Get Real Get Better: A Framework for Developing Agile Program Management in the U.S. Navy Supported by the Application of Advanced Data Analytics and AI
Abstract
:1. Introduction
2. Background: Project and Program Management
2.1. Agile Project Management
2.2. AI-Supported Agile Project Management
2.3. Human–AI Collaboration in Project Management
2.4. Program Management in the U.S. Navy
3. Data-Driven Organizations
3.1. The Power of Data-Driven Decision-Making
3.2. Becoming a Data-Driven Organization
- Establish an Authoritative Data Lake: Create a centralized database to store and manage all project-related data, ensuring easy access for program managers (PMs) and other stakeholders. While there is a need to identify authoritative data sources, the data oftentimes may not exist or do not exist in the form that is needed for appropriate analyses. In this case, program managers should seek ways to develop new data pipelines that would allow for the collection of new and meaningful data;
- Develop Data Collection Standards: One potential pitfall for organizations hoping to adopt a data-driven mindset is the inconsistency in which data are collected and reported. To avoid this, program managers should seek to implement uniform data collection standards across different departments, programs, and projects, making it easier to compare and analyze data. Developing data dictionaries or metadata repositories is critical here. A data dictionary is defined as a centralized repository of information about data, such as its meaning, relationships to other data, origin, usage, and format. By posting this information in an easily accessible location and ensuring the dictionary is available to users across all levels of the organization, the program office can ensure that data are being used in a reliable and consistent fashion. This also ensures that conclusions drawn from the use of those data are consistent and explainable to users;
- Invest in Data Management Tools: The adoption of a data-driven culture begins with the consistent use of data management tools. Program managers should equip themselves and their professionals with data management tools, including machine learning and statistical modeling techniques, to enable them to derive valuable insights from the collected data. In addition, organizations should seek out new and meaningful ways to visualize their data. Data visualization is as critical a part of the data analytic process as any other. End users simply cannot derive any meaningful interpretation or action from raw data. Visual aids that emphasize the power of the data, the influence and impact of the raw data in drawing specific conclusions, and that demonstrate trends and patterns in the data are all critical pieces for data adoption. Visualization tools such as Qlik, Tableau, and even Excel allow users to convert raw data of any kind and make them more interpretable for stakeholders at all levels;
- Encourage a Data-Driven Culture: Finally, organizations should seek to promote a culture that emphasizes the importance of data-driven decision-making at all levels of the organization. Organizational leaders should try to avoid situations that encourage intuitive decision-making and rely more on decision-making processes that allow them to trace those decisions back to authoritative data. In this case, leading by example will also create an environment within a program office that encourages analytical thinking. The inculcation of this framework begins by encouraging those behaviors that are consistent with data-driven values. This includes providing ongoing training, creating incentives, and recognizing achievements in data-driven program management.
4. Methodology
- Enhanced Decision Making: access to well-structured data will allow PMs to gain a deeper understanding of their programs, helping them to identify potential risks and opportunities for improvement;
- Optimized Resource Allocation: data-driven insights can guide PMs in making more informed decisions about resource allocation, ensuring that scarce resources are deployed effectively;
- Fostered Collaboration: the availability of relevant data can promote information sharing and collaboration between different departments and stakeholders, resulting in more efficient project management and better outcomes;
- Improved Accountability: data transparency will improve accountability by enabling PMs to track progress more accurately, making it easier to identify and address inefficiencies.
4.1. Business Analytics Use Case
4.2. Advana Jupiter
4.3. The Qlik Platform
5. Agile Program Development in the U.S. Navy
5.1. Applications of Data Analytics and Machine Learning
5.2. How the Agile Approach Impacts Program Management in the U.S. Navy
6. AI and Data Science in Agile Project Management
6.1. Artificial Intelligence and Machine Learning Applications
6.2. A Case for Deep Learning
6.3. Generative AI
6.4. ChatGPT: Example
6.5. Implications of Generative AIs for Agile Program Management Development
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Software delivery | Deliver working software frequently, from a couple of weeks to a couple of months, with a preference for a shorter timescale. Too often, projects are conducted in a manner where there may be an initial envisioning session. Beyond that, developers may fail to interact with either thought leaders or end users. This was carried out to avoid this through continuous and transparent communication with all stakeholders. |
Collaboration | Businesspeople and developers must work together daily throughout the project. The development team must work directly with end users to better understand their problems and how algorithms should be designed to address those root problems. |
Motivation | Build projects around motivated individuals. Give them the environment and support they need, and trust them to complete the job. This is carried out to empower the development teams by giving them the opportunity to build innovation. The teams’ creativity was perceived as a key ingredient to ultimate success. |
Communication | Face-to-face conversation continues to be the most efficient and effective method of conveying information to and within a development team. While the demands of a distributed world were recognized, as much face-to-face collaboration as possible was encouraged to facilitate the sharing of ideas and more open innovation. |
Demonstrations | Working software is the primary measure of progress. The goal is to share the success via live demonstrations with the stakeholders. |
Development | Agile processes promote sustainable development. The sponsors, developers, and users should be able to maintain a constant pace indefinitely. An emboldened collaborator will continue to seek ways to improve processes. |
Promotion | Continuous attention to technical excellence and good design enhances agility. Strong technical achievements, as often as possible, are promoted. |
