Please use this identifier to cite or link to this item: https://dair.nps.edu/handle/123456789/5409
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dc.contributor.authorCharles Pickar-
dc.contributor.authorRaymond Franck-
dc.date.accessioned2025-05-12T20:57:32Z-
dc.date.available2025-05-12T20:57:32Z-
dc.date.issued2025-05-12-
dc.identifier.citationAPAen_US
dc.identifier.urihttps://dair.nps.edu/handle/123456789/5409-
dc.descriptionSYM Paperen_US
dc.description.abstractThe complexity and scale of defense projects necessitate innovative project management and scheduling approaches. Digital twins, a digital representation of physical entities, transform how projects are planned, executed, and monitored. This paper explores the definition, applications, advantages, and challenges of implementing digital twins in project management. Additionally, integrating artificial intelligence (AI) predictive delay analysis processes provides an advanced framework for optimizing execution and risk mitigation. This paper examines (a) how real-time digital replicas and AI-driven predictive analytics using defense acquisition data can enhance decision-making, efficiency, and project outcomes in defense project management and (b) how prediction markets might enhance the timeliness and quality of information for program management—leading to better program outcomes. While the technical advances are impressive, they rely on information. Also, program management involves human skills and knowledge. Prediction markets have demonstrated promising capabilities to provide timely and accurate information for program management—with or without state-of-the-art technical means.en_US
dc.description.sponsorshipAcquisition Research Programen_US
dc.language.isoen_USen_US
dc.publisherAcquisition Research Programen_US
dc.relation.ispartofseriesAcquisition Management;SYM-AM-25-357-
dc.subjectProgram Managementen_US
dc.subjectAcquisition Schedulesen_US
dc.subjectData Scienceen_US
dc.subjectProject Schedulingen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectDigital Twinen_US
dc.titleTiming is Everything: Schedules, Models, and Analysisen_US
dc.typeTechnical Reporten_US
Appears in Collections:Annual Acquisition Research Symposium Proceedings & Presentations

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