Transforming
Life Sciences.

A therapy that works in a Phase III trial does not automatically work in a European market. This distinction — obvious in principle, consistently underestimated in practice — is the root cause of one of the most frustrating patterns in pharmaceutical commercialisation: a drug that clears every clinical and regulatory hurdle, then stalls indefinitely at the reimbursement gate, available in theory and inaccessible in practice. The gap between marketing authorisation and meaningful patient access is not a bureaucratic inconvenience. It is a strategic failure, and in most cases it is preventable.

The proliferation of national HTA processes across Europe has made market access structurally more complex than at any previous point in the industry's history. Companies must now navigate not one approval process but twenty-seven, each with its own evidentiary standards, comparator requirements, economic thresholds, and institutional culture. Germany's AMNOG process rewards incremental benefit over an active comparator. France's HAS evaluates medical service rendered and improvement in medical benefit on separate scales. England's NICE applies a cost-effectiveness threshold that is explicit but contested. Italy and Spain layer regional variation on top of national decisions. The result is that a single product launch in Europe is, in practice, a portfolio of parallel market access campaigns — each requiring different evidence packages, different value narratives, and different stakeholder strategies.

Why Clinical Success Does Not Guarantee Access

The disconnect between clinical evidence and market access outcomes is rarely accidental. It reflects a systematic mismatch between the questions clinical trials are designed to answer and the questions payers actually ask:

1. The comparator problem: Phase III trials are designed to satisfy regulators, who typically accept placebo-controlled or best-available-therapy comparisons at the time of trial design. HTA bodies evaluate value against the actual standard of care at the time of submission — which may have changed substantially. A trial that demonstrates superiority over a comparator that payers no longer consider relevant provides limited dossier value, regardless of its p-values.
2. The endpoint gap: Regulatory endpoints — overall survival, progression-free survival, objective response rate — are not always the endpoints that drive reimbursement decisions. Payers increasingly want patient-reported outcomes, quality-of-life data, and real-world effectiveness evidence that clinical trials frequently do not collect, or collect inadequately. A dossier built on surrogate endpoints alone will face hard scrutiny in most major EU markets.
3. The economic evidence deficit: Health economic models are not an afterthought to be produced in the six months before submission. They require assumptions about treatment pathways, resource utilisation, and long-term outcomes that must be validated against local data and structured around each country's specific willingness-to-pay thresholds. Companies that build a single global economic model and adapt it minimally for each market routinely find it rejected as insufficiently localised.
4. Late payer engagement: Payers across Europe have signalled consistently — through scientific advice processes, early dialogue mechanisms, and HTA decisions themselves — that they want to be engaged before the evidence package is finalised, not after. Companies that treat payer engagement as a post-approval activity arrive at negotiations without the intelligence they need to price credibly or defend value effectively.

The Access Strategy as a Commercial Asset

The most consistently successful market access outcomes share a common feature: the access strategy was built in parallel with the clinical development programme, not retrofitted onto it. This means several things in practice.

Target product profiles are reviewed not only through a regulatory lens but through a payer lens — asking, for each proposed indication and each target market, what the HTA body will require to demonstrate added value, and whether the planned evidence generation is sufficient to meet that bar. Health economics and outcomes research is embedded in the development programme from Phase II, ensuring that the economic model is grounded in trial-derived data rather than published literature and assumptions. Patient advocacy engagement is begun early, recognising that patient organisations increasingly have formal roles in HTA processes in several EU countries, and that their framing of unmet need shapes the political environment in which access decisions are made.

Pricing architecture deserves particular attention in the European context. Reference pricing linkages between EU member states mean that the price agreed in one market automatically constrains the negotiating range in others. A concession made in a smaller market to achieve early access can propagate across the portfolio in ways that were not modelled at approval. Managing launch sequencing — deciding which markets to enter first, in what order, and at what price — is therefore not a commercial afterthought but a strategic decision with multi-year financial consequences.

What Good Looks Like

Companies that consistently achieve broad, sustainable access in Europe share recognisable characteristics. They treat access as a core commercial competence, not a regulatory function. They invest in local market intelligence — understanding not just the formal HTA process in each country but the informal dynamics: which clinical opinion leaders carry weight with the relevant HTA committee, what the payer's current budget pressures are, where political support for the therapy's indication exists and where it does not.

They build value dossiers that speak the payer's language — structured around clinical need, comparative effectiveness, and economic impact on the health system, not around the commercial positioning developed for physician detailing. And they approach price negotiations with a clear understanding of the floor below which launch is economically unviable, the ceiling above which access will be restricted, and the range within which a durable agreement is achievable.

Clinical proof of concept is necessary. It is not sufficient. The companies that understand this earliest — and build the access infrastructure accordingly — are the ones whose therapies reach patients. The rest produce data that lives in dossiers.

In early 2025, execon was engaged by a mid-sized European Contract Development and Manufacturing Organisation (CDMO) specialising in oral solid dosage forms and injectables. The company had recently secured a significant contract with a top-20 pharma partner and was under pressure to expand its manufacturing capacity. Two new filling lines and a dedicated cleanroom for high-potency APIs were in the pipeline — investments totalling over €18 million.

There was just one problem: the cash wasn't there.

A closer look at the balance sheet revealed the culprit. Days of Inventory Outstanding (DIO) stood at 163 days — more than five months of stock sitting in warehouses and production areas. For a company turning over roughly €85 million annually, that represented nearly €38 million of working capital tied up in materials, intermediates and finished goods. The CFO had been wrestling with the number for two years. "We know the inventory is too high," she told us in the first meeting. "What we don't know is what to do about it."

A System Running on Intuition

The root cause wasn't negligence — it was complexity managed manually. The CDMO produced over 340 SKUs across 60+ customers, each with its own forecast patterns, lead time constraints and contractual service level commitments. Planners were managing replenishment through a combination of ERP outputs, personal experience and conservative buffers accumulated over years of near-miss stockouts.

"We had one planner who knew exactly which APIs were risky and which weren't," the supply chain director explained. "But that knowledge lived entirely in his head." When that planner took medical leave for six weeks in 2024, the team added roughly €4 million of safety stock "just to be safe." Much of it was still sitting there when we arrived.

Step 1: Making Sense of the History

The first phase involved an AI-assisted analysis of 36 months of historical data — purchase orders, goods receipts, production orders, customer deliveries and demand signals. The data was messy: inconsistent lead time recording, duplicate SKUs, and forecast accuracy that varied wildly between product families.

Using machine learning clustering techniques, we segmented the 340 SKUs into meaningful groups based on demand volatility, supplier lead time variability and margin contribution. The analysis surfaced uncomfortable truths: 34% of SKUs with the highest stock levels had demand coefficients of variation below 0.2 — meaning they were highly predictable and had been dramatically over-stocked for years. Meanwhile, 18 high-margin products with genuinely volatile demand had no differentiated stock strategy at all.

