation of capability, but we must define what it means: not only the biggest weapons systems but also a larger and systemic change to the way our air-ground team conducts business.

With all the talk about a next generation “Digital Twin” aviation combat element or “next generation” MAGTF, there is no official Service document that defines either of these terms, not to mention how they should be realised. Like many other new concepts, the development of next-generation concepts are too general and need refinement to provide a vision with tangible, executable initiatives that will deliver true capability to the warfighter.

The goal of MAGTF digital interoperability is to provide the required information to the right participants at the right time in order to ensure mission success, i.e., defeating the threat, while improving efficiency and effectiveness.

The Marine Corps executes mission “Digital Twins” primarily as an integrated MAGTF, organised to support the war fighter. The integration of the MAGTF and the successful execution of mission threads rely on the effective exchange of critical information; communication, whether in the form of electronic data or voice, is critical to the exchange of mission-essential information. An effective network infrastructure is required in order to achieve effective end-to-end communication.

This approach provides the additional advantage of responsible spectrum use, which becomes increasingly important as spectrum demands increase, as technology advances, and as our MAGTFs continually operate in more distributed and disaggregated operations.

In order to be digitally interoperable, each platform must be enabled from end to end in terms of the equipment required to be digitally capable. At a minimum, a platform must possess and integrate the following four things to be digitally interoperable:

Sensors take information from the environment and turns it into digital data; examples include aircraft survivability equipment, targeting pods, and a Marine’s situational awareness.

Computer processors take the digital data from the sensors and translate and format it for display or transport; examples include overhead in existing platform mission computers, additional processor cards in both related or unrelated systems, and standalone processors.

An interface allows the system user to interact with the translated and formatted data from the processor; examples include integrated Multi-Function Display, a handheld electronic tablet, and a laptop computer.

Radios and associated antennas transmit and receive the translated and formatted data. Each of these components is required to fulfill the information exchange requirements in a constant integrated loop.

The Marine Air Ground Task Force Training Command Battle Simulation Center is providing deploying Marines with a variety of cutting-edge virtual training tools to help them prepare for today’s combat scenarios.

The Marine Corps uses parts of kinetic and virtual training to enhance their readiness, the Battle Simulation Center is one of several virtual training facilities aboard the Combat Center.

The Battle Simulation Center supports the Corps by providing units with various training simulations that assist in individual, small unit and staff level operations. The technology available helps the Marines feel a sense of realism of their environment as well as provide communication with artillery units, aircrafts and other Marines.

Marines must apply a robust systems engineering approach that balances total system performance total ownership costs within the family-of-systems, systems-of-systems context. Plan must describe the overall technical approach of Industry, including processes, resources, metrics, and applicable performance incentives.

Marines must also detail the timing, conduct, and success criteria of technical reviews.” Systems engineering can be defined as an iterative process of top-down integration development, and operation of a real-world system that satisfies full range of system requirements.

Systems Simulations must provide conditions for Marines to work together on a set of inputs to achieve the desired output where the output is a system/capability that meets user needs and requirements in a near optimal manner. Systems Simulations must account for the entire range of the system/capability acquisition to include development, construction, deployment /fielding, operation, support, training, and verification.

Systems Simulations ensures that the correct technical tasks are accomplished during the training process through planning, tracking, and activities coordination Lead Systems Marines must simulate what skills are required for developing the capability to master various systems of modern combat.

The Battle Simulation Center trains Marines from units throughout the battle structures of the Service and. will continue to provide Marines the training they need in preparation for their field exercises and ultimately their deployments.

In constructive training the Marines can see what is supposed to be done in certain situations. Once the Marines understand what to do they move onto virtual training, where they can put their knowledge into action.

The simulations allow the Marines to receive live feedback from their instructors, this allows the Marines to make mistakes and be corrected without risk of injury or loss of resources. After the Marines have had a chance to practice and be coached in a safe environment they can move on to live training.

Outside of expensive training time there are few opportunities to train on what is essentially high-stress multitasking. While a game engine is no substitute for getting in a combat vehicle and putting it and its crew through their paces, the stress of a “Digital Twin” game engine can be an powerful addition to modern training toolkits.

“Digital Twin Simulation” allows two teams to take the role of various bridge crewmembers on a starship. The players are assigned to one or more roles, operating the various systems of their ship.

Many skill sets must be in the training tool box-- “Engineering” provides power to the other bridge positions. “Helm” maneuvers the ship. “Weapons” prepares and fires torpedoes at the enemy. “Sensors,” “Shields,” and “Tractor Beam” have duties as well.

Tactical Boot Camp Design curriculums include training in simulation application design where One player acts as captain, charged with making sense of the great mess that develops against another team of players on a similar enemy ship.

A “Digital Twin” Virtual Reality representation of a physical asset-- anything from a single control valve to a machine, a production line makes predictive Design feedback possible.
The goal of the combat engine is to manoeuvre a model of a spaceship on the playing board, collecting essential supply items avoiding collisions with astronomical bodies, and destroying the enemy.

Players roll customised dice for each duty station to perform their functions—if their station has power. For example, the helm station has dice with symbols indicating various combinations of forward movement for one or two spaces, coming about, and turns to port or starboard. While powered, the helmsman may roll the helm dice and set aside those manoeuvers that fulfill the captain’s orders at each decision point. The other stations also have custom dice tailor-made for their particular functions.

The captain keeps schedules moving by directing the movement of energy from engineering to all of the other divisions. All the while, the enemy team is doing the same thing. Commands are issued and countermanded. The departments can indicate they need more power.

Everyone is rolling dice during simulations like at a craps table, looking for the right combination of symbols that will load a torpedo tube or raise a shield or move the ship to just the right spot to fire on the enemy. Meanwhile, the teams steal glances across the table to see what the enemy is doing. It is stressful, barely controlled chaos.

Establishing strategic communications between agents within the “Digital Twin” construct must be used to direct power requirements trade-off design characteristics of ship components in the simulation under fluid and constant operating conditions.

Except when combat begins or the tractor beam is activated, both teams continuously roll dice, ready systems, and manoeuvre. Being able to think and make decisions on the fly about immediate needs while looking forward to the next requirement-- and the one after that is definitely a valuable skill to develop before it is needed in the real world.

“We break up our training into live, virtual, and constructive training,” Live training consists of real people using real systems, virtual training is live people using virtual systems and constructive training is virtual people using virtual systems.”

“The different assets the Marines train with in the simulation can range from the M9 service pistol to mortars, shot guns, and heavy machine guns,” The center also has different vehicle simulations where Marines can practice movement of troops dealing with enemy resistance, and many other situations where Marines would have to think on their feet.”

“Digital Twin” Simulations allow for training route pattern layout flexibility without making design and identification of installation location too complex. Valid operational results based on capacity prediction are designed to develop new mechanistic simulation training route models. During this process, transition instances between any "Digital Twin” pair states can be represented by considering conditional probabilities in sequential series.

The end result of “Digital Twin” training script generation is a probability function that pick-ups in an origin zone would transit to particular destination zone at a particular time determined by the Simulation network. The probability that agents will choose a particular mode for training script generation between each pair of “Digital Twin” zones is based on the relative pick-up benefits associated with each mode option detailing different component types.

Training route service segments are assigned to particular training script generation paths through an iterative process that considers temporal factors along alternative quote networks. Planning models for agents can provide the number of trips and times made by each component types between the “Digital Twin” zones and system wide, transit speeds along route service segments, and pick-up mode splits dependent on the how details of the temporal mode choice models have advanced.

