Supply Chain Vulnerability Identification Using Big Data Techniques

NPS NRP Project PosterSupply Chain Vulnerability Identification Using Big Data TechniquesN4 - Fleet Readiness & LogisticsThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Unmanned Surface Logistics Concept of Support

NPS NRP Executive SummaryUnmanned Surface Logistics Concept of SupportN4 - Fleet Readiness & LogisticsThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Unmanned Surface Logistics Concept of Support

NPS NRP Project PosterUnmanned Surface Logistics Concept of SupportN4 - Fleet Readiness & LogisticsThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Aviation Depot Maintenance Throughput Optimization

NPS NRP Executive SummaryAviation Depot Maintenance Throughput OptimizationN8 - Integration of Capabilities & ResourcesThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Improving DoD Energy Efficiency: Combining MMOWGLI Social-Media Brainstorming With Lexical Link Analysis (LLA) to Strengthen the Defense Acquisition Process

Disclaimer: The views represented in this report are those of the authors and do not reflect the official policy position of the Navy, the Department of Defense, or the federal government.Excerpt from the Proceedings of the Tenth Annual Acquisition Research Symposium Logistics ManagementThe research presented in this report was supported by the Acquisition Research Program of the Graduate School of Business & Public Policy at the Naval Postgraduate School. To request defense acquisition research, to become a research sponsor, or to print additional copies of reports, please contact any of the staff listed on the Acquisition Research Program website (www.acquisitionresearch.net).Prepared for the Naval Postgraduate School, Monterey, CA 93943.Approved for public release; distribution is unlimited

Medical Supply Chain Impacts of Pandemic Preparedness and Response

NPS NRP Executive SummaryIn the wake of the 2020 COVID-19 pandemic Commander, Naval Surface Forces U.S. Pacific Fleet(CNSP) is reevaluating current shipboard allowances of medical supplies/Personal Protective Equipment, PPE (masks, gloves, etc..), and disinfecting supplies (hand sanitizers, disinfecting solutions). This study seeks to positively impact COVID-19 response at the ship and fleet levels through an evaluation of notional modifications to authorized medical and shipboard allowances and prepositioned stocks to increase resiliency for the next pandemic. To accomplish this, we intend to first gather policy data concerning required shipboard Authorized Medical Allowance List (AMAL), including consumable supplies which are presently tailored toward mass casualty situations, not pandemics. We will review OPNAV, PACFLT, Fleet Forces, and Navy Medicine guidance regarding pandemic preparedness and response and will integrate our findings with the AMAL review process. We will also gather current CNSP After-Action Reports and Lessons Learned arising from the COVID-19 pandemic to understand likely usage rates and compare them with on-hand inventory. We will also examine available shipboard storage and determine how best to plan for both mass casualties and a future pandemic. We will also examine available pre-positioning sites and their storage availability to determine how best to leverage shore supply storage assets. This effort will then be explored and informed using either mathematical based, stochastic simulation or a multi-variate optimization leading to improved understanding and identification of weak and strong areas in our on-hand inventory readiness as well as our ability to accomplish recommended levels of resupply. Our findings will result in recommended minimum requirements for on-hand shipboard inventory (PPE, disinfectant, etc.) and determine the best pre-positioning of larger stocks ashore which can then be delivered to and sustain fleet assets though an extended pandemic. These revised inventory plans can then inform updated inventory and re-supply policy that can maintain readiness, support US Navy missions, and save lives.Commander, Naval Surface Forces (CNSF)U.S. Fleet Forces Command (USFF)This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Medical Supply Chain Impacts of Pandemic Preparedness and Response

