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There is a recurring pattern in how pharmaceutical companies approach artificial intelligence: leadership commits to a transformation agenda, a technology partner is selected, a pilot is designed — and then the project quietly stalls. Not because the algorithm failed. Because the data was not ready. Batch records in PDF scanned from paper. Yield figures calculated differently across three sites. Equipment maintenance logs that exist in six formats across a legacy CMMS and two spreadsheet trackers. The AI had nothing reliable to learn from.

The uncomfortable truth is that most pharma companies do not have a data strategy problem. They have a data operations problem. The governance, processes, and accountability structures that would produce clean, consistent, machine-readable data at scale have never been built — because they were never required to run the business. Until now.

Why operations is where data quality is won or lost

Data quality in pharmaceutical manufacturing is determined overwhelmingly at the point of creation: on the shop floor, at the equipment interface, in the batch record, in the deviation log. If those inputs are inconsistent — different units, different taxonomies, different levels of granularity depending on who recorded the entry — no downstream cleaning exercise will fully recover them. The cost of remediation grows non-linearly with the distance from the source.

This is why data readiness for AI is fundamentally an operations problem, not an IT problem. The decisions that determine whether useful data is created happen in manufacturing, supply chain, and quality operations — in how processes are designed, how equipment is configured, how operators are trained, and how exceptions are documented. Technology can capture and store data efficiently; it cannot compensate for operational processes that were never designed to produce it consistently.

Execon worked with a contract development and manufacturing organisation that had invested significantly in a data lake and analytics platform before addressing its source data. Two years in, the platform contained data from twelve manufacturing systems — but yield comparisons across campaigns were unreliable because batch size definitions were inconsistent, in-process control data was recorded manually at different frequencies across shifts, and deviation categorisation used three different taxonomies that had evolved independently across sites. The analytics team spent the majority of its time reconciling data rather than generating insight. The investment in the platform was sound; the sequence was wrong.

The four layers of the data foundation

Getting the foundation right before committing to AI requires work across four layers, each of which depends on the one below it.

The first is data definition: establishing agreed, documented definitions for the key entities and measures that the organisation needs to reason about. What is a batch? How is yield calculated — and is it calculated the same way across sites, products, and process steps? What constitutes a critical process parameter versus an in-process control? These questions sound basic. In most organisations, the answers are inconsistent, undocumented, or contested. Resolving them is an operational and organisational task, not a technical one.

The second is data generation: redesigning the operational processes and equipment configurations that create data to produce it consistently and in a machine-readable form. This means moving from paper-based and hybrid recording to electronic batch records, from operator-discretion data entry to system-enforced formats, from periodic manual readings to automated sensor capture where the process warrants it. Each of these changes requires operational redesign, validation, and change management — not just system implementation.

The third is data integration: connecting the systems that hold operational data — MES, LIMS, ERP, QMS, CMMS — so that related records are linked rather than siloed. A deviation record in the QMS that cannot be automatically associated with the batch record in the MES and the maintenance event in the CMMS requires manual correlation every time an analyst needs to understand root cause. At scale, that manual work makes any meaningful pattern detection impractical.

The fourth is data governance: the ownership, accountability, and process structures that maintain data quality over time. A one-time remediation project that is not followed by durable governance will degrade within eighteen months. Governance in an operational context means clear ownership of master data by function, a defined process for resolving data quality issues when they are identified, and metrics that make data quality visible to operations leadership rather than invisible to everyone except the IT team.

Where AI genuinely adds value — once the foundation is there

The case for AI in pharmaceutical operations is real, but it is not uniform. The use cases that consistently deliver value share a common characteristic: they involve pattern detection across large volumes of consistent, structured operational data where the signal is too subtle or too complex for human analysts to identify reliably.

Predictive yield modelling — identifying process parameter combinations that reliably predict end-of-batch yield before the batch is complete — is a genuine value creator for high-value biologics and complex small molecules. Equipment predictive maintenance, where sensor data from critical manufacturing equipment is used to anticipate failure before it causes a batch loss or a deviation, has demonstrated strong ROI in multiple asset-intensive manufacturing settings. Automated anomaly detection in in-process control data, flagging deviations from expected process trajectories in real time rather than after batch release review, can meaningfully reduce investigation burden and improve process understanding.