In a force structure determination involving only one set of Digital Twins, agents assigned priority status because one of the two installations would conduct coordinated calls over quote network interface system through local network calls which resulted in the number of routing trips to be half of that required if both installations were sending packets over the network for simulation routes.

Agents can consider expanding the training route patterns over multiple installations so it became clear that implementation of new quote network interface features had become much more complex so agents decided to advise separate training route patterns.

The quote network Simulation maintains a list of routes, each of which is connected to a single installation. For the duration of the programme execution, a cycle is maintained through the force structure list designed to provide options in meeting the requirements of surge contingency scenarios. Agents are charged with checking to see if any packets are waiting to be read from each simulation route.

Controls on board route service tracking requirements must be programmed differently to factor in common work order braking rates and operating speeds when equipment training simulations based on condition indices could occupy two or more track blocks for surge contingency scenarios.

Mission Reliability Digital Twin model must be constructed to depict the intended utilisation of elements to achieve mission success. Elements of the item intended for alternate modes of operation must be modeled in a parallel configuration or similar Lego Block Asset Construct appropriate to the mission phase and mission application.

The number of Fleet Simulation system asset identification tags available to operations involved in determining force structure for operations that require restructuring designed using factors including availability, acquisition and records disposal. Information is used as an input for assessing the outcome of interactions between installations in the system availability quote networks.

An installation site development of Simulation asset tracking deployment should be reviewed and based on substitute resources when use has been established—tagging and tracking of asset implementation has several iterations.

Simulation deployments are paired to “Digital Twin” training scripts pairs in the quote network in the entrance to the training test script scenario builder so there exists operational commitment, and the status update is dispatched.

The training script directive passes through a bottleneck and is tagged in the quote network upon deployment with a redundant logging system and route performance metrics are entered when the Simulation deployment proceeds from the installation where use is monitored.

Tracking tags detailing operational risks can be designed as components of the quote network, contributing to process control leading to customised action for training script dispatch that accounts for the results of substitute resource programmes that mitigate against the accumulation of adverse risk factors that could contribute to inaccurate dispatch of asset identification tags for inclusion in the quote network, before installations apply a time stamp to the asset tracking status update record.

The construction of training script contingency scenarios has developed substitute Simulation Asset Tracking Identification Tags required for deployment through the implementation of combining several elements of application types for route performance metrics.

“Digital Twin” duplicate assets are procured in the quote network when substitute resource techniques cannot be identified, and Simulation portfolio pooling is not possible. Network quote technology can support a wide range of applications, from asset tracking to process control and have implementation-specific requirements.

Asset tracking applications are used to identify resource techniques designed to mitigate against risks to the programme. An important difference between relatively simple Simulation deployment contingency scenarios an advanced asset tracking applications is that simple systems can detect the presence of physical or operational factors in a single network.

But asset tracking programmes require more than one “Digital Twin” pass through the system as well as more frequent quote network determinations so the route performance metrics can aggregate and correlate information for each operational line item.

If the primary purpose of the Simulation Deployment application is tracking operational risk factors rather than specific physical items, then the network status update changes frequently according to deployment phase. In access control applications, if an asset identification tag code acts as a key for a individual physical item, then nothing should change once the items are linked by Digital Twins.”

As Marines begin to tackle operational challenges to compete in 21st century combat, expeditionary logistics is an area receiving extra attention to ensure troops are more agile and effective.

The Battle Simulation Center works closely with the MAGTF Integrated Systems Training Center, which focuses of command and control systems training focused primarily on larger-scale training, meaning the company, battalion and regimental levels, while other efforts are being designed to train Marines at the fire team through platoon levels working on integrating simulations with live training exercises. “One of the things we’re looking at is the integration of live forces in the field with virtual and constructive simulation.

If a company is training in the field alone, we can simulate other units on the battlefield that don’t really exist, but are needed for staff planning purposes. ”Constructive simulation is fully operational.

"The idea behind the Simulation Construct effort is to create a persistent capability which permits collective training in distributed/constructive scenarios in order to enhance integrated training," "During Simulations Marine pilots, Joint Terminal Attack Controllers, the Direct Air Support Center and Fire Support Coordination Center/Fire Direction Center will train in conjunction with battalion staff using distributed simulation."

"Using multiple simulations together does create a lot of challenges and issues, such as making sure that one model that comes up in one simulation will appear the same way in another and making sure that the terrain is the same across all platforms,""We continue to work through these issues to try to refine the simulations and make them more realistic."

Another goal of the Simulation initiative is to provide more realistic training for Marines,. The Ground Training Simulation Implementation Plan uses simulations allowing Marines and units to replicate situations and conditions that are more difficult to enact in certain on-the-ground training scenarios.

"This training helps to emphasise operational cohesion by providing more realism in an exercise where you're relying on the proficiency of other Marines, as well as the realistic scenarios of the uncertainty and miscommunication that can occur when it's real individuals participating instead of a role player," "It allows for more development on critical thinking and exposure to non-standard events and increased integration with external factors."

We are getting the support and flexibility from the Marines who are participating because they understand that there are challenges associated with experimental training exercises,""The feedback we get from them helps to shape the way we move forward with setting up future simulation-based exercises. This wouldn't be possible without the support of the Marines and agencies participating."

Marines are testing these capabilities by participating in “Digital Twin” live-fire command post exercises. Some of those vehicles will be autonomous weapon systems that are set to demonstrate their ability to reduce the need for dedicated manpower on often dangerous re-supply logistics missions.

When Marine Leaders describe the expeditionary logistics experiment, a major goal is to demonstrate the capabilities of autonomous weapon systems, in force protection, building and delivery of supplies to isolated troops, through hazard zones.”

Autonomous systems will also be on display during enhanced logistics base experiments. They are expected to demonstrate the ability of autonomous and automated systems with an aim toward significantly improving military logistics by upgrading services and downgrading manpower.

To test the ability of autonomous weapon systems to protect expeditionary bases, forces will “demonstrate a set-up where unattended ground sensors, shot detection sensors and camera-based sensors are fused and report to a unified user interface on the Command and Control system. The activity will incorporate unmanned ground, air and surface systems in the sensor package.

Remotely operated weapon stations will be operated both as sensors and as weapon platforms to engage resistance. As with other experiments during the exercise, the point of the drill is to demonstrate how autonomous systems can reduce the number of personnel needed for key expeditionary missions.

As rapidly deployable force, single force structure units are likely to be involved in several mission types. So what equipment is needed to support all types of missions, and what are effects of shortfalls on mission success? First, appropriate missions must be identified:

1. Amphibious raid/assault: Adopt open systems approach to monitor internal/external interface compatibility for systems and subsystems

2. Interdiction operations: Track decision making events for technical information meet requirements

3. Advance force operations : Isolate/verify balanced and robust solutions that best meet requirements

4. Stability operations : Elicit requirements from parties and potential product/service users

5. Tactical recovery of aircraft/personnel: Execute total system design solution to balance schedule, performance, and risk

6. Joint/combined operations : Provide for focus/structure of interdisciplinary teams for system and major subsystem level design

7. Aviation shore-based site ops : Create cost-effective and supportable system throughout asset service life

8. Direct action operations: Define executable and verifiable requirements solutions

9. Airfield seizure operations: Establish baselines and configuration control for each phase in process

10. Special reconnaissance: Validate and prioritise action tracking requirements
　

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Top 10 Blockchain Tech Establish Market Network Connection Required for "Digital Twin" Expression

11/11/2018

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“Digital Twin” Stakeholders have reached their limit of patience with Blockchain hype, confirming something is going wrong. Sure enough, all major firms exploit that and already offer some Blockchain services, directing customers to their limited, “corporate bullshit” toolset. They advertise superior services but offer only narrow class of use cases.

