Key Takeaways
- Pharma planning looks like a supply chain problem. It is a data problem, and that distinction only becomes visible when you try to automate.
- In made-to-order pharma manufacturing, every customer order is a unique production event. There is no inventory buffer to absorb a planning error. The schedule is the commitment.
- McKinsey's pharma operations benchmarking shows top-quartile manufacturers reach final delivery in half the time of average manufacturers, and more than five times faster than bottom-quartile ones. The difference traces to planning frequency and process standardisation, not capacity investment.
- When automation arrives, it does not just replace manual work. It exposes what the manual process was hiding: data quality problems, undocumented process logic, and system dependencies that nobody had formally mapped.
- The fear that a legacy system cannot coexist with a modern planning layer is the most expensive assumption in pharma operations. It is also, in most cases, wrong.
The planning problem nobody has formally named
Every week, inside pharmaceutical manufacturing and ingredient businesses across the US and UK, the same sequence of events plays out.
A planner arrives on Monday morning and opens a spreadsheet. On one side: every open customer order, each with a specific product, quantity, and ship date. On the other: the available production resources, blenders, lines, batches, raw materials, spread across one site or several. The planner’s job is to make those two things meet, for every order, by Wednesday.
No system does this automatically. The planner does it through a combination of experience, institutional knowledge, and manual reconciliation that has accumulated over years. They know which blender handles which formulation. They know which orders can be consolidated into a single batch without violating quality parameters. They know that a last-minute order change on Tuesday afternoon means a call to the production manager, which may mean an additional shift.
Pfizer’s VP of Digital Manufacturing, Mike Tomasco, described the industry’s starting position plainly in 2025: many pharma processes are still just paper processes that have been scanned rather than truly digitised, with a lot of groundwork to do before advanced technology can be usefully deployed. Pfizer’s own Global Supply network, built from more than 30 legacy pharmaceutical companies, found that obtaining actionable insights across sites was genuinely difficult before their multi-year transformation programme began.
The cost of this invisibility is real. It shows up as overtime when a last-minute order change requires a manual schedule rebuild. It shows up as missed ship dates when reconciliation takes longer than the customer window allows. It shows up in a planning team that spends Monday to Wednesday doing work that should take hours, leaving Thursday and Friday for the decisions that actually require human judgement.
Deloitte’s 2025 Smart Manufacturing Survey found that 46% of manufacturers report moderate to significant challenges filling planning and scheduling roles. That talent pressure compounds the system problem: when planning depends on individual expertise rather than system capability, every hiring gap or departure is also an operational risk.
Made to order: why standard supply chain thinking does not apply
Understanding why this problem is harder in pharma than in most other manufacturing sectors requires understanding one structural reality: most pharma manufacturers, and almost all CDMOs, operate on a made-to-order basis.
In a made-to-stock model, a manufacturer builds to a forecast. Inventory absorbs the mismatch between forecast and actual demand. A planning error does not immediately become a customer problem. It becomes a stock level adjustment.
In a made-to-order model, there is no buffer. Every customer order is a unique production event. The batch that will fill that order does not exist until the order arrives. The production schedule is not a production plan. It is a set of customer commitments, each one specific, each one time-bound, each one carrying consequences if it fails.
The planning complexity this creates is compounding. Each order requires not just a production slot, but the right equipment at the right time with the right cleaning status, the right raw materials allocated and available, and the right batch size that consolidates efficiently with other orders while still meeting each individual ship date.
In a pharmaceutical colour coating operation, for example, this means batches must be sequenced by colour intensity, light-to-dark, to minimise cleaning time between runs. A blender that runs a dark formulation cannot immediately run a light one without a full washdown. The planner carries this sequencing logic in their head. The ERP does not.
The assumption that no standard platform can handle this specificity, that the complexity is too unique, too customised, too embedded in individual planners to run on a standard system, is the belief that keeps most pharma operations stuck. Deloitte’s 2024 biopharma survey found that 82% of respondents said their supply chain digitalisation journey began less than five years ago. For most pharma manufacturers, this journey is only just beginning.
What the industry's digital transformation experience actually shows
The challenge of modernising pharma planning infrastructure is well documented in public reporting from the sector’s largest operators. The pattern that emerges is consistent.
Pfizer’s Global Supply transformation, running across a network inherited from more than 30 legacy companies, required a multi-year effort specifically because each facility had its own systems, datasets, and operational standards. Getting a unified view across that network was not a technology problem. It was a data standardisation and process alignment problem that the technology could only solve once the underlying groundwork was in place.
Novartis publicly named its ERP modernisation programme the Lean Digital Core ERP Transformation, appointing a Head of Data specifically for the initiative. The name itself signals what the programme discovered: the core constraint in modernising an enterprise ERP is not the platform. It is the lean, clean data foundation that the platform requires.
