Middle Tier of Acquisition (MTA)

AAF  >  MTA  >  Rapid Prototyping Path

Middle Tier of Acquisition (MTA) Rapid Prototyping

 

This MTA path is to rapidly develop fieldable prototypes within an acquisition program to demonstrate new capabilities within 5 years.

How to use this site

Each page in this pathway presents a wealth of curated knowledge from acquisition policies, guides, templates, training, reports, websites, case studies, and other resources. It also provides a framework for functional experts and practitioners across DoD to contribute to the collective knowledge base. This site aggregates official DoD policies, guides, references, and more.

DoD and Service policy is indicated by a BLUE vertical line.

Directly quoted material is preceeded with a link to the Reference Source.

Lifecycle View of Rapid Prototyping

Requirements Acq Strategy and Documents Contracting Costs and Funding Entrance Exit MTA Execution MTA Completion Reporting and Governance Prototype Development Test and Demo Prototype O&S Transition Program

Reference Source: DoDI 5000.80, Paragraph 1.2.b-c

 

The MTA pathway is intended to fill a gap in the DAS for those capabilities that have a level of maturity to allow them to be rapidly prototyped within an acquisition program or fielded, within 5 years of MTA program start. The MTA pathway may be used to accelerate capability maturation before transitioning to another acquisition pathway or may be used to minimally develop a capability before rapidly fielding.

 

TThe rapid prototyping path provides for the use of innovative technologies to rapidly develop fieldable prototypes to demonstrate new capabilities and meet emerging military needs. The objective of an acquisition program under this path will be to field a prototype meeting defined requirements that can be demonstrated in an operational environment and provide for a residual operational capability within 5 years of the MTA program start date. Virtual prototyping models are acceptable if they result in a fieldable residual operational capability. MTA programs may not be planned to exceed 5 years to completion and, in execution, will not exceed 5 years after MTA program start without Defense Acquisition Executive (DAE) waiver.

See Overview & Highlights and FAQs for unique considerations for the MTA pathway.

Watch this 39-second video clip about prototypes and learning through doing from the Defense Panel Discussion on MTA (June 2018):

  • Mr. Ben Fitzgerald (then Director of Acquisition and Sustainment Strategy Office) interviewing Dr. Will Roper, Assistant Secretary of the Air Force (Acquisition, Technology & Logistics) and Dr. Bruce Jette, Assistant Secretary of the Army (Acquisition, Technology & Logistics) 

Prototype: A model built to evaluate and inform its feasibility or usefulness.  Non-physical models are acceptable if the non-physical model is the residual operational capability to be fielded.

 

DoDI 5000.80

Residual Operational Capability: For rapid prototyping programs, residual operational capability will be considered any military utility for an operational user that can be fielded.

 

DoDI 5000.80

Why Prototyping?

Reference Source: DoD Prototyping Handbook, Nov 2019

The National Defense Strategy emphasizes adopting a risk-tolerant approach to capability development through the extensive use of prototyping and experimentation to drive down technical and integration risk, validate designs, gain warfighter feedback and better inform achievable and affordable requirements, with the ultimate goal of delivering capabilities to the Joint warfighter at the speed of relevance. In order to retain U.S. global technological dominance, DoD must adopt and mature this approach quickly, using all existing authorities at its disposal and new authorities provided by Congress in recent law.

4.2.1 Purpose for Prototyping.

A review of prototyping literature reveals a nearly endless list of reasons why S&T, R&D, and acquisition professionals conduct prototyping projects. All of these reasons can be synthesized into one fundamental purpose: to generate information that supports a decision.

These decisions take place along the full spectrums of the S&T, R&D, and acquisition lifecycle domains. Some decisions occur at formal milestones while others are simply made by program and project managers (referred to as PMs for the remainder of the guidebook) in the course of managing their projects. These decisions are often expressed in the form of a question, or directly supported by an answer to a question, and cover a wide spectrum of topics, including decisions related to the prototype’s technology, program management challenges, and how the technology is to be employed. Examples of some of these questions can be found in Table 2. Prototyping projects should be designed to generate data sets that support a specific decision.

Benefits of Prototyping

Reference Source: DoD Prototyping Handbook, Nov 2019

In addition to the reasons S&T, R&D, and acquisition professionals choose to prototype, the literature also identifies numerous benefits that can be recognized from prototyping.

4.2.2.1 Rapid Learning.

Prototyping can enhance rapid learning through the use of the “test-analyze-fix-test” (TAFT) approach to capability development. Using this approach, prototypes undergo repetitive iterations of the TAFT process as long as funding and schedule permit or until the desired performance is achieved or the purpose is realized. This approach helps reveal problems early and enables developers to evaluate the modifications they make to mitigate the problems.

4.2.2.2 Accelerated Demonstration.

Prototyping can be used to demonstrate the value of new concepts, technologies, components, systems, and applications earlier in the technology development process than would have been possible if the final development article was used for testing.

4.2.2.3 Rapid Delivery of Capability to the Field

Prototyping can also be used to develop and demonstrate solutions to existing and emerging operational capability gaps. When these prototypes are deemed viable solutions, they are often left in the field to be used by operators as solutions to their pressing needs.