Step 2: Right Strategy for Each Product

Armed with the segmentation, we designed a differentiated stocking strategy for each cluster. High-volume, predictable products moved to a continuous review system with statistically derived reorder points. Volatile, high-value products were assigned dynamic safety stocks recalculated weekly based on updated demand signals. Slow-moving and obsolete candidates — 47 SKUs in total — were flagged for rationalisation.

For the first time, the planning team had a logic-driven framework rather than a patchwork of rules of thumb. Target stock levels were set with explicit service level trade-offs: a 98.5% fill rate commitment for key accounts, 95% for standard customers — numbers that could be defended to the board and adjusted as the business evolved.

Step 3: A Dashboard That Replaced Thirty Spreadsheets

Before the project, the team managed inventory health through a combination of ERP reports and manually maintained Excel files — some of which, by the team's own admission, were "updated when we have time." Overstock situations went unnoticed for weeks, and emerging stockouts were caught late, triggering expensive expediting.

We built a set of HTML-based dashboards connected to the ERP system, pulling live inventory data every four hours. The dashboards were purpose-built in HTML — lightweight, browser-accessible without any additional software, and deployable across the planning team in days. They gave planners a real-time view of four key signals:

• Out-of-stock: zero on-hand with open customer orders — requiring immediate escalation
• Near out-of-stock: coverage below reorder threshold — triggering replenishment
• Overstock: coverage exceeding the maximum threshold — flagging for demand pull-forward or production pause
• Stale inventory: materials approaching expiry with no planned consumption — flagging for rework or write-off

Within the first month of go-live, the dashboard identified €2.1 million of overstock that had accumulated undetected, and flagged three API batches within 90 days of expiry that would otherwise have been written off entirely.

Step 4: Closing the Loop with Production Scheduling

The final — and most technically ambitious — component was a scheduling algorithm that acted on the dashboard signals. Rather than relying on planners to translate inventory alerts into production decisions manually, the algorithm proposed adjustments to the weekly schedule: pulling forward campaigns where near-stockout conditions were emerging, pushing back or splitting batches where overstock was accumulating.

The algorithm operated as a recommendation engine, not an autonomous system — planners reviewed and approved all changes. "We were nervous about letting a machine touch the schedule," the supply chain director admitted. "But in practice it flags things we would have missed, and saves us about two hours of scheduling work every morning."

The Results

Twelve months after implementation, Days of Inventory Outstanding had fallen from 163 to 108 — a reduction of 34%, freeing approximately €14.5 million of working capital. The CDMO used that cash to partially self-fund its capacity expansion, reducing the debt facility needed from external lenders by 40%.

Service levels, rather than declining as inventory fell, actually improved: customer-facing fill rates rose from 91.3% to 97.1% as the right products were stocked at the right levels. Planner overtime — a chronic problem during busy periods — dropped by more than half.

The CFO closed the final project review with a remark that stayed with us: "We thought this was a technology project. It turned out to be a decision-making project. The AI just made the decisions visible."

Ask the CFO of almost any mid-size pharmaceutical manufacturer what their biggest operational challenge is, and the answer will rarely be a single crisis. It will be a slow accumulation of friction — too many products, too many suppliers, too many exceptions, too many small batches that disrupt the schedule, too many packaging variants that exist for reasons no-one can fully reconstruct. Portfolio complexity in pharma manufacturing is rarely designed. It accretes. And by the time it becomes visible as a financial problem, it has typically been compounding quietly for years.

The numbers, when properly assembled, tend to be striking. In our experience working with generics and specialty pharma manufacturers, direct complexity costs — changeover time, minimum batch penalties, regulatory tail maintenance, excess safety stock on slow-moving SKUs — routinely account for 8 to 14 percent of manufacturing cost of goods. Indirect costs, which include the management attention consumed by exceptions and firefighting, the quality events disproportionately concentrated in non-core products, and the working capital tied up in inventory that moves slowly, add further. Complexity, in aggregate, is often one of the largest addressable cost items in the business. It is also one of the least visible, because it hides in the normal.

How Complexity Accumulates

Understanding why pharma portfolios proliferate is the starting point for addressing the problem. The mechanisms are consistent across companies:

1. Acquisitions without rationalisation: Every acquisition brings a new product portfolio. Integration programmes focus on people, systems, and commercial infrastructure — product rationalisation is deferred as commercially sensitive, then forgotten. The combined entity carries two portfolios indefinitely.
2. Customer-driven fragmentation: Large customers — hospital groups, wholesalers, export distributors — request product variants: different pack sizes, different labelling languages, different strengths. Each request is commercially reasonable in isolation. Collectively, they multiply the SKU count and splinter manufacturing runs.
3. Specification inflation: Product specifications, particularly for packaging materials, are set conservatively at launch and rarely revisited. A carton board weight specified for a product launched fifteen years ago may reflect requirements that no longer exist — but changing a specification requires a quality assessment, regulatory notification, and validation batch, so it remains unchanged by default.
4. The tail that nobody owns: In most pharma manufacturers, no single function is accountable for the total cost of a low-volume SKU. Commercial sees the revenue. Manufacturing sees the production disruption. Finance sees neither clearly. The result is that products generating minimal margin and significant operational complexity persist indefinitely, because removing them requires a cross-functional decision that no-one is incentivised to initiate.

The True Cost of Complexity — and Where It Hides

Quantifying complexity costs requires going beyond standard cost accounting, which typically averages costs across products rather than allocating them to the activities they actually generate. An activity-based view of the manufacturing P&L reveals costs that conventional reporting obscures:

1. Changeover and cleaning time: In a solid oral dosage facility producing 200 SKUs, changeover and cleaning between campaigns may consume 20 to 30 percent of available production time. Reducing SKU count by 20 percent does not reduce changeover time by 20 percent — the saving concentrates disproportionately, because the eliminated products tend to be the ones generating the most frequent, disruptive, and costly line changes.
2. Regulatory maintenance: Every registered product carries an ongoing regulatory burden — periodic safety update reports, variations, post-approval commitments, renewals. For a tail SKU generating €150,000 of annual revenue, the fully loaded regulatory maintenance cost can represent 10 to 20 percent of that figure. This cost is invisible in most P&Ls.
3. Quality and deviation events: Complexity and quality incidents are correlated. Non-routine products — those manufactured infrequently, on non-standard equipment, or with unusual process parameters — generate a disproportionate share of out-of-specification results, deviations, and investigations. Each event consumes QA resource that has a clear opportunity cost in a constrained function.
4. Inventory and working capital: Slow-moving SKUs require safety stock to protect against demand variability, but their low and irregular consumption means that stock ages, approaches expiry, and is frequently written off. Working capital tied up in tail inventory is capital not available for investment in core products or capacity.