Simplification | Simplicity, the art of maximizing the amount of work not completed, is essential to the developed development approach. The teams should not be burdened with administrative work that inhibits their ability to deliver new and innovative product lines. |
Teams | The best architectures, requirements, and designs emerge from self-organizing teams. However, while collaboration across the effort is optional, it occurs organically. |
Adaptation | The team regularly reflects on how to become more effective, then tunes and adjusts its behavior accordingly. In-progress reviews, ad hoc scrum teams, and other means for self-reflection have been used as the key facilitators. |
>>Provide Some Examples in Which Generative AI Can Be Used to Assist Program Managers.<< |
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ChatGPT Reply: |
Generative AI, such as GPT-3, can be used to assist in program management in a variety of ways. |
Here are some examples: |
1. Requirement Gathering: |
Program managers can use generative AI to help with requirement gathering. They can provide the AI with the project’s scope and goals, and the AI can generate a list of requirements that need to be fulfilled to achieve those goals; |
2. Risk Management: |
AI can help in identifying potential risks in the program by analyzing data from past projects and identifying patterns that indicate potential problems. This can help the program manager take proactive measures to mitigate those risks; |
3. Resource Allocation: |
Program managers can use AI to assist in the allocation of resources such as personnel, equipment, and funding. AI can help in predicting future resource needs based on past performance data and other relevant information; |
4. Schedule Optimization: |
Generative AI can assist program managers in optimizing schedules by analyzing historical data to identify the best way to sequence tasks and allocate resources; |
5. Decision-Making: |
AI can assist program managers in making better decisions by providing them with data-driven insights and recommendations. This can help the program manager make informed decisions that are more likely to lead to successful outcomes; |
Overall, generative AI can help program managers streamline their processes, improve decision-making, and increase the chances of program success. However, it is important to note that AI should be used as a tool to assist program managers rather than a replacement for human decision-making. |
Department of Defense (DoD) program management involves complex and high-stakes projects, and machine learning can be used to support decision-making, risk management, resource allocation, and other aspects of program management. Here are some examples of machine learning techniques that can be used in DoD program management: |
1. Predictive analytics: |
Predictive analytics can be used to forecast future outcomes and identify trends based on historical data. This can be useful for predicting project completion dates, identifying potential cost overruns, and estimating resource needs; |
2. Natural language processing (NLP): |
NLP can be used to analyze unstructured data, such as emails, chat messages, and reports. This can help program managers identify potential risks and issues that may not be captured in structured data; |
3. Computer vision: |
Computer vision can be used for surveillance, image analysis, and other tasks that require processing large amounts of visual data. This can be particularly useful in the defense industry, where there is a need for situational awareness and real-time decision-making; |
4. Reinforcement learning: |
Reinforcement learning can be used to optimize resource allocation and scheduling. This approach involves using algorithms to learn from experience and improve over time based on feedback; |
5. Deep learning: |
Deep learning can be used for a variety of tasks, including image and speech recognition, natural language processing, and predictive analytics. This approach involves using neural networks to learn from data and make predictions or decisions based on that learning. |
In addition to these techniques, there are also specialized machine learning algorithms and frameworks that have been developed specifically for defense applications, such as the Defense Advanced Research Projects Agency’s (DARPA) Explainable Artificial Intelligence (XAI) program. |
Ultimately, the choice of machine learning techniques will depend on the specific needs of the DoD program and the available data. |
Enhanced creativity | Program managers can leverage generative models to brainstorm and visualize multiple scenarios, designs, or solutions, which can then be refined based on specific stakeholder feedback. |
Automated task completion | Generative AI can automate the generation of code, reports, or other outputs for specific repetitive or well-defined tasks, freeing program managers to focus on more complex or creative aspects. |
Feedback integration | Generative models can be retrained or fine-tuned for program management purposes based on feedback, aligning with the agile practice of regular reflection and adaptation. |
Rapid prototyping | The use of generative AI can quickly produce multiple prototypes or solutions to a variety of program management tasks, adhering to the agile principle of early and continuous delivery. |
Skill requirements | The introduction of generative AI in agile program management will require new skill sets for program managers, including data analytics and AI training, which should be considered during the planning and execution phases of the program. |
Resource allocation | The use of generative AI will require additional resources, such as high-performance computing and specialized AI-powered software tools, which should be accounted for during program development, planning, and execution. |
Quality assurance and testing | The use of generative AI will require new testing and quality assurance procedures to ensure the accuracy and reliability of program management outputs. |
Ethical and governance concerns | The use of generative AI in agile program management should be guided by ethical and governance considerations to ensure the responsible and ethical use of these technologies. |
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Haase, J.; Walker, P.B.; Berardi, O.; Karwowski, W. Get Real Get Better: A Framework for Developing Agile Program Management in the U.S. Navy Supported by the Application of Advanced Data Analytics and AI. Technologies 2023, 11, 165. https://doi.org/10.3390/technologies11060165
Haase J, Walker PB, Berardi O, Karwowski W. Get Real Get Better: A Framework for Developing Agile Program Management in the U.S. Navy Supported by the Application of Advanced Data Analytics and AI. Technologies. 2023; 11(6):165. https://doi.org/10.3390/technologies11060165
Chicago/Turabian StyleHaase, Jonathan, Peter B. Walker, Olivia Berardi, and Waldemar Karwowski. 2023. "Get Real Get Better: A Framework for Developing Agile Program Management in the U.S. Navy Supported by the Application of Advanced Data Analytics and AI" Technologies 11, no. 6: 165. https://doi.org/10.3390/technologies11060165