DMDII projects demonstrate and apply digital manufacturing technologies to increase the competitiveness of American manufacturing.

DMDII is pleased to share our newest project call in the Design, Product Development & Systems Engineering Technology Thrust: DMDII 18-01 – AI Design Advisor. For more information, please read DMDII’s Request for Proposal and Proposal Preparation Kit and watch a recap of our Project Call Vision Webinar.

If you are interested in submitting a response to the project call, please join us for a Virtual Pitch Session Webinar on July 23rd. During the webinar, teams and individuals looking to form teams will have the opportunity to share an overview of their proposed solution and receive preliminary feedback from the DMDII community.

Enterprise award projects are research, development, and demonstration projects that are selected through a competitive project call and evaluation process. These projects are required to address specific technology focus areas driven by the Institute’s strategic investment plan. A combination of private and federal funding is used to execute these projects, and the teams are generally comprised of both academic and industry members of DMDII.

Partner Innovation Projects (PIPs) are research, development and demonstration projects through DMDII that use private funding to execute the project. PIPs leverage UI LABS fundamental principles of collaboration and fit within DMDII scope and mission.

Project focus areas fall along different parts of the manufacturing process:

18-01 Project Call Summary – AI Design Advisor

DMDII’s newest project call within our Design, Product Development & Systems Engineering Technology thrust asks, “How can we better equip design engineers to leverage information available from downstream enterprise activity as feedback to inform better designs?” By deploying artificial intelligence systems on the wealth of data produced by activities like manufacturing, assembly and MRO, this project seeks to enable powerful insight generation that yield orders-of-magnitude increases in productivity through smarter decision-making during the design phase. Proposers must consider how such systems can interact with CAD programs to best deliver these insights real-time to design engineers. 



Structural Composites – Blade Multidisciplinary Design and Analysis – 14-01-06

Lead Organization: Green Dynamics Inc.

Other Organizations: MetaMorph Inc.; University of Delaware; Vanderbilt University; PTC, Inc.; MSC Software Corporation; Pennsylvania State University, Applied Research Laboratory; SimInsights Inc.

Awarded: July 2015

Description: Partners integrated a suite of analysis tools under a common intuitive user interface specifically focused on wind turbines. Successful implementation of this software approach will reduce barriers to entry for smaller composite material developers and shorten cycle times for current manufacturers all while providing a comprehensive cost and manufacturing model to prevent overruns.

Automated Manufacturability Analysis Software “ANA” – 14-01-07

Lead Organization: Iowa State University

Other Organizations: American Foundry Society, John Deere, The Lucrum Group,, North American Die Casting Association, Pennsylvania State University Applied Research Laboratory, Steel Founders’ Society of America, Tech Soft 3D, University of Alabama at Birmingham

Awarded: February 2016

Description: This project will create a manufacturability analysis package that can work on any platform to provide real-time feedback on critical manufacturing issues. The ANA project builds upon work from the AVM project to develop commercially viable software that will provide feedback to designers at the conceptual design phase. The resulting analysis software will enable conceptual designers to receive immediate feedback on their designs early in the manufacturing process, cutting down the often lengthy conceptual design phase of components. The outcomes of this project will enable significant reductions in manufacturing costs, product launch costs, and time to market.

Elastic Cloud-Based Make – 14-01-10

Lead Organization: GE Global Research

Other Organizations: Rolls-Royce, Penn State University Advanced Research Laboratory, Northwestern University, Iowa State, Oregon State, Rochester Institute of Technology, Quad City Manufacturing Lab

Awarded: December 2015

Description: Developing a low-cost way for small and medium-sized manufacturers to access new technology in advanced modeling, simulation, and analysis.

Mind the Gap – Filling the Gap between CAD and CNC with Engineering Services – 14-02-02

Lead Organization: STEP Tools, Inc.

Other Organizations: Penn State ARL, Vanderbilt University

Awarded: February 2015

Description: The Mind the Gap project aims to develop and deliver cloud services to optimize and monitor computer-controlled (CNC) machining. The new services will operate on 3D digital models, which are easier to share and modify than traditional code-based models.

