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Novonesis

Updating the operating model

Industry
Biotechnology
Public information as of
January 2026

A4BEE prepared this analysis from publicly available sources. It reflects our own reading of Novonesis's published strategy and is not endorsed by, or produced in cooperation with, Novonesis. Company website

Strategic priorities

Novonesis operates across 4 stated priorities, with the most concrete near-term plan anchored on 6-9% organic sales cagr.

Accelerating innovation pipeline with 45+ new product launches annually and expanding into high-growth biosolutions markets for food, health, and planetary sustainability.

Realizing cost and sales synergies from the Novozymes-Chr. Hansen merger through standardized operations and digital efficiency gains.

Reducing Scope 1 and 2 emissions by 75% by 2030 through energy monitoring, process optimization, and sustainable manufacturing practices.

Challenges we see

  • Digital Integration

    Post-Merger ERP Fragmentation

    The 2024 merger of Novozymes and Chr. Hansen created a combined entity with disparate financial and supply chain systems that currently lack unified visibility across the organization.

    EUR 60 million ERP investment signals critical operational silos that delay synergy realization and accurate CSRD compliance reporting.

  • Operations Manufacturing

    Yield Variability in Fermentation

    Large-scale fermentation of enzymes, probiotics, and HMOs across 30+ production sites depends on precise parameter control where minor deviations cause batch failures.

    Legacy equipment lacks real-time KPI visualization, leading to "lost batches," high manufacturing downtime, and inability to achieve the "Golden Batch" consistency.

  • Digital Operations

    Digital Maturity Gap in R&D Labs

    The Lyngby Innovation Campus and global R&D facilities house 2,000 researchers but 57% of staff cite "lack of knowledge" as a barrier to digital progress.

    Reliance on "Excel Islands" and manual data transfers between air-gapped lab equipment introduces data integrity risks and slows the prototype-to-production pipeline.

  • Operations Manufacturing

    IT/OT Divergence Across Sites

    Most production sites utilize legacy SCADA systems that do not communicate with enterprise cloud layers, preventing real-time AI-driven process control.

    The absence of a standardized OPC UA communication backbone blocks the deployment of predictive analytics and increases cybersecurity exposure across converged facilities.

  • Compliance Regulatory

    CSRD Compliance and ESG Reporting

    The 2024 Annual Report was Novonesis's first prepared under EU CSRD requirements, mandating granular real-time energy and water monitoring at every machine.

    Legacy factories lack automated data capture for Scope 1 and 2 emissions, risking regulatory Compliance gaps and reputational damage to sustainability commitments.

Opportunities, by urgency and business impact

Each bubble is one opportunity, numbered to match the list below. Further right means it bites sooner; higher means a bigger effect on the business. A bigger bubble means a bigger implementation effort.

Source: A4BEE analysis of public sources

  1. Batch Failure Reduction

    Fermentation processes are inherently unstable, with yield variability causing "lost batches" and high manufacturing downtime across 30+ global production sites.

    Implement real-time KPI visualization and "Golden Batch" intelligence using digital twins to enable proactive process control and reduce batch failures.

  2. Lab Data Integrity and Automation

    57% of R&D staff cite "lack of knowledge" as a digital barrier, with scientists using manual Excel workflows and paper notebooks that create data silos and audit risks.

    Deploy integrated lab digitalization platforms to automate data capture, eliminate manual transcription, and accelerate the 45+ annual innovation launches.

  3. IT/OT Connectivity Standardization

    Legacy SCADA systems at production sites do not communicate with enterprise cloud layers, blocking real-time process intelligence and predictive maintenance.

    Establish OPC UA-based communication backbone to bridge IT/OT gap and enable AI-driven process optimization across all fermentation facilities.

  4. ESG Data Automation for CSRD

    CSRD compliance requires granular, real-time energy and water monitoring at every machine, but legacy factories lack automated data pipelines to ESG dashboards.

    Build industrial data platforms that automate the "Data work" from shop-floor sensors to sustainability reporting dashboards for Scope 1/2 compliance.

  5. Biomanufacturing Workforce Digitalization

    Leadership acknowledges "urgent need" to strengthen the biomanufacturing workforce pipeline, with 3.8 million new manufacturing jobs projected over the next decade requiring digital skills.

    Transform passive operators into "Digital Operators" through structured training, UX-driven interfaces, and change management programs that bridge the "Scientist-Algorithm Gap."

What we'd propose

  • Enterprise AI

    Real-Time Fermentation Intelligence Platform

    Deploy industrial data platform with advanced KPI visualization and "Golden Batch" comparison capabilities to enable proactive process control across fermentation operations.

    • Ontology layer

      A shared semantic model so lab, process, and quality data describe the same things the same way.

