By Paul Salmon FCILT, FSCM
In Defence logistics, forecasting has long been the cornerstone of availability. Whether for engines, avionics, or vehicle parts, planners rely on data-driven models to predict failure rates, schedule maintenance, and align inventory with operational tempo. But when it comes to medical spares — the equipment, consumables, and components that sustain deployed healthcare capability — the rules of the game change.
Forecasting medical spares is not just difficult; it is one of the most volatile and mission-critical challenges facing Defence supply chains today. Traditional statistical models struggle against unpredictable demand, rapid obsolescence, and life-or-death consequences. While a delayed vehicle part may ground an aircraft, a shortage of ventilator circuits or sterile surgical kits can cost lives.
So why are medical spares so much harder to forecast than traditional spares? And what solutions exist to make this area of the supply chain more resilient?
Why Medical Spares Defy Traditional Forecasting
1. Unpredictable Demand Driven by Operations
Traditional engineering spares benefit from measurable reliability metrics: Mean Time Between Failure (MTBF), service life, and usage cycles. With decades of maintenance records, Defence logisticians can predict, with reasonable accuracy, when an aircraft part or vehicle component will fail.
Medical demand, by contrast, is shaped by operational tempo and casualty profiles. A humanitarian mission after an earthquake requires a completely different profile of medical items than a counter-insurgency operation or high-intensity warfighting. Demand can spike overnight — a mass-casualty event, a pandemic, or a sudden expansion of deployed forces — making forecasts based solely on historical data almost meaningless.
2. Shelf-Life and Expiry Constraints
Engineering parts can often be stockpiled for years, if not decades. A spare gearbox does not “expire.” Medical spares, however, are often perishable. Drugs, blood products, diagnostic reagents, and sterile kits all have finite shelf lives. Even advanced equipment like ventilators or imaging machines may include consumables that degrade in storage.
This creates a paradox: Defence must over-provision to guarantee availability but risks costly wastage when items expire unused. The balance between readiness and efficiency is far narrower than in traditional engineering domains.
3. Stringent Regulatory and Quality Barriers
Medical equipment is regulated to the highest standards. Substituting components, reusing expired consumables, or turning to non-certified suppliers is rarely an option. Where Defence engineers can occasionally employ workarounds — cannibalising parts, using equivalent materials — medical logisticians are constrained by strict safety and ethical imperatives.
For example, a CT scanner coil or ventilator valve must meet civilian healthcare regulations, regardless of operational urgency. This lack of flexibility intensifies the forecasting challenge.
4. Fragile, Globalised Supply Chains
The Defence demand for medical spares is tiny compared with the vast civilian healthcare market. Military procurement has limited leverage over global suppliers, who prioritise civilian hospital networks. During the COVID-19 pandemic, this fragility was laid bare: Defence, like national health services, competed for limited supplies of PPE, oxygen concentrators, and testing kits.
Furthermore, many high-tech spares have single-source suppliers. If that manufacturer halts production, Defence can be left with long lead times and no alternatives. This supply fragility, combined with demand unpredictability, makes forecasting particularly fraught.
5. Data Gaps in Defence Context
Traditional spares forecasting benefits from decades of detailed usage data. Defence can calculate failure rates for a Chinook transmission or a Challenger road wheel with confidence. But casualty data and medical usage profiles are thankfully rare. Each new operation presents novel demand patterns that do not map neatly to past missions.
This means Defence has a thin dataset to build predictive models on — and forecasting without data is closer to guesswork than science.
6. Zero-Tolerance for Shortages
Perhaps the most profound difference is ethical. In engineering, some degradation of availability may be tolerated. An aircraft can be grounded until a part arrives; a vehicle can be cannibalised. But in medical logistics, the acceptable rate of stock-out is zero. A shortage of surgical consumables, diagnostic reagents, or anaesthetic drugs can immediately endanger lives.
This creates an inherently conservative forecasting posture: planners must assume worst-case scenarios, holding deeper buffers than would be considered efficient in other domains.
The Cost of Getting It Wrong
Defence cannot afford to see medical supply chains as simply another variant of engineering logistics. The stakes are higher. If forecasting errors ground an aircraft, the mission is delayed. If forecasting errors leave a deployed field hospital without critical consumables, lives are lost, strategic credibility is damaged, and Defence risks political fallout.