Automated Assembly Planning: From CAD model to Virtual Assembly Process – 14-02-04

Lead Organization: Oregon State University

Other Organizations: ESI North America

Awarded: July 2015

Description: This project aims to develop a computational tool to automatically transform a CAD (Computer-Aided Design) assembly into a set of assembly instructions with as little initial user commitment as possible. Quick predictions of an assembly plan will provide feedback to both design and industrial engineers so that they can see how their decisions impact assembly time and cost. For manufacturing companies that choose to use the developed toolset, it could result in millions of dollars in savings.

Automatic Tolerancing of Mechanical Assemblies from STEP AP203: Completion of Adaptive Vehicle Make Tasks – 14-02-05

Lead Organization: Design Automation Lab, Arizona State University

Awarded: July 2015

Description: This project will investigate algorithms to automate tolerance synthesis of mechanical assemblies. This will include first order Geometric Dimensioning and Tolerancing (GD&T) based only on geometric conditions for assemblability and partial support for second order (based on limited design intent, viz. fits and fasteners). This will result in lower product cost due to better tolerance control, lower scrap rate, and quicker product development time by reducing trial and error in tolerance allocation.

Advanced Variance Analysis & Make – 14-08-01

Lead Organization: Rolls-Royce Corporation

Other Organizations: 3D Systems, Georgia Institute of Technology, Microsoft, National Center for Supercomputing Applications (NCSA), Penn State University Applied Research Laboratory, Southwest Research Institute (SwRI)

Awarded: January 2016

Description: The Advanced Variance Analysis & Make project uses high-performance computing to demonstrate how data coming off of a machine relates to the part made by that machine. It will indicate whether an anomaly in the data is, in fact, related to an anomaly in performance and/or adherence to a design specification for the part.

The analyses will form the basis of a database of production anomalies available through the Digital Manufacturing Commons. Manufacturers will use the resulting data in real time to correct an anomaly if it will affect a part’s performance, or to ignore the data anomaly if there is no evidence of impact on the part’s capabilities, saving time and money during the manufacturing process.

O3 – Operate, Orchestrate, and Originate – 14-06-05

Lead Organization: STEP Tools, Inc.

Other Organizations: ITI-Global (International TechneGroup Incorporated), Mitutoyo America, SystemInsights

Award Date: December 2015

Description: Developing a web environment that will enable users to orchestrate machining and measurement processes from tablets and smart phones. Time and money are wasted when a machining program creates a part that does not conform to the design requirements of the customer. O3 will allow users to check machining programs for conformance from remote locations. When conformance is not met, the service will allow the process to be adjusted using apps. The servers that will be used to host O3 tools will be located at DMDII, and the tools will be available to all manufacturers through the Digital Manufacturing Commons.

Adaptive Machining Tool Kit – 14-07-01

Lead organization: GE Global Research

Other organizations: Western Illinois University, Metrologic Group, University of Wisconsin-Madison, Sivyer Steel, Genesis Systems

Award date: June 2016

Description: Developing a plug-and-play kit that allows CNC machines to adjust to variations in the geometry of manufactured components, reducing the need for manual intervention.

Integrated Manufacturing Variation Management – 14-07-02

Lead Organization: Caterpillar Inc.

Other Organizations: Missouri University of Science and Technology, University of Illinois Urbana-Champaign

Award Date: January 2016

Description: Generating a system by which a manufacturer, in an automated fashion, can compensate for machine tool workspace (machine tool) errors induced due to part, fixture, tooling, or machine tool errors. This should allow for large reductions in setup times for new parts, new fixtures, or parts that see a large variation in the rough condition as delivered to the machining operation while minimizing human interaction in the machining setup process. The innovation over the present state of technology will yield significant improvement in process reliability and efficiency in the entire value stream.

Intelligent Adaptive Machining Fixtures for Castings (IAMFixR) – 14-07-03

Lead Organization: Product Development & Analysis (PDA) LLC

Other Organizations: Arizona State University’s Design Automation Lab, American Foundry Society, Steel Founders’ Society of America

Award Date: August 2015

Description: Developing a set of methods and a software enabler, called “IAMFixR” to reduce the setup time for machining of large castings and fabrications and to virtually eliminate scrapping any of these high value parts. The team aims to incorporate the casting industry standard into a 3D model and use digital technology to capture the changing dimensions of features critical to machining operation for every part produced in a production environment.

