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Polfarmex

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 Polfarmex's published strategy and is not endorsed by, or produced in cooperation with, Polfarmex. Company website

Strategic priorities

Polfarmex operates across 4 stated priorities, with the most concrete near-term plan anchored on quality firewall.

Restore and guarantee absolute product safety following the Furosemidum/Nasen recall through automated inspection and verification systems to strengthen resilience against future quality issues.

Successfully transition from legacy generics to high-complexity biologics by bringing human insulin analogues from R&D to pilot to production scale with Mabion-grade digital maturity.

Grow contract manufacturing business by attracting international clients through radical transparency and real-time production visibility dashboards.

Challenges we see

  • Quality Manufacturing

    Telling products apart on the packaging line

    A packaging mix-up between the Furosemidum and Nasen lines led to a product recall, with regulatory attention from the Chief Pharmaceutical Inspector (GIF).

    Where line clearance and blister checks during a changeover rest on a manual step, telling one product's blister from another depends on operator attention at that moment; reading what is actually on the line makes that check automatic.

  • Digital Integration

    IT/OT Architecture Fragmentation

    Polfarmex operates SAP as the financial ERP alongside the INTENSE Platform for workflows, creating bifurcated data silos where operational data does not flow automatically into business systems.

    Humans serve as "middleware" manually typing data between screens, creating data entry bottlenecks, transcription errors, and delayed visibility into production status.

  • Operations Manufacturing

    Legacy Equipment Black Box Problem

    The 84,000 m2 Kutno plant contains equipment spanning three decades; while the 2022 blistering line is smart, 1990s-era fluid bed dryers and mixers are "dumb" assets with no data connectivity.

    You can only optimize what you can measure; legacy machines do not report temperature, vibration, or pressure data to central systems, holding back predictive maintenance and OEE optimization.

  • Compliance Regulatory

    Biologics Process Complexity

    Polfarmex is developing human insulin analogues, requiring transition from simple chemical synthesis to highly sensitive biological processes where slight variances in bioreactor parameters can ruin batches.

    Traditional "quality by testing" approach is insufficient for biologics; they need "Quality by Design" with real-time process monitoring, ALCOA+ data integrity, and EMA-compliant digital lab infrastructure.

  • Operations Operations

    Labor Scalability Constraints

    With 700+ employees and aggressive capacity expansion through new packaging lines, Polfarmex faces labor constraints in the competitive Kutno industrial hub where skilled workers are finite.

    Scaling production linearly with headcount is inefficient; manual palletizing, data entry, and machine tending create bottlenecks and increase human error on critical pharmaceutical operations.

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. Automated Quality Inspection Gap

    The Furosemidum/Nasen recall indicated that manual inspection and legacy sensors are largely reactive against cross-contamination during high-speed packaging operations with multiple product changeovers.

    Deploy AI-powered computer vision systems integrated with automated line clearance to scan every blister before insertion, implementing "Positive Release" logic that halts packaging automatically on mismatch detection.

  2. Shop Floor Data Silos

    Production data from machines, batch completion signals, and lab results do not flow automatically into SAP or INTENSE, requiring manual transcription that introduces delays and errors.

    Deploy an Industrial Data Platform that sits between Shop Floor (Machines), INTENSE (Workflow), and SAP (Finance) to automate data flow using OPC UA protocols and eliminate manual data-entry bottlenecks.

  3. Legacy Asset Connectivity

    1990s-era equipment operates as black boxes without connectivity, preventing visibility into machine status, performance metrics, and predictive maintenance opportunities across the aging equipment base.

    Retrofit legacy PLCs and machines with IoT gateways using control board technology to extract signals without replacing multimillion-dollar equipment, enabling OEE optimization and predictive maintenance.

  4. Bioprocess Digital Twin Absence

    Insulin biosimilar development requires managing non-linear biological processes where slight parameter variances can ruin batches, but current systems lack predictive modeling and simulation capabilities.

    Implement Digital Twins for insulin bioreactors to simulate runs and predict batch outcomes before physical production, accelerating time-to-market and reducing expensive wet-lab failures.

  5. CDMO Client Transparency

    Contract manufacturing clients demand real-time visibility into their production orders, but Polfarmex lacks digital dashboards to provide external partners with transparent status updates, quality checks, and inventory data.

    Build secure cloud-based client portals where CDMO customers can view real-time production status, quality checks, and inventory, differentiating Polfarmex from opaque low-cost competitors.

What we'd propose

  • Digital CDMO

    AI-Powered Vision Inspection System

    Deploy computer vision and AI-based anomaly detection on packaging lines to verify pharmacological codes on every blister against cartons, implementing automated line clearance with positive release logic.

    • 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

    Industrial Data Platform for IT/OT Convergence

    Build middleware layer connecting shop floor machines, INTENSE workflow system, and SAP ERP to automate data flow and eliminate manual transcription between operational and business systems.

    • 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

    Legacy Equipment Retrofitting Program

    Deploy IoT gateways and control board technology to extract operational data from legacy PLCs and equipment without replacing existing machines, enabling predictive maintenance and OEE optimization.

    • 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

    Digital Lab for Biosimilar Development

    Implement Digital Twin technology and GxP-compliant data infrastructure for the insulin biosimilar laboratory, enabling process simulation, ALCOA+ data integrity, and accelerated EMA regulatory submissions.

    • 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
  • Enterprise AI

    CDMO Transparency Dashboard

    Build secure cloud-based client portal enabling Polfarmex's contract manufacturing customers to view real-time production status, quality metrics, and inventory levels for their orders.

    • 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 maturity: today and target

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

Source: A4BEE analysis of public sources
Data Integration 35 → 80
SAP and INTENSE operate in silos; shop floor data requires manual transcription; no unified data platform connects OT to IT systems
Quality Automation 25 → 90
Packaging recall reflected reliance on manual inspection; no computer vision or automated line clearance deployed on critical packaging lines
Legacy Connectivity 30 → 75
1990s equipment runs as closed black boxes while the modern 2022 lines are smart but not yet integrated with it; retrofitting the older assets closes that distance.
Lab Digitalization 40 → 85
Biotech lab established but lacks Digital Twin, LIMS integration, and ALCOA+ compliance infrastructure needed for insulin biosimilar development
Cloud & Analytics 25 → 70
No client-facing dashboards for CDMO transparency; limited predictive analytics; data remains locked in local systems without cloud accessibility
Process Automation 45 → 80
New packaging lines installed but manual palletizing and machine tending persist; AR/VR training and cobot deployment opportunities unrealized

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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 Polfarmex, 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].