Delays in tech transfer directly impact time-to-market – the single most important driver of commercial value in pharmaceutical development.  So, with over one hundred years of experience between us, the authors of this white paper from DTG and NGT Biopharma set out to investigate the fundamental root causes of Tech Transfer time and describe a desired future state along with the factors critical to getting there. Because, as the authors observe:

“Tech Transfer is where value is either realised or silently lost”

Download the full report here.

The Hidden Bottleneck

The first key insight that emerged from the discussion was the distinction between digital systems and digital readiness.  Most organisations have invested heavily in systems but these environments are not operating as an integrated whole.  R&D is exploratory by nature, and its systems are built for flexibility and creativity. Manufacturing depends on structured, validated and standardised models designed to lock things down for regulatory compliance. That mismatch creates the bottleneck: there is no data continuity.

Where we do find a higher level of investment in digital infrastructure is where expenditure is high: clinical development. Absorbing around 70% of total R&D spend, and with high regulatory stakes, investment has been made in seamless, standardised data exchange across organisational boundaries. We only need to look at the COVID model to know that, when the will power and focus are there, data can flow to the right next step. But the industry has been slow to apply those hard-won lessons to tech transfer.

Where value is lost: the hidden inefficiencies

The inefficiencies in tech transfer are recognised but perhaps what is not acknowledged as well is just how fundamentally embedded they are in systems and processes.  As Mark Buswell summarised it,

“Tech transfer is effectively a knowledge workflow – but it’s not managed as one”

The full report describes key areas of inefficiency:

• Data re-entry – data is repeatedly re-entered manually, transferred as PDFs and reformatted across systems
• Lack of workflow orchestration – co-ordination relies on via emails, meetings and sequential tasks rather than being managed as a digital workflow
• Validation Tax – approximately 90% of validation cost is static documentation rather than being data-driven and behaviour-based
• Partnership Value Loss – increasing reliance on CDMO partnerships has added complexity along with non-standard systems and data

At the core of all of this is a persistence of the PDF economy.  Despite operating increasingly sophisticated internal systems, organisations routinely export data into static formats such as PDFs for external communication.  Certificates of Analysis for example are still commonly shared via email as documents.  These files are reviewed, interpreted and often manually re-entered into receiving systems – reintroducing friction into what could be a seamless digital process.

“We generate data digitally, but we exchange it like it’s 1995”

The full report explores the “Psychology of the PDF” and how digital transformation must address trust.

Mindset & Leadership

Digital transformation is often seen as a technology issue, but in reality it is a leadership and operating model challenge – specifically the vacuum of transformation leadership with end-to-end ownership.  Leaders recognise the potential value of a more integrated, data-driven environment.  What is scarce is the combination of deep domain knowledge and digital fluency required to translate that awareness into action.  In the full report, Mark Buswell tells the story of arriving as an experienced consultant to support a team in reducing an 18 month tech transfer timeline.  The team were debating whether they could achieve 16 months, Mark arrived aiming for one.  The shocked reaction highlighted an important leadership insight: a failure of imagination compounded by an organisation that had never been required to ask a different question.

The tech transfer operating model takes on an additional dimension when viewed through a sustainability lens.  John Arthur highlights a critical but often overlooked issue: the fragmentation of sustainability data across the product lifecycle.  Without integrated data flows, organisations cannot effectively measure Scope 3 emissions from their supply chain – representing up to 90% of a product’s carbon footprint – and procurement cannot optimise supplier selection to lower this.  The environmental ambition of leadership may prove to be the catalyst that further drives investment in data flow and integration.

Rethinking the CDMO Ecosystem

When tech transfer extends beyond organisational boundaries, fragmentation intensifies.  The increasing use of CDMOs has transformed the industry, enabling flexibility and scalability.  But it has also introduced new layers of relationship, data and knowledge flow complexity.

Data integrations across sponsor-CDMO boundaries are typically managed on a case-by-case basis – each connection becoming a bespoke effort, resulting in a network of point-to-point integrations that are difficult to scale and maintain.  Economic incentives reinforce the problem: CDMOs operate on tight margins and require clear return on digital investment, while pharma sponsors tend to prioritise speed and cost over long-term data integration.  The result is a digital transformation deadlock. 

The full report cites industry examples of how companies and CDMOs are collaborating together and achieving tangible, quantified results.

The Future State

The future state is about orchestration, not fragmentation – enabling data to flow across the lifecycle, shifting focus from systems to data. Ownership around end to end systems will move from siloes to ecosystems with leaders who own transformation end-to-end. Sustainability is embedded into decision-making from the earliest stages and digitally integrated partnerships – grounded in shared data environments – are built.

To move toward this future state, we identified seven factors critical to success that do not require a complete system overhaul – more of a shift in mindset from systems to data and from projects to platforms:

1. Define a clear data strategy aligned with business outcomes
2. Invest in modern data architecture, including data fabrics and standardised ontologies
3. Pilot digital-to-digital integration with key CDMO partners
4. Utilise existing industry consortia to explore shared platform models that enable ecosystem connectivity
5. Implement workflow orchestration tools to replace email-based coordination
6. Embed sustainability metrics into early-stage process design decisions
7. Evolve validation approaches to support greater agility and iterative development

The organisations that win will not be those with the best systems, but those with the foresight to recognise that as clinical development accelerates – through synthetic data arms, adaptive trial designs and AI-driven discovery – then CMC and Manufacturing execution will become the critical bottleneck.  The organisations that invest now and master data flow will define the next era of pharmaceutical manufacturing.