Both examples point to the same underlying reality. Pharma manufacturers are not failing to invest in digital planning capability. They are discovering that the investment only delivers when the data and process layer underneath it is solid enough to support it.
CDER warning letters jumped 50% in FY2025, with more than a third citing GMP violations tied directly to documentation failures including incomplete batch records and missing entries. Many of those failures trace back to the same root: a gap between what the system holds and what actually happened on the floor, a gap that manual workarounds have quietly widened over years and that nobody sees until an inspection or an automation initiative makes it visible.
What automation actually exposes: an InspireXT client story
InspireXT worked with a global pharmaceutical colour coating and film coating manufacturer, an InspireXT client, to address the made-to-order planning problem described above. The engagement is instructive specifically because of what it revealed once automation began running, not just what it delivered.
The starting position was a planning team running a sophisticated manual process around an ERP system that could not support automated scheduling. Each sales order line converted to a batch automatically. Everything after that, which batches to consolidate, which blender to assign, how to sequence production across the week’s orders, was done manually by a planner using a custom spreadsheet. The process worked because the planners were skilled. It consumed most of their working week and left no room for demand anticipation.
The client’s internal belief was that no standard platform could handle the complexity of what their planners were doing. The process was too specific and too dependent on institutional knowledge that had never been formally documented.
InspireXT’s response was not an argument. It was a session called Day in the life of a planner, running the client’s own Monday morning process live on the new system in front of the planning team. A custom component was built inside the platform to handle the blender assignment algorithm: a logic engine that evaluates formulation type, batch size, resource availability, cleaning status, and ship date priority simultaneously. This was the largest single piece of development work in the engagement.
But 70% automation was not the most significant outcome. When automated planning began running at the client, the system flagged something the manual process had never surfaced. Bulk density values, a critical parameter in the blender assignment logic, were incorrect for more than 20% of the product records going through the planning process.
The planners had been compensating for this silently, through experience, for years. They knew which products behaved differently from their system records and adjusted accordingly without documenting it. The data problem was invisible because the manual process that absorbed it was also hiding it.
When automation arrived, the compensation disappeared. The data problem became visible. And because it was now visible, it was correctable.
This is the pattern that the industry’s wider experience confirms. Pharma manufacturers are not struggling to plan because they lack technology. They are struggling because years of manual workarounds have created a layer of undocumented process logic and silent data correction that sits between what the system holds and what the business actually does. Automation does not fix that layer. It reveals it.
The integration question every CIO gets stuck on
For most pharmaceutical manufacturers considering planning modernisation, the conversation eventually reaches a specific obstacle. Not budget. Not process complexity. Not change management.
The legacy system.
The client had a highly customised ERP environment the business had no intention of replacing. Years of configuration had made it, practically speaking, irreplaceable in the short or medium term. The question was whether a modern planning layer could sit on top of it without creating an integration that breaks every time a quarterly platform update lands.
This concern is not irrational. In a pharmaceutical environment where the ERP is tightly coupled to batch records, serialisation, and quality management, a broken integration is not a technical inconvenience. It is a production risk.
InspireXT resolved this using the platform vendor’s own public APIs: interfaces designed to remain backward compatible through every quarterly update. The integration is not custom in the fragile sense. It is built on a foundation the vendor has committed to maintaining.
Master data synchronises between the two systems on a two-hour cycle. Records with missing or incorrect attributes do not fail silently. They are flagged, corrected, and picked up automatically in the next scheduled run. The error-handling logic means the integration does not propagate bad data. It surfaces it.
The client kept their ERP. They have a modern planning layer running on top of it. The integration has not broken during a quarterly update. The decision was not a technology bet. It was an engineering decision made with a clear understanding of the architecture underneath it.
What this means for pharma manufacturers in 2026 and beyond
The pattern this engagement illustrates is not specific to one manufacturer. It describes the situation at most pharma manufacturing and ingredient businesses operating at scale today.
Made-to-order production with tight customer windows. Planning processes that work because skilled people have learned to compensate for system gaps. Legacy ERP environments that are stable, customised, and here to stay. A deep institutional belief that the process complexity is too unique for a standard platform. And a digital transformation journey that, for 82% of biopharma companies, is less than five years old.
McKinsey’s benchmarking across 25 global pharmaceutical companies found that reaching top-quartile throughput performance from bottom-quartile would be worth two percentage points of EBIT. The path there runs through planning frequency and process standardisation. For CDMOs specifically, the commercial consequence is direct: the manufacturers winning contracts in 2026 are those whose planning systems can make a delivery commitment with confidence, not those with the most capacity on paper.
For excipient and ingredient manufacturers, the parallel sits in specification management. The complexity is different, thousands of product specifications, multi-market regulatory requirements, customer-specific documentation, but the structural condition is the same. Manual processes absorbing complexity that systems should handle. Data quality problems hidden inside workarounds that have become institutionalised over years.