Site Visit Executive looked at the problem and collapses the apparent wide-range of Blockchain services into couple of topics: supply line optimisation and streamlining network marketing processes.

Blockchains themselves are not the solution to anything yet. Instead, they are the path to experiments that guarantees results.

Before DoD has Blockchain in some form, no new “Digital Twin” model can work. Spatial navigation became possible after DoD got GPS sensors in its pockets; Situational awareness became possible after DoD got the sensors connected.

Not side products but instead secondary products of Blockchains have broadly adopted tokens and develop into applicable results such as “Bucket Brigades”.

Bucket brigade systems are a tool to build robust simulated robot control systems. This choice is sufficient to achieve adequate levels of performance for a variety of behaviours. The parallel implementation of the bucket brigade system is sure to speed up the training process and implement robotics controller.

Bucket brigade systems provide guidance shortening the number of cycles required to learn task rules using only a few training examples starting with randomly generated classifiers. Bucket brigades compensate for lack of time to deploy field-ready tech.

Most DoD Leaders associate bucket brigades with disasters, but they are mostly used in normal conditions to load or unload something, for example a truck of ammo supplies. Another example of Bucket Brigades is warning beacons,

In every case, Bucket Brigades compensate for the lack of technology, temporary or permanent. Information transfer is compromised by technological shortcomings of all networks not able to allow for multiple interpretations or deliver the information with fidelity.

The multi-agent approach provides a specific modeling and simulation alternative to known math/science system model tech for simulating manoeuvre process.

In a tech set without Bucket Brigades, now a stronger than ever trend to automate with machine learning, each node is as stupid as a box of rocks, unless it is perfectly prepared for the job. Each element has to be precisely calibrated or the system is in major trouble. Even if some aperture is allowed, an error propagation is a strong and adverse phenomenon.

Blockchain solutions have information about how many times a resource was used as reward and for what segment of equipment, so you can see in what operational theatre the discount effect is greatest. Robots can write and pass comments on it.

We set up experiment involving “Digital Twin” robots learning to work together: one robot ideally handing off Tokens to the other, which in turn carries them to a final destination. Discounting rewards results in the first robot receiving significantly less reward than the second one.

And, importantly, the discount power grows when other customers are more relevant to you.

How can that possibly work?

Tokens are transferred between Digital Twin agent pairs in the Bucket Brigade, without any centralised system watching or authorising interactions.

Tokens aren’t anything new. Each of us use incumbent tokens daily without even noticing: keys, tickets, receipts, reservations, network certificates etc. Ordinary tokens are already vital, but they are not very smart. More importantly, they are expensive to issue and even more expensive to maintain, with a large portion of barriers to implement system associated with security.

How can tokens change the way DoD does business and consumption of operational resources?

As open token-carrying platforms become more widespread, anyone can maintain an unlimited number of token output. Soon, piles of tokens will represent everything that can be counted in an economically meaningful way.

Within token-enabled supply chains, every participant seamlessly contributes to the quality automated event flow. Most economic acts can be done through token exchange, issuance or redemption. When tokens circulate, things you normally run operations, and procurement on happen “by themselves”, with much less overhead than normal.

As many Blockchain bridge connections are manifest in operations, tokens promise new productive economic scenarios when random “Digital Twin” token pairs can become mutually usable in changing conditions as a result of more reasonable inputs so many essential operational parameters can be articulated with greater precision.

On one hand, smart decision-making can be rewarded immediately in tokens. On the other hand, it always costs some number of some tokens to do something. So DoD operatives either make an economically-responsible decision or do not contaminate the feed, abstaining from any involvement.

When being passed continuously between units, tokens be very, very smart when it comes to changing scenarios.

Passing Tokens from one unit in the Bucket Brigade to another is a fundamentally local event. No one else but the units party to this exchange is required and if we consider distributed token-carrying platform connection bridges between them as free-access, self-maintaining ownerless entities.

Examples of that ubiquitous miracle of local interaction are everywhere: The great complexity of physical phenomena troops encounter is the result of endless iterations of similar “local acts”: circles on the water don’t need a concentric dispatcher.

Bucket Brigade Junctions in token interactions can be much smarter: since the constructs can also bear an often-needed note of context rather than the iron extremes present in automated operations.

But what about more complex things than just transfer of value, such as level of local interaction relevance under changing operational conditions where quality of information is not possible to quantify..

Network Marketing Example Network problems can be fixed with Bucket Brigade Tokens.

Establishing field agents for product/process design creates agent-based tools to construct market places among members of a Distributed design team to coordinate set-based design of a discrete build product. Designers of components are empowered to "Buy" and "Sell" desired characteristics engineers are motivated to assume.

Here we describe the entities interacting in the market space and outline the market space required to make trade-off decisions on each characteristic of a design. Agents representing each component "Buy" and "Sell" units of these characteristics. A component that needs more latitude in a given characteristic, i.e. more weight can purchase increments of that characteristic from another component, but may need to sell another characteristic to raise resources for this purchase.

In network marketing, each participant essentially has one core asset- the position/order tracking tag in the network recorded with fidelity. Tokens “locked” into the system provide privileges in using the system as intended. It could be revenue share in a typical marketing scheme or it could be a discount on risk charged by the system charges for its services.

In Distributed problem solving, we typically assume a fair degree of fidelity is present: the agents have been designed to work together; or the payoffs to self-interested agents are only accrued through collective efforts; or engineering relationships between units has introduced disincentives for agent individualism; etc.

Distributed problem solving concentrates on competence; as anyone who has played on a team, worked on a group project, or performed on a football team can tell you, simply having the desire to work together by no means ensures a competent collective outcome.

We have described Single Phase of cooperation life-cycle on Enterprise-to-Enterprise level search for possible product support collaborators. First, agents have to contact possible partners. There is wide field for future research in the domain of automatic searching and contacting possible partners.

This approach ensures the trustworthiness of the partners transferred from real-life to the agents cooperation. Each agent is equipped by the addresses and the security certificates and every partner can be authenticated using standard key methods. Every agent can be connected to many partner agents according to defined internal cooperation rules.

Once the agents are connected together, each agent provides the list of available product support capabilities to partners. It is possible to propose different capabilities to different partners. During this phase agents form basic cooperation network, receiving information suitable for effective collaboration in the next phases. During the life-cycle of the cooperation, agents subscribe information of the changes on product support resources on already established cooperation.

Building Blocks required for Digital Twin manoeuvre are quite similar to building blocks required to implement use of Blockchain with trusted status updates of connected instances.

To deliver value, connections must span wide mission space. Implementation of connections must not be tied to distinct established steps or location, but must be time sensitive to maximise transmission and minimise with respect to sense/response between the edge and core mission space.

Have we encountered Block and Connection concepts elsewhere?

Yes we have. Multi-Agent Systems are present in Building Block Constructs, with each Agent representing one block component viewed a baseline unit contribution to Digital Twin Model.

The convergence of Digital Twins and Blockchain is evident. Enterprises dissociated by modular structures and associated by function in operational sequences presents series of steps subdivided into blocks -- not only things/objects but also multi agent models, unit of work, process, verification decisions, outliers, feedback, metrics etc.

Component Sequence Builds make it easy to represent objects, processes, and decision outcomes. Connected blocks can support simulation agents networks joined by common Digital Twins. For example, alignment concepts described in previous reports specific to appropriate blocks can lead to useful platforms.

