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Empowering the Defense Acquisition Workforce to Improve Mission Outcomes Using Data Science (2021)

Chapter: 2 Defense Acquisition Process, Data, and Workforce: The Short Version

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Suggested Citation:"2 Defense Acquisition Process, Data, and Workforce: The Short Version." National Academies of Sciences, Engineering, and Medicine. 2021. Empowering the Defense Acquisition Workforce to Improve Mission Outcomes Using Data Science. Washington, DC: The National Academies Press. doi: 10.17226/25979.
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2

Defense Acquisition Process, Data, and Workforce: The Short Version

THE ACQUISITION PROCESS

To support the missions and needs of the U.S. military, the Department of Defense (DoD) needs a wide variety of platforms, weapons, supplies, and contractor services. Under the purview of the Under Secretary of Defense for Acquisition and Sustainment, the development and purchase of systems, services, and interdependent capabilities are governed by DoD’s tailored acquisition policy, illustrated at a high level in Figure 2.1. The systems and capabilities are acquired via relevant pathways. Each pathway has specific instructions and supporting guidance and processes instructions that help govern and facilitate the way DoD develops and procures the associated capabilities. For example, a capability that is urgently needed by the operating forces usually follows a compressed development and production schedule of less than two years, but the authorities and definition of “urgent” is carefully controlled by Congress given the flexibilities they offer. In contrast, a major military capability is typically developed and procured over a longer period of time, often years with continued procurement and sustainment for decades. Aircraft, ships, submarines, spacecraft, and large ground vehicles fall into this category, but so do smaller vehicles or small unmanned systems. These capabilities are divided into subcategories depending on their cost. The higher the cost, the more senior the oversight and the more reporting that is required by Congress. Software systems follow another process and defense business systems yet another. These processes differ based on the nature of what is being acquired. For example, buying and upgrading software needs to follow a very different design and

Suggested Citation:"2 Defense Acquisition Process, Data, and Workforce: The Short Version." National Academies of Sciences, Engineering, and Medicine. 2021. Empowering the Defense Acquisition Workforce to Improve Mission Outcomes Using Data Science. Washington, DC: The National Academies Press. doi: 10.17226/25979.
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Image
FIGURE 2.1 Adaptive Acquisition Framework.
SOURCE: Department of Defense. “DoD Instruction 5000.02: Operation of the Adaptive Framework.” 2020. https://www.esd.whs.mil/Portals/54/Documents/DD/issuances/dodi/500002p.pdf?ver=2019-05-01-151755-110.

Suggested Citation:"2 Defense Acquisition Process, Data, and Workforce: The Short Version." National Academies of Sciences, Engineering, and Medicine. 2021. Empowering the Defense Acquisition Workforce to Improve Mission Outcomes Using Data Science. Washington, DC: The National Academies Press. doi: 10.17226/25979.
×

procurement process than the process necessary to design and procure a new tactical aircraft or bomber with major physical elements. Finally, DoD acquires many contractor services, which follow yet another process.

ACQUISITION DATA USE AND CONTEXT

To acquire anything in DoD, authoritative determinations must be made for its necessity (through a requirements process) and funding (through a budget, authorization, and appropriation process). Each of the military departments has its own data systems to support the acquisition, requirements, and budget processes. While some data flows from the military departments into common DoD-wide data systems, there are still extensive data sets within each department using differing terminologies—even when referring to the same concept or piece of information—which magnifies the complexity of accessing and analyzing data across DoD.

The nature of what is being purchased governs the specificity and review process associated with the acquisition process and the tailoring of that process to the specific system in question. An expensive major weapon system (more than $3 billion in constant 2020 dollars) requires an operational requirement approved by senior military leaders and a budget allocation requested by the Secretary of Defense and the President for authorization and appropriation by Congress. Data and data analytics are extremely valuable in producing, for example, an accurate cost estimate when estimating the budget that will be required for procurement as well as when evaluating whether a postulated or prototyped weapon system will actually fulfill the operational need. Approval authority often is delegated to less senior officials as the cost of the program decreases; however, data analytics that support a needs assessment and a cost estimate are still needed at all levels.

