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The Department of Defense (DoD)’s Joint Logistics Enterprise, which spans both supply chain and logistics operations, provides the means to muster, transport, and sustain military power anywhere in the world at a high level of readiness. The Joint Logistics Enterprise is immense: for the Air Force alone, the number of aircraft is equivalent to four commercial airline fleets and the supply chain inventory to sustain them is as large as multiple Fortune 500 companies combined. “The DoD Joint Logistics Enterprise is immense,” said John Paschkewitz, DARPA program manager in the Strategic Technology Office.
“Most people don’t realize that the Air Force alone operates a fleet of aircraft four times the size of one of the largest U.S. airlines. The supply chain inventory to sustain the Air Force fleet has been estimated to be as large as multiple Fortune 500 companies combined. Add the Army, Navy, and Marine Corps’ needs, and you see how enormous the department’s global logistics and supply systems are, dwarfing any commercial logistics system.”
This scale is managed by partitioning, with separate organizations managing supply chains, distribution, and procurement, resulting in thousands of separate logistics information management systems across the DoD. The resulting “open system of systems” is driven by business processes, with information systems ranging from 1970’s legacy mainframe software to modern enterprise resource planning packages. Logistics planners and operations center staffs (e.g., J-3’s) must navigate dozens of information systems (IS’s) to address basic situational awareness and reactive planning queries. These systems frequently track attributes on a common weapons system, component or process, but the data in these systems may be latent, incorrect, or deliberately inaccurate to mitigate risk (e.g., hoarding or requesting spares beyond what is needed) as well as in a range of incompatible formats. Fusing this data to create a coherent picture of state is a laborious, human-driven process. Obtaining further insight to diagnose why certain states exist or predicting future states (or prognosis) is frequently only possible following exhaustive analysis after the fact given the fractured and stove-piped nature of the enterprise.
To operate successfully in an increasingly contested global security environment, however, the logistics enterprise needs to change how it operates. In particular, the enterprise needs to overcome its reliance on thousands of disparate legacy information systems, which can’t provide the status of millions of military parts, supplies, and pieces of equipment, which are stocked and shipped around the world. The Defense Science Board Task Force on Survivable Logistics urged the Pentagon to use artificial intelligence and machine learning to bolster military logistics using predictive analysis, demand forecasting, production scheduling, anomaly detection, and supply-chain optimization.
To address this challenge, DARPA announced the LogX program with the goal to develop and demonstrate software for real-time logistics and supply chain system situational awareness (diagnosis), future state prediction (prognosis), and resilience at unprecedented scale and speed. LogX aims to build a capability to work alongside existing logistics information systems that exploits the recent migration of logistics information to digital formats and the cloud.
In contrast to the DoD, the commercial sector has achieved a higher level of functionality in its information systems, but the nature of the DoD problem is different: commercial systems are built on business processes that are largely demand-pull as opposed to planned supply push. Additionally, the integration of business processes, information systems, and data across the supply chain in the most sophisticated corporate environments makes causal reasoning and forecasting considerably easier.
The nature of military logistics in a contested environment places a higher value on resilience and distributed operations than is economically attractive for many commercial settings, leading to fundamentally different system architectures. However, the commercial sector has matured and demonstrated agile execution concepts that unify information systems, business processes, and demand-driven distribution strategies that may apply to military settings.
“DARPA is exploring the flexibility of systems that can be composed by operators to meet varying mission needs as part of its Mosaic Warfare concept, which moves the enterprise from one that is ‘built to order’ with long response times and low flexibility to an agile ‘assemble to order model,’” Paschkewitz said. “This shift will require profound changes in inventory management, positioning, and logistics information awareness beyond the obvious changes in weapons systems and tactics. It will also require a different approach than commercial Just In Time (JIT) systems to have adequate resilience. A new logistics approach is required to viably compose and sustain a Mosaic fighting force, and information awareness is the critical enabler.”
DARPA is looking for potential proposers with expertise in leading-edge logistics information systems and analysis; knowledge in applying AI to resilient/adaptive supply chain management, supply chain due diligence; real-time awareness across heterogeneous logistics/supply systems; and distributed, probabilistic state estimation techniques on networks of networks.
LogX Program
The goal of the LogX program is to develop and demonstrate software for real-time logistics and supply chain system situational awareness (diagnosis), future state prediction (prognosis) and resilience at unprecedented scale and speed. LogX will build a capability to work alongside existing logistics information systems that exploits the recent migration of logistics information to digital formats and cloud-based deployment.