The Rationalisation Playbook

Effective portfolio rationalisation in pharma is neither simple nor fast — regulatory obligations, customer commitments, and supply continuity requirements constrain the speed of any simplification programme. But the methodology is well-established, and the value is consistently material:

1. Build the true P&L by SKU: The starting point is always an activity-based cost allocation that assigns manufacturing, quality, regulatory, and working capital costs to individual products. This exercise alone is often revelatory — it is not uncommon for 20 to 30 percent of a portfolio to be making no positive contribution on a fully loaded basis.
2. Segment the portfolio: Products fall into three broad categories: core (high volume, high margin, strategically important), viable (positive contribution, manageable complexity), and tail (negative or marginal contribution, disproportionate operational burden). The tail rarely represents more than 10 to 15 percent of revenue, but frequently accounts for 30 to 40 percent of operational complexity.
3. Rationalise specifications, not just products: Before discontinuing a product, ask whether its cost and complexity can be reduced through de-specification — simplifying packaging grades, consolidating SKUs with minor strength or pack size differences, or removing country-specific variants that no longer serve a strategic purpose. Often, the right answer is not discontinuation but simplification.
4. Manage the discontinuation process: Regulatory notifications, customer communication, and supply continuity obligations make discontinuations slow. A rolling programme with 18 to 24 month timelines per product, managed as a formal project with governance and commercial sign-off, is more effective than ad hoc decisions taken product by product.

Where AI Changes the Equation

Artificial intelligence does not change the fundamental logic of complexity management — the economics of tail products are what they are, regardless of the analytical tool used to surface them. But AI materially accelerates and sharpens the analysis, and introduces capabilities that were not previously available at scale.

Machine learning models can now segment product portfolios by true cost-to-serve with a precision and speed that manual activity-based costing cannot match — processing years of production, quality, logistics, and demand data simultaneously to produce a complexity-adjusted P&L at the individual SKU level within days rather than months. Natural language processing tools can scan specification databases, quality records, and regulatory dossiers to identify simplification opportunities — flagging packaging materials that appear over-specified relative to current guidelines, or strength variants whose prescribing patterns suggest they serve a negligible patient population. And predictive models can simulate the working capital and service level impact of discontinuation decisions before they are implemented, giving commercial and supply chain teams the confidence to act without the fear of unintended stockouts.

In our experience, AI-assisted portfolio analysis consistently surfaces simplification opportunities that conventional analysis misses — not because the data was not available, but because the volume and interconnectedness of the signals exceeded what any team could process manually. The question for pharma manufacturers is no longer whether to use these tools, but how quickly to deploy them.

Complexity as a Strategic Choice

The goal of complexity management is not the smallest possible portfolio — it is the right portfolio. Some complexity is worth carrying. Products that serve niche patient populations, anchor distributor relationships, or provide strategic optionality in specific markets may justify their cost. The discipline is in making that trade-off explicitly and consciously, rather than by default.

Pharma manufacturers that treat complexity as a strategic variable — auditing it regularly, pricing it accurately, and managing it actively — consistently outperform peers of equivalent scale and portfolio breadth on manufacturing margins, working capital turns, and quality metrics. Those that allow it to accumulate unchecked find that the operational burden compounds year by year, until what began as a manageable nuisance becomes a structural constraint on the business.

The hidden cost of complexity is not really hidden. It is simply unread — sitting in the data, waiting for someone to look.

The medical device industry has always operated under pressure — from stringent regulatory oversight to complex global supply chains and rapidly evolving clinical needs. But the shocks of the past decade — a global pandemic, geopolitical tensions, raw material shortages, and accelerating technological change — have elevated operational resilience from a back-office concern to a board-level priority. For medical device companies, the stakes are uniquely high: supply failures do not just affect revenue, they affect patients.

Operational resilience, in this context, means more than the ability to recover from disruption. It means designing organisations, supply chains, and manufacturing operations that can absorb shocks, adapt rapidly, and continue delivering safe, effective products — even when the environment becomes unpredictable.

The Dimensions of Resilience

Building operational resilience in medical devices requires attention across several interconnected dimensions:

1. Supply Chain Resilience: Single-source dependencies and just-in-time models proved fragile during COVID-19. Leading companies are now diversifying supplier bases, building strategic safety stocks for critical components, and mapping their supply chains multiple tiers deep — understanding not just their direct suppliers, but the suppliers of their suppliers.
2. Manufacturing Agility: The ability to flex production volumes, switch lines, or relocate manufacturing in response to demand shifts or site disruptions is increasingly valuable. Modular manufacturing concepts, cross-trained workforces, and investments in Automation reduce reliance on single sites or specialist skills.
3. Regulatory Preparedness: Regulatory compliance cannot be a bottleneck in a crisis. Companies with well-maintained technical files, proactive relationships with notified bodies, and established change management processes are better positioned to respond quickly — whether to a product modification, a field safety corrective action, or a supply chain substitution.
4. Digital and Data Infrastructure: Real-time visibility across the supply chain, demand sensing, and predictive maintenance all depend on sound data foundations. Companies that invested in ERP modernisation, IoT-enabled manufacturing, and integrated planning tools entered the disruptions of recent years with a significant advantage.
5. Organisational Resilience: Structures, governance, and culture matter as much as technology. Cross-functional crisis teams, clear escalation protocols, and leadership with the mandate to act decisively are essential — as is a workforce culture that surfaces problems early rather than absorbing them silently.

The Challenges Ahead

Despite growing awareness, many medical device companies face structural barriers to resilience:

1. Portfolio and Complexity Creep: Decades of acquisitions and product line extensions have created sprawling portfolios with thousands of SKUs, each with its own supply chain, regulatory dossier, and manufacturing footprint. Simplification is often the most powerful resilience lever — but it is also one of the hardest to execute.
2. Cost vs. Resilience Trade-offs: Building redundancy — dual sourcing, safety stock, flexible capacity — costs money. In an industry under sustained pricing pressure from hospital groups and healthcare systems, making the business case for resilience investments requires demonstrating tangible financial value, not just risk mitigation.
3. Regulatory Fragmentation: Operating across the EU MDR, US FDA, and a growing list of country-specific requirements adds complexity to every supply chain decision. A component substitution that is straightforward operationally may require parallel regulatory submissions across a dozen jurisdictions.
4. Talent Scarcity: Skilled quality, regulatory, and supply chain professionals are in short supply across the industry. Retaining institutional knowledge, building succession pipelines, and accessing specialist expertise at speed — particularly during a crisis — is a persistent challenge.
5. Technology Integration Gaps: Many companies operate with fragmented IT landscapes — legacy ERP systems, disconnected planning tools, and manual quality processes. Integrating these systems is expensive and time-consuming, yet without it, real-time visibility and data-driven decision-making remain aspirational.