FactBoard: Real-Time Data-Driven Visual Decision Support System for the Factory Floor – 15-02-08

Lead Organization: Iowa State University

Other Organizations: Boeing, Factory Right, John Deere, ProPlanner

Awarded: March 2016

Description: This project will develop FactBoard, a shop floor decision support system that will convert thousands of data inputs from logistics and production systems into a collection of visual dashboards—all in real-time. The dashboards will consist of mobile support displays that can be accessed by a variety of users, from plant managers to factory floor foremen. FactBoard will enable manufacturers to make quick adjustments to respond to resource changes, saving them time and money. Many companies are often not in a position to make major upfront investments in shop floor data collection, so FactBoard would ultimately enable manufacturers to use existing data effectively while increasing the quality of information and decision-making as additional data sources become available in the future.

SPEC-OPS: Standards-based Platform for Enterprise Communication enabling Optimal Production and Self-awareness – 15-03-02

Lead Organization: Palo Alto Research Center (PARC)

Other Organizations: ITAMCO, MTConnect Institute, System Insights

Awarded: March 2016

Description: SPEC-OPS aims to provide a first-of-its-kind platform to tightly integrate machine tools and the multiple systems involved in the total manufacturing process, such as manufacturing execution systems, enterprise resource planning systems, dynamic planning and scheduling and process analytics. The capability to integrate multiple systems to transfer data back and forth does not exist today, and SPEC-OPS is the first major effort to address the challenge. The final platform will result in savings in planning, scheduling, execution, and maintenance time for manufacturers.

Manufacturing Work Instructions on Wearable and Mobile Devices with Augmented Reality – 15-04-01

Lead Organization: Rochester Institute of Technology

Other Organizations: Harbec, Optimax, OptiPro

Awarded: March 2016

Description: This project aims to move shop floor instructions off of paper and into interactive, easy-to-use wearable technology. Using augmented reality technology, users will be able to see how to complete a task in real time, with virtual guides showing them what—and what not—to do. At the same time, the system will collect valuable real-time shop floor data that is not typically captured and harness it to improve future manufacturing processes. The system will be based on open standards to achieve another key goal of the project: the creation of technology that is cost-effective for SMEs.

Authoring Augmented Reality Work Instructions by Expert Demonstration – 15-04-03

Lead Organization: Iowa State University

Other Organizations: Boeing, Daqri, Design Mill, John Deere, Purdue University

Awarded: March 2016

Description: This proposal seeks to create work instructions for augmented reality systems by developing the Augmented Reality Expert Demonstration Authoring (AREDA) product. The end product will be a simple and intuitive method to quickly create augmented reality work instructions using 3D cameras with advanced image processing and computer vision algorithms. The cameras will track experts as they manipulate parts to complete a project, capturing minute details and translating them into virtual instructions. AREDA stands to benefit companies like project team partners John Deere and Boeing by making assembly line training more cost-effective through augmented reality.

Systems Design Using the Digital Thread (AME) – 15-05

Currently, tools and methods used in the design of products and systems have very limited or no capacity to support real-time automated or semi-automated guidance for decision making in life cycle considerations. Design requirements known as “-ilities” include producibility, serviceability, usability, sustainability. Early design for “-ilities” guidance would enable more producible, serviceable, usable, sustainable, safe and lower-cost designs with shorter product development cycles and fewer design iterations. There is a need for solutions that enable and integrate the wide array of stakeholders across the value chain, including suppliers, OEMs and customers.

The goal of this project “Systems Design using the Digital Thread” is to demonstrate technologies that can use data from across the product lifecycle and from across the value chain to improve product design and manufacturing.

Operating System for Cyberphysical Manufacturing (IM) – 15-06

The ultimate utility of digital manufacturing will be reflected in the ability of enterprises to quickly and effectively respond to changing business and market demands. This, in turn, will necessitate the ability to dynamically organize and reconfigure their resources, starting from the individual machines through auxiliary systems to the entire supply chain. Current enterprises and manufacturing systems are generally narrowly specialized to address the needs of a specific set of products and/or manufacturing tasks. As a result: (a) they are difficult to reconfigure and repurpose to a broader set of products/tasks; (b) they do not have standard interfaces for data exchange with external systems; and (c) new functions and capabilities are difficult and costly to implement and justify. This project call focuses on alleviating this impasse by developing an operating system for the management of resources and of their focused utilization.