      DETAIL

    • Predictive models

      Models trained on the historical data plane that flag deviations before they become scrap.

      DETAIL

    • Decision surfaces

      Single pane of glass that surfaces model output to the right role at the right moment.

      DETAIL

    • Shorter lead time from data capture to decision.
    • Records that audit on their own, not on inspection day.
    • Scale without adding the same headcount.

    We've done this before

    See how we do Enterprise AI
  • Digital Lab

    Laboratory Digitalization and Integration

    Modernize R&D lab operations by integrating equipment, automating data capture, and eliminating manual workflows to accelerate innovation pipeline and ensure data integrity.

    • Unified data backbone

      Connect instruments and LIMS into a single data spine so QC and CDMO records are queryable across sites.

      DETAIL

    • Paperless workflows

      Move lab execution from paper to instrument-captured records with full audit trail.

      DETAIL

    • Continuous QC release

      Review-by-exception dashboards that flag only the records needing scientist attention.

      DETAIL

    • Shorter lead time from data capture to decision.
    • Records that audit on their own, not on inspection day.
    • Scale without adding the same headcount.

    We've done this before

    See how we do Digital Lab
  • Digital CDMO

    IT/OT Convergence Architecture

    Establish secure, standardized communication infrastructure that bridges shop-floor operational technology with enterprise IT systems to enable real-time process intelligence.

    • OT/IT convergence

      Pull sensor and controller data off the line into a shared data plane in real time.

      DETAIL

    • Batch intelligence

      Golden-batch comparison and deviation detection running on the same data plane.

      DETAIL

    • Production release flow

      Closed-loop between QA, MES, and ERP so batch record review and release follow the data, not the paperwork.

      DETAIL

    • Shorter lead time from data capture to decision.
    • Records that audit on their own, not on inspection day.
    • Scale without adding the same headcount.

    We've done this before

    See how we do Digital CDMO
  • Enterprise AI

    ESG Data Platform for CSRD Compliance

    Build automated data pipelines from shop-floor sensors to sustainability dashboards to enable real-time energy and emissions monitoring for regulatory compliance.

    • Ontology layer

      A shared semantic model so lab, process, and quality data describe the same things the same way.

      DETAIL

    • Predictive models

      Models trained on the historical data plane that flag deviations before they become scrap.

      DETAIL

    • Decision surfaces

      Single pane of glass that surfaces model output to the right role at the right moment.

      DETAIL

    • Shorter lead time from data capture to decision.
    • Records that audit on their own, not on inspection day.
    • Scale without adding the same headcount.

    We've done this before

    See how we do Enterprise AI
  • Digital Lab

    Digital Operator Transformation Program

    Deploy comprehensive change management and training program to transform traditional operators into digitally-enabled workforce capable of use advanced automation systems.

    • Unified data backbone

      Connect instruments and LIMS into a single data spine so QC and CDMO records are queryable across sites.

      DETAIL

    • Paperless workflows

      Move lab execution from paper to instrument-captured records with full audit trail.

      DETAIL

    • Continuous QC release

      Review-by-exception dashboards that flag only the records needing scientist attention.

      DETAIL

    • Shorter lead time from data capture to decision.
    • Records that audit on their own, not on inspection day.
    • Scale without adding the same headcount.

    We've done this before

    See how we do Digital Lab

Digital maturity: today and target

Scored out of 100 across six dimensions. The target is what Novonesis's own published ambition implies — not a perfect score.

Source: A4BEE analysis of public sources
Data Integration 35 → 80
ERP fragmentation and legacy SCADA silos require EUR 60M investment to unify; disparate systems across former Novozymes and Chr. Hansen operations
Process Automation 40 → 85
Manual data entry and Excel-based workflows persist in labs; 57% cite knowledge gaps; fermentation control lacks real-time optimization
Analytics Capability 45 → 90
Strong R&D data generation but insights delivered days post-experiment; Golden Batch comparison not yet operationalized at scale
IT/OT Convergence 30 → 75
Legacy SCADA systems disconnected from enterprise cloud; OPC UA standardization not deployed across 30+ production sites
Workforce Digital Skills 35 → 70
Urgent workforce development need acknowledged; Digital Operator training programs required to support 3.8M projected new manufacturing roles
Sustainability Monitoring 40 → 85
CSRD compliance first achieved in 2024; granular machine-level energy monitoring needed for 75% emission reduction target by 2030

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Related reading

This is an independent analysis prepared by A4BEE from publicly available information as of January 2026. It reflects A4BEE's own interpretation and opinion, is not affiliated with, endorsed by, or verified with Novonesis, and may be incomplete or inaccurate. All company names and trademarks are the property of their respective owners. To request a correction or removal, contact [email protected].