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Getinge

Updating the operating model

Industry
Pharmaceuticals
Public information as of
January 2026

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

Strategic priorities

Getinge operates across 4 stated priorities, with the most concrete near-term plan anchored on digital therapies & smart workflows.

Transitioning from standalone hardware to integrated, data-driven clinical ecosystems with IoMT solutions like TwinView and FleetView platforms enabling remote monitoring and predictive maintenance.

Targeting 6-10% organic growth in Life Science segment through biopharma recovery, single-use bioprocessing via HPNE acquisition, and 25% faster drug delivery through lab digitalization.

Deploying the Automatiq robotic platform (Envoy, Depot, Loaders, Hub) to address CSSD staffing crises and reduce instrument turnaround time while enabling "one more surgery per OR per day."

Challenges we see

  • Compliance Regulatory

    FDA Quality Remediation Backlog

    recalls affecting vaporizer and EVH systems have paused promotional activity for the Cardiosave and Cardiohelp systems, in line with FDA recommendations.

    Legacy quality management systems could not simulate real-world edge cases during R&D and manufacturing phases, leading to reactive rather than predictive quality control.

  • Digital Integration

    M&A Integration Technology Debt

    Aggressive acquisitions (Paragonix, Healthmark, HPNE, Fluoptics, Talis Clinical) created heterogeneous IT landscapes with information silos and incompatible software stacks requiring integration into myGetinge portal.

    Fragmented data environments across acquired entities impede unified customer experience and real-time operational visibility, delaying synergy realization.

  • Digital Operations

    SAP S/4HANA Migration Complexity

    Transition from legacy SAP ECC to S/4HANA must be completed before 2027 maintenance deadline, requiring parallel system operation and significant data cleansing across global operations.

    Decades of "customization debt" with bespoke code conflicts with SAP's "Clean Core" strategy, limiting ability to use real-time data for financial and operational decisions.

  • Operations Manufacturing

    Joining records across systems

    Life Science segment laboratories operate in fragmented data environments with "paper-on-glass" digital forms that inhibit effective decision-making and metadata generation for FDA 21 CFR Part 11 compliance.

    Manual processes create information sharing gaps between analytical development and quality control, slowing time-to-market for pharmaceutical partners by an estimated 25%.

  • Digital Regulatory

    IT/OT Cybersecurity Convergence Risk

    Connected medical devices (ventilators, anesthesia machines, bypass pumps) classified as critical infrastructure require integration into hospital IT environments while maintaining highest cybersecurity standards under EU NIS 2 Directive.

    Security breaches in IT networks can potentially spread to OT networks controlling life-saving devices, with legacy systems lacking "secure-by-design" frameworks for products like TwinView.

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. Quality Management System Modernization

    Quality management has been largely reactive, with legacy systems unable to simulate edge cases during the R&D phase. The 90% quality record backlog reduction was achieved through reactive remediation rather than predictive prevention.

    Implement a "Digital Quality by Design" framework utilizing predictive modeling and digital twins to stress-test medical hardware before clinical deployment, transforming quality from a cost center to a competitive differentiator.

  2. Sterile Reprocessing Automation Gap

    High instrument turnaround time and staffing crisis in Central Sterile Supply Departments (CSSDs) limit surgical throughput. The Automatiq platform hardware exists but lacks sophisticated middleware to integrate with hospital IT infrastructure and MES systems.

    Provide the orchestration layer that connects Automatiq Hub to hospital IT infrastructure through sophisticated middleware and API management, enabling "lighthouse" deployments in Getinge's own facilities.

  3. Bioprocessing Data Fragmentation

    The Life Science segment's HPNE acquisition created single-use bioprocessing capabilities but lacks unified digital platform integration. Laboratories use "paper-on-glass" systems that generate no granular metadata.

    Develop a "Single-Use Ecosystem" digital platform using digital twins to simulate bioprocesses from discovery to commercial launch, integrating GEW 888 neo cGMP washer IO-Link data with enterprise MES systems.

  4. Connected Device Security Architecture

    Servo TwinView and FleetView platforms increase connectivity but create cybersecurity vulnerabilities. Converged IT/OT environments lack unified risk management as EU NIS 2 Directive mandates integration of OT systems into cross-organizational security concepts.

    Design a Hybrid Security Operations Center (SOC) monitoring both IT and OT environments with AI/ML anomaly detection, providing "Security-by-Design" frameworks for new product launches like Vasoview Hemopro 3.

  5. Manufacturing Predictive Maintenance Gaps

    Getinge's global manufacturing footprint across France, China, Germany, Poland, Sweden, Turkey, Netherlands, UK, and US lacks Industry 4.0 smart factory capabilities needed to achieve 2028 margin targets of 16-19%.

    Expand IO-Link predictive maintenance strategies across production lines, implementing machine learning algorithms analyzing vibration, heat, and cycle-time data to maximize Overall Equipment Effectiveness (OEE).

What we'd propose

  • Enterprise AI

    Digital Quality by Design Platform

    Implement a predictive quality management framework that uses digital twins and advanced analytics to simulate medical device behavior under edge-case conditions, preventing recalls before products reach clinical use.

    • 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 CDMO

    Automatiq Orchestration Layer Development

    Design and implement the middleware and API management layer that enables Automatiq robotics (Envoy, Depot, Loaders) to smooth integrate with hospital MES, ERP systems, and existing IT infrastructure.

    • 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
  • Digital Lab

    Life Science Digital Platform Unification

    Create an integrated digital ecosystem connecting HPNE single-use bioprocessing assets, GEW 888 neo cGMP washers, and laboratory instruments into a unified platform with FDA 21 CFR Part 11 compliance.

    • 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

    Hybrid IT/OT Security Operations Center

    Design and deploy a unified Security Operations Center monitoring both information technology and operational technology environments for connected medical devices, ensuring EU NIS 2 compliance.

    • 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
  • Digital CDMO

    Smart Factory Predictive Maintenance Platform

    Deploy Industry 4.0 predictive maintenance capabilities across Getinge's global manufacturing footprint using sensor data, machine learning, and real-time analytics to maximize equipment effectiveness.

    • 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

Digital maturity: today and target

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

Source: A4BEE analysis of public sources
Data Integration 45 → 85
M&A acquisitions created heterogeneous IT landscapes with information silos; SAP S/4HANA migration in progress but customization debt limits real-time data leverage
Process Automation 55 → 90
Automatiq robotics launched but lacks middleware integration; IO-Link sensors deployed but not connected to enterprise MES; paper-based processes persist in Life Science labs
Predictive Analytics 35 → 80
Quality management remains reactive despite 90% backlog reduction; predictive maintenance limited to basic IO-Link monitoring; no digital twin deployment for product validation
Connected Products 60 → 90
TwinView and FleetView platforms operational but IT/OT security architecture incomplete; NIS 2 compliance gaps; myGetinge portal not fully integrated with acquired company systems
Cybersecurity Maturity 40 → 85
Critical infrastructure devices lack secure-by-design frameworks; no unified IT/OT SOC; legacy systems vulnerable to lateral movement attacks from IT networks
Sustainability Tech 50 → 85
Net Zero 2030 targets set but smart utility monitoring not deployed at scale; digital product passports not implemented; sustainable materials adoption (DPTE-BetaBag) early stage

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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 Getinge, 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].