Complexity of Logistics Networks for 2040 and Beyond
With rapid globalization and technological advancements manifesting at rising rates, the complexity of logistics networks has increased exponentially. This is most evident in the military sector where the Army is creating interconnected systems and sensors across various domains. With logistics networks having increased in their complexity due to challenges in maritime environments, the tyranny of distance, finite infrastructure, and contested environments, new and improved networks are just as vital now and in the future as they were in World War II. This article describes how important this new model is in shaping and navigating logistics networks for 2040 and beyond.
Model Overview
The Network Flow Contested Logistics (NFcL) model is a logistics planning tool designed to simulate logistics network operations by sea, air, and land between and within a contested theater of operations. The model’s goal is to evaluate the effectiveness of various logistics network configurations across multiple scenarios. It is intended for use when a logistics network is contested, or otherwise under stress or disruption, to determine where the model is vulnerable and resilient. The NFcL model can simulate an uncertain environment of distribution points to determine the optimal flow of supplies, minimize the impact of disruptions to the military supply chain, and ensure the continuity of operations and wargame alternative scenarios and their impact on the logistics network. When the optimal flow cannot be achieved due to disruption or a contested environment, users can identify courses of action to find a solution where maximum or nearly maximum flow potential is achieved.
Model Development
The NFcL model was developed initially to study the effects of Army theater-level contested logistics in the 2040 timeframe. Under sponsorship of the Army Futures Command’s Futures and Concepts Center Directorate of Concepts, the modeling began as a contested logistics board game from the Center for Naval Analyses, which was later modified for use in the Army’s Future Study Program. The model matured from its initial versions of spreadsheet-based simulations into a more complex network flow model with more detail. During this process, the modeling improved to provide visualization of supply flow and to model different types of supplies and transportation modes with higher fidelity.
An interactive graphic user interface allows users to make changes directly in the model and see results immediately (like manipulating the network structure and supply method distribution). The updated model is structured to host multiple types of supply classes, such as fuel and ammunition, while simulating what types of transports are preferred for the stipulated type of supplies. Its versatility is predicated on the multiple data ingestions it uses to predict requirements for best case sustainment scenarios. As sustainment capacity is computed using the supplies and transports being modeled, a resulting max flow metric is calculated.
Assumptions and Optimization
As model enhancement progressed, the team researched premises that provided the most accurate depiction of what assumptions were accepted as certain and truthful regarding how joint force combatant commands relate to logistics operations worldwide. The model, therefore, assumes strong and credible relationships between allied countries, with a focus on integrated deterrence to increase capability, improve interoperability, and strengthen trust. It considers all classes of supply and includes features such as alternate ports for sea, air, rail, and basing, and what methods are optimal for evacuating casualties. The model optimizes flow by routing supplies to ports and zones with the highest given weights. The weights are determined by factors such as numbers of units requiring supplies, reachability from a neighboring port, proximity to zones requiring supplies, and zones with adequate supplies present. In a future contested environment, optimizing transportation routes will help reduce fuel consumption, save time, protect and mitigate supply assets from adversarial targeting, and improve the logistics process.
Model Objectives
The model has three main objectives: analyze the impact of forecasted logistics demand on a contested network, assess the network’s capacity, and quantify the model’s results to understand the logistics network and to be able to make future improvements.
The analysis of forecasted logistics demand on a contested network reveals several key considerations. First, it is essential to assess whether the current number of transports and facilities can sustain present and future demands between network nodes. This involves evaluating the capacity and efficiency of the logistics infrastructure. Once the network structure (i.e., nodes) is identified and supplies are known, the model contains an algorithm to determine viable sets of transports that meet the supply and network requirements. Second, the resilience of a network flow system against disruptions can be determined through a course of action analysis, which helps in understanding the network’s ability to adapt and recover from potential disruptions. Additionally, identifying critical distribution points within the network is crucial, since these are pivotal for maintaining the flow of goods and services. Lastly, pinpointing potential chokepoints is necessary to anticipate and mitigate any areas where congestion or delays could significantly impact the overall efficiency of the logistics network.
Characterizing the capacity of a contested logistics network involves several critical steps. The highest volume of goods that the network can efficiently handle must be determined. Exploring various network configurations and examining options for optimal network nodes are essential to ensure that the logistics network is robust and efficient under contested conditions. Identifying transportation requirement force pools is also crucial, because it helps in understanding the resources needed to maintain and enhance network operations. Additionally, the effects of technological improvements, such as automation and robotics, on the contested logistics network must be considered. These advancements can significantly enhance the efficiency, speed, and reliability of logistics operations.
Analyzing the model’s results involves several key aspects. Assessing network nodes’ access and usage provides insight into the efficient use of each node. Evaluating the capacity of these nodes is crucial to understanding their ability to handle current and future demands. Visualizations of demand and forecasted demand flows help identify patterns and potential bottlenecks within the network. These modeling results can reveal vulnerability points within the network, highlighting areas that are most susceptible to failure or inefficiency under contested conditions. Consequently, these insights are vital for developing strategies to enhance the resilience and robustness of the logistics network while making future model improvements.