The goal of this project, “Operating System for Cyberphysical Manufacturing” (to be referred to as OSCM), is to develop an operating system for manufacturing that provides both horizontal and vertical resource management from the lowest hardware to the highest enterprise level. It represents a foundational technology that provides standardized physical and information/control interfaces for universal access, targeted tools for planning, simulating, executing, monitoring operations, and provides a means of networking manufacturing resources. Once developed, it will constitute the foundation DMDII will use as the basis for further digital technology developments.

Hardware/Software Tool Kit for Real-Time Machine and Process Diagnostics, Monitoring, and Self-Correction – 15-14

A key limitation of conventional manufacturing machines is their lack of intelligence. In general, manufacturing machines are not self-monitoring or error-correcting, or capable of adapting to variations without custom developed application-specific sensing systems and algorithms. The development and maintenance of such intelligent systems is expensive and often requires technical expertise that is not widely available. There is a need for commercially available intelligent solutions for the above-stated functionalities that are low cost, scalable, and capable of plug-and-play type real-time interoperability. This topic aims to implement machine intelligence into manufacturing machines. It is also aimed at promoting the adoption of relevant standards for sensing systems, sensing system communications and integration into manufacturing machines and systems. The scope includes both new machines having built-in sensors and intelligence as well as legacy machines and systems that have been retrofitted with sensors and intelligence.

Agile Manufacturing to Compensate for Production Variability – 15-15

Every manufactured component is imperfect. Some imperfections are within acceptable limits, while other imperfections require the component to be reworked or cause the component to be rejected. Variations in component geometry and composition are managed through quality processes, and through engineering design practices that result in intended performance even when imperfections exist. Digital manufacturing technologies allow for new ways to manage the variability within a batch of manufactured components. The goal of this topic is to demonstrate revolutionary new approaches to measuring the geometry and composition of manufactured components, and to use this data in other parts of the digital thread. The ultimate goal is to use digital manufacturing technologies to mitigate production variability, and to reduce the time and cost to develop and maintain manufactured products.

Open Source Software Applications for Digital Manufacturing – 15-16

A major goal of DMDII is to democratize American manufacturing by enabling and empowering small businesses, entrepreneurs, and individuals to design and manufacture products. Large manufacturing organizations typically have significant resources to support digital manufacturing technologies, while small businesses typically lack these resources. The digital manufacturing capability gap between large manufacturing businesses and small businesses creates significant inefficiencies for the entire manufacturing value chain, and has a negative impact on the manufacturing industrial base of the United States.

DMDII defines the digital thread as data across the entire lifecycle of a manufactured product. This includes all of the product and process data that is generated in every part of the manufacturing value chain. The DMC is a web-based platform that can be used to aggregate, integrate, and analyze the digital thread.

DMDII aims to build an online community of users who use the DMC platform for both public and private purposes, who contribute open content including data and software, and who will participate in the online community.

Analytical Solutions for Life Cycle Feedback – 16-01

In today’s industrial practice, tools and infrastructure do not generally facilitate flow of product and process information across the product lifecycle, creating inefficiencies in product development, production, quality, and after sales service. Often, designers do not have information describing the constraints and capabilities of operations and processes employed in manufacturing, supply chain, and field service. Processes employed during early product development lack insight into information about the manufacturing processes need to produce the product, how products are used by the customer and how the product performs overs its life. Similarly, manufacturing functions and service personnel have no visibility into the intent of the product designer, the various analyses performed to validate design decisions, or the results of early tests and failure reporting. Finally, service functions such as maintenance, repair, and overhaul – and the supply chain associated with that – lack specific, scientific visibility into the state of products as they are released from the manufacturing process.

The capacity to feed relevant data forward and backward to feed the numerous opportunities for greater insight has the potential to result in better decision making at all parts of the product lifecycle, and improved designs that more efficiently and effectively satisfy customer requirements.

The main objective is to reduce total lifecycle costs of complex systems by collecting data from different parts of the product lifecycle, and by allowing this data to flow across the product lifecycle and to use this information to improve decision making. These lifecycle costs may be reduced through (for example) improved product design, reduction in manufacturing costs, improved quality and yield, or reduction in service costs. The key technical challenges are the capture and analysis of relevant product and process data, and the ability to derive insights in different parts of the product lifecycle based on this data. The solution should support real time updates and decision making.