In both cases, the answer is consistent. The complexity is real. The scepticism is understandable. The assumption that the problem is unsolvable on a standard platform is, in most cases, wrong.
How InspireXT approaches this problem
InspireXT works with pharmaceutical manufacturers and ingredient companies to close the gap between the operating model the business is running and the systems underneath it.
The work starts with understanding the planning reality in practice, not process documentation, but the actual daily workflow. What the planning team does manually that the system does not support. Where the data quality problems sit. What the integration constraints look like from the inside. The Day in the life of a planner approach is not a methodology. It is the fastest way to understand what the system gap actually costs before committing to a direction.
The solution is built around Oracle ERP Cloud, Oracle SCM Cloud, and Databricks, connected to the legacy infrastructure that is staying, using architectures that do not create fragile dependencies. Where process complexity requires custom logic, as the blender assignment algorithm required at one client, that logic is built inside the platform’s extension framework. Custom logic inside the platform travels with upgrades. Custom logic outside it breaks during them.
The outcome is a planning function that runs on systems rather than on people. A supply chain team that makes commitments based on data rather than experience. A legacy ERP that continues doing what it does well, with a modern planning and data layer sitting cleanly on top of it.
Key Questions Leaders Are Asking
What is the main reason pharma supply chain planning keeps failing despite significant technology investment?
Most pharma planning failures are not technology failures. They are data and process failures that technology has been deployed on top of. Manual workarounds built over years create a layer of undocumented process logic and silent data correction that sits between what the system holds and what the business actually does. When automation arrives, it removes the workaround and makes the underlying problem visible. Organisations that treat this as a sign the implementation has failed are misreading the signal. The data problem existed before the automation. The automation made it addressable.
Why is made-to-order pharma manufacturing harder to plan than made-to-stock?
In a made-to-stock model, inventory absorbs the gap between forecast and actual demand. In a made-to-order model, there is no buffer. Every customer order is a unique production event requiring the right equipment, the right materials, and the right batch sequencing to meet a specific ship date. The planning system must account for formulation requirements, resource availability, cleaning constraints, and delivery priority simultaneously for every order. A planning error does not become an inventory adjustment. It becomes a missed delivery to a pharmaceutical customer who cannot absorb it.
Can a modern planning system coexist with a legacy ERP without creating integration risk?
Yes, with the right architectural decision. The integration risk most CIOs fear is a custom connection that breaks during a platform update. This risk is avoidable when the integration is built on the platform vendor’s own public APIs, which are designed to remain backward compatible through quarterly updates. At this client, master data synchronises on a two-hour cycle and records with missing attributes are held and flagged rather than propagated silently. The legacy ERP has remained unchanged and stable throughout the engagement.
What does automation typically expose in pharma planning environments that manual processes were hiding?
The most consistent discovery is data quality problems that manual planners had been compensating for through experience. At one InspireXT client, critical product attributes were incorrect for more than 20% of the records going through the planning process. The planners knew which products behaved differently than the system suggested and adjusted accordingly, without documenting it. When automation arrived, the compensation disappeared and the data problem became visible and correctable. This pattern appears consistently across pharma planning environments where manual workarounds have become institutionalised over years.
What is the commercial case for fixing pharma planning infrastructure?
McKinsey’s benchmarking across 25 global pharmaceutical companies found that moving from bottom-quartile to top-quartile throughput performance would be worth two percentage points of EBIT. Top-quartile manufacturers reach final delivery in half the time of average manufacturers. The path runs through planning frequency and process standardisation. For CDMOs specifically, the manufacturers winning contracts in 2026 are those whose planning systems can make a delivery commitment with confidence, not those with the most capacity on paper.
What signals indicate a pharma manufacturer is ready for planning modernisation?
Four signals appear consistently. The planning team spends more than half its week on schedule reconciliation rather than decision-making. Customer delivery commitments are made without a real-time view of network capacity. A planner’s departure creates an operational risk rather than a staffing problem. And a legacy ERP migration has been deferred because nobody is confident about what is in the current system. Any one of these signals suggests the system gap is costing more than the modernisation would.
How long does a pharma planning transformation typically take?
The timeline depends on the complexity of the existing estate and the scope of custom logic required. A discovery phase using InspireXT’s PLM Insight accelerator, which maps the current technical estate before any migration commitment is made, typically completes in days rather than months. This de-risks the scoping decision and prevents the mid-project surprises that cause most pharma IT projects to overrun. The blender assignment algorithm at one InspireXT client, which was the most complex single element of that engagement, took the most development time but was completed within the original project scope.
If the planning reality described in this piece is familiar, the manual schedules, the legacy system that is staying, the belief that the complexity cannot be solved, InspireXT would like that conversation.