Here we present a practical application of product support provider network interaction to a major weapons system. Since the first operational deployment of the F-35B, the Marine Corps has seen part shortages/delays, poor reliability of certain parts, long repair times and inaccurate delivery times.

DoD has no formal mechanism to share after-action reports on a distributed network so the problem is that the service has kept on its own internal records system. “Without the F-35 program office sharing or making available operational lessons learned through a new or existing network communications mechanism, the services are at risk of not having access to key information that could affect their movements, exercises, operations, and sustainment of the aircraft,.

The formal sharing of lessons learned over the network would be extremely useful to the Services as they ramp up their F-35 deployments overseas. Since the DoD has no formal means to communicate these reports, the Corps has largely relied on informal means such as personal relationships and telephone calls to officials in the other services.

It’s a major problem to be solved since services branches are planning to expand F-35 operations so we are making a recommendation to the DoD to create a formal network mechanism or means to communicate and share F-35 after action reports across the military.

"The goal is to prevent lessons learned from being captured in a vacuum within each military service, but rather to have them captured and shared among the joint force to create, among other things, better doctrine, policy, training and education.

Marine Corps has noticed supply-chain problems that could be solved using Blockchain networks including shortage of parts in the F-35 supply chain; longer repair time for certain parts and inaccurate estimated arrival time for these parts

ALIS is a high-tech computer system that informs maintainers of aircraft upcoming maintenance and parts required to help sustain the aircraft. Marine Corps is “uncertain how long the F-35 can effectively operate” if the Autonomic Logistics Information System, or ALIS becomes “disconnected from the aircraft,.

At an exercise near Twentynine Palms, California, the Corps recorded “issues related to the tents used to house the ALIS” and the “need for maintaining network connectivity, and the limited reach-back support for ALIS.”

During another exercise. squadron noted accomplishments such as the “F-35 using its sensors to share data with legacy platforms” and better stealth capability over other aging aircraft. They also reported the need for classified facilities “to meet basic cooling and power requirements for housing the ALIS servers.

DoD plans to continue to evaluation of ALIS's performance, and that it agrees "future testing is worthwhile, so information is made more accessible across the services operating the F-35 has already been accepted by the Joint Program Office and the Pentagon,.

DoD said it is interested to start communicating many F-35 issues such as product support requirements through a new "Network Bank" registry for lessons learned, but did not specify where or how the forum will be based. "As F-35 operational exercises grow, the department will continue to share lessons learned through the existing operational advisory group and supportability advisory group.
　
DoD F-35 program is at a critical juncture. With aircraft development nearing completion within the next few years, DoD must now shift its attention and resources to sustaining the growing F-35 fleet. While production accelerates, DoD’s reactive approach to planning for and funding the capabilities needed to sustain F-35 operations has resulted in significant readiness challenges—including multi-year delays in establishing repair capabilities and spare parts shortages.

There is little doubt that the F-35 brings unique capabilities to the military, but without revising sustainment plans to include the key requirements and decision points needed to fully implement the F-35 sustainment strategy, and without aligned funding plans to meet those requirements, DoD is at risk of being unable to leverage the capabilities of the aircraft it has recently purchased. Furthermore, until it improves its plans, DoD faces a larger uncertainty as to whether it can successfully sustain a rapidly expanding fleet.

DoD plan to enter into multi-year, performance-based contracts with the prime contractor has the potential to produce cost savings and other benefits. However, important lessons are emerging from its pilot agreements with the contractor that are intended to inform the upcoming multi-year contract negotiations. To date, DoD has not achieved the desired aircraft performance under the pilot agreements, but it continues to move quickly toward negotiating longer-term contracts—which are likely to cost tens of billions of dollars—by 2020.

Contractor is assigned task of integrating sustainment support for the system, including that for the F-35 supply chain, depot maintenance, and pilot and maintainer training, as well as providing engineering and technical support.

According to program officials, the establishment of a new Product Support Network Integrator is an acknowledgement that DoD needs to take a more significant role in providing sustainment support for the F-35.

DoD did not plan for and fund stocks of materials needed to repair parts at the depots material incorrectly assuming material would be included as part of the contracts for establishing repair capabilities at the military depots.

So DoD has had to fund and negotiate additional contracts for the material. Late requirements identification and lack of funding to support repairs for many components is not expected to be delivered to depots until months/years after tech capabilities to conduct repairs have been established.

Without examining whether it has the appropriate metrics to incentivise the contractor or a sufficient understanding of the actual costs and technical characteristics of the aircraft before entering into multi-year, performance-based contracts, DoD could find itself overpaying for sustainment support that is not sufficient to meet warfighter requirements.

Finally, on a broader level, DoD projected costs to sustain the F-35 fleet over its life cycle have risen over the last several years despite the department’s concerted efforts to reduce costs.

Already the most expensive weapon system in DoD history, these rising costs are particularly concerning because the military services do not fully understand what they are paying for. This puts them in a precarious position as they consider critical trade-offs that might make F-35 sustainment more affordable. Without improving Network Communications with the services to help them better understand how the sustainment costs they are being charged relate to the capabilities that they receive, the services may not be able to effectively budget for the F-35 over the long term.

DoD has limited visibility into the support provided by the contractor along with the actual costs for which the services are responsible, until after the contract is signed. These transparency concerns are complicated by the fact that the services are paying into shared pools for F-35 sustainment, and the costs they are being charged for some requirements—such as for spare parts—cannot be directly tracked to an item that the services own or support that is specifically provided to an individual service.

As we have outlined in this report, Blockchain is an emerging technology for decentralised and transactional supply line connection monitor sharing across a large group of supplier Network intersections. It enables new forms of distributed supply line connection monitor networks, where agreement on shared states can be established without trusting a central integration point. A major difficulty for architects designing applications based on blockchain is that the technology has many configurations and variants. Since blockchains are at an early stage, there are limited number of product support success stories or reliable technology evaluation available to compare different blockchains.

Blockchain brings significant improvements to supply chain management for manufacturers and precision parts suppliers. Blockchain is the digital and decentralised exchange of value technology that records all transactions without the need for an intermediary. Businesses aim to manufacture goods — whether end products, solutions or precision parts of the highest quality, for the best price, with the greatest technical support, and according to agreed timelines.

Blockchains enable the creation of intelligent, embedded and trusted programme supply line connection monitor, letting suppliers build terms, conditions and other logistics parameters into contracts and other transactions. It allows suppliers to automatically monitor agreed upon value figures, delivery times and other enabling conditions, and automatically negotiate and complete transactions in real time. This impacts cost/benefit of work orders, maximises efficiency and allows for multiple avenues leading to supply line connection monitor.

A blockchain is a shared, distributed, secure supply line network connection monitor that every participant on product support service routes can share, but that no one entity control. In other words, a blockchain is a supply line connection monitor that stores work order routing records. The routing intersection is shared by group of service route supplier participants, all of whom can submit new records for inclusion.

Blockchain records are only added to the supply line connection monitor based on the agreement, or consensus, of a majority of the supplier group. Additionally, once the records are entered, they can never be changed or erased. In sum, blockchains record and secure supply line route dispatch information in such a way that is becomes the agreed-upon record for groups like F-35 stakeholders of important contract terms and enabling conditions.

Smart contracts can be instantly/securely sent and received over the Blockchain Network reducing exposure/delays in back office dispatching. As an example, oversight of Purchase Requests could be securely implemented with greater transparency and also potential battlefield applications messaging system could be leveraged during instances in which troops are attempt to communicate back to HQ using secure, efficient and timely logistics system.