The logistics enterprise can be viewed as a three-layered “network of networks” system: the physical supply chain and logistics distribution networks, the information networks that connect understanding of demand to supply and distribution, and financial networks that resource the activity.
LogX will consider the impact of information dynamics and networks on the state of the physical logistics and supply chain enterprise, to include both military and commercial elements. The impact of financial instruments on information dynamics (e.g., contracts and commodity futures) will be considered as inputs in program-specified scenarios. Given this emphasis, concepts for communications systems, physical infrastructure, delivery platforms, robotics/drones, and other physical effectors are out of scope and excluded. Although information security is a key concern for LogX, concepts primarily focused on or dependent upon blockchain implementation are also explicitly excluded.
Within the information layer, LogX will provide the ability to accelerate and improve the effectiveness of logistics operations in contested environments through enhanced situational awareness of both logistics distribution and supply chain activity. The program will explore logistics information operations with some assumptions about how they are likely to evolve over the next ten years. That evolution is likely to involve a migration to cloud-based infrastructure and data centralization, but not necessarily implementation of modern software architectures or extensive standardization, interoperability, or integration. However, proposers should assume that relevant data and/or information systems of interest will be accessible from within a cloud computing infrastructure.
Obtaining coherent and detailed understanding of the total logistics system, such that impacts of changes on one part of the system are understood by other parts, is extraordinarily difficult. The system is especially vulnerable to the ripple effect, in which disruptions cascade throughout the system and are amplified by the architecture of the enterprise, and the bullwhip effect, in which local decisions are made without regard to the state of the global enterprise, causing catastrophic swings in inventory and readiness. Both of these failure modes are exacerbated by inadequate situational awareness driving operational decisions.
LogX will address the core challenges of:
- Obtaining, sharing, and understanding information across the Joint Logistics Enterprise regarding the current and future state (diagnosis and prognosis) of both logistics and supply chain systems;
- Automated and scalable understanding of the complex business processes, information flows, data sources, uncertainties, and system models to identify relevant system state variables;
- Automated and rapid estimation of current and future states (diagnosis and prognosis) based on the collection of logistics and supply chain data, which may be incomplete, incorrect and/or inconsistent.
Logistics Model
LogX is predicated on the ability to achieve state estimation. In this framing, the logistics enterprise is considered to be a distributed, networked dynamical system, where the state is a complete description of the system at a point in time. The variables that provide this complete description are the state variables. The state variables for the logistics and supply chain systems include both information (e.g., business process status, part orders, cargo manifests) and physical distribution (e.g., inventory levels, aircraft flight tracking) quantities. Logistics and supply chain system state variables are generally discrete in time (e.g., the number of parts in an order is an integer quantity) but may be stochastic.
Some state variables may not be directly observable since there are no sensors and may need to be inferred, and others may be redundant, incorrect, or incomplete. State estimation is the process by which an estimate of the underlying, partially observed system state is obtained. The methods of Filtering Theory are an examples of techniques to do so. Framed this way, data collection and fusion can be framed as an uncertainty minimization problem, providing a natural means to address the complexity of the logistics information awareness.
The state estimation will be both dynamic and distributed: dynamic because the underlying state variables may change in time (or cannot be approximated as steady state) and distributed because there is no central state estimator. Instead, there are multiple users across the Joint Logistics Enterprise (e.g., a DLA buyer, a USTC planner, or a Navy logistics officer) each obtaining state estimates that can be shared across the LogX system and combined to create an improved overall global system statemestimate.
By taking this approach, there are clear analogies to electrical power grid control and resilience as well as system health monitoring, with the important difference that for the logistics enterprise, limited mechanistic models exist for the combination of the information and physical systems. A mechanistic model is necessary to move beyond descriptive (“what is happening?”) and correlation-based diagnosis (“why did this happen?”) to causational diagnosis and prognosis (“what will happen and why?”). To construct and dynamically update such models, LogX will leverage a range of nascent innovations in automated reasoning and knowledge engineering for complex systems.
Program Description and Structure
The user-facing software product of the LogX program will be a mission-centered applications service (hereafter apps service) to provide situational awareness and estimates of future state that can be deployed across the Joint Logistics Enterprise. The mission-centered applications will not be a static single-function software capability, but an “insight-as-a-service” capability that provides personalized, timely information to make sound decisions in the context of the user’s mission. This capability will be deployed from the cloud-based computing environments the military and commercial logistics and supply chain communities are migrating to. Proposers should plan for the apps service to be accessible from any number of platforms (tablet,smartphone or computer). The apps service will utilize a microservices architecture to provide scalable and powerful system understanding and foresight as a service to a range of potential users. This microservices-based software architecture has been matured and utilized at scale by commercial entities to provide personalized services on demand to users, and the goal of LogX is to do so for logistics/supply chain diagnosis and prognosis.