Yet the cost of over-forecasting is also significant: expired drugs, wasted stock, sunk cost in obsolete equipment, and a footprint of surplus inventory that is difficult to rotate or repurpose. Medical logistics therefore operates in a “no-win” tension: too little is catastrophic, too much is wasteful.
Towards Solutions: Making Medical Forecasting More Resilient
Despite these challenges, Defence is not powerless. There are practical strategies that can reduce unpredictability, strengthen supply assurance, and improve the resilience of medical forecasting.
1. Predictive Modelling and Scenario Planning
Rather than relying solely on historical demand, Defence can use scenario-based forecasting models. These models simulate different operational profiles — humanitarian, peacekeeping, high-intensity conflict — and estimate medical demand accordingly.
Advances in AI and machine learning can help by ingesting diverse datasets: casualty modelling, epidemiology, climate data, and even intelligence feeds. For example, forecasting malaria prophylactic demand can be linked to predictive climate models of mosquito activity.
Predictive analytics will never eliminate uncertainty, but it can narrow the cone of unpredictability and help Defence prepare tailored contingency stocks.
2. Pooled Stockpiles and Regional Hubs
No single Defence organisation can cost-effectively hold all possible medical spares. A collaborative model — whether across coalition allies (NATO, 5 Eyes) or between Defence and national healthcare systems — could create pooled inventories of critical spares.
Shared regional hubs, with transparent access rules, would reduce waste while ensuring surge capacity. The EU’s rescEU stockpile of medical countermeasures, created after COVID-19, demonstrates how pooled models can work at scale. Defence could replicate this within an alliance framework.
3. Strategic Partnerships with NHS and Industry
The NHS and civilian healthcare sector have vastly larger buying power and supplier relationships. Defence can leverage this through framework agreements, shared contracts, and joint procurement mechanisms.
For example, Defence and NHS could co-purchase high-demand consumables like IV sets or oxygen masks, enabling Defence to “ride” on NHS volume discounts while accessing established supply routes. Similarly, Defence could partner with manufacturers to guarantee surge production in crises, sharing capacity agreements with civilian healthcare providers.
4. Rotation Through Civilian Systems
One of the best mitigations against expiry is stock rotation. By feeding Defence-purchased consumables into NHS supply chains before they expire — and replenishing with fresh stock — waste can be dramatically reduced. This model has been trialled for blood products and certain pharmaceuticals, with promising results.
This requires close integration with national healthcare logistics systems but offers a route to align readiness with efficiency.
5. Digital Supply Chain Visibility
End-to-end visibility of medical inventories, from depot to deployed field hospital, is critical. Too often Defence medical spares are managed in fragmented silos, with poor real-time data on consumption rates, expiry dates, and stock location.
Digital “control towers” for medical logistics — integrating IoT-enabled tracking, automated expiry monitoring, and AI-driven demand sensing — could transform forecasting accuracy. In effect, forecasting becomes a real-time, adaptive process, not a static plan.
6. Professionalising Medical Logistics
Finally, Defence must continue to professionalise the medical logistics cadre. Forecasting medical spares requires a unique skillset: part logistician, part clinician, part data scientist. Training, accreditation, and cross-pollination with civilian healthcare logisticians will strengthen this capability.
Professional bodies like CILT and the Health Logistics Association can play a role in embedding common standards, frameworks, and CPD pathways for Defence medical supply chain professionals.
Conclusion: From Forecasting to Readiness
Medical spares will never be as predictable as traditional engineering spares. The volatility of operations, perishability of consumables, and ethical imperatives of patient care make forecasting inherently difficult. But difficulty is not an excuse for fragility.
By embracing predictive modelling, pooling resources, forging NHS and industry partnerships, and digitising visibility, Defence can shift from a mindset of “forecasting demand” to one of “readiness for uncertainty.”
In contested, complex operational environments, medical logistics is not just a support function. It is a strategic enabler of force credibility and humanitarian responsibility. If the aircraft is grounded, the mission waits. If the operating theatre is grounded, the mission fails.
In that light, getting medical spares forecasting right is not optional. It is a matter of operational success, strategic credibility, and human life.
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