OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Advanced Infrastructure & Advanced Manufacturing, Sustainment & Logistics The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws. OBJECTIVE: The objective of this SBIR Solicitation is to support the discovery (through market research), identification (through innovation outreach), research (through feasibility studies), development (through prototyping), test (through experimental, developmental, and operational testing), evaluation (through clear metrics), and maturation (through technology readiness assessments) of existing leading-edge commercial industry 4.0 technologies (also depicted in figure 1): Autonomous Robots Modeling and Simulation System Integration Internet of Things (IoT) Cybersecurity Controls Cloud Computing Augmented Reality Artificial Intelligence (AI) and Predictive Analytics Commercial industry 4.0 technologies will directly influence the agency's DCs from existing inefficient operations into modern 21st-century Smart-warehouses wherever practicable. Best business practice elements from DoDI 5000.80 MTA will be incorporated wherever possible to support the prototyping activities. Existing commercial industry 4.0 technologies have already been implemented in the manufacturing industry to increase efficiencies in the manufacturing floor. In addition, existing commercial industry 4.0 technologies have been implemented in warehouses to increase mass efficiencies, for instance, Amazon has developed its smart-warehouses with robust focus on industry 4.0 technologies to increase efficiencies in Amazon's warehouses to meet the need of its customers. Problem statement: Inventory Management - Inventory management practices and procedures are inefficient, consuming significant resources. DLA Distribution incurs continuous Business risks due to maintaining high levels of inventory that exceed requirements and weaknesses in inventory accuracy. Currently, DLA DCs have a requirement of 100% inventory reconciliation and verification to account for every single item stored in the warehouses, and to ensure every physical item stored match the stock record. The current process of inventory reconciliation is labor intensive, it takes long lead times to process, and it leads to inaccuracies of data (due to human error). The impact of this problem is seen in the current increase of labor hours, long lead times, and the increase of operational and labor cost of every DCs. Concept statement: DLA DCs lack automation in comparison to the private sector, as result this creates inefficiencies in warehouse operations. Currently, warehouse operations consume abundant amount of resources, i.e., time, human labor, paper, documentation, cost, data error, misplacement of material, bottle necks, etc. Most of current DLA Distribution systems are approximately 20 years old to include the Distribution Standard System (DSS) and the Equipment Control System (ECS) that obtained Full Operational Capability (IOC) in 1999. Many of the current warehouse systems are unsustainable, inefficient, and present many cybersecurity challenges. It is important to state that legacy systems were not designed with cybersecurity considerations, with DLAs mission changing over the past 20 years, and cyber threats are now on the forefront. It is critical to DLA Distribution to replace legacy system with state-of-the-art technology/systems that are designed with cybersecurity considerations, and evaluated through Research, Development, Test and Evaluation (RDT&E) activities. DESCRIPTION: The execution of this SBIR effort will require a strong partnership between technology managers of DLA Information Operations (J6), and distribution managers at DLA Distribution (J4) to identify, research, develop, test, evaluate, and determine the feasibility, and maturity of smart-warehouse commercially available industry 4.0 technologies to seamlessly integrate within the DLA's network, the warehouse management system (WMS), and the warehouse execution system (WES). Note: Integration with the WMS/WES cannot occur until at a minimum TRL 7-9. This will be a Phase II requirement. Offerors should state the TRL on their proposals. Existing commercially available industry 4.0 technologies determined as feasible with an overall value proposition will then be recommended for transition and fielding into the DLA distribution operational environment throughout the DLA enterprise of DCs. It is acknowledged that some commercially available industry 4.0 technologies may not provide a return on investment (ROI) or value proposition throughout the DLA enterprise of DCs due to mission operational tempo and location. Nevertheless, transforming DLA's DCs into smart warehouses is anticipated to gain efficacies whenever possible via automation for labor-intensive warehouse tasks, thereby creating warehouse operations that are more cost-effective and efficient. Additionally, this DMP R&D Charter envisions upon completion of rigorous prototyping, test, and evaluation of existing commercially available industry 4.0 Technologies, such as autonomous robots, modeling, and simulation, system integration tools, IoT, cloud computing, augmented reality, and artificial intelligence for predictive analytics with significantly improved cybersecurity controls. Figure 2 depicts DLA's Traditional Warehouse (current state) versus DLA's 5G Smart-warehouses (future state). TRLs are the most common measure for communicating the readiness of new technologies or new applications of existing technologies to be incorporated into a system or program and describe the increasing levels of technical maturity based on demonstrated (tested) capabilities based on demonstrations of increasing fidelity and complexity measured on a 1-9 scale, where level 1 generally represents paper studies of the basic concept, moving to laboratory demonstrations around level 4, and ending at level 9, where the technology is tested and proven, integrated into a product. PHASE I: Feasibility Study Not to exceed 6 months This phase encompasses only requirement analysis with no prototype development. This phase entails: Identification of Capability Gaps: Offeror(s) will collaborate with the DLA Distribution Stakeholders to identify capability gaps within the current DLA distribution environment and how these gaps can be closed by implementing Smart-warehouse technologies. The capability gap analysis must identify the problem statement(s) as defined by DLA stakeholders, describe the current As-Is problems, and define an acceptable redesigned capability by identifying the changes required to generate the