The apps service will ingest diagnostic or prognostic user queries (or hypotheses) regarding the logistics system or supply chain. Given a specific query (or hypothesis), the apps service will employ composition services to dynamically combine, or compose, microservices for data collection, reasoning, learning and state estimation in response. These cloud-based microservices are functionally modular programs that have clearly defined purposes. The LogX microservices stack will ensure that the knowledge required to answer a query or test a hypothesis is maintained separately from the services that collect data, assemble it into a model and reason on it, as well as from the data itself, providing flexibility, security, and a natural path to scaling.
Finally, the apps service will instantiate a mission-specific user interface (UI) application to provide the resulting insight to the user. The LogX program will have two technical areas (TAs): the Mission-Centered Applications Service (TA1) and Cloud-Based Microservices (TA2), which will have two tracks: automated knowledge engineering and dynamic/distributed state estimation.
These technical areas will be complemented by a Government-side test and evaluation (T&E) team that will provide data, ground truth adjudication, and an elastic compute environment for software development. The Government T&E team plays a critical role in the overall conduct of the program. The T&E team will define test scenarios for each sprint and provide a range of data as Government Furnished Information (GFI) for both causal model and state estimate construction that will be bounded by the test scenarios in the program.
Historical data from logistics information or enterprise resource planning (ERP) systems from DLA, United States Transportation Command (USTC), Military service, and commercial sources (ranging from information in SAP and/or Oracle data formats to legacy structured data);
The program will utilize an adaptive, agile approach to software development. The apps service and underlying microservices will be demonstrated through a series of sprints to progressively address more difficult logistics/supply chain diagnostic/prognostic queries. The program will leverage historical data available from military and commercial settings as ground truth to build confidence in the validity of the resulting analysis. Proposers should plan for a sequence for “macroscale” strategic supply chain problems, “microscale” operational and tactical logistics and distribution problems, or combinations of the two every 8-12 weeks as part of the technology development plan.
LMI wins LogX contract
In March 2020, VA-based LMI announced the award of a contract with the Defense Advanced Research Projects Agency (DARPA), Strategic Technology Office (STO), to support the LogX research and development (R&D) prototype program. The program focuses on discovering and responding to logistics network disruptions. DARPA’s LogX R&D program creates and demonstrates a new paradigm for logistics information awareness across the global logistics enterprise, enabling responsive and resilient operations. LMI has partnered with NTELX, the Florida Institute of Technology, Data Machines Corp., and Cougaar Software Inc. to develop and execute a test and evaluation (T&E) framework for the program.
As part of the program, risks of data disruptions to the global supply chain will be examined to uncover technologies that can find, prevent, mitigate, and recover from these disruptions. The program solicits technologies in two areas: mission-centered applications with context-centric situational awareness and foresight and cloud-based microservices for dynamic knowledge extraction, state estimation, and causal modeling. LMI will lead the T&E team in defining, replicating, and exploring logistics information challenges to evaluate the prototypes’ success in understanding dynamic logistics networks. When creating the T&E framework, the team will develop the experimental data environment, create and integrate scenarios, and evaluate vendor technologies alongside DARPA STO.
In today’s rapidly changing world, LMI’s next-generation strategy integrates our core logistics domain experience with advanced analytics. “We are excited to support the DARPA LogX program as it represents a key element of LMI’s next-generation logistics strategy, powered by advanced analytics. The research platform LogX, enabled by our holistic approach to logistics (HALO), addresses some of our nation’s most complex and urgent logistics priorities,” said Pete Pflugrath, vice president of LMI’s logistics service line.
Comprehensive, real-time situational awareness, future state prediction, and the resilience of the Department of Defense (DoD) global logistics enterprise are fundamental to mission success and survivability. This project will bring DoD closer to effectively realizing the benefits of software-defined networks, the internet of things, artificial intelligence, secure boundaryless collaboration, and smart data encryption. DARPA’s LogX R&D prototype program lays the groundwork for these emerging technologies and LMI’s T&E team will facilitate its success. “The award of this DARPA LogX R&D program will enable us to continue delivering excellent service to our clients through unparalleled expertise in our advanced analytics and logistics service lines. We are committed to supporting our clients by offering the most forward-thinking solutions to their largest challenges,” said Robert Lech, vice president of LMI’s defense market.