Built-in supplier incentives to assure the security of every transaction and asset in the blockchain allows routing technology at intersections to be used not only for transactions, but as a product registry system for recording, tracking and monitoring all assets across multiple value suppliers. This secure information can range from information about parts or contract work-in-progress such as product specifications and purchase orders.

Because blockchain is based on shared consensus among different suppliers, the information on the blockchain is reliable. Over time, suppliers build up a reputation on the blockchain which demonstrates their credibility to one another. Furthermore, because trust can be established by the supply line connections, third party monitor of routing intersections between two suppliers will no longer be necessary.

In order to establish sufficient trust to become involved in a blockchain supply line connection monitor, the motives and goals of DoD and involved suppliers must be clear. The reputation of the participants becomes transparent and grows over time. It is important that suppliers in the routing market space can trust each other in order to share information and increase efficiency in shared processes.

Blockchain networks also open the door for machine-to-machine transaction capabilities to enable the transformation of a traditional supply line connection, where work order transactions and contracts must be maintained by each DoD dispatcher agent in market interaction with suppliers.

1. Cut procurement costs and production time by zeroing in directly on the right suppliers who can create the correct, high-quality parts

2. Speed prototype evaluation to test and modify design before production at a more competitive cost

3. Work with pre-certified precision parts suppliers and machine shops to procure more in less time

4. Eliminate supplier evaluation time, expedite orders and lower risk with suppliers automatically identified with needed technical expertise, location, machinery, design capability, and capacity

5. Custom quotes or automated blanket quotes for ongoing part needs simplify part orders, avoid production delays, and help manage profitability

6. Purchasers and parts manufacturers begin working together immediately on order fulfillment with instant review of qualifications and equipment

7. Increase parts and supplies quality control by comparing, reviewing, and approving qualified machine shops and precision parts suppliers

8. Get real-time insight and transparency into parts fabrication, production, and delivery

9. Improve supply chain efficiency with automated quote management, instant notification of quotes with approval

10. Orders, designs and fabrication are secure and provides protection not always afforded to smaller manufacturing firms or suppliers
　

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Top 50 Elements Required for Simulation Training: Application of Station Tasks Instruct Schedule Process

11/1/2018

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Simulation can become an effective training tool to improve competence of Marines providing mechanism for determining if Marines are ready for action on a much more comprehensive basis than through its current examinations. Stronger station base must be developed to address issues of standardisation and validation.

Training programs using simulation often insert simulation into existing courses rather than customising the course to ensure that the simulation contributes effectively to the course training objectives. One result has been a lack of standardisation in simulator-based courses.

The major benefits of simulation will be realised with a more structured approach to the use of simulation for training. Benefits include ability to use simulators to train regardless of conditions, allowing instructors to terminate training scenarios at any time and training scenarios can be performed under risk-free conditions, repeated, recorded and played back.

The training simulation effort is technically limited to five aspects of warfighting: ship-to-shore manoeuvre itself, amphibious fire support/effects, clearing amphibious assault lanes, amphibious command & control, C4 communication & amphibious information warfare.

However, Marines trying not to bound the effort too rigidly because someone somewhere might submit a totally unexpected idea that changes the way we look at amphibious operations. We don’t want to limit training proposals in any way.

Interim Airlift concept proposes existing aircraft “snatch pickup” of "Logistics Glider" from Sea base helipad, flight deck, or nearby surface for aerial sustainment of tactic manoeuvre expedition force.

A next step towards achieving vision of connecting multiple simulators spread across the battlespace is the integrated training station to house, all under one roof, simulators for pretty much anything in the carrier strike group. We’ll be able to integrate them all together. Eventually we will be able to pipe in feeds from live aircraft out on our range – that’s the live part, and then vice versa hopefully we can pipe what’s being seen in the simulators, or what’s being constructed in the simulators, out to the live aircraft as well.

Professional development of Marines has in the past been based on a strong tradition of on-the-job learning. There is a wide range of Marine simulators in use worldwide. Capabilities simulators range from radar only to full-scale ship-bridge simulators capable of simulating a 360-degree view.

Marine simulators can simulate a range of vessels in scenarios of real generic operating conditions e.g., ports and harbors. Simulators can be used to train Marines in a number of skills, from rules of the road and emergency procedures to bridge team resource operations.

A simulator does not train; it is the way the simulator is used that yields the benefit. It is easy to be impressed by the latest, largest full-mission simulator, but what is more important than the technology is how training methods are applied and whether it increases training effectiveness significantly, incrementally, or at all.

Physical scale-model, or manned-model, simulators are scale models of specific vessels that effectively simulate ship motion and handling in fast time. These models are especially effective for teaching shiphandling and manoeuvre skills.

New training structures assess training effectiveness from specific simulator features. While some work has supported the notion that higher levels of fidelity add to training effectiveness, others do not. For example, there is no evidence that in air carrier community that motion systems add to the training effectiveness of a simulator. Despite the widespread acceptance of motion systems, evidence is inconsistent.

We decided to work out simulation problems after units depart on deployment. There would be no impact on the response plan, which by then would have run its course. No one could object to the complexity of the task as the players involved would be trained and certified units. The fleet could focus them on whatever warfighting tasks seemed most critical, separate from a set training regimen.

It is difficult to determine the validity and degree of equivalency between simulator training and shipboard experience without an evaluation of transfer. The issue is if it can be determined that skills learned in a simulator can be employed aboard ship.

The most systematic way to test the application of this training to shipboard performance would be to systematically compare shipboard performance of simulator-trained individuals as group to performance of a group whose only difference is the lack of simulator training. Logistically, these studies are difficult to execute within the air carrier sector and may be even more difficult to execute in sectors lacking systematic organisational structure.

Marines duties and responsibilities are dictated by their work space, operating in the sometimes highly stressful and demanding work space of automated ships, short turnaround times in port, smaller crew sizes, and self-contained independence of long sea voyages. Deck officers must be knowledgeable in skills ranging from watchkeeping, navigation, cargo handling, and radar.

Marine pilots are highly skilled, functioning independently in scenarios requiring understanding the operation of ship-bridge equipment and manoeuvre capabilities of a wide range of vessels and to be able to safely manoeuvre through shallow and restricted waters. Pilots must also be knowledgeable in local working practices of ports and terminal operations.

In Applying simulation to training requirements, it is important to consider differences among simulators, that is, the different levels of simulator component capabilities. A high degree of realism is not always required for effective learning transfer. Often it is not necessary to use the most sophisticated simulator to meet all training objectives.

Levels of realism and accuracy required should match the training objectives. Simulators are used for Marine performance evaluation. These evaluations are usually informal and take the form of debriefings during the course of training. Occasionally, however, simulators are used for more structured evaluations.

Systematic application of the instructional design process offers a strong model for the structuring of new courses and the continuous improvement of existing courses. Instructors must ensure that all training objectives are met and themselves be trained to ensure that the simulator-based training courses meet the training objectives.

An effective training programme addresses Marines training needs with respect to knowledge, skills, and abilities. It exploits all media, from personal computer-based training to limited-task and full-mission simulators and applies the appropriate training tool to the specific level of training. For example, it would not be necessary to use a full-mission simulator for early instruction in rules-of-the-road training.

Systematic approach to training promotes convergence toward full-mission expertise by developing basic modules of skills in several steps. This approach encourages the assembly of ever-larger skills modules until the trainee can exploit training on a full-mission simulator.

Differences in instructional techniques can result in a significant range of material that can be covered. The way material is covered also affects the relative value of the learning experience. These factors may be affected by simulator features and fidelity; however, limitations in these areas can be minimised or offset to a large extent for certain instructional objectives. For example, we found bridge team training could utilise creative instructional design can be used to compensate for limitations in simulator capabilities.