Industrial Internet of Things (IIoT) Retrofit Kit for Legacy Manufacturing – 16-02

The realization of digital manufacturing critically hinges on the ability to securely and easily capture, transfer, and analyze data from production machine tools. This requires multi-functional, simple discoverable, and affordable sensing technologies that can be easily integrated into both new and legacy systems, and that possess plug-and-play functional characteristics. While many modern machine tools possess sensing and control systems, the data communications and digital interfaces are frequently complex and/or proprietary. The lack of plug-and-play type digital integration is an obstacle to achieving seamless digital operation of these machines within the manufacturing enterprise. This project call focuses on applying standards and demonstrating plug-and-play digital integration that enables machine tool data collection, transfer, and analysis. The expected result is significant reduction in the cost and complexity of machine tool digital integration. The ultimate goal is a smart factory that has full systems integration of hardware, software, and data.

Modern machine tools can measure significant amounts of data, and they can be connected to networked resources. However, most machine tools that are in use today lack many of the sensors and the communication capabilities necessary to integrate them into the Industrial Internet of Things (IIoT). Legacy machines often were not installed with sensor devices that are common and inexpensive today or they lack the capability of sending the collected data to the outside world through a standard “gateway.” These machines are often highly-utilized, valuable components of manufacturing facilities, but they were created and installed at a time when computing resources were more expensive, more complicated, and more limited. These legacy machines are valuable, productive, and are not going to be replaced in the near future. They are typically not digitally connected, or the connection that they have performs poorly by current standards, or relies on proprietary hardware and software. Existing connectivity work-arounds are often unique and ad-hoc (computers emulating paper tape readers, for example). As these systems age obsolescence becomes a reality therefore maintaining and updating the connection hardware and software becomes more difficult.

Seamless Work Flows from Design to Fabrication – 16-03

Unlike additive manufacturing, subtractive manufacturing (e.g. machining) does not offer the ability to simply press a print button to translate a three-dimensional model into a physical part. In general, subtractive manufacturing requires three-dimensional models to be analyzed using computer aided manufacturing (CAM) software. The CAM data entry process typically requires significant manual user input to ensure that the part is manufacturable with the available machine tools, tooling packages and fixtures of a particular operation. The requirements for manual user input increases as part complexity increases and as the use of multi-axis machine tools increases. Additional user input is required to accommodate for materials properties, a variety of available tooling properties, and machining parameters (e.g., feeds and speeds). These circumstances have led to the underutilization of complex and multi-axis machine tools while produce complex geometries through incorporation of suboptimal machining trajectories

There is a need for software solutions that significantly reduce the manual input and expertise required to rapidly translate designs into fabricated parts in order to fully use the capabilities of available machine tools.

Real-Time Optimization of Factory Operations – 16-04

The operations of a modern manufacturing facility are typically controlled by multiple information systems that do not interact with one another or the information systems upstream/downstream in the manufacturing enterprise. Consequently, overall work flow is accomplished with unnecessary hand-offs and manual interventions. Businesses rely on these silos of information for decision support, which leads to local optimization; global optimization guided by an end-to-end work process with integrated procedural execution across the manufacturing value chain is limited due to the poor interoperability. Critical decision making such as production planning, scheduling and timing, order sequencing, and the assignment of resources including materials, workforce, resource, software and assets within the manufacturing enterprise is adversely affected by this environment. Inadequate decision support results in added expenses and inefficient operations.

To counter this, there is need to unify engineering analysis, production planning/control, and real-time factory floor data to enable operationally dynamic, closed loop control of the manufacturing environment. Decisions may be best served by real-time visibility into the status of production equipment and personnel on the plant floor, machine throughput, inventory movement, labor/material/utilities consumption, machine setups, and plant logistical sequencing. Rather than replacement of existing information systems with a single, monolithic approach, there is a need to provide integration across the collection of systems that use relevant data to generate a set of options for decision-makers to take action.

Factory decisions can be greatly improved by the utilization of business intelligence technologies, such as those which facilitate the transformation of raw data into meaningful and useful information for analysis purposes, online analytical processing, data mining and analytics, complex event processing, active performance management, text mining, and predictive and prescriptive analytics. Real-time optimization of factory decisions is best served by combining data derived from a variety of both external and internal sources and orchestrating those decisions in the context of an integrated work flow environment.

