Software-as-a-Service

Vishal Prasad at Mareana

How are digital innovations offering a new way of working for contract development and manufacturing organisations?

In the pharma industry, ‘virtual’ is often equated with ‘fragmented’. While the asset-light model provides capital efficiency, it frequently creates a transparency tax that erodes operational sovereignty. When manufacturing is outsourced, the critical data required for good practice compliance remains trapped within the siloed systems of various contract development and manufacturing organisations (CDMOs). Sponsors do not just lose access to granular data; they lose the context of the process itself. They surrender real-time visibility, forced to rely on periodic, summarised reports that obscure the true health of their production cycle.

This trade-off has become a significant liability. To regain control, leaders are adopting a digital control layer with the help of artificial intelligence (AI). This foundation establishes digital symmetry between sponsors and their decentralised CDMO networks. It acts as a continuous bridge between the sponsor’s quality standards and the CDMO’s execution, ensuring that operational distance does not result in a loss of control. By synthesising fragmented data into a unified narrative, this layer transforms the relationship from one of passive oversight to one of active orchestration.

Virtual pharma executives must prove control without owning the factory, and regulators hold the sponsor accountable for all quality standards. Visibility remains the primary hurdle. The US Food and Drug Administration (FDA) links over 60% of drug shortages to manufacturing quality failures.1

These risks often stay hidden from sponsors until a crisis occurs. Legacy sponsor-CDMO relationships rely on lagged observation. Companies wait weeks for batch records and then transcribe data into spreadsheets, hoping for audit compliance. This is not control. It is a strategic risk.

Modern leaders use batch genealogy to bridge the sponsor-partner divide, which creates a real-time digital mirror of the physical manufacturing process. By ingesting and harmonising data from disparate CDMO enterprise resource planning, laboratory information management system and even paper records, the system maps parent-child relationships across the entire production network. This provides a single, query-ready lineage that ensures credibility is a verifiable reality, backed by immutable evidence rather than blind trust.

The virtual model creates heavy cycle-time friction. Reports suggest that global pharma inventory write-offs average 4% annually, which represents $11bn in lost product.2 Variability and quality failures drive these costs. In virtual models, product sits in quarantine for 45 to 60 days. This manual quality assurance (QA) review traps working capital and slows innovation.

Exception-based review fundamentally changes the productivity of a sponsor’s quality unit when overseeing CDMO production. In traditional manual workflows, a sponsor’s QA team must scrutinise every page of a batch record sent by the partner, irrespective of whether the data is correct. AI automates this verification, checking hundreds of parameters against specifications in seconds and only flagging the data that contains true anomalies or errors. By shifting the focus from ‘checking everything’ to ‘reviewing exceptions’, sponsors drastically increase throughput and reduce the risk of human oversight, significantly accelerating the batch release cycle. This shift accelerates the order-to-cash cycle; speed hedges against supply chain volatility.

Virtual pharma firms are lean; small teams manage massive global workloads and headcount limits organisational intelligence. Due to this, PwC research suggests 80% of process deviations stem from human factors in documentation.3

Agentic co-pilots act as force multipliers. These systems leverage graph retrieval-augmented generation (GraphRAG), an architecture that combines the reasoning power of large language models with the structured precision of a knowledge graph. Unlike general AI that might hallucinate, GraphRAG restricts the AI’s ‘knowledge’ to the validated relationships within your batch genealogy. This ensures that every answer is factually grounded and provides a clear audit trail back to the source data.

For example, a chief operating officer can ask why Site A yield is drifting below the validated baseline. They then receive a root-cause analysis in seconds. AI removes the headcount bottleneck, allowing lean teams to perform deep-dive investigations that previously required weeks of manual data synthesis.

The virtual model for pharma manufacturing is no longer a compromise. Sponsors can insource intelligence while outsourcing assets. This creates agility that vertically integrated giants cannot match. A five-person team can now orchestrate a global network with precision. Leaders should not ask how much they can save by outsourcing; they should ask how much control they can regain through a digital layer. Digital orchestrators will define the next generation of the industry.

References:

1. Visit: fda.gov/media/131130/download

2. Visit: nventic.com/insights/inventory-write-offs-inpharmaceutical-manufacturing

3. Visit: pwc.be/en/news-publications/2025/reducinghuman-error-in-the-pharma-quality-environment.html

Mr Vishal Prasad, co-founder and chief product officer of Mareana, has over 30 years of industry experience and a broad background in design, development, architecture and management. He is adept at natural language processing and statistical analysis to solve multiple business problems in pharma manufacturing, supply chain and sustainability.