16-17 GLP-1 - Big appetite

Nearly one in ten adults in Great Britain have either recently used GLP-1 weight loss medications or are seriously considering doing so.

Pharmaceutical supply chains were not designed for that, especially for a cold chain product delivered on a weekly basis.

Their efficacy has driven enormous media interest, with many people sharing transformational experiences.

Between early 2024 and early 2025, an estimated 1.6 million adults across England, Wales and Scotland used GLP-1s to support weight loss and health goals.

Globally, the market is booming and is expected to grow from around $14 billion in value two years ago to nearly $50 billion by 2030.

Domestically, this very high level of demand has significantly exceeded NHS England’s original prescribing expectations, which adds to the wider pressure on pharmaceutical supply chains due to demand in other countries.

How can pharmaceutical businesses cope with unusual, unprecedented spikes in demand, while evolving their processes to maximise market growth?

Pharmaceutical supply chains are designed around predictable prescription demand and long planning cycles. However, consumer-driven demand spikes from ADHD medications to children’s pain relievers to oncology drugs have repeatedly exposed how brittle that model can be.

GLP-1s are the latest and largest stress test.

Production challenges aside, getting product to market for large and complex organisations like the NHS tests existing supply chain relationships and processes.

In the case of GLP-1 medications, the challenge is complicated by the need for temperature-controlled storage and transport. As volumes increase, maintaining cold chain integrity across manufacturing, warehousing, distribution and last mile adds further operational pressure.

The changing nature of how patients access their medication, with the growth of digital pharmacies, home delivery services and healthcare platforms, increases the complexity of temperature-controlled distribution planning.

In supply chain planning, demand forecasting must fully integrate with product availability and delivery against the backdrop of stringent regulatory compliance. Gaps at any point can quickly expose weaknesses in planning processes and visibility.

The supply chain impact of GLP-1 popularity extends well beyond the medications themselves. As usage has grown, so too has demand for adjunct medications, particularly treatments used to manage side effects like nausea. Weight loss also drives sales of aesthetic treatments, including dermal fillers and skin-tightening procedures. Many of these products are also temperature-sensitive, adding further complexity to cold chain logistics.

The ripple effects extend even further, beyond healthcare and into consumer markets. People using GLP-1 drugs often eat smaller portions, reduce snack consumption and increase protein. Weight loss can also prompt people to buy new clothes.

The underlying impact on supply chains is that even modest changes in behaviour related to GLP-1s can introduce unexpected volatility into forecasting and inventory planning.

How can pharmaceutical businesses position themselves to deal with demand volatility?

Traditional planning models that rely heavily on historical data struggle to keep pace with dramatic behavioural shifts. Ideally, supply chain teams need access to more timely demand and distribution indicators, including information that sits outside core processes such as prescription trends.

This requires greater end-to-end visibility across the supply network so stakeholders can understand how demand changes work their way back up the chain from healthcare providers at the sharp end through to delivery performance and production.

The most effective supply chain platforms help organisations connect the dots by integrating data from planning, procurement, manufacturing, logistics and distribution processes to create a single operational view.

Supply chains have revolved around the collection and analysis of data at each stage of the process for years. However, with AI and machine-learning models, huge volumes of information can be analysed much earlier than legacy analysis processes and technologies.

These tools offer a transformational level of insight into risks and opportunities in the supply chain in real time.

Consider this scenario, for example. A pharmaceutical manufacturer is supplying GLP-1 medications to multiple national markets. Prescription data from pharmacies and healthcare platforms indicates a specific and sudden surge in demand in one region.

Because planning, logistics and distribution data are connected on a unified platform, changes in demand are flagged early to supply chain planners.

AI models are used to analyse the likely impact on each stage in the supply chain before evaluating possible responses such as adjusting production volumes or prioritising deliveries to the affected market.

Planners can review the recommended actions and approve adjustments before shortages begin to affect pharmacies or healthcare providers. As new data continues to arrive, the system monitors performance and updates forecasts, allowing the organisation to respond continuously.

The GLP-1 boom is likely to serve as a useful case study for the pharmaceutical industry on various levels. One key lesson is that historic demand baselines are less reliable when behavioural shifts occur at scale.

Organisations need planning processes that can detect demand signals earlier than before and in a way that enables them to adapt quickly. This requires effective integration between healthcare providers, pharmacies, distributors and manufacturers, supported by shared data and visibility.

The GLP-1 boom will not be the last time consumer behaviour outpaces pharmaceutical supply chains. But it should be the last time the industry is caught planning for yesterday’s demand.

The organisations that invest now in real-time visibility, integrated demand signals and cold chain agility will not just survive this shift – they will be the ones ready for the next one.