☰

Luba

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

Strategic priorities

Luba operates across 4 stated priorities, with the most concrete near-term plan anchored on eco-consciousness.

Commitment to achieving 100% plastic-free and biodegradable product portfolio by 2030, transitioning to airlaid paper and cotton nonwoven substrates while maintaining production speed.

Maximizing return on investment from the 2021 production expansion through high-speed operations at 250,000 packages per day with minimal downtime.

Developing 150+ new formulations annually across Baby Care, Medical, Pet, and Household categories, maintaining position as Poland's only dedicated wet wipe specialist.

Challenges we see

  • Operations Manufacturing

    High-Frequency Changeover Management

    With 150 new formulations per year across diverse product categories (Baby Care, Med, Household, Pet), production lines must be frequently stopped, cleaned, and recalibrated. Changing between formulation types involves purging liquid lines and swapping nonwoven rolls.

    Manual changeover management increases time-to-first-good-part, directly eroding capacity gains from high-speed machinery and reducing overall equipment effectiveness.

  • Operations Manufacturing

    Biodegradable Substrate Processing

    The shift toward plastic-free substrates like airlaid paper and cotton nonwovens is mandated by EU SUP Directive requiring 50% plastic reduction by 2025. These materials have lower tensile strength than synthetic blends.

    At high production speeds, web breakage risk is significant with sensitive biodegradable materials, potentially causing hours of unplanned downtime without closed-loop IoT tension control systems.

  • Digital Integration

    R&D-to-Production Tech Transfer Gap

    Luba's R&D department produces one new formulation every 2.4 days, but transition from laboratory formulation to factory-scale run operates as a black box. R&D data on viscosity and absorption rates is kept separate from production floor SCADA data.

    Trial-and-error during first production runs of new products wastes high-cost raw materials and production time, with no digital platform linking lab data to manufacturing parameters.

  • Compliance Regulatory

    Paper-Based GMP/BRC