Low-Cost Robotics and Automation – 16-05

While robotics and other automation technologies offer significant benefits to manufacturing organizations in terms of productivity, speed, and quality, the cost of these technologies has limited their widespread use. Most robotics and automation solutions are expensive, singularly purposed, challenging to reprogram, and require physical or isolation from humans for safety purposes. This expense and inherent lack of flexibility especially limits their utility for small manufacturing businesses, who often cannot afford any impactful level of automation. Several applications of note include:

  • Cost, agility, and orchestration amongst automation assets are drivers for many companies. Affordable interoperability via overarching resource planning is a priority in this context;
  • Remote inspections of difficult to reach areas of manufacturing assets/sites, including sensor arrays to capture environmental and asset condition data – examples include aerial drones for inspections at height, robotic arms or extensions to inspect small space areas, etc;
  • Confined space entry by mobile robots instead of by humans, to perform maintenance and inspection tasks with a goal to completely end the need for confined space entry by humans;
  • Mobility of robots in high volume environments, especially for material handling operations;
  • Precision manufacturing in support of eliminating the sources of variation affecting final product quality and consumer experience.

Furthermore, many small businesses and high-mix manufacturing environments lack the floor space to accommodate the footprint needed for automation technologies and material handling solutions which require significant extra space for safety. The non-recurring cost to support many automation platforms remains high due to the complexity of (re)programming and test needed to enable safe and productive operation. Finally, the operation and maintenance of these systems requires specialized skills.

There is a need for flexible automation solutions that have a significantly reduced cost of ownership and are more easily installed, operated, and maintained. Rapid repurposing of automation or robotics capabilities (like a human), facilitates amortization of costs to justify investment. This topic requires a step change improvement in the affordability and ease of use of robotics and other automation. Such solutions must be extremely affordable and must be highly reconfigurable and adaptable, as well as exhibiting the precision, repeatability and productivity of conventional automated solutions while demonstrating the capability to be quickly repurposed at a cost which makes them accessible for small to medium sized businesses.

Supply Chain MBE/TDP Improvement – 14-06-01

Lead Organization: Rolls-Royce Corporation

Other Organizations: 3rd Dimension, Anark Corporation, ITI-Global (International TechneGroup Incorporated), Lockheed Martin, Microsoft, Purdue University

Awarded: January 2016

Description: This project seeks to push Model-Based Enterprise (MBE) technologies forward by using MBE technology to streamline the design stage of the manufacturing process. This involves the use of intelligent 3D models to eliminate the need to translate to different formats, including 2D drawings, when transferring information between original equipment manufacturers (OEMs) and other companies within the supply chain.
Using MBE ties data related to tolerance, life of product, and other product specs to the 3D model during the design phase and allows it to be used for all stages of the process, eliminating issues of unclear or inaccurate drawings. The combination of the 3D model and accompanying information is referred to as the technical data package (TDP). As a framework and best practices for MBE/TDP are standardized and disseminated more widely, they will become more useful and accessible to SMEs. As a greater percentage of the supply chain embraces MBE, the number of potential suppliers to the DoD and other major manufacturers will increase.

Enabling Real-Time Supply Chain Visibility Through Predictive Analytics – 15-12-02

Lead Organization: University of Washington

Other Organizations: GE Global Research, ITAMCO, University of Illinois Urbana-Champaign

Description: This project will allow both buyers and suppliers to reduce their overall supply chain costs and come up with a more resilient manufacturing strategy through enhanced real-time visibility into part deliveries and demands by creating a model that will ensure that the part availability predictions are always conservative, redundant predictive factors are removed, greater weightage is given to recent purchase orders during model development, and confidence intervals are provided on the prediction accuracy based on the quality of the available data. Additionally, the project will provide insights on how to best visualize complex supply chain data given different stakeholders’ tasks and goals

Assessing, Remediating and Enhancing DFARS Cybersecurity Compliance in Factory Infrastructure – 15-01-01

Lead Organization: Imprimis, Inc. (i2)

Other Organizations: SPIRE Manufacturing Solutions, Western Cyber Exchange

Awarded: December 2015

Description: This project seeks to create, test and implement a uniform cybersecurity standard for DMDII, with the goal of improving cybersecurity and supply chain security across the manufacturing industry. It reflects feedback from DMDII’s large manufacturing partners who have expressed the need for improved supply chain management. The project will review DoD cybersecurity standards for contractors, assess the costs, capabilities, and training manufacturers need to meet them, then develop a case study to aid manufacturers in meeting them. Ultimately, more manufacturers will be able to become DoD cybersecurity compliant, adding more potential contractors into the DoD and manufacturing pipeline.