Transforming
Life Sciences.

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 an automated dashboard pulling live data from the ERP system every four hours, giving 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."

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.

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.