Principles for Building Resilience

There is no universal blueprint for resilience, but the companies that navigate uncertainty most effectively tend to share a set of common practices:

1. Know Your Risks: Invest in structured risk identification — supply chain mapping, scenario planning, and regular stress-testing of critical processes. Risks that are visible can be managed; those that are invisible become crises.
2. Prioritise Based on Patient Impact: Not all products and supply chains deserve equal resilience investment. Focus first on critical devices — those where supply failure directly threatens patient safety — and build your resilience architecture outward from there.
3. Simplify Before You Optimise: Complexity is the enemy of resilience. Rationalising portfolios, suppliers, and manufacturing sites reduces the surface area for disruption and creates the headroom to invest more deeply in what remains.
4. Build Relationships, Not Just Contracts: The companies that fared best during recent supply crises were those with deep, trusted relationships with key suppliers — relationships built over years, not forged in panic. Supplier development, transparency, and mutual investment pay dividends when allocation decisions are made under pressure.
5. Integrate Resilience Into Strategy: Resilience cannot be a project or a task force. It must be embedded into the strategic planning cycle, with explicit targets, executive ownership, and resource allocation — treated with the same rigour as growth or cost objectives.
6. Invest in Digital Visibility: End-to-end supply chain visibility is foundational. Companies that cannot see their inventory, demand, and supplier status in near real time are flying blind in a crisis. Prioritise the data and system investments that make this possible.
7. Practise, Don't Just Plan: Resilience plans that live in documents rarely survive first contact with reality. Regular simulation exercises, escalation drills, and after-action reviews build the organisational muscle memory that makes the difference when disruption actually arrives.

Uncertainty is not a temporary condition — it is the new baseline. For medical device companies, operational resilience is not a defensive investment. It is a source of competitive advantage, enabling faster response to market opportunities, stronger customer relationships, and a licence to operate that is earned through consistently reliable supply. The companies that build resilience into their DNA today will be the ones best positioned to grow tomorrow.

Industrial companies in developed countries have been increasingly going digital during the last 10 years as a means to cut costs or to differentiate themselves from competitors. In the pharmaceutical and biotech industry the digital journey started mostly in marketing — from virtual detailing for HCPs, social media campaigns, or influencer partnerships with KOLs to more complex AI-powered patient engagement platforms — and research & development — from use of AI in drug discovery and digital twins to use of real world evidence in trial design or machine learning in biomarker identification. Only recently, mostly driven by advancements in AI and blockchain technologies, pharma companies moved their digitalization focus towards manufacturing (pharma 4.0) and supply chain.

Opportunities

The pharma supply chain — driven by high complexity and fragmentation — offers many opportunities for improvement through digitalization:

1. Enhanced Supply Chain Visibility: Real-time tracking of raw materials, intermediates, and finished products ensures better control and traceability, reducing risks such as counterfeit drugs and theft.
2. Improved Forecasting and Inventory Optimization: AI and predictive analytics can analyze historical and real-time data to accurately forecast demand, reducing stockouts and overstocking.
3. Faster Drug Delivery and Patient-Centric Models: Digitization enables just-in-time manufacturing and faster response to market demands, especially for personalized medicine and rare disease drugs.
4. Increased Regulatory Compliance: Blockchain and IoT technologies can provide an immutable audit trail for drug production and distribution.
5. Enhanced Risk Management: Early warning systems using AI and real-time data can predict and mitigate risks such as supply disruptions, quality issues, and non-compliance.
6. Sustainability Goals: Digital tools can optimize routes, reduce waste, and support sustainable packaging initiatives.

Challenges

At the same time, pharma supply chains face challenges that must be managed carefully:

1. Data Silos and Integration: Many pharmaceutical companies use disparate systems, leading to fragmented data that hinders the creation of a unified digital ecosystem.
2. Regulatory and Compliance Complexity: Compliance with global regulations (FDA, EMA, WHO) can slow down implementation.
3. High Implementation Costs: Deploying advanced technologies such as IoT, AI, and blockchain requires significant upfront investment.
4. Cybersecurity Risks: As supply chains become more digital, they become targets for cyberattacks.
5. Change Management and Resistance: Employees and partners may resist adopting new technologies.
6. Scalability Across Global Operations: Rolling out digital transformation initiatives globally while adapting to local conditions can be complex.
7. Quality and Data Reliability: AI and predictive models rely on high-quality data, which is not always readily available.

Principles for Success

While there is no "golden recipe," a few general principles are widely accepted as a good base:

1. Define a Clear Vision and Strategic Goals: Outline a clear vision for the future supply chain aligned with organizational objectives.
2. Build Data-Centric Foundations: Establish a robust data strategy covering collection, storage, and governance.
3. Foster Agile and Collaborative Operations: Adopt two-speed approaches — maintaining operational reliability while rapidly piloting digital innovations.
4. Leverage Emerging Technologies: IoT, AI, and blockchain are transforming supply chains — cloud-based solutions and APIs streamline integration with legacy systems.
5. Balance Risk and Scalability: Start with minimum viable products (MVPs) and gradually integrate successful pilots into the broader organization.
6. Enhance End-to-End Visibility: Big data and analytics tools enable companies to track supply chain performance and make data-driven decisions.
7. Drive Continuous Innovation: A culture of continuous innovation ensures that digital transformation remains dynamic and responsive to market changes.
8. Invest in Talent and Leadership: Cultivate digital skills within the workforce and attract new talent with expertise in digital and analytics — balancing traditional expertise with innovative digital capabilities.

In spring 2024, execon was engaged by a European medical device company specialising in connected diabetes management technology. The company had developed a next-generation continuous glucose monitoring (CGM) system — a wearable sensor with a 14-day wear life, a companion app for real-time trend analysis, and integration with major insulin pump platforms — and had secured CE Mark under EU MDR and FDA 510(k) clearance. The product was clinically strong. The commercial infrastructure was not.

The ambition was substantial: a coordinated launch across six countries — Germany, France, Spain, and Italy in the European Union, and the United States and Brazil in the Americas — within an eighteen-month window. The VP of Commercial had been explicit in the first briefing: "We have a strong product and the regulatory clearances we need. What we do not have is a plan for executing six launches simultaneously without collapsing under our own complexity."

The Weight of Six Markets

Multi-country medical device launches are not simply national launches multiplied by six. Each market brings its own regulatory filing requirements, reimbursement pathways, tender cycles, distributor relationships, clinical evidence expectations, and pricing environment. For a connected device with a digital health component — integrating patient data, cloud connectivity, and companion software — the complexity compounds: data privacy regulations differ, software as a medical device (SaMD) classification requirements vary, and interoperability standards with hospital systems are rarely aligned.

The company had attempted to manage this complexity informally, with country managers coordinating independently and a lean central team providing light oversight. The result was a launch plan that existed in twelve different versions across as many spreadsheets, with no single source of truth on timelines, regulatory status, or resource requirements.