Execon supported a mid-size biologics manufacturer in building the data foundation required to implement a predictive process monitoring capability. The engagement began with a six-week data readiness assessment across two manufacturing sites — mapping data sources, identifying gaps in completeness and consistency, and documenting the operational process changes required to close them. The foundation work took fourteen months: electronic batch record implementation across three product families, integration of the LIMS and MES on a shared data platform, and master data governance structures with clear ownership in manufacturing operations. The AI model implementation that followed took three months. The sequence was right. The model worked.

The strategic implication

Pharmaceutical companies that invest in AI before investing in the operational data foundation will continue to generate pilots that cannot scale. The companies that will extract durable value from AI in operations are those that treat data quality as an operational discipline — resourced, managed, and measured with the same rigour as yield, right-first-time, and on-time delivery.

The question is not whether to pursue AI. The question is whether the organisation is willing to do the less glamorous work first — the process redesign, the master data governance, the integration architecture — that makes the AI worth building. The foundation is not a prerequisite that gets in the way of transformation. It is the transformation.

The playbook that built the blockbuster era no longer works. In specialty pharma, the sales representative calling on a physician is no longer the centre of gravity in the commercial model. Payers hold more power, formulary restrictions are tightening, and health technology assessment bodies in key European markets are applying scrutiny that no amount of detailing can overcome. Yet many specialty pharma commercial organisations remain structured — and resourced — as though the prescription still begins and ends with the prescriber.

Commercial excellence today means something harder: aligning every lever of the commercial model — medical affairs, market access, patient services, digital engagement, and field force — into a system that creates value for all stakeholders simultaneously. The companies doing this well are outperforming their peers not because they have more sales representatives, but because they have built capabilities that most organisations still treat as support functions.

The access layer has become the commercial layer

In specialty pharma, reimbursement and formulary position now determine commercial success more reliably than share of voice. A therapy that achieves broad label but restricted reimbursement in major European markets will chronically underperform its clinical potential. Yet commercial organisations routinely underinvest in the access capabilities needed to secure and defend that reimbursement — health economics, outcomes evidence generation, payer engagement, and managed entry agreement design.

Execon worked with a mid-size specialty pharma company entering three new European markets with a high-value oncology asset. Their existing commercial structure was built around hospital specialists; their market access function was a team of two, reporting late into the launch planning cycle. We helped redesign the launch model to lead with access: payer value dossiers co-authored with medical affairs, real-world evidence commitments embedded in managed entry agreements, and key account management structures that owned payer relationships rather than just physician relationships. The result was formulary inclusion timelines that were materially shorter than the company's previous launches — and patient access that was not rationed by budget caps.

Field force effectiveness is not a headcount problem

The instinct to add headcount when commercial performance disappoints is understandable but usually wrong. In specialty indications, the addressable prescriber universe is narrow, and additional representatives calling on the same specialists produce diminishing returns quickly. What deteriorates first is not coverage — it is quality. Too many sales organisations measure activity rather than impact: calls made rather than calls that changed something.

Commercial excellence in specialty pharma requires a sharper answer to three questions for every call: what does this prescriber need to hear that they have not yet heard? What is the barrier — knowledge, experience, reimbursement confidence, or patient identification? And what combination of field force, medical, and digital interaction will move that barrier?

In an engagement with a specialty rare disease company, Execon analysed two years of commercial activity data and found that prescriber conversion was almost entirely driven by a small subset of interactions — those involving a specific combination of clinical case discussion and reimbursement navigation support. Calls that did not include both elements showed near-zero correlation with prescribing behaviour. The commercial redesign that followed reduced field force size by fifteen percent while concentrating resources on high-impact interaction types. Within two quarters, new patient starts had increased and cost per patient acquisition had dropped significantly.

Patient services as a commercial capability

In specialty pharma, the patient journey from diagnosis to treatment initiation is rarely straightforward. Prior authorisation requirements, specialty pharmacy logistics, patient out-of-pocket costs, and adherence challenges all create attrition between prescription and treatment. Most commercial organisations treat patient services as a compliance function. The leading ones treat it as a commercial capability.