Before we had the simulator, Marines were really slow in the first few days on the range because that’s the first time that they did it. But now getting some practice time in, you get better control and better performance on the range with the live assets, so it makes it more efficient. So the simulator is really useful, it’s invaluable as far as getting Marines ready to go.

Ship-bridge simulators and manned models can be effective in the development and renewal of Marine pilot skills in a number of significant areas including bridge team resource administration,, shiphandling, docking and undocking scenarios, bridge watch keeping, rules of the road, and emergency procedures.

Although current computer-based simulators are limited in their ability to simulate ship manoeuvre trajectories in shallow and restricted waterways and ship-to-ship interactions—capabilities important to pilot shiphandling training-- simulator training in areas such as bridge team/resource management can be of value to pilots.

Special-task simulators could be used effectively in Marine training. A limitation affecting widespread use is little availability of desktop simulations and interactive courseware. Marines must selectively sponsor development of interactive courseware with embedded simulations to facilitate understanding of information and concepts that are difficult or costly to convey by conventional means.

Use of simulations offers an effective mechanism for accessing not only Marines knowledge but ability to apply that knowledge, to prioritise tasks, and to perform several tasks simultaneously, all functions routinely required aboard ship. Must develop a framework for integrating simulation into training program before it undertakes more extensive use of simulation in training.

Must update and expand relevant task and subtask assessments for application to the Marines training needs. For the instructional design process to be effective, the course design should include the definition of training needs based on the steps required to complete identified tasks and subtasks for specific functions. Assessment must include dimensions that have been missing with respect to behavioural elements and specific steps needed to execute each subtask.

Standards for simulator-based training courses should be considered in the development of a plan for allowing substitution of simulator-based training for required sea time in the limited cases. The ratio of simulator time to sea time should be determined on a course-by-course basis and should depend on the quality of the learning experience, including the degree to which the learning transfers to actual operations.

The accuracy and fidelity of ship-bridge simulators can vary significantly from training station to station. These differences derive from the differences among original models used to develop the simulations and from station operator modifications to models after installation of the simulations.

Often, training station operators periodically modify simulation models after the initial validation. This process of continually modifying simulation models can result in inconsistent training programmes, as successive training sessions may be conducted with different simulations.

To address these concerns, simulators and simulations must be validated, all modifications must be documented and the simulation revalidated. The extent to which accuracy of a simulation needs to be validated will depend on the proposed use of the simulation.

Equivalency of simulation to real life has not been systematically investigated because existing task assessments are not adequate for this purpose and systematic application of task assessments based on performance have not been developed for this purpose.

The work of Marines is task-oriented. To be able to effectively apply simulator technology, it is important to systematically measure simulator effectiveness for training and to develop a mechanism to use simulators to improve the effectiveness of the transfer of skills and knowledge.

Ship control and navigation are visually supported tasks, especially in confined areas. Learning visual skills is an important process in the development of proficiency in control and navigation. In many simulators, the visual simulations are provided with systems that have limited capabilities to represent some stimuli. The result can be distortion of distance perceptions as an observer moves around the simulated bridge.

It is possible to stimulate lessons by participating in a simulation involving a crew change, a watch relief, two ports unfamiliar to the new watch officers, and a transit speed that was excessive for the situation but not readily apparent. As the scenario unfolded, bridge team members created enough pressures and problems for themselves without any instruction. The need for more effective passage planning and improved communications among bridge team members was no less apparent than it might have been in a situation artificially influenced by role reversals or problems inserted by the instructor.

The impact on training effectiveness of ship operational characteristics—such as vibration, sound, and physical movement of the bridge in roll, heave, and pitch—has not been verified and should be investigated before applying these systems to simulators.

Marines must assess the impact on training effectiveness of apparent limitations in simulator visual systems. If these limitations have a negative impact on training effectiveness, visual systems must be developed that overcome or minimise the negative aspects of current systems.

Comprehensive assessment addresses the large number of problems resulting from a lack of understanding within the Marines of the capabilities and limitations of an automated system. For example, when the radar signal-to-noise ratio is poor, the automatic radar plotting aids may "swap" the labels of adjacent targets.

If Marines are not aware of this limitation, Marines may be navigating under false assumptions about the position of neighboring vessels, increasing the chances of a casualty. Comprehensive assessment will identify misconceptions about automated systems that could then be remedied through training or equipment redesign.

Marines must undertake structured assessments of the need for simulation of vibration, sound, and physical movement. These assessments should include consideration of the possibly differential value of these various sources of information in different types of training scenarios.

Manned models are an effective training device for illustrating and emphasising the principles of shiphandling. They are particularly effective in providing hands-on ship manoeuvre in confined waters, including berthing, unberthing, and channel work. Manned models can simulate more realistic representations of bank effects, shallow water, and ship-to-ship interactions than electronic, computer-driven ship-bridge simulators.

The ability of a simulator to closely replicate manoeuvre trajectory of ship is a strong measure of the usefulness and value of the simulator for training. At present, simulation of ship manoeuvre trajectory is well developed in normal deep-water, open-ocean cases. In cases involving shallow or restricted waterways, ship-to-ship interactions, and extreme manoeuvre, fidelity may be significantly reduced.

Conduct of full-scale real-ship experiments would significantly advance the state of practice in model development. These experiments could supplement the limited information available for shallow and restricted water, slow speed, and reverse propeller operational information.

Marines must develop standards for the simulation of ship manoeuvre. Fidelity of the models must be validated through a structured, objective process. Standard models must be selected and tested in towing tanks and the results compared to selected full-scale real-ship trials of the same ships to provide benchmark metrics for validation and testing of simulators.

What we want to be able to do in the future, and this training station is the first step, is machine-to-machine metrics gathering. Allowing us to gather large amounts of metrics- so not just necessarily how they did on that event, in the actual actions they took on that event, but we can also gather historical metrics on the aircraft, its system, how well the systems have held up.

We can look at, automatically, machine-to-machine, look at the pilot and how proficient he is, how much flight time Marines received recently, and that will all help us build that bigger picture so we can inform leadership with the best simulation information we can give them.