desired To-Be capability to eliminate the capability gaps. Requirements Analysis: Offerors will identify the tasks required and conditions needed to meet DLA's needs using new or modified technologies, consider the possibility of conflicting requirements, and analyze, document, validate, and manage software or system requirements. This analysis is critical to the success or failure of the Smart-warehouse concept; it must be documented, actionable, measurable, testable, traceable, related to DLA's identified business needs or opportunities, and defined to a level of detail sufficient for the system design. End-User Requirements: Offerors will identify the tasks the end-user(s) need to be able to carry out to successfully perform their jobs and optimize the processes required for Smart-warehouses. Concept of Operations (CONOPS): The offeror must create a CONOPS for a Smart-warehouse concept that supports both routine and wartime distribution warehouse operations. The concept of operations covers utilizing Smart-warehouse technologies within DLA distribution warehouses during routine operations (e.g., Department of Defense (DoD) Enterprise Architecture; OV-1, etc.). Functional Requirements: Offerors must define the functions of the Smart-warehouse and describe the functional inputs and outputs of the Smart-warehouse. (Any inputs that are unattainable should be documented and assigned a corresponding risk that details the effect on the project). These requirements may involve calculations, technical details, data manipulation and processing, and other functionality that defines what the Smart-warehouse is supposed to accomplish; these requirements are captured in use cases. System Requirements: Offeror(s) must identify the functionality needed by a system to satisfy the DLA Distribution customer's requirements. The selected offeror(s) must determine the system requirements that most effectively meet the end user's needs. Preliminary Metrics: Identify Key Performance Parameters (KPPs), Key Performance Indicators (KPIs), Key Systems Attributes (KSAs), and other relevant operational metrics. Technology Readiness Assessments (TRAs) as required: Identify TRL and validated by the government. Assess and demonstrate the Smart-warehouse technology prototype(s) are capable of technology maturity (TRL 4 -9) of Technology Readiness. PHASE II: Prototype Development, T&E - Not to exceed 24 Months After completing Phase 1, and based upon what they learn from Phase 1, a proposal for Phase II can be submitted. This phase encompasses prototype development, T&E for technology maturation including: Prototype Development: Using elements from Phase I and addressing DLA Distribution defined user requirements, functional requirements, and system requirements a prototype (s) is developed for Experimentation, Developmental Test and Evaluation (DT&E), Early Operational Assessment (EOA), and Initial Operational Test and Evaluation (IOT&E). Experimentation, DT&E, EOA, IOT&E: Using Government designated testing location/environment, conduct test and evaluation, integration as feasible with the DLA Warehouse Execution System (WES) and/or implement government cybersecurity controls with the Smart-warehouse prototype(s) to demonstrate functionality within the Operational Environment (OE) for cybersecurity certification. T&E against the preliminary metrics identified in Phase 1 (KPPs, KSIs, KSAs and operational requirements) and refined the metrics as required. Technology Readiness Assessments (TRAs): Assess and demonstrate the Smart-warehouse prototype(s) are capable of technology maturity (TRL 4-7) of Technology Readiness throughout Phase 2 and achieve Level (TRL) 7- 9 upon completion of Phase 2 for transition. Note that integration with DLA's WMS and WES cannot occur until at a minimum TRL 7-9. PHASE III DUAL USE APPLICATIONS: Dual Use Applications: At this point, there is no specific funding associated with Phase III. During Phase I and Phase II, the progress made should result in a vendor's qualification as an approved source for a Warehouse Inventory Management system and support participation in future procurements. COMMERCIALIZATION: The manufacturer will pursue the commercialization of the Warehouse Inventory Management technologies and designs developed to apply to the warehouse environment -- the processes developed in preliminary phases and potential commercial sales of manufactured mechanical parts or other items. The first path for commercial use is at DLA's twenty-four Distribution Centers and twenty Disposition Centers. When fielded, DLA estimates 20 - 24 units, but the number of units could be more. REFERENCES: 1. Buffi, A., Tellini, B., "A Novel Phase-based Method for UHF-RFID Tag Localization via UAV", 2019 IEEE 5th International forum on Research and Technology for Society and Industry (RTSI), pp.370-375, 2019. 2. Gope, P., Millwood, O., Saxena, N., A provably secure authentication scheme for RFID-enabled UAV applications , Computer Communications, Volume 166, 2021, Pages 19-25, ISSN 0140-3664, https://doi.org/10.1016/j.comcom.2020.11.009 3. Greco, G.; Lucianaz, C., Bertoldo, S., Allegretti, M., Localization of RFID tags for environmental monitoring using UAV , Electronico (2015), pp. 480-483, DOI:10.1109/RTSI.2015.7325144 4. Jasrotia, D., Manisha J. Nene, "Localisation using UAV in RFID and Sensor Network Environment: Needs and Challenges", 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), pp.274-279, 2019. 5. Kachroo, A., Vishwakarma, S., Dixon, J.N, Abuella, H., Popuri, A., Abbasi, Q.H., Bunting, C.F., Jacob, J.D., Ekin, S., "Unmanned Aerial Vehicle-to-Wearables (UAV2W) Indoor Radio Propagation Channel Measurements and Modeling", IEEE Access, vol.7, pp.73741-73750, 2019. 6. Karan, E., Christmann, C., Gheisari, M., Irizarry, J. and Johnson, E. (2014). A Comprehensive Matrix of Unmanned Aerial Systems Requirements for Potential Applications within a Department of Transportation Construction Research Congress 2014 American Society of Civil Engineers 0 964-973 A. 7. Quino, J., Maja, J.M., Robbins, J., Fernandez, R.T., Owen, Jr., J.S., Chappell, M., RFID and Drones: The Next Generation of Plant Inventory , AgriEngineering 2021, 3, 168-181. https://doi.org/10.3390/agriengineering3020011 KEYWORDS: Drone, Warehouse Inventory Management, Warehouse, Distribution, Inventory, Inventory Management, Logistics, Simulation, Modeling and Simulation, Sustainment, Availability, Reliability, Maintainability, Supportability, Software Development, Machine Learning, Neural Networks, Real-time Computational Intelligence, Data Science, Software Architecture, Deep Learning.