Step 1: Establishing a Single Launch Control Tower

The first intervention was structural. We established a central Launch Control Tower — a governance framework that brought together the heads of regulatory affairs, market access, supply chain, commercial, and medical affairs under a unified programme structure with weekly cadence, shared dashboards, and a single decision log.

The diagnosis was immediate: the six country plans were operating on incompatible assumptions. Germany's team was planning for a January 2025 reimbursement listing; France's market access team had assessed a twelve-month evaluation process that would preclude any meaningful revenue before Q3 2025. The US commercial team had sized its salesforce for a broad hospital launch, while Brazil's local partner had built a plan premised on a narrow private insurance channel. None of these plans had been reconciled.

"We had six correct answers to six different questions," the VP of Commercial reflected. "We needed one correct answer to the same question."

Step 2: Sequencing the Launch — Not All Countries Are Equal

With the landscape mapped, we designed a differentiated launch sequencing strategy. Not all six markets warranted simultaneous investment, and not all regulatory and reimbursement timelines permitted it.

Germany was the anchor market — the largest European CGM market, with a fast-track reimbursement pathway under §139c SGB V for digital health applications and established guideline support for CGM in both type 1 and type 2 diabetes. A January 2025 launch was achievable and financially material. France and Spain were positioned as wave-two markets, with launches targeted for mid-2025, contingent on completing local clinical evidence submissions and HTA processes. Italy, with a more fragmented regional reimbursement structure, was sequenced last in the EU cohort.

In the Americas, the US was prioritised — but scope was narrowed. Rather than a broad hospital launch, we recommended a focused entry through endocrinology practices and integrated delivery networks with established CGM protocols, building the evidence base and commercial infrastructure before a broader expansion. Brazil required a separate operating model: import licensing under ANVISA, local post-market surveillance obligations, and a reimbursement landscape dominated by supplementary health (plano de saúde) rather than public tender.

The sequencing decision freed approximately €4.2 million of planned launch investment to be redeployed to markets and activities with higher near-term returns — a conversation that had been technically obvious for months but required an external forcing function to execute.

Step 3: Navigating the Regulatory Patchwork

CE Mark and FDA clearance were necessary, not sufficient. Each market presented a distinct regulatory overlay that required active management.

In Germany, the product's companion app required classification as a Class IIa medical device software under EU MDR Article 22 — a classification that had not been anticipated in the original CE Mark submission. A supplementary technical file was required, adding eight weeks to the German launch timeline.

In France, the Haute Autorité de Santé requested a comparative clinical dataset against the current market-leading CGM system — data the company had generated in a prior clinical study but had not formatted for HTA submission. Reformatting and translating the clinical evidence package consumed six weeks of medical affairs capacity that had not been planned for.

Brazil presented the most significant regulatory hurdle: the product's cloud connectivity infrastructure was hosted on servers outside Brazil, triggering LGPD (Lei Geral de Proteção de Dados) compliance requirements that had not been assessed. Resolving this — through a combination of data residency adjustments and contractual safeguards with the local distributor — added ten weeks to the Brazilian timeline and required legal input from three jurisdictions.

"Every time we thought we had the regulatory picture complete, a new layer appeared," the Head of Regulatory Affairs told us. "What we lacked was a structured way of surfacing those layers before they became blockers."

Step 4: Building the Evidence and Reimbursement Dossiers

Medical device reimbursement in the EU and Americas is not harmonised — and for a connected diabetes device, the evidentiary bar is rising. Payers increasingly require real-world evidence of clinical utility, health economic modelling demonstrating cost-effectiveness, and outcomes data from populations that reflect their own.

We supported the medical affairs and market access teams in building five separate reimbursement dossiers — Germany, France, Spain, the US (for managed care contracting), and Brazil — each adapted to the local HTA framework, epidemiological context, and payer priorities. Germany's dossier centred on digital health application evidence requirements. France's was structured around the CNEDiMTS submission format. The US dossier was built for managed care medical directors, with emphasis on total cost of care modelling across diabetic complications.

The process surfaced a critical gap: the company's existing health economic model had been built for a homogeneous European population. Adapting it for Brazil — with a different epidemiological profile, cost structure, and treatment pathway — required a six-week rebuild that the medical affairs team had neither the capacity nor the local expertise to execute internally. We brought in a specialist health economics partner to close that gap.

Step 5: Aligning Supply Chain and Commercial Infrastructure

A product that cannot be consistently supplied is a product that cannot be launched. The CGM system's 14-day sensor carried a twelve-week manufacturing lead time, a cold-chain distribution requirement, and a shelf life of eighteen months — all of which created significant planning complexity across six markets with different launch timing, demand uncertainty, and distribution infrastructure.

We worked with the supply chain team to build a launch inventory plan that balanced the risk of stockouts in anchor markets against the carrying cost of excess inventory in wave-two markets. For Germany and the US, safety stock equivalent to sixteen weeks of demand was built ahead of launch. For France and Spain, inventory was staged at a European 3PL with the flexibility to redirect between markets as actual demand signals emerged.

Brazil required a separate supply chain configuration: import licensing timelines meant that stock needed to be in-country eight weeks before launch, and the local distributor lacked the cold-chain certification required for the sensor. Qualifying an alternative logistics provider and completing the cold-chain audit added five weeks to the Brazilian preparation.

The Results

Twelve months after the programme launched, the scorecard was clear. Germany delivered ahead of plan: first-year revenues of €9.3 million against a target of €8.5 million, with reimbursement listing secured in week three of January 2025. The US focused launch exceeded its endocrinology channel targets by 22%, and two integrated delivery network contracts were signed ahead of schedule. France and Spain were on track for mid-2025 activation; Brazil launched six weeks later than the original plan but within the revised timeline established at the outset of the programme.

Across all six markets, the structured sequencing and shared governance had avoided an estimated €6.1 million of wasted investment in premature market activities — tenders entered too early, salesforce hired before reimbursement was secured, inventory shipped to markets not yet ready to receive it.

The VP of Commercial's closing assessment was measured but direct: "We would have launched in all six markets in the same month and failed in most of them. We launched in the right order, and we succeeded in the ones that mattered first."

In November 2024, execon received a call from the deal team of a mid-market European private equity fund at 7pm on a Thursday. They had just entered exclusivity on the acquisition of a Turkish pharmaceutical manufacturer specialising in oral solid dosage forms — tablets, capsules, and film-coated generics — with an enterprise value of approximately $100 million. The fund had won a competitive auction. The problem: they had ten days to complete due diligence before the exclusivity window closed.

"We need you in Istanbul by Monday," the partner told us. "We have a data room with over 3,000 documents, a management presentation we don't entirely believe, and a factory tour we need someone who knows what to look for."