4.2.2.3 Fail Fast, Fail Cheap to Learn Fast and Save Money.

“Fail Fast, Fail Cheap” is a term of art that the prototyping community uses to describe the great value of prototyping. Dr. Griffin echoed that philosophy in his April 18, 2018 statement to Congress. Rather than avoiding failure, Dr. Griffin encourages the Department to adopt a “willingness to learn from failure” as it uses prototyping and experimentation to quickly deliver innovative solutions.7 This philosophy seeks to use the simplest and least expensive representative model possible (rather than an expensive final development article) to quickly determine the value of an approach, concept, or technology through incremental development and evaluation. When testing reveals something isn’t working as expected or desired (i.e., a “failure”), the prototype design can either be modified and reevaluated, or decision makers can pivot to a different approach. The faster prototyping “fails,” the faster learning can occur, and the faster decisions can be made regarding the next appropriate step in the development or innovation process. “Failing fast” with prototyping enables the DoD to drive down technical risk, inform requirements, and ensure an integrated and interoperable capability before either weighing down the research and engineering phase of an acquisition with costly procurement decisions or weighing down a procurement program with costly technical risk.

What is a "Successful" Prototype?

Reference Source: DoD Prototyping Handbook, Nov 2019

While prototyping is a useful tool in the S&T, R&D, and acquisition professionals’ toolbox when used appropriately, concepts such as “Fail Fast, Fail Cheap” run counter to the culture of success within DoD’s acquisition community. The NDS points out that DoD’s over-optimized processes hinder timely delivery of capability to the Joint warfighter. These processes have inculcated a level of risk aversion that hampers the use of prototyping to inform decisions. In its report,“Weapon Systems: Prototyping Has Benefited Acquisition Programs, but More Can Be Done to Support Innovation Initiatives” (GAO-17-309), the Government Accountability Office (GAO) states that “DoD has become increasingly risk averse” and further asserts that risk aversion stifles innovation.

One way of mitigating this risk-averse culture is institutionalizing a new definition of what constitutes prototyping “success” and “failure.” Quite simply, since at its core prototyping is meant to generate a data set to inform a future decision, a prototyping project “succeeds” if it provides that data set—even if the prototype itself does not work. Likewise, a prototyping project that does not generate a data set to inform a future decision “fails.” Perspectives of “success” and “failure” in prototyping should have less to do with the prototype itself and more to do with the data that the prototyping project generates. The reality is that, by their nature, prototypes should be expected to “fail” frequently—that’s part of the prototyping and learning process. Some might question then: what is the benefit of developing something that fails? It is the concept of “Fail Fast, Fail Cheap” that provides the justification for exploring technology development that subsequently fails to perform.

In order to show that prototyping projects that fail to deliver a capability actually succeed in their intended purpose, developers must clearly identify up front the purpose of the prototyping project, what information is going to be learned, and the value of that information. That way, even if the prototype fails, the developer can point to the metrics of success identified during the planning process to justify the expenditure and demonstrate how, while the prototype may have failed, the prototyping project succeeded.

 

Prototyping Tools

Reference Source: DoD Prototyping Handbook, Nov 2019

The increased use of prototyping within and outside of DoD in recent years is partially attributable to the tools and methods available today that enable the rapid building, testing, rework, and retesting of prototypes as many as several times each day. The following are brief summaries of some of the common tools that can be used for physical and virtual prototypes.

 

4.3.1 Prototyping Tools for Physical Prototypes.

4.3.1.1 Additive Manufacturing (AM).

Often referred to as three-dimensional (3D) printing, AM reads in data from a Computer-Aided Design (CAD) file and adds successive layers of liquid, powder, or other material layer-by-layer to build a 3D object. Common materials used for AM include plastic, metal, and concrete.

4.3.1.2 Computer-Aided Design (CAD).

Mainly used for detailed engineering of 3D models or 2D drawings of physical components, CAD enables designers to use computer systems to help them design products. Benefits of CAD include lower product development costs and a shortened design cycle.

4.3.1.3 Hardware-In-The-Loop.

Hardware-In-The-Loop is a tool that uses simulation to test physical prototypes. Real signals from the prototype are transmitted through input/output devices of a test system that simulates a fully assembled product, enabling test and design cycles to occur as though the real-world system is being used.

 

4.3.2 Prototyping Tools for Virtual Prototypes.

4.3.2.1 Advanced Modeling and Simulation (AMS).

AMS is a tool that uses advanced computing capabilities to create models and simulations that closely align with actual physical systems. AMS allows the user to not only observe physical processes in order to gain a better understanding of what happens and how it happens, but it also creates new ways of studying the physical processes that occur.

4.3.2.2 Artificial Intelligence (AI).

AI is the science pertaining to machines mimicking cognitive functions that are typically associated with the human mind, such as “learning” and “problem solving”. AI works by combining large amounts of data with fast, iterative processing and intelligent algorithms that allows the software to learn automatically from patterns or features in the data, making it possible for machines to learn from experience, adjust to new inputs, and perform human-like tasks.

4.3.2.3 Machine Learning (ML).

ML is a type of AI that is based on software programming of statistical techniques that give computer systems the ability to “learn” with data and build analytical models, without minimal human intervention. ML enables users to automatically produce models that can quickly and accurately analyze large, complex sets of data.

4.3.2.4 Augmented Reality (AR).

AR is an interactive experience that layers virtual information over a live camera feed into a headset or through a smartphone or tablet device giving the user the ability to view 3D computergenerated images superimposed on the physical world. AR can also be used to identify changes needed in the system design, CONOPS, or other inputs without the need to manufacture a physical prototype.

4.3.2.5 Mixed Reality (MR).

MR brings together real-world and digital elements that allow the user to see and immerse themselves in the physical world around them as they interact with a virtual environment. With MR systems, the user interacts with and manipulates both physical and virtual items and environments using next-generation sensing and imaging technologies.

4.3.2.6 Virtual Reality (VR).

VR is an interactive computer-generated experience that takes place within a simulated environment, providing users with visual, auditory, and other types of sensory feedback on the use of a simulated system. Similar to AR, VR can be used to identify changes needed in the system design, CONOPS, or other inputs without the need to manufacture a physical prototype.