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For the US to remain in stride with warfighting demands across vast distances, now is the time to rethink US military logistics and integrate commercial supply chain practices and capabilities.
Defense operations can catch up with two decades of rapid advances in technology that have enabled commercial supply chains globally. These Industry 4.0 technologies, including artificial intelligence and machine learning used in predictive analytics, supply chain intelligence and more autonomous functionality, can quickly bridge gaps and increase speed in contested defense scenarios. The result can be greater cargo visibility, smarter warehousing and better transportation utilization.
Challenges during the COVID-19 pandemic highlight the need for greater resiliency, responsiveness and military supply chain visibility. The U.S. Navy can get ahead of economic, cyber and other contested scenarios that could disrupt logistics. The Department of Defense (DoD) can make the investment to re-architect a defense supply chain and logistics infrastructure that fosters resilience, velocity and agility at enterprise scale before the next major challenge.
Throughout 20th century conflicts, the cornerstones of US military dominance were an agile warfighting force; a defense industrial base that excelled at research-driven manufacturing; and a global logistics capability that set the bar for planning, stockpiling and large-scale transportation. That logistics dominance, however, is eroding as readily available technological tools enable potential adversary abilities to deny access and impede sustainment operations. In addition, new disruptive and asymmetric capabilities in the cyber and space domains have global reach. At the same time, hypersonic weapons put logistics at risk from a great distance. Recent enemy cyber threats to a US low-orbit satellite and to US energy pipeline infrastructure show that attacks can be extremely focused and result in acute domestic risk.
The US military can work more closely with commercial partners and allies to identify vulnerabilities and build agility into critical supply chains. In this military supply chain transformation, private partnerships can foster greater diligence by tapping commercial supply chain lessons and big data to ferret out solutions to major issues, such as a lack of supply availability, limited to no end-to-end visibility and a lack of resiliency.
There are four actions that military leaders can take to overcome today’s challenges and create a more predictive and secure supply chain that functions in all phases and conditions in a contested logistics scenario.
Design a more modern and agile IT infrastructure to operate logistics when communications are compromised. Systems can have multiple nodes and support deep, predictive analytics that function afloat, ashore or at enterprise scale without connectivity.
Many companies and federal agencies have created such networks, which can be phased and adaptive when an IT system is degraded.
Client story
▉ The challenge
A US federal government entity, with a national scope, mined years of maintenance and logistics data and used artificial intelligence (AI) to unlock predictive capabilities.
▉ The solution
The EY team helped the entity reach its goal of better operational execution and improved maintenance and scheduling, with cost savings.
▉ The outcome
The entity can now predict equipment failures with 75% to 90% accuracy from three to seven days in advance by using a repeatable methodology. It can also better identify maintenance process improvement opportunities through more accurate reporting.
Gaining better E2E visibility can start with a supply chain risk management assessment that can expose risks at critical supply points, including energy infrastructure, as well as deep relationships at resupply locations, at ports, and with allied and commercial partners. A vulnerability assessment can expose and remediate weaknesses among DoD partners, especially commercial and foreign partners, and build agility across all supply classes.
Another E2E tool is the creation of a supply chain digital twin, a model to generate predictive analysis and visualizations on potential supply chain disruptions across the world. Built on real-time data, simulation, machine learning and reasoning, a digital twin can improve the decision-making process.
Client story
▉ The challenge
A US federal government entity sought to transform how it assesses, manages and mitigates risks in subtiers of its global supply chain by addressing everything from raw materials for tech hardware to the financing of research and development.
▉ The solution
The EY team helped the entity dig into suppliers’ investor and outsourcing relationships, and technology and products, to identify and reduce foreign influence to strengthen access to materials across a multinode network.
▉ The outcome
The entity, with data from across the supply chain, was able to increase assessment and measurement of risk.
Client story
▉ The challenge
A global chemicals company created a supply chain digital twin, with the help of the EY team, to understand developing risks, predict supply chain disruption and take steps to mitigate risks before they occur.
▉ The solution
This company’s complex global supply chain includes multiple ocean freight legs, third-party suppliers and bills of materials going back more than five levels.
▉ The outcome
By modeling its supply chain with digital process mining and advanced analytics – coupled with scenario planning to simulate possibilities and get ahead of changes – the company was able to make better decisions and reduce risk in its supply chain.
By knowing what is needed when and where, AI can advance predictive logistics and execute preplanned push logistics.
Commercial industrial supply chain resiliency was lacking at the height of the COVID-19 pandemic, which resulted in lessons in U.S. Navy supply chain readiness. During supply surges in conflict, when cybersecurity breaches could disrupt the supply chain, the Navy of the future needs the ability to mobilize supply production at any location, especially in the context of great power competition.
The use of Industry 4.0 technology enablers at scale can deliver data-driven insights to commanders and wrench turners alike. In addition to forecasting with AI and machine learning, this can include predictive maintenance and other applications:
There are also lessons to learn from the US government and private industry coordination of medical supply chains and logistics at the height of the COVID-19 pandemic. The problem: critical medical supplies were largely produced outside the United States. After initial medical supply inventories were exhausted, there was no ability to quickly expand production to meet the need. The critical lesson was that you can’t immediately surge what you don’t have, even with the Defense Production Act (DPA). The nation had limited visibility on medical supply production, lacked surge contracts and lacked data infrastructure to augment visibility on everything from raw materials to alternative sources. However, with private commercial supply data combined with government COVID infection statistics, shipments eventually could be distributed quickly and equitably. The lessons showcase that military supply chain leaders can get ahead of the next challenge and collaborate with the private sector, and illustrate the need for greater DoD agility. A contested logistics situation would be even more difficult than the COVID-19 pressures on labor, materials sourcing, production and delivery.
Be prepared to maintain, command and control a resilient, multinode supply chain in a contested environment, which will allow you to view the Common Operational Picture (COP) and see where everything is and how it moves.
This may require enterprise-to-edge, operationalized data from the cloud to a laptop. This can lead to a competitive advantage in conflict by providing commanders with a comprehensive logistics picture that can predict problems and prescribe supply chain solutions across the enterprise. Planning may need to go into construction of decision support tools and commercial connectivity between the DoD, commercial partners and allies. This should address adversaries’ sophisticated cyber and other strike capabilities, including kinetic bombardment and focused energy attacks. In other words, operations data and information are part of the broader logistics picture and are used to create solution sets during disruptions across the network.
As the military transforms for the next conflict with a hybrid force of manned and unmanned systems, now is the time to transform supply chains and logistics capabilities to keep pace with the speed of conflict and sustain maritime advantage.
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