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Purpose:  Develop intelligent anticipatory tools that efficiently manage, track, redirect, account for and distribute supplies to forward units engaged.

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Presentation on theme: "Purpose:  Develop intelligent anticipatory tools that efficiently manage, track, redirect, account for and distribute supplies to forward units engaged."— Presentation transcript:

1 Purpose:  Develop intelligent anticipatory tools that efficiently manage, track, redirect, account for and distribute supplies to forward units engaged in decisive action/operations Results:  Identification & prioritization of onward movement, leveraging and optimizing all conveyance modes, ensuring timely delivery, and allowing gaining unit to predict arrival  Visibility and accountability between multiple transit nodes irrespective of command level and class of supply  Real-time distribution management for sustainment operations in austere and remote environments Payoff:  Improved capability to tactically transport and reliably deliver consumables to Forward Operating Bases (FOBs) and smaller satellite bases in remote, dispersed, austere locations  Reduced supplier and equipment risk, including improved efficient and safe methods for equipment retrograde operations Milestone Schedule and Cost Milestone FY13 ($K) FY14 ($K) FY15 ($K) FY16 ($K) FY17 ($K) Distribution Management Tool Distribution Modeling Mobile-Based Planning & Assessment Distribution Analytics Systems Integration 6.2 T40 11432628270026002000 6.2 T42 355349 6.3 T08 600439436400500 Total20983416313630002500 Distribution Management Tool 3 63 6 4 6 Total $14.1M 54 46

2 Distribution Management Tool MeasureCurrentObjectiveTRL Prediction of Delivery (FOB, COP, PB) Not Available+/- 2 days5 Visibility of Distribution Network < 50% known location of transaction 95% known location of transaction 6 Mobile ApplicationsNot AvailableAccessible Anytime/Anywhere 6 2 7. How are we leveraging other technology efforts?  Substantial technical participation from Army and Joint movement managers with access to their respective talent and resources  Coordination with PEOs developing related logistical software applications  Expanding upon the lessons learned from G4/LIA logistics data visibility efforts such as NGWC, EBAL, VITaL and Mobile Applications 8. Other Attributes:  Technology Protection Plan: Yes  International Program: No  Endorsements: —DA G4/LIA —PEO EIS —USTRANSCOM (SDDC) —CASCOM —PEO C3T  Technology Transition Agreement: In Process; TTAs expected with labs, PEO EIS, USTRANSCOM (GTN/IGC), MTS, PEO C3T, and others as identified.  Affordability Metrics: TBD. Trade studies/Operations Analyses on going to establish value and affordability metrics. 6. Quantitative metrics: 1. What is the problem?  Forward units lack the ability to accurately identify sustainment requirements and execute distribution decisions considering force protection  Incompatible strategic and tactical distribution systems  Lack of timely delivery of supplies and equipment to the Warfighter 2. What are the barriers to solving this problem?  Visibility limited as items transition from strategic move into tactical move  Tactical movement information scattered throughout multiple systems & networks and manual processes (Excel spreadsheets) are used to fill technology gaps  Information gap between strategic and tactical movements  Delivery information not completely automated 3. How will you overcome those barriers?  Incorporate semantic modeling to increase information understanding across heterogeneous logistic systems  Integrate mobile computing technologies across the distribution process to extend actionable information far into the field (e.g., COP & PB)  Leverage spatial & temporal analysis to improve decision making associated with the distribution network 4. What is the capability you are developing?  Identification and prioritization of onward movement so gaining unit can predict arrival  Visibility and accountability between multiple transit nodes irrespective of command level and class of supply  Capability to prioritize delivery requirements of needed material 5. What is the result/product of this effort?  Synchronization of information from multiple deployment and distribution data sources for improved tactical logistical SA  Ability to match requirements to resources  Distribution network model for predicting network flow  Enhancement of relevancy of Legacy Systems

3 Distribution Management Tool 2 9. Describe any partnerships/collaborations with external organizations/companies in the execution of this program. Is ERDC considered a leader or a follower in these arrangements (OGA, Industry, or Academia?) ERDC is the leader in the development of the DMT. The DMT will utilize LIA’s NGWC mesh network technology as a primary data source, and DA G4/LIA continues to support the effort. The DMT program is aligned with the development of the PM-TIS product TOPS. 10. Is this work considered a disruptive technology? Why? This program is not in and of itself considered disruptive. It is utilizing basic technologies that have been developed elsewhere as well as developing complementing technologies. Various disconnected primitive tools exist at present to address some goals of this project. However, they are stove-piped and do not readily share information to achieve optimal performance of supply delivery. This project will capitalize on the introduction of innovative mobile technology to provide insight into warehouse inventory, supply transit, and delivery windows. The flexibility introduced thereby greatly enhances the tactical commander’s ability to more accurately identify troop requirements and execute distribution decisions. 11. What new intellectual property are you generating based on this activity? (Patents, new knowledge, new technologies, innovative materials, etc.) All of the software being developed for this program will be completely government owned. Diversity of constraints drives the computational complexity of the vehicle routing problem (VRP). To meet timelines for effective sustainment of operations, this complexity may be short-circuited by the introduction of metaheuristics and various machine learning strategies, which, when properly integrated, may be patentable. Most current VRP algorithms are serially-based. As massively-multicore systems become more pervasive, optimizers may be parallelized for faster time to solution. All new technology will be robust, extensively tested, and well-documented for ease of use by all levels of command.


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