The Constraints of Compressed Diligence

Ten-day due diligence on a manufacturing asset is not simply compressed — it is a fundamentally different exercise. There is no time for iterative analysis, no margin for false starts, and no opportunity to revisit questions that were not asked in the first site visit. The risk is not just missing something — it is anchoring on the wrong things and not having the time to course-correct.

The target operated two production facilities on the outskirts of Istanbul, employed approximately 620 staff, and generated revenues of $78 million with an EBITDA margin of approximately 14%. Its portfolio comprised 180 registered SKUs across 23 therapeutic categories, with primary exposure to the Turkish domestic market (62% of revenues) and export sales to 34 countries, predominantly in the Middle East, North Africa, and Central Asia. The company held EU GMP certification for a subset of its lines and was in active pursuit of WHO prequalification for two key volume products.

AI as a Force Multiplier

Before the team boarded the flight to Istanbul, we had already processed the entire data room. Using AI-assisted document analysis, we ingested over 3,200 files — regulatory submissions, batch records, quality agreements, customer contracts, financial statements, and correspondence with TITCK (the Turkish Medicines and Medical Devices Agency) — and generated a structured intelligence picture within six hours of data room access.

The AI layer did three things that would have taken a conventional diligence team three to four days to replicate manually: it identified all regulatory inspection findings from the past five years and mapped them to specific production lines and quality systems; it extracted all customer concentration metrics from contracts and cross-referenced them with the revenue data in the financial model; and it flagged 47 documents containing language inconsistent with the management narrative — commercial agreements with termination clauses, quality deviations that had not been disclosed, and a regulatory warning letter from TITCK that had been buried in a folder of routine correspondence.

"By the time we landed in Istanbul," our project lead noted, "we already knew what questions to ask and where the bodies were likely to be buried."

Step 1: Operations Assessment — Reading the Factory

The first two days on the ground were spent at the manufacturing sites. For an OSD manufacturer at this scale, the factory tells a story that no data room can fully convey: the state of equipment maintenance, the discipline of shopfloor procedures, the confidence of operators when asked to walk you through a deviation report.

The primary facility — a 12,000 sqm site built in 2011 — was running at approximately 68% capacity utilisation across its four compression lines and two film-coating units. The equipment was well-maintained and largely within calibration. The shopfloor was organised. But three things gave us pause: validated cleaning procedures for two compression lines had not been revalidated following a line modification eighteen months earlier; environmental monitoring data showed a pattern of exceedances in the granulation suite that had been closed out without adequate trending; and the maintenance management system was entirely paper-based, making equipment reliability impossible to assess without a manual audit of individual logs.

The secondary facility — used primarily for packaging and secondary operations — was more concerning. It was operating under a temporary GMP certificate issued after a TITCK inspection had identified fifteen observations the previous year, three of them classified as major. The corrective action plan was in place, but two of the three major observations remained open at the time of our visit.

Step 2: Quality and Regulatory Risk — The TITCK File

The AI-flagged TITCK correspondence proved to be the most significant finding of the diligence. A warning letter, dated eight months prior, related to a stability study anomaly on the company's highest-volume export product — a metformin 1000mg tablet destined for Gulf Cooperation Council markets. Management believed the matter was closed. It was not.

A review of the complete correspondence thread — over 140 documents, processed and sequenced by the AI tool — revealed that TITCK's response to the company's corrective action submission had not yet been received. The product remained on conditional shipping approval pending regulatory clearance. If clearance was not received, the product — representing approximately $8.2 million of annual export revenue — faced a potential market suspension.

This finding was not discoverable from the management presentation, the financial model, or the summary regulatory schedule provided by the vendor's advisors. It required reading the original Turkish-language TITCK correspondence in full — which the AI tool had translated, sequenced, and flagged within the first hours of data room access.

Step 3: Commercial Assessment — Beyond the Revenue Line

The commercial diligence ran in parallel with the operations work. The target's revenue story was superficially attractive: consistent growth of 12% per annum over five years, a diversified export footprint, and a stated strategy of premiumising the portfolio toward higher-margin specialty generics.

The AI-assisted contract review told a more nuanced story. Of the 34 export markets, six accounted for 71% of export revenues. Three of those six were serviced through a single distributor — a family-owned trading company based in Dubai — operating under a master distribution agreement with exclusivity in eleven countries and a change-of-control clause allowing termination with 90 days' notice upon a change in ownership. The acquisition, if completed, would trigger that clause.

This distributor relationship had not been disclosed as a material contract in the vendor's data room index. It surfaced through the AI contract analysis, instructed to flag any agreement containing change-of-control, exclusivity, or termination provisions. Management's response — that the relationship was "long-standing and not at risk" — did not change our assessment: $11 million of annual revenue terminable with 90 days' notice upon closing was a material contingent liability requiring either contractual resolution or a price adjustment.

Step 4: Synthesis — Building the Adjusted Investment Case

By day seven, we had enough to build the adjusted investment case. The findings were consolidated into a risk-rated issue log — 23 items in total, classified by severity and reversibility — and translated into financial impact scenarios for the fund's model.

The three headline findings were: the TITCK regulatory overhang on the metformin product ($8.2 million revenue at risk, 60% estimated probability of resolution within twelve months); the open GMP observations at the secondary facility (estimated $1.8 million remediation cost, eighteen-month timeline); and the distributor change-of-control risk ($11 million revenue at risk, requiring contractual negotiation prior to closing). In aggregate, base-case adjusted EBITDA for Year 1 post-acquisition was 23% below the vendor's model.

The fund used the findings to reopen price negotiations. A $6.5 million price reduction was agreed, along with an escrow arrangement covering the TITCK regulatory risk and a pre-closing requirement for the vendor to obtain a written continuity waiver from the Dubai distributor.

The Results

The deal closed on schedule. The compressed ten-day timeline was made possible by AI-assisted document processing that effectively doubled the analytical capacity of the diligence team without doubling headcount or cost. Three findings that would likely have been missed — or discovered only after closing — were identified, quantified, and priced into the transaction.

The combined financial impact of these findings, had they remained unaddressed, would have reduced Year 1 EBITDA by approximately $2.8 million against the acquisition model. The fund's operating partner summarised the experience at the post-close debrief: "Ten days felt impossible. It turned out to be enough — but only because the first six hours of AI analysis told us where to spend the next ten days."

In early 2025, execon was engaged by a mid-sized generics pharmaceutical group with an established commercial footprint across the Middle East and North Africa. New local content regulations in several Gulf Cooperation Council markets and Egypt had created an urgent mandate: a defined proportion of products sold in-country needed to be manufactured within the region to qualify for public tender participation. Two major tender cycles — with combined annual revenues of approximately $14 million — were contingent on demonstrating local manufacturing capability by a non-negotiable regulatory deadline.