An effective patient support programme does more than remove friction — it generates commercial intelligence. Every touchpoint with a patient or caregiver reveals where the system is breaking down: which payers are creating prior authorisation delays, which specialty pharmacies are slow to dispense, which patient populations are abandoning therapy at which point. This intelligence, systematically captured and fed back into the commercial model, allows rapid intervention. Without it, organisations discover problems when they appear in quarterly numbers — too late to course-correct within a launch cycle.

Integrating medical affairs into the commercial engine

The boundary between commercial and medical affairs is both a regulatory requirement and, in many organisations, a dysfunction. Medical affairs is often structured to operate in parallel with — rather than in support of — the commercial model. Evidence generation plans are not linked to commercial priorities. Medical science liaisons operate on separate call plans with no coordination with field force activity. Publication strategy is driven by scientific interest rather than market shaping needs.

In specialty pharma, where physician decision-making depends heavily on clinical evidence and peer influence, this separation is costly. Execon has helped several companies design integrated commercial-medical operating models that preserve the independence required by regulation while eliminating the structural disconnects that slow both functions. The key is shared data — common customer records, coordinated engagement plans, and joint review of where scientific and commercial priorities align — not shared incentives, which create compliance risk.

Building for the next access environment

The access environment in specialty pharma will continue to tighten. More health technology assessments, more real-world evidence requirements, more value-based contracting. The companies building commercial excellence now — investing in access capabilities, redesigning field force models, professionalising patient services, and integrating medical and commercial — are not just optimising for today. They are building the organisational capabilities that will determine who wins the next wave of specialty launches.

The sales call will remain part of the model. But the companies that treat it as the model will find the gap between clinical promise and commercial performance growing wider every year.

The investment thesis looked compelling at deal close. The management presentation was persuasive. The financial model showed a clear path to multiple expansion. And then the first hundred days begin — and the gap between the deal narrative and the operational reality starts to reveal itself, one conversation, one factory walk, one commercial review at a time. For private equity investors in life sciences, this gap is not exceptional. It is the rule. The question is not whether it exists, but how quickly and intelligently the new ownership group responds to it.

The first hundred days after close are not primarily a diagnostic exercise. They are an execution exercise — a period in which the right priorities must be identified, sequenced, and begun before the organisation settles back into the inertia that pre-deal momentum temporarily disrupted. Companies that treat this window as a discovery phase typically emerge from it with a comprehensive assessment and a slide deck. Companies that treat it as an activation phase emerge with initiatives underway, quick wins on the board, and a management team that has experienced what the new ownership expects from them. The difference in value outcomes, compounded over a three-to-five year hold, is substantial.

The Sequencing Problem

Most value creation failures in PE-backed life sciences companies are not failures of strategy. They are failures of sequencing. The right initiatives are identified but launched simultaneously, overwhelming management bandwidth. Or structural changes are attempted before quick wins have established credibility and momentum. Or the financial agenda is pursued without the operational foundation that makes it sustainable.

The first hundred days should be structured around three distinct horizons. In the first thirty days, the priority is stabilisation and signal-setting: understanding what is actually true about the business, establishing the governance cadences that will drive accountability, and identifying two or three rapid interventions — pricing corrections, working capital releases, cost eliminations — that demonstrate both competence and intent. In days thirty to sixty, the focus shifts to foundation-building: defining the value creation plan with the specificity that allows it to be tracked, aligning the management team around it, and launching the first wave of structural programmes. In days sixty to one hundred, the emphasis moves to momentum: early proof points from the structural programmes, a clear picture of where talent gaps exist, and a revised financial model that reflects the operational reality rather than the deal assumption.

The Commercial Agenda

In most life sciences acquisitions, commercial performance is both the primary value driver and the area of greatest uncertainty at close. Revenue assumptions in the investment model are typically based on market data, analogue transactions, and management projections — none of which fully capture the quality of the commercial organisation, the sustainability of key customer relationships, or the realistic addressable opportunity in each segment.

The first commercial priority in the hundred-day window is almost always pricing. Pharma and medical device companies that have been management-owned or under-invested frequently carry pricing that has not been reviewed systematically in years — with list prices that have not kept pace with inflation, discount structures that have proliferated without discipline, and tender management that lacks the rigour to defend margin under pressure. A pricing audit in the first sixty days typically surfaces two to four percentage points of margin improvement that can be actioned within the hold period without volume risk, if approached correctly.

