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OrlenPołudnie

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

Strategic priorities

OrlenPołudnie operates across 4 stated priorities, with the most concrete near-term plan anchored on bioethanol ii generation leadership.

Pioneer second-generation bioethanol production in Poland using agricultural residue (straw) to meet EU Fit for 55 regulatory requirements and National Indicative Target compliance.

Transform from traditional oil refining and waste oil regeneration to a closed-loop biorefinery model that maximizes resource value while minimizing waste through biomass cogeneration and biogas production.

Achieve operational energy independence through a 48 MW biomass power plant, 40 MW photovoltaic farm, and integrated biogas facility to reduce carbon footprint and production costs.

Challenges we see

  • Digital Manufacturing

    Legacy Asset Digital Integration

    The Jedlicze facility operates extensive legacy infrastructure for oil regeneration and solvent production that lacks native digital communication capabilities, creating data silos that prevent unified operational visibility.

    Inability to implement predictive maintenance across the entire asset base results in unplanned downtime and suboptimal equipment lifecycle management, directly impacting OPEX in a period of financial pressure from PLN 1.2 billion asset writedowns.

  • Operations Integration

    IT/OT Convergence Security

    The new B2G complex and 48 MW cogeneration plant must integrate with existing DCS and SCADA systems while maintaining strict industrial security standards per NIS2 directive and IEC 62443 requirements.

    Where network segmentation between the PCN (Process Control Network) and enterprise IT systems is not tightly defined, critical production processes carry cybersecurity risks, with potential for production disruption or regulatory compliance gaps.

  • Digital Operations

    Biotechnology Laboratory Digitalization

    The B2G complex requires 50+ specialized biotechnologists who need real-time access to fermentation parameters, VCD measurements, and process KPIs that are currently tracked manually in Excel spreadsheets.

    Manual data handling in biological processes creates data islands that slow optimization cycles, increase error rates, and hold back the rapid response needed to maintain bioethanol yield targets of 25,000 tons annually.

  • ESG Regulatory

    ESG Reporting Automation

    ORLEN Group's 2024-2030 Sustainability Strategy and CSRD compliance require comprehensive tracking of emissions, water usage, and waste across the Jedlicze biorefinery operations including the new biomass and photovoltaic installations.

    Manual collection of environmental data from distributed systems is time-consuming and error-prone, and makes calculating the carbon footprint of each bioethanol batch required for regulatory certification and market positioning harder to achieve.

  • Operations Manufacturing

    Bioprocess Continuous Production Stability

    Second-generation bioethanol production from straw involves continuous fermentation processes where interruptions destabilize biological activity in bioreactors, requiring sophisticated monitoring beyond traditional refinery controls.

    Without real-time foam detection, automated KPI calculation, and predictive analytics, process deviations can lead to batch losses, enzyme waste, and make reaching the design capacity needed for investment payback harder to achieve.

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. Fragmented Legacy Equipment Data

    Existing pumps, centrifuges, and mixers in oil regeneration and solvent production lines lack IoT connectivity, preventing end-to-end visibility into equipment health and energy consumption patterns across the Jedlicze facility.

    Deploy retrofitting solutions with IoT sensors and edge gateways to connect legacy assets to a central Industrial Data Platform, enabling predictive maintenance that the CEO has identified as a strategic priority.

  2. Disconnected Bioprocess Laboratories

    New biotechnology teams will rely on offline analytical instruments and manual Excel tracking for critical parameters like Viable Cell Density, preventing real-time process optimization and creating compliance risks.

    Implement integrated Digital Lab solutions connecting analyzers (Roche, Beckman Coulter) to unified dashboards with automated KPI calculations, enabling scientists to optimize bioethanol yield without IT intervention.

  3. Manual ESG Data Compilation

    Sustainability reporting requires aggregating data from the 48 MW cogeneration plant, 40 MW PV farm, biogas facility, and production processes manually, consuming significant time and creating audit risks under CSRD requirements.

    Build an ontology-based Industrial Data Platform with automated data pipelines that continuously calculate carbon footprint per batch, enabling dynamic ESG compliance and supporting ORLEN Group's 2030 sustainability targets.

  4. Cybersecurity Gaps in IT/OT Integration

    Connecting the new B2G complex DCS systems with enterprise IT for data analytics creates security vulnerabilities in the Process Control Network, risking production disruption and NIS2 non-compliance.

    Implement secure IT/OT convergence architecture using OPC UA communication backbone with Zero Trust principles and IEC 62443-compliant network segmentation, enabling safe data flow from sensors to dashboards.

  5. Bioreactor Foam Management

    Fermentation of straw-based feedstock generates unpredictable foam that can block filters and contaminate equipment, traditionally requiring constant operator observation and manual antifoam dosing.

    Deploy computer vision systems for non-invasive 24/7 foam monitoring with autonomous antifoam dosing control, eliminating human error risk and enabling the Lights Out operational model for continuous bioethanol production.

What we'd propose

  • Digital CDMO

    Industrial IoT Retrofitting for Legacy Assets

    Deploy IoT sensors and edge computing solutions to connect legacy refinery equipment to a central data platform, enabling predictive maintenance and operational visibility across the Jedlicze facility's established production lines.

    • 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

    Integrated Digital Laboratory Platform

    Transform biotechnology laboratory operations by connecting analytical instruments to a unified data platform with automated KPI calculations, enabling real-time process optimization for the B2G bioethanol production complex.

    • 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

    ESG Data Platform and Carbon Footprint Automation

    Build an ontology-based Industrial Data Platform that automatically aggregates environmental data from production assets, energy systems, and utilities to enable real-time ESG reporting and per-batch carbon footprint calculation.

    • 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

    Secure IT/OT Convergence Architecture

    Design and implement a cybersecurity-compliant integration architecture that enables safe data flow between operational technology systems and enterprise IT platforms while maintaining industrial network isolation and regulatory 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 Lab

    Computer Vision Bioreactor Monitoring

    Deploy non-invasive camera-based monitoring systems with AI-powered foam detection and autonomous control algorithms to ensure continuous bioethanol production stability without operator intervention.

    • 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 OrlenPołudnie's own published ambition implies — not a perfect score.

Source: A4BEE analysis of public sources
Data Integration 35 → 80
Legacy refinery assets operate as data silos; new B2G complex offers greenfield opportunity for unified data architecture
Process Automation 40 → 85
Traditional refinery controls established but biological processes require advanced closed-loop automation for continuous fermentation
Predictive Analytics 25 → 75
CEO prioritizes predictive maintenance but current implementation limited; ML-based anomaly detection not yet deployed across asset base
IT/OT Security 45 → 90
Basic network segmentation exists but NIS2 and IEC 62443 compliance requires comprehensive Zero Trust architecture implementation
ESG Reporting 30 → 85
Manual data collection for sustainability metrics; CSRD deadline requires automated carbon footprint calculation per batch
Laboratory Digitalization 20 → 80
New biotech team will need fully integrated digital lab; current state relies on Excel and manual instrument data capture

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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 OrlenPołudnie, 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].