The scope was substantial. Eight products needed to be transferred to alternative manufacturers within 18 months: six oral solid dosage forms (tablets and capsules spanning cardiovascular, anti-infective, and CNS indications) and two sterile products (a lyophilised injectable and a liquid fill ophthalmic). Three of the eight were insourcing transfers — bringing production into the group's own recently upgraded manufacturing facility in Jordan. The remaining five were out-transfers to two contract manufacturers, one in Egypt and one in the UAE.

Why Product Transfers Are Harder Than They Look

Pharmaceutical product transfers are among the most technically and organisationally demanding exercises in the industry. Even a single oral solid transfer between established partners routinely takes 18–24 months from initiation to first commercial batch. The reasons are structural, not bureaucratic.

Formulations do not behave identically on different equipment. Granulation dynamics, compression forces, coating parameters, and dissolution profiles all shift when a product moves to a new manufacturing line — even when equipment specifications appear equivalent on paper. Every analytical test method must be formally transferred to the receiving laboratory and demonstrated to produce equivalent results. Regulatory dossiers must be updated across all registered markets, each with its own submission format, timeline, and administrative requirements. For sterile products the burden is substantially higher: lyophilisation cycles must be re-developed and validated, aseptic process simulations completed, and the regulatory package is an order of magnitude more complex than for oral solids.

Beyond the technical work, transfers are organisational challenges. Critical process knowledge resides with individuals, not documents. Sending sites have commercial priorities of their own. Receiving sites are building capabilities in parallel with receiving products. And when supply is already on market, any interruption risks stockouts, patient harm, and potential tender delisting — there is no margin for error.

Step 1: Stratify Before You Execute

The first intervention was a rapid transfer readiness assessment across all eight products and three receiving sites, completed within the first three weeks. Rather than treating the portfolio as a uniform queue, we stratified by risk: technical complexity, regulatory pathway length, equipment equivalence gaps, analytical method transfer burden, and supply criticality.

The outputs were immediate and uncomfortable. The lyophilised injectable — widely assumed to be straightforward given the receiving site's existing lyophilisation capability — surfaced as the highest-risk transfer in the portfolio. A review of existing batch records revealed that the lyo cycle had never been formally validated at the sending site: it had been transferred informally from the originator years earlier and run on operator knowledge and institutional habit. There was no validated design space to transfer. We would effectively be developing the cycle from scratch at the receiving site, not transferring it. Identifying this eight weeks earlier than it would otherwise have appeared allowed us to initiate lyo cycle development in parallel with technical transfer documentation — a sequencing decision that ultimately saved approximately eleven weeks on the critical path.

Step 2: Build One Integrated Plan, Not Three Separate Ones

With eight products moving across three sites in two countries, the instinct of each receiving site was to manage its own transfer independently. The sending manufacturer had its own scheduling priorities. The regulatory consultants in each jurisdiction were working to their own timelines. We imposed a single integrated Master Transfer Plan with a unified critical path, weekly cross-site governance, and a shared exception log visible to all parties.

The first cross-site governance call surfaced a critical scheduling conflict: the Jordan facility had allocated the same granulation suite to two of the oral solid transfers in the same four-week window, assuming the technical batch campaigns would run sequentially. They were not — both were on the critical path simultaneously. Resolving this required two days of capacity negotiation and would otherwise have caused a six-week delay, invisible until the conflict actually occurred on the shop floor.

Step 3: Run Technical and Regulatory Workstreams in Parallel

In conventional transfer programmes, regulatory submissions are prepared after technical transfer is complete: development report, validation data, and stability results assembled, then submission filed. This sequential logic adds four to eight months to every transfer.

We structured all eight transfers on a rolling submission model. Regulatory dossier templates were prepared and pre-populated from existing manufacturing authorisations before technical work began. Analytical method transfer protocols were submitted to receiving site QC laboratories before the first technical batch, allowing laboratory preparation to proceed in parallel with process development. Stability studies were initiated at the earliest permitted timepoint — immediately after the first successful technical batch — rather than after process validation was complete.

For the three markets where the regulatory authority permitted scientific advice meetings, we engaged early: presenting the transfer strategy, the proposed comparability approach, and the stability bridging protocol before any data was available. Two of the three authorities provided written agreement on the acceptability of the methodology, eliminating the risk of a rejection at the point of submission.

Step 4: Close the Knowledge Transfer Gap

Documentation is necessary but not sufficient. The most consequential process knowledge — why a step works, what operators watch for, what happens at the edges of the design space — rarely lives in batch records or SOPs. We implemented structured knowledge transfer workshops at the sending site for each product, attended by the receiving site's process development and QA teams.

For the oral solids, the focus was granulation end-point determination and compression sensitivity — areas where the sending site's operators had developed reliable indicators through experience that existed in no written document. For the sterile products, workshops covered aseptic line setup, interventions management, and the specific behaviour of the liquid fill under varying temperature conditions.

For the lyophilised product, we arranged for the Jordan facility's process development scientist to spend four weeks embedded at an independent lyo development centre alongside the sending site's formulation scientist. The lyo cycle was effectively co-developed by both teams — not transferred from one to the other — which accelerated validation and built the receiving site's ownership of the process from the outset.

Step 5: Manage Supply Continuity as a Workstream

With products already on market, supply continuity during the transfer window was not a background assumption — it was an active workstream. We built a transfer inventory model for each product: current stock levels, consumption rates by market, residual shelf life at transfer completion, and safety stock requirements to bridge the gap between last commercial batch at the sending site and first commercial batch at the receiving site.

For two of the oral solids, the model revealed that existing stock would not bridge the transfer timeline at expected consumption rates. The sending manufacturer was contracted for a precisely timed buffer campaign — calculated to avoid accumulating excess inventory that would cannibalise the receiving site's first commercial batches. For the ophthalmic product, a short shelf life meant the buffer campaign had to be scheduled within a narrow eight-week window tied to the receiving site's first commercial batch approval — a dependency only visible because the supply and transfer timelines had been integrated into a single model from the outset.

The Results

Fourteen months after programme initiation — against an 18-month baseline for a transfer programme of this scope — six of the eight products had received commercial manufacturing approval at their respective receiving sites, with first compliant batches released to market. The two sterile products were on track for approval within the following six weeks, within the original deadline.

The overall programme timeline was compressed by approximately 31% against the reference baseline: driven primarily by the parallel technical and regulatory workstream design (saving eight to ten weeks per product), the early lyo cycle development initiation (eleven weeks), and the cross-site governance model that caught and resolved the granulation suite conflict before it caused a delay. Both target tender cycles were entered with compliant local manufacturing declarations. The combined revenue secured across the two tenders represented $13.2 million annually against the $14 million at-risk figure — a shortfall attributable to a competitor price reduction in one product category during the transfer window, not to programme execution.