At the other end of the spectrum, many non-developmental goods (such as parts or commercial supplies) and services are procured through purchasing contracts. These capabilities are also aligned with operational needs and may involve formal requirements and specific budgeting, but the oversight for underlying procurements may not be individually governed by the pathways outlined in Figure 2.1. Instead, a budget is allocated and the DoD official responsible for procuring the capability is expected to buy what is needed by getting a reasonable price. Again, data analytics are extremely valuable when assessing both the quantity of needed parts and supplies and the corresponding “fair” or “best value” price.

Suggested Citation:"2 Defense Acquisition Process, Data, and Workforce: The Short Version." National Academies of Sciences, Engineering, and Medicine. 2021. Empowering the Defense Acquisition Workforce to Improve Mission Outcomes Using Data Science. Washington, DC: The National Academies Press. doi: 10.17226/25979.
×

THE ACQUISITION WORKFORCE

The DoD acquisition workforce community is roughly 182,671 people strong as of the third quarter of FY 2020 (DoD 2020a) and a broad collection of disciplines that include program managers; engineers of nearly every discipline including test, manufacturing, and production specialists; financial and contracting professionals; and logisticians. About 10 percent are military and 90 percent are contracted support (DoD 2020; Schwartz et al. 2016).

Ensuring that DoD has a competent and trained acquisition workforce has been a focus and concern for decades. Dating back to at least the Packard Commission in 1986, there have been efforts to improve the management and training of the overall acquisition workforce. The Defense Acquisition Workforce Improvement Act of 1990 requires the Secretary of Defense to establish education and training standards, requirements, and courses for the civilian and military acquisition workforce. Those responsibilities are generally delegated to the USD(A&S). This Act also established the Defense Acquisition University (DAU), headquartered in Fort Belvoir, Virginia. In addition to DAU, members of the acquisition workforce may receive training at the military service academies—the Air Force Institute of Technology, the Naval Postgraduate School, and the National Defense University—and in public and private colleges and universities. While many members of the acquisition workforce receive education and training at these institutions, only DAU is dedicated to acquisition.

Because of its prominent role in training the acquisition workforce, committee members visited DAU to gain an appreciation for what it was teaching the DoD acquisition community with regard to data science. During its visit in January 2020, the committee learned that DAU offered few dedicated courses in data science, but that several courses included aspects of data analytics. During the same visit, discussions with members of a hands-on course for program managers disclosed that data use and data analytics issues were not consistently embedded in practical exercises and that some class members felt unprepared to find or use data in decision making. To facilitate application, most data use and data analytics training are embedded with acquisition disciplines rather than taught as a separate skill. These observations are consistent with recent analyses by Anton et al. (2019). However, in October 2020, DAU released “CENG 002: Data Analytics for DoD Acquisition Managers Credential.” According to the DAU catalog, CENG 002 is a 32-hour course introducing data science to mid-level acquisition workforce managers in all career fields seeking to use data to inform decisions. Its timely release is indicative of the rapidly changing data science environment and DoD’s sense of urgency in upskilling its workforce to better understand and embrace the possibilities of data. Other

Suggested Citation:"2 Defense Acquisition Process, Data, and Workforce: The Short Version." National Academies of Sciences, Engineering, and Medicine. 2021. Empowering the Defense Acquisition Workforce to Improve Mission Outcomes Using Data Science. Washington, DC: The National Academies Press. doi: 10.17226/25979.
×

approaches to data science and data analytics training for the acquisition workforce are covered in Chapter 6 of this report.

In summary, the DoD acquisition processes are as varied as the types of products and services required to support the military departments. Data are complex and often characterized differently within different programs or systems. Finally, members of the acquisition workforce are very skilled but—reflecting the many heterogeneous processes they support—have diverse sets of skills, roles, and experiences. Given the diversity and complexity of acquisition in DoD, a single, all-encompassing solution for improving workforce capability in data use is unlikely. However, a framework for training and educating the workforce—customizable to processes, skillsets, and roles—may enable enhanced data use.