1. Identify requirements for different trainee population backgrounds

2. Set training objectives/goals requirements

3. Determine course content/material requirements

4. Implement timetables for training evaluations

5. Correlate resource requirements with training objectives

6. Match specific instructional techniques to curriculum content

7. Identify trainee assessment requirements

8. Establish instructor experience, qualification, selection, training

9. Select type of training media

10. Estimate cost benefit/effectiveness of the training programme.

11. Select simulators designed to meet training needs not structuring training to fit simulator.

12. Measure training performance against predefined criteria.

13. Continue training until required proficiency level is reached.

14. Anticipate requirements for refresher training to maintain skill level

15. Evaluate structure of trainee performance prior to, at conclusion of, and after programme

16. Conditions and attitudes in the work space must be conducive to transfer of training.

17. Create standards/objectives for individual simulator exercises content

18. Provide content for simulation exercise instructions/debriefings

19. Design instructions based on special task compared to full-mission simulator

20. Account for ship type and model fidelity of manoeuvre

21. Assess type/structure/length of exercise scenario objectives

22. Establish level of fidelity/accuracy of visual scene

23. Account for accuracy of trajectory prediction and validation requirements

24. Account for high front-end cost of simulator-based training compared to cost of on-the-job learning

25. Provide training structure for trainees to substantiate their statements

26. Establish basis for instructor recruitment/selection

27. Value instructional capability to operate/integrate simulator resources

28. Develop inclusion of bridge/radar operations subject matter

29. Maintain up-to-date content of ship operational navigation/traffic technology

30. Communicate with industry address requirements and details of training courses

31. Prepare all necessary course material/equipment

32. Validate ship models, production manoeuvre scenario metrics

33. Prepare for coordination of course schedules and training strategy

34. Conduct/supervise debriefings

35. Develop ship-bridge simulator-based learning system

36. Build design workshop to implement simulation instruct strategy

37. Provide for simulation exercise design/grading

38. Establish command experience knowledge of simulator capabilities;

39. Demonstrate expert shiphandling skills

40. Value current general knowledge of industry/trainee sectors

41. Determine training requirements for assessment of job-tasks and subtasks;

42. Meet training objectives include performance measures

43. Establish training techniques include assess if simulation meets objectives

44. Determine duration of training programme and debriefing techniques

45. Validate integration of simulator/simulation with curriculum

46. Establish standards for design/validate exercise scenarios

47. Qualify training instructors based on guidelines/standards

48. Plan for establishing sequencing of simulator training

49. Account for effect of short/extended course duration on learning

50. Transfer training effectiveness by different population categories

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Top 50 Mission Objectives Achieve Force Operations Capacity/Solutions Provide Full Spectrum Expedition Support

11/1/2018

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1. Organise operating forces, support/sustainment base and unique capabilities to respond to the complex spectrum of crises and conflicts.

2. Provide combatant commanders with scalable, sustainable, interoperable, expeditionary, combined-arms MAGTFs

3. Promote service component, functional component, and joint task force headquarters command and control capabilities to achieve equipment interoperability

4. Enhance MAGTF interoperability with efficient command and control system combined with surveillance, and reconnaissance capability

5. Expand capabilities to observe, visualise and shape operational area and attack critical vulnerabilities

6. Discharge responsive, integrated, and balanced expeditionary fires leveraging improvements to organic surveillance, joint target acquisition, aviation, and indirect fires

7. Build capabilities to operate under austere conditions across the spectrum of conflict while reducing dependence on existing infrastructure.

8. Execute information operations capabilities for amphibious pre-positioning, aviation, and land mobility, manoeuvre, and sustainment capabilities into operating forces.

9. Network operational communications, information, and intelligence systems with joint and allied forces and provide a global access capability to information resources.

10. Promote experimentation to include ways to accomplish acquisition, logistic, and support tasks through technological innovations, outsourcing, and other techniques.

Top 10 Enterprise Strategy Process to Provide Weapons Systems Users with Tools to Improve Readiness
　
1. Available

Degree to which a system, subsystem or equipment is in a specified operable and committable state at the start of a mission, when the mission is called for at an unknown, i.e. a random, time.

2. Compatible

Capacity for systems to work together without having to be altered to do so--user must be able to open orders in either product-- products of the same or different types, or different versions of the same product.

3. Transport

Quality of equipment, devices systems permits ability to be moved from one location to another to interconnect with locally available complementary equipment, devices, systems or other complementary facilities.
　
4. Interoperable

Condition achieved among communications systems or items of communications-electronics equipment when information or services can be exchanged directly and satisfactorily between them and/or their users.

5. Reliable

Measure quality, time and speed performance-- want to operate as long as possible without losses; and when you have losses, you want to fix them as quickly as possible.

6. Field Use

Service support embedded with field agents to ensure equipment readiness and mission success-- utilise expertise in supply/logistics support, assures parts availability & repair services.

7. Maintainable

Service of restoring failed equipment, machine or system to its normal operable state within a given inspection timeframe, using established practices and procedures.

8. Logistics Support

Integrated and iterative process for developing materiel strategy to optimises functional support, leverage existing resources, guide the system engineering process to quantify ownership cost over service life and decrease the logistics footprint

9. Training

Uses regular or existing workplace tools, machines, documents, equipment, knowledge, and skills necessary for unit to learn to effectively perform job assignments

10. Simulation

Mechanical devices enable trainees to use some actions, plans, measures, trials, movements, or decision processes prepare for use with must be designed to repeat, as closely as possible, the physical aspects of equipment and operational surroundings trainees will find at work place.
　
　
Top 10 Mobile Control Authorities Combine Equipment Plan/Route Schedule Marine Deployment Support

1. Dispatch Deploy Control Centers

Dispatch control centres plan, route, and schedule personnel, supplies, and equipment movements over point of origin to port of debarkation to final destination or movements within area of operations. In some cases, the agencies are permanent.

For example, every MAGTF should have a full-time distribution and transportation section. For smaller MAGTFs, this may be no more than one Marines at the combat service support operations centre In other cases, movement control agencies are temporary.

Battalions, squadrons, regiments, and groups establish temporary movement control centres when their organisations are moving. Local standing operating procedures establish the composition and procedures for deployment control centres.

2. Materiel Transit Operation Centre

The Marine air-ground task force deployment and distribution operations center is the MAGTF commanders agency responsible for the control and coordination of all deployment support activities. It is also the agency that coordinates with geographic combatant commanders unit, and transportation component commands. When the MAGTF operates as part of a joint force, transit requirements are coordinated via operations centre for all geographic combatant commander’s service components.

3. Mobile Capability Command

Operational capability is located within the MAGTF command element, conducting integrated planning, provides guidance and direction, and coordinates and monitors transportation resources in its directorate role for MAGTF’s theater and tactical distribution processes.

4. Materiel Distribution Centre

The Materiel Distribution Centre is MAGTF’s distribution element with responsibility to provide general dispatch and receipt services consolidated distribution services and to maintain asset visibility to enhance throughput velocity and sustain operational tempo.

While in garrison, centre will make every effort to integrate/collocate with the base materiel transit operation, in order to maintain distribution competence. For deployed operations, together with logistics combat element, function to establish and operate the distribution network under deployed scenarios conditions.

5. Distribution Liaison Cells

Distribution liaison cells are task-organised distribution elements structured to perform tasks aboard Marine expeditionary units or forward operating areas to include but not limited to providing support for deploying MAGTFs.

6. Terminal Operations Organisations

Terminal operations organisations are integral to deployment and distribution systems, providing support at strategic, operational, and tactical nodes. Terminal operations organisations are established to include port operations group, beach operations group, railhead operations group, and the movement control agency of the landing force support party task-organised, manned, and augmented as required, to perform these tasks.

7. MAGTF Movement Control Centre

Standing organisation and the subordinate element to allocate, schedule and coordinate internal transportation requirements based on MAGTF commanders priorities supports the planning and execution of MAGTF ground movement scheduling, equipment augmentation, transportation requirements, materiel handling equipment, and other movement support on theater controlled routes, and register requirements to the joint movement centre for support. In addition, coordinates activities with installation operations and supporting commands

8. Major Subordinate Command Unit Movement Control Centre

Division and wing commanders deploy forces to support operational MAGTFs, directing transportation and communications assets needed to execute deployments. Each command activates its unit to support marshaling and movement of assigned subordinate units established down to the battalion, squadron, or independent company, as required, to serve as the unit transportation capacity directorate.

9. Base Operations Support Group

Bases from which Marine Corps operate forces unit forces deploy establish base operations support groups to coordinate their efforts with those of the deploying units. Bases operations support groups coordinate and manage transportation, communications, and other functional support requirements beyond organic capabilities to supported units during deployment.