The group's VP of Operations summarised the outcome with characteristic directness: "We knew the timeline was impossible on paper. What we didn't know is that 'impossible' and 'never been done before' are different things."

In the autumn of 2023, execon was engaged by a specialty pharma company preparing to launch a prescription cardiovascular treatment across nine European markets — Germany, France, the United Kingdom, Italy, Spain, Denmark, Sweden, Norway, and Finland. The product had received EMA approval and carried a compelling clinical profile: a first-in-class mechanism targeting residual cardiovascular risk in patients with elevated triglycerides despite stable statin therapy, backed by a large-scale outcomes study demonstrating a statistically significant reduction in major adverse cardiac events. The science was strong. The commercial readiness was not.

The central challenge was structural: the company had nine to twelve months to build operational and commercial infrastructure in nine markets simultaneously — roughly half the eighteen to twenty-four months that pharma launch practice considers a minimum. Affiliates had been incorporated in the major markets, but several remained thinly staffed, without general managers in place, and without the network relationships that reimbursement negotiations in Europe require. The mandate was clear: design a go-to-market playbook that was realistic within the available window, identified the critical path in each country, and avoided the expensive mistakes that compressed launch timelines characteristically produce.

A Dual Research Approach

The engagement was structured in three phases. The first involved structured interviews with seventeen internal stakeholders across the nine affiliates — general managers, medical affairs directors, market access leads, commercial directors, and marketing managers — to map current state, identify operational pain points, and capture the questions each country team most needed answered before launch.

The findings were striking in their consistency. Across almost every market, the same cluster of problems surfaced: reimbursement timelines too tight for adequate KOL and agency engagement, promotional materials incomplete or absent, no shared digital asset management system, and country SOPs largely inherited from a US corporate infrastructure that did not map to EU regulatory realities. In Germany, the salesforce structure created ambiguous reporting lines that generated friction between external representatives and internal key account managers. In France, the required pharmaceutical operator licence — a legal prerequisite to commercialise drugs in the country — had been outsourced to a third party, creating a dependency risk that experts unanimously recommended be insourced before launch. In the Nordics, no clear plan existed for managing the supply chain complexity arising from four countries with four different distribution models.

The second phase engaged fifteen external industry experts — seasoned commercial, medical, and market access executives with direct experience launching products in each of the nine markets. Their input validated and sharpened the internal picture, and added a layer of country-specific tactical intelligence that no internal team could have generated alone.

The Critical Path: Reimbursement and KOL Engagement

Across all nine markets, the expert consensus on the single most critical pre-launch activity was identical: build the reimbursement dossier early and invest in KOL engagement well before submission. In European pharmaceutical markets, a product's price and reimbursement outcome is rarely determined by the data alone — it is shaped by the relationships between the company's medical and market access teams and the clinical opinion leaders, payer committees, and patient advocacy groups whose endorsement or opposition frames the negotiating context.

With a compressed timeline, this had direct sequencing consequences. In Germany — the anchor market whose reference price would set the ceiling for Italy, Spain, and other Mediterranean countries — the priority was an early, rigorous engagement with IQWIG and the relevant cardiology KOL community, combined with a cost-effectiveness narrative anchored in hospitalisation reduction data rather than unit price comparisons. Italy's launch was deliberately sequenced three or more months after Germany's reimbursement outcome, in recognition of Italy's explicit practice of referencing German pricing.

In the United Kingdom, the post-Brexit regulatory environment added a layer of complexity: post-approval activities were now managed through the MHRA rather than the EMA, requiring a distinct regulatory strategy and a salesforce clustering approach that treated England and Northern Ireland together and the Republic of Ireland separately, in line with post-Brexit trade arrangements.

Building the Operational Foundation

Across all nine countries, the external experts were consistent on a second priority: hiring general managers with established local networks before attempting any meaningful engagement with agencies, payors, or KOLs. This was not a conventional view — many pharma launches prioritise clinical and medical roles ahead of general management. But in markets where reimbursement committees and hospital formulary decisions are relationship-dependent, an affiliate GM without existing relationships to the key decision-makers is structurally disadvantaged from the outset. In Sweden, specifically, external experts identified the engagement with regional pharmaceutical teams across Sweden's twenty-one health regions as a pre-launch workstream of equal importance to the dossier itself.

The operational review also surfaced a gap that internal teams had not fully reckoned with: the absence of an EU-wide launch planning and project management function. Each affiliate was managing its launch preparation independently, with no cross-country visibility on timelines, dependencies, or emerging risks. Milestones were not tracked against a common framework. Best practices from markets that had already launched were not being systematically shared with those that had not.

The recommendation was structural: implement a central launch coordination office — a lean governance layer with weekly cadence, shared milestone tracking across all nine markets, and a clear escalation path for country-level blockers. The Germany launch, the most advanced, became the reference model from which other affiliates could extract learning rather than repeat avoidable mistakes.

Country-Specific Critical Actions

The operational guides delivered for each country translated the strategic priorities into country-specific action plans. Beyond the common threads, several markets required bespoke interventions.

In France, the insourcing of the pharmaceutical operator status — a process that takes approximately nine months to complete — was identified as a non-negotiable pre-launch requirement. The outsourced arrangement that had been put in place created legal exposure and a dependency that could not be unwound quickly if the third-party relationship deteriorated. A local law firm specialising in healthcare was engaged to confirm the path.

In Sweden and the broader Nordic region, the supply chain complexity was unique and underappreciated. Each of the four countries operates a distinct distribution model: Denmark requires direct wholesaler purchases; Sweden mandates a local distributor stock point to comply with a law requiring product availability within twenty-four hours of request anywhere in the country, including on islands; Norway allows monthly deliveries to pharmacy chains directly from the central 3PL; Finland can be served through the same Swedish distributor. A Scandinavian-speaking supply chain manager was recommended as an early hire — a role whose operational scope did not fit neatly into any of the standard affiliate organisational templates.

The Outcome

The engagement delivered a set of country operational guides covering all nine markets, each structured around market access, medical affairs, commercial, regulatory, pharmacovigilance, supply chain, and general management. The guides gave the executive team a clear picture of what needed to happen in each country, in what sequence, and with what organisational prerequisites — a level of operational granularity that the organisation had not previously consolidated in a single view.

Perhaps more importantly, the process surfaced the risk the company had not fully articulated internally: that the compressed timeline was manageable only with a deliberate prioritisation of activities. Not everything on the launch checklist could be completed in nine to twelve months. The recommendation was not to attempt all of it — it was to identify the ten to fifteen activities that would determine success or failure, sequence them correctly, and execute them with precision. The remaining items could follow in the two years after launch.

The Chief Operating Officer's assessment at the final readout was measured and direct: "We came into this project knowing we were behind. We leave it knowing exactly what 'behind' means and what we need to do about it."