NPS NRP Project PosterIn the wake of the 2020 COVID-19 pandemic Commander, Naval Surface Forces U.S. Pacific Fleet(CNSP) is reevaluating current shipboard allowances of medical supplies/Personal Protective Equipment, PPE (masks, gloves, etc..), and disinfecting supplies (hand sanitizers, disinfecting solutions). This study seeks to positively impact COVID-19 response at the ship and fleet levels through an evaluation of notional modifications to authorized medical and shipboard allowances and prepositioned stocks to increase resiliency for the next pandemic. To accomplish this, we intend to first gather policy data concerning required shipboard Authorized Medical Allowance List (AMAL), including consumable supplies which are presently tailored toward mass casualty situations, not pandemics. We will review OPNAV, PACFLT, Fleet Forces, and Navy Medicine guidance regarding pandemic preparedness and response and will integrate our findings with the AMAL review process. We will also gather current CNSP After-Action Reports and Lessons Learned arising from the COVID-19 pandemic to understand likely usage rates and compare them with on-hand inventory. We will also examine available shipboard storage and determine how best to plan for both mass casualties and a future pandemic. We will also examine available pre-positioning sites and their storage availability to determine how best to leverage shore supply storage assets. This effort will then be explored and informed using either mathematical based, stochastic simulation or a multi-variate optimization leading to improved understanding and identification of weak and strong areas in our on-hand inventory readiness as well as our ability to accomplish recommended levels of resupply. Our findings will result in recommended minimum requirements for on-hand shipboard inventory (PPE, disinfectant, etc.) and determine the best pre-positioning of larger stocks ashore which can then be delivered to and sustain fleet assets though an extended pandemic. These revised inventory plans can then inform updated inventory and re-supply policy that can maintain readiness, support US Navy missions, and save lives.Commander, Naval Surface Forces (CNSF)U.S. Fleet Forces Command (USFF)This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Leverage AI to Learn, Optimize, and Wargame (LAILOW) for Strategic Laydown and Dispersal (SLD) of the USN Operating Forces

NPS NRP Project PosterThe SECNAV disperses Navy forces in a deliberate manner to support DoD guidance, policy and budget. The current SLD process is labor intensive, takes too long, and needs AI. The research questions are: - How does the Navy weight competing demands for naval forces between the CCMDs to determine an optimal dispersal of operating forces? - How does the Navy optimize force laydown to maximize force development (Fd) and force generation (Fg) efficiency? We propose LAILOW to address the questions. LAILOW was derived from the ONR funded project and focuses on deep analytics of machine learning, optimization, and wargame. Learn: When there are data, data mining, machine learning, and predictive algorithms are used to analyze data. Historical Phased Force Deployment Data (TPFDDs) and SLD Report Cards data among others, one can learn patterns of what decisions were made and how they are executed with in the past. Optimize: Patterns from learn are used to optimize future SLD plans. A SLD plan may include how many homeports, home bases, hubs, and shore posture locations (Fd) and staffs (Fg). The optimization can be overwhelming. LAILOW uses integrated Soar reinforcement learning (Soar-RL) and coevolutionary algorithms. Soar-RL maps a total SLD plan to individual ones used in excursion modeling and what if analysis. Wargame: There might be no or rare data for new warfighting requirements and capabilities. This motivates wargame simulations. A SLD plan can include state variables or problems (e.g., future global and theater posture, threat characteristics), which is only observed, sensed, and cannot be changed. Control variables are solutions (e.g., a SLD plan). LAILOW sets up a wargame between state and control variables. Problems and solutions coevolve based on evolutionary principles of selection, mutation, and crossover.N3/N5 - Plans & StrategyThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Leverage AI to Learn, Optimize, and Wargame (LAILOW) for Strategic Laydown and Dispersal (SLD) of the USN Operating Forces

NPS NRP Technical ReportThe SECNAV disperses Navy forces in a deliberate manner to support DoD guidance, policy and budget. The current SLD process is labor intensive, takes too long, and needs AI. The research questions are: - How does the Navy weight competing demands for naval forces between the CCMDs to determine an optimal dispersal of operating forces? - How does the Navy optimize force laydown to maximize force development (Fd) and force generation (Fg) efficiency? We propose LAILOW to address the questions. LAILOW was derived from the ONR funded project and focuses on deep analytics of machine learning, optimization, and wargame. Learn: When there are data, data mining, machine learning, and predictive algorithms are used to analyze data. Historical Phased Force Deployment Data (TPFDDs) and SLD Report Cards data among others, one can learn patterns of what decisions were made and how they are executed with in the past. Optimize: Patterns from learn are used to optimize future SLD plans. A SLD plan may include how many homeports, home bases, hubs, and shore posture locations (Fd) and staffs (Fg). The optimization can be overwhelming. LAILOW uses integrated Soar reinforcement learning (Soar-RL) and coevolutionary algorithms. Soar-RL maps a total SLD plan to individual ones used in excursion modeling and what if analysis. Wargame: There might be no or rare data for new warfighting requirements and capabilities. This motivates wargame simulations. A SLD plan can include state variables or problems (e.g., future global and theater posture, threat characteristics), which is only observed, sensed, and cannot be changed. Control variables are solutions (e.g., a SLD plan). LAILOW sets up a wargame between state and control variables. Problems and solutions coevolve based on evolutionary principles of selection, mutation, and crossover.N3/N5 - Plans & StrategyThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.