REFERENCES

Anton, P.S., M. McKernan, K. Munson, J.G. Kallimani, A. Levedahl, I. Blickstein, J.A. Drezner, S. Newberry. 2019. Assessing Department of Defense Use of Data Analytics and Enabling Data Management to Improve Acquisition Outcomes. RR-3136-OSD. Santa Monica, CA: RAND Corporation. https://www.rand.org/pubs/research_reports/RR3136.html.

DoD (Department of Defense). 2020a. “Defense Acquisition Workforce Key Information: Overall.” FY20Q3. June 30. https://www.hci.mil/docs/Workforce_Metrics/FY20Q3/FY20(Q3)OVERALLDefenseAcquisitionWorkforce(DAW)InformationSummary.pptx.

DoD. 2020b. “OSD/JS Privacy Program.” https://www.esd.whs.mil/Portals/54/146/Privacy/Frequently%20Asked%20Questions%20(2017%20update).pdf.

Schwartz, M., K.A. Francis, and C.V. O’Connor. 2016. “The Department of Defense Acquisition Workforce: Background, Analysis, and Questions for Congress.” Congress Research Service. July 29. https://fas.org/sgp/crs/natsec/R44578.pdf.

Suggested Citation:"2 Defense Acquisition Process, Data, and Workforce: The Short Version." National Academies of Sciences, Engineering, and Medicine. 2021. Empowering the Defense Acquisition Workforce to Improve Mission Outcomes Using Data Science. Washington, DC: The National Academies Press. doi: 10.17226/25979.
×
Page 19
Suggested Citation:"2 Defense Acquisition Process, Data, and Workforce: The Short Version." National Academies of Sciences, Engineering, and Medicine. 2021. Empowering the Defense Acquisition Workforce to Improve Mission Outcomes Using Data Science. Washington, DC: The National Academies Press. doi: 10.17226/25979.
×
Page 20
Suggested Citation:"2 Defense Acquisition Process, Data, and Workforce: The Short Version." National Academies of Sciences, Engineering, and Medicine. 2021. Empowering the Defense Acquisition Workforce to Improve Mission Outcomes Using Data Science. Washington, DC: The National Academies Press. doi: 10.17226/25979.
×
Page 21
Suggested Citation:"2 Defense Acquisition Process, Data, and Workforce: The Short Version." National Academies of Sciences, Engineering, and Medicine. 2021. Empowering the Defense Acquisition Workforce to Improve Mission Outcomes Using Data Science. Washington, DC: The National Academies Press. doi: 10.17226/25979.
×
Page 22
Suggested Citation:"2 Defense Acquisition Process, Data, and Workforce: The Short Version." National Academies of Sciences, Engineering, and Medicine. 2021. Empowering the Defense Acquisition Workforce to Improve Mission Outcomes Using Data Science. Washington, DC: The National Academies Press. doi: 10.17226/25979.
×
Page 23
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The effective use of data science - the science and technology of extracting value from data - improves, enhances, and strengthens acquisition decision-making and outcomes. Using data science to support decision making is not new to the defense acquisition community; its use by the acquisition workforce has enabled acquisition and thus defense successes for decades. Still, more consistent and expanded application of data science will continue improving acquisition outcomes, and doing so requires coordinated efforts across the defense acquisition system and its related communities and stakeholders. Central to that effort is the development, growth, and sustainment of data science capabilities across the acquisition workforce.

At the request of the Under Secretary of Defense for Acquisition and Sustainment, Empowering the Defense Acquisition Workforce to Improve Mission Outcomes Using Data Science assesses how data science can improve acquisition processes and develops a framework for training and educating the defense acquisition workforce to better exploit the application of data science. This report identifies opportunities where data science can improve acquisition processes, the relevant data science skills and capabilities necessary for the acquisition workforce, and relevant models of data science training and education.

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