10. Station Operations Support Group

Marine Corps operating forces air stations deploy establish station operations support groups coordinate efforts with those of deploying units. Air stations have transportation, communications, and other assets useful to all commands during deployment.
　
Top 10 Considerations for Sufficient Logistics Plans for Marine Expeditionary Operations

1. Expeditionary Action Phases

Because MAGTFs are organised to conduct operations under austere conditions Marine forces and MAGTF commanders provide the operational logistics capabilities necessary for conducting expeditionary operations, while tactical logistics are provided by MAGTF commanders and their subordinates. This expeditionary or temporary operations support will be withdrawn after the mission is accomplished. Expeditionary operations involve action phases which have strategic, operational, and tactical considerations.

2. Deployment

Deployment is the movement of forces to the area of operations. Deployment is initially a function of strategic mobility. Operational-level movement in theater completes deployment as forces are concentrated for tactical employment.

Deployment support permits the MAGTF commanders to marshal, stage, embark, and deploy their commands. Although deployment is a strategic and operational-level concern, tactical-level units may be required to assist the deployment.

3. Entry.

Entry is the introduction of forces into theatre accomplished by sea or air, but in some cases forces may be introduced by ground movement from an adjacent expeditionary base. Logistics capabilities are used in the entry phase to develop entry points e.g., an airfield or port, an assailable coastline, a drop zone, an accessible frontier.

4. Enabling Actions

Enabling actions are preparatory actions taken by the expeditionary force to facilitate the eventual accomplishment of the mission. Enabling actions may include seizing a port, or airfield for the introduction of follow-on forces and the establishment of necessary logistics and support capabilities. In case of disruption, enabling actions may involve the initial restoration of order and stability. In open conflict, enabling actions may involve use of force to stop competitor advance/capabilities, or capturing key terrain required for conduct of decisive actions.

5. Departure or Transition.

Because expeditions are by definition temporary, all expeditionary operations involve a departure of the expeditionary force or a transition to some form of permanent presence.. Departure is not as simple as the tactical withdrawal of the expeditionary forces from the scene because action requires withdrawing the force in a way that maintains the desired situation while preserving the combat capabilities of the force. For example, time must be taken for reload of ships to restore sustainment capabilities because either force may be instantly ordered to undertake another expeditionary operation.

6. Forward-Deployed Logistics Capabilities

Marine Corps maintains force reserve program me allow MAGTFs to sustain themselves for a significant period of time during combat operations. Sustainment gives MAGTFs the required endurance until theater-level supply is established.

Sustainment resources forward deployed with MAGTFs are augmented and replenished with reserve materiel and land prepositioning programmes. The resulting logistics self-sufficiency is fundamental, defining characteristic of expeditionary MAGTFs.

7. Reserve Materiel.

Combination of non-deployed force-held assets and reserve system programmed purchases collectively serve to ensure levels above MAGTFs can deploy with sufficient equipment and supplies to support period of contingency operations to provide reasonable assurance force can be self-sustaining until resupply channels are established. Usually,, MAGTF deploys with sufficient aviation-specific equipment and supplies.

8. Maritime Prepositioning Force.
Maritime Prepositioning Force.is combination of prepositioned materiel and airlifted elements with limited sustainment capabilities. Smaller MAGTFs may be sustained ashore for more or less time depending on the size of the force, the number of preposition forces support of that force, and other variables such as inclusion of an aviation logistics support ship.

9. Prepositioned Programs.

Prepositioned vehicles, equipment, and supply stocks used for regional contingencies are configured to support a MAGTF. Stocking goals for prepositioned programme are the same as the prepositioned ships amd requirements can be filled with this equipment if directed.

10. Marine Expeditionary Planning Organisation

Preparation of plans for future operations are directed by administrative sections responsible for execution of expeditionary plans. Subordinate elements and smaller MAGTFs conduct the same planning with greater focus on the current battle and smaller size to dictate operational modifications.
　
Top 10 Implications of Emerging Marine Corps Logistics Concepts

1. Equipment Technological developments require logistics teams to be more innovative and forward-thinking than their predecessors. Emerging concepts for the 21st century could yield significant savings in manpower, supply inventories and maintenance costs, while at the same time increasing responsiveness, efficiency, and effectiveness of support.

2. Advancing Technologies

To further develop the operational capabilities inherent advancing technologies that are applicable to Marine Corps information and logistics systems and equipment are needed to reduce the logistics footprint and reliance on facilities ashore. Further, close liaison with industry will be essential to take advantage of technological breakthroughs.
3. Logistics Information Systems

The Marine Corps, in conjunction with the Navy, must develop and field logistics systems that will provide near real time, over-the-horizon logistics information. These systems also need to be able to determine future over-the-horizon, surface, and aviation assault support requirements.

4. Development and fielding

Air and surface refueling capabilities will need to be present in the over-the-horizon logistics information essential to success, reducing the logistics footprint ashore, especially when sea-based logistics method is required.

5. Sea-basing

Sea-based logistics is yet another emerging support concept that requires technology, coupled with innovative thinking, to become a viable reality. When providing a sea-based logistics capability the Marine Corps needs to ensure that this capability is fully integrated with amphibious ships,, aviation logistics support ships, hospital ships, combat logistics force ships, offshore petroleum discharge systems, and logistics over-the-shore systems.

6. Total Asset Visibility

Total asset visibility systems, combined with improved business practices, can enhance expeditionary logistics anticipatory and more responsive to support the increased number and frequency of requirements to units at greater distances dispersed over a larger battlefield. Effective and accurate total asset visibility systems will be essential for rapid identification of Logistics Operations requirements, location in storage, immediate access, and tracking transportation assets for delivery. Successful unit logistics support will depend heavily on total asset visibility systems to maintain responsiveness—especially in expeditionary operational scenarios.

7. Distribution Systems

Planners must develop future distribution systems that provide rapid and responsive means to receive, store, access, break down, repackage, transport inland, and distribute on demand smaller unit packages. Innovations will be necessary in the packaging of unit daily requirements that will facilitate direct delivery from the container to the user. Improvements in shipboard selective warehousing, access, and offload technologies need careful examination to address the increased demand of deliveries, increased frequency of smaller sustainment slices on limited transportation assets. Sea-basing will demand that distribution systems provide the means to accomplish at sea, or preclude having to do at all, the functions that currently necessitate general offload and buildup ashore.

8. Supply

Expeditionary logistics capabilities could decrease the need to stockpile or warehouse supplies. Emerging technologies in commercial enterprise, military warehouse modernisation and potential extension to shipboard or even container designs may potentially improve receipt, storage, accountability, and issue operations to the point where one supply warehouse person could do the work in a fraction of the time. Sizable cost savings could also result from increased use of commercial sources for commonly used items, tools, services, and repair parts. This could eliminate the current methods used to procure, store, and maintain large inventories of repair parts or backup subassemblies.

9. Maintenance

Shipboard maintenance requirements of on-board equipment need accurate identification as well as reduction, wherever possible. Technology can yield significant benefits in this area. The advances here can be realised through incorporation of built-in maintainability and reliability features in equipment and supplies. Longer shelf lives for various supplies can substantially reduce on-board equipment maintenance and the rotation of needed supplies.

Equipment reliability and Availability Technology reduces the number of maintenance actions required to ensure equipment readiness and simplify repair. Significant savings become feasible in facilities, inventories, manpower, and the money required to maintain them.

Enhanced technological developments will also lead to growing procurements of commercial end items versus military-unique end items. Such efforts greatly reduce equipment cost, increase availability of and accessibility to commonly used parts, reduce mean time to repair, and increase overall equipment readiness.

10. Retention of Amphibious Capability

State-of-the-art technological logistics enhancements underscore Marine Corps naval character and why it must continually strive to improve its capability to conduct amphibious operations. The skills and knowledge built on amphibious capability are essential tools for influencing technological and tactical advances that produce time, manpower, cost, and other benefits.
　

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