The second commercial priority is portfolio focus. PE-backed life sciences companies almost invariably carry products, indications, or geographies that consume commercial resource disproportionate to their contribution. Identifying and beginning to exit these positions in the first hundred days frees capacity — management attention, salesforce time, marketing spend — that can be redirected toward the highest-value opportunities in the portfolio.

The Operations Agenda

Operational value creation in life sciences is slower than commercial value creation, but it is often more durable. Manufacturing efficiency improvements, supply chain redesign, and procurement savings are harder to reverse than pricing actions, and they improve the structural cost position of the business in ways that support both margin expansion and exit valuation.

In the first hundred days, the operational agenda should focus on establishing the baseline. What does OEE actually look like across production lines, once the informal adjustments and scheduling workarounds are stripped out of the data? What is the true cost-to-serve by product, once changeover time, regulatory maintenance, and quality incidents are allocated properly? Where is working capital tied up — in slow-moving finished goods, in excess raw material safety stock, in receivables that have been allowed to extend beyond contract terms? These questions are rarely fully answered before close. The hundred-day period is the window to answer them, and to begin the first structural responses.

The People and Governance Agenda

Value creation programmes in PE-backed companies succeed or fail on the basis of whether the right people are in the right roles, with the right incentives, operating within a governance structure that creates genuine accountability. Each of these conditions is frequently absent at close, and establishing them is not a soft priority — it is the infrastructure on which everything else depends.

The hundred-day governance agenda should establish a monthly operating review at which commercial, operational, and financial performance is reviewed against plan, with variance explanation required and corrective action tracked. It should establish a value creation steering committee that meets quarterly to review progress against the investment thesis and resequence priorities as the business evolves. And it should complete an honest talent assessment — identifying where the management team has the capability to execute the transformation, and where interim or permanent reinforcement is needed before the programmes stall.

One hundred days is not long enough to transform a life sciences business. It is long enough to determine whether the transformation will succeed — and to put in motion the decisions, the programmes, and the accountability structures that will make it do so.

There is a version of management consulting that keeps its distance by design. The analysis is delivered, the recommendations are presented, the engagement closes — and the hard work of implementation is handed back to an organisation that may or may not have the capacity, the clarity, or the will to execute. This model has its place. But it is not the only model, and in life sciences — an industry where the gap between strategic intent and operational reality tends to be wide, and where the cost of that gap is measured in patient access, regulatory risk, and value destruction — it is often not the right one.

Interim management by experienced consultants offers a fundamentally different proposition: not advice from the outside, but execution from within. The consultant takes the seat — Head of Supply Chain, VP Manufacturing, Interim Commercial Director — and operates with the authority, accountability, and daily exposure that the role demands. The external perspective does not disappear; it becomes an asset deployed from a position of genuine organisational power. Problems that are invisible to advisors become obvious to incumbents. Levers that are unavailable to consultants become accessible to managers.

Why the Combination Works

Senior consultants who move into interim roles bring a specific combination that is difficult to replicate through conventional hiring. They have pattern recognition built across multiple companies and situations — they have seen what good looks like in manufacturing performance, supply chain design, or commercial organisation, and they recognise the distance between current state and that benchmark almost immediately. They are not building institutional knowledge from scratch; they arrive with it. And because their tenure is defined and their incentives are aligned with delivery rather than longevity, they move faster and with less political caution than permanent hires typically can.

The "from within" dimension matters more than it might appear. Access to the right conversations, the ability to make and enforce decisions, the credibility that comes from being accountable for outcomes rather than recommendations — these are not marginal advantages. In a transformation programme, they are often the difference between a change that sticks and one that stalls at middle management.

In Manufacturing: Discipline From the Inside

At a mid-size specialty pharma manufacturer facing persistent OEE underperformance and a backlog of deviation investigations, the standard consulting response — a diagnostic and an improvement roadmap — had been tried and had not moved the needle. What was needed was someone inside the operation with the authority to restructure the shift management model, reset accountability between production and quality, and personally drive the weekly performance review cadence until the new rhythm became self-sustaining.

execon took on the Interim Director of Manufacturing role for nine months. The improvement programme that followed was not new in its content — root cause analysis, line balancing, OEE cockpit design — but it was executed with the urgency and consistency that only comes from someone whose name is on the outcomes. OEE improved by 18 percentage points over the engagement period. More durably, the operating model — the cadences, the accountabilities, the escalation logic — survived the handover to the permanent hire.