Functionality
Despite the ongoing complexity of logistics operations, the NFcL model is user friendly and designed for non-technical personnel. It accepts inputs such as network nodes, inventory levels, demand, storage capacity, network flow capacity, transport pools, and transportation routes. Users can freely add or remove any data point. This allows the model to run on a turn-by-turn basis, adjusting results and outcomes iteratively.
The NFcL model allows for the selection of transportation nodes for forces deploying to and returning from certain locations that best sustain the military unit. It also allows a selection of pathways to achieve sustained logistics in a contested area, and the evaluation of multiple strategies to understand the impact of the contested environment on units; on sea, air, and land transports; and on resources. The model can also provide visual representations of supply flows and transportation routes under various conditions.
The NFcL model is also designed to simulate scenarios at the strategic, operational, and tactical levels. It provides insights into node and transport usage, helping to estimate the significance of new and existing nodes and transports, and suggesting ways to improve simulation results. As users manipulate the data to account for locations of units, transports, and efficiency of supply resources or port capacities, users can adjust the model to obtain the decisions or results they desire. At strategic levels, decision makers can weigh the model’s maximum throughput to plan for contingencies, enhance operational efficiency, and make better-informed decisions during conditions of uncertainty or degradation. At the operational level, sustainers can plan where to preposition supplies and sustainment assets to improve efficiency or capacity. Finally, at the tactical level, Soldiers and teams can strategize quickly on how best to distribute supplies to ensure tactical units receive their supplies efficiently and with minimal disruption.
Relevance of Network Flow
The military’s global all-domain concept continues to evolve at unprecedented rates, with services investing in advanced warfare technologies. As newer technologies pervade the battlespace, managing an already-diverse supply chain becomes a game changer.
The potential benefits of an effective network flow are numerous. Primarily, if the network model can mitigate the effects of disruption in a contested environment, risk to the force is minimized and the military supply chain can move supplies more rapidly, with more reliability, and with increased supply-chain resiliency. A well-designed network flow model can plan for disruptions and devise strategies to mitigate them.
Insights and Future Work
The NFcL model has been applied to theaters of interest, providing insights on port usage and transportation requirements. It offers insights on the relative usage of each port, its proximity and relevance to other nodes, the potential for primary and secondary port substitution, and the challenges of supplying land zones and ports farther from the coasts.
Future work on the model includes adjustments for more accurate measurements of each port’s capacities, more sophisticated routing heuristics, analysis on the effects of automation, and more efficient and effective resource allocation, including resource efficiencies using multi-modal transport methods. The most significant changes will likely involve new methods for delivering and interdicting supply shipments, such as electric and autonomous vehicles, deliveries via drone, and smart ports. The effects of technological enhancements, such as automation and robotics, will also improve network flow outcomes to the extent possible given the model’s capacity for data inputs.
The integration of these advancements not only optimizes network flow but also plays a crucial role in critical scenarios as military leaders and staff plan logistics operations in an overtly complex future environment.
Summary
The NFcL model can help expedite the movement of supplies in times of military conflict, where the timely delivery of supplies can often determine the outcome of a mission. By determining the strengths and weaknesses of the supply chain, the NFcL model can ensure that the right resources reach the right place at the right time. Moreover, the model can enhance the reliability and resiliency of the supply chain from beginning to end. When potential bottlenecks are addressed proactively, the model can ensure that the supply chain remains unaffected by external disruptions. This, in turn, contributes to the overall resiliency of military operations, enables more accurate planning, and improves the network’s logistical capabilities. Consequently, as the Army gears up and prepares to train and execute the 2040 warfighting design, the need for a robust and efficient network flow model is more pressing than ever.
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Erica Herzog is a lead decision analyst in McLean, Virginia, where she supports Army modernization, sustainment, and contested logistics work for DoD sponsors. She holds a Master of Arts degree in procurement and acquisitions management from Webster University and a Master of Science degree in strategic studies from the Army War College. She is an Army Reserve officer with 28 years of military experience and is Lean Six Sigma Black Belt certified.
Oliver Stein is a senior software engineer in the Bedford, Massachusetts, area where he works on Army modernization and contested logistics, focusing on wargaming platform development and logistics modeling. He holds a Master of Science degree in computational operations research from the College of William & Mary.
Dr. Suzanne DeLong is a principal applied operations research analyst in the Hampton Roads area where she works on Army modernization, contested logistics, and future warfare concepts at Joint Base Langley-Eustis. She holds a Doctor of Philosophy degree in systems engineering from the University of Virginia. She is a retired Army officer, having served as a 49A Operations Research/Systems Analyst and in the Air Defense Artillery Branch.
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This article was published in the winter 2025 issue of Army Sustainment.
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