In Supply Chain: Redesign Under Load

A generics manufacturer integrating a recent acquisition faced a supply chain that had doubled in complexity overnight: two ERP systems, overlapping supplier bases, conflicting planning processes, and a commercial organisation making promises the supply function could not reliably keep. The integration programme needed someone who could simultaneously keep the operation running and redesign it — a combination that requires both operational competence and transformation experience that is rarely found in a single permanent hire on a fast timeline.

As Interim Head of Supply Chain, execon ran the function and led the integration workstream in parallel. The dual mandate — operate and transform — is where the consultant-as-interim model is at its most effective. The external perspective drove the design of the future-state operating model; the organisational authority drove its adoption. Within twelve months, the two supply chains were operating as one, service levels had stabilised, and a single S&OP process was live across the combined business.

In Commercial: Building Before the Permanent Team Arrives

Pre-launch commercial build-out is among the highest-stakes activities in the pharmaceutical calendar, and among the most common contexts where interim leadership creates disproportionate value. A biotech approaching its first European launch had a strong medical and regulatory organisation but no commercial infrastructure: no market access framework, no pricing and reimbursement strategy, no field force model, no KPI architecture. The permanent VP Commercial had been identified but would not be in seat for four months — a period that, in a launch timeline, is not spare capacity but critical path.

execon stepped in as Interim VP Commercial, built the launch readiness framework, led the payer engagement strategy across three priority markets, and established the commercial operating model that the incoming permanent leader inherited as a functioning organisation rather than a blank page. The transition was deliberate: the interim role was designed from the outset to create a handover, not a dependency.

The Handover Is the Deliverable

The measure of a successful interim engagement is not what the interim manager achieved while in post — it is what the organisation is capable of after they leave. The most effective interim leaders in life sciences are those who build while they run: who install the processes, develop the team, and establish the rhythms that allow performance to continue and improve without them. This orientation — toward enablement rather than indispensability — is what distinguishes interim management done well from a long consulting engagement with a different title.

For life sciences companies navigating transformation, vacancy, or rapid change, the question is not whether to bring in external expertise. It is whether to keep that expertise at arm's length, or to put it where the work actually happens.

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.

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.

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.

The life sciences and medical devices industries have no shortage of ambitious strategies. Boards approve sweeping transformation programmes. Leadership teams craft compelling narratives about growth, efficiency, and patient impact. Consultants deliver polished decks. And yet, year after year, a striking proportion of these strategies fail not in their conception but in their execution. The ideas are sound. The implementation is not.

This gap between strategy and outcome is not unique to life sciences — but the consequences here are more acute. A failed commercial launch at a pharma company is not just a missed revenue target; it may mean patients waiting longer for a new therapy. A stalled operational transformation at a medical device manufacturer is not just an efficiency problem; it may compromise product quality and regulatory standing. Understanding why execution fails — and how to fix it — is therefore one of the most consequential management questions in the sector.

Why Execution Fails

In our experience working across pharma, biotech, and medical device companies, execution failures tend to cluster around a recognisable set of root causes:

1. Strategy Without Operational Translation: Many strategies remain at an altitude that is inspiring but unactionable. A global pharma company may commit to "becoming the leader in patient-centric oncology" without ever translating that ambition into concrete changes to its commercial model, supply chain, or medical affairs function. Strategy that cannot be converted into specific decisions, resource allocations, and behavioural changes is a vision statement, not a plan.
2. Misaligned Incentives: Even when the strategy is clear, execution stalls if the incentive structures reward different behaviours. A medical device company pursuing a services-led growth model will struggle if its salesforce is still compensated purely on device volume. A pharma company committed to cross-functional collaboration will find that commitment tested if business units are competing for the same P&L targets. People follow incentives — and if incentives point in a different direction from the strategy, incentives win.
3. Underestimating Change Management: Transformation programmes in life sciences routinely underinvest in change management. The assumption — often implicit — is that once leadership endorses a new direction, the organisation will follow. It rarely does. At a major European medical device group, a multi-year ERP implementation delivered on time and on budget, yet adoption remained stubbornly low eighteen months post go-live because the change management programme had been scoped at a fraction of the technical investment. The system worked. The people did not change.
4. Initiative Overload: Life sciences organisations are particularly prone to launching too many initiatives simultaneously. A mid-size pharma company we worked with had 47 active transformation workstreams at the point of engagement — each with a sponsor, a budget, and a project manager, but collectively consuming far more leadership attention and organisational bandwidth than was available. The result was a portfolio of half-executed initiatives, each moving slowly, few reaching completion. Priority is not about what you say yes to; it is about what you say no to.
5. Weak Governance and Accountability: Execution requires someone to be accountable — not collectively responsible, but personally accountable. In matrix organisations, which are the norm in large pharma and device companies, accountability diffuses. Programme steering committees meet quarterly. Escalation paths are unclear. Decisions that require cross-functional trade-offs linger unresolved for months. By the time the problem surfaces at the right level, the window to correct course has often closed.
6. Loss of Momentum: Strategies are typically launched with energy and commitment. That momentum is fragile. Leadership changes, budget cycles, regulatory setbacks, or simply the passage of time erode the organisational will to sustain difficult change. At a global biotech company, a commercial transformation that had strong initial traction lost momentum after a change in regional leadership — and was quietly deprioritised before its most important elements had been embedded.

How to Fix It

There is no single intervention that guarantees execution success. But the companies that consistently translate strategy into outcomes share a set of deliberate practices:

1. Translate Strategy into the Operating Model: Every strategic priority should have a clear owner, a defined set of changes to processes, structures, and capabilities, and measurable milestones. A useful test: if you cannot describe what will be different — specifically — in the way the organisation operates twelve months from now, the strategy has not yet been translated into execution.
2. Align Incentives Ruthlessly: Review compensation, performance management, and resource allocation through the lens of the strategy. If the metrics and rewards do not reinforce the strategic priorities, change them. This is uncomfortable work — it requires confronting legacy structures and vested interests — but without it, execution will always be swimming against the current.
3. Invest in Change Management as a First-Class Discipline: Change management is not a communications plan. It is a structured programme to shift behaviours, build capabilities, and sustain adoption. In our experience, life sciences companies that invest 15–20% of programme budgets in change management consistently outperform those that treat it as an afterthought. For medical device companies navigating regulatory change or ERP transformation, this investment is particularly critical.
4. Ruthlessly Prioritise: Limit the number of strategic initiatives in flight at any one time. A useful heuristic: the number of initiatives your organisation can execute well is probably half the number currently underway. Stopping initiatives is as important as starting them — and significantly harder. Build the governance discipline to say no.
5. Establish Clear Accountability: For every critical initiative, there should be a single named owner with the authority, resources, and mandate to deliver. Steering committees advise; they do not own. Where cross-functional decisions are required, establish clear decision rights and escalation protocols in advance — not at the moment of conflict.
6. Manage Momentum Actively: Sustaining execution energy over multi-year programmes requires deliberate effort. Celebrate intermediate milestones. Maintain visibility of progress at the senior level. Connect the work to the patient and clinical outcomes it is ultimately designed to serve — in life sciences, this is a particularly powerful source of organisational motivation. And when leadership changes, invest explicitly in continuity: the incoming leader needs to understand not just the strategy but the execution context.
7. Build Execution Capability as a Core Competence: The most resilient life sciences organisations treat execution as a capability to be developed and sustained, not a one-time effort. This means investing in programme management talent, building internal consulting capabilities, and creating institutional knowledge about how change happens in the organisation — what works, what does not, and why.

Strategy execution is not glamorous work. It does not generate headlines or feature prominently in investor presentations. But it is the discipline that determines whether the ambitions of life sciences companies — ambitions that ultimately serve patients and healthcare systems — are realised or remain aspirational. In a sector where the stakes are as high as they are in pharma and medical devices, closing the gap between strategy and execution is not a management nicety. It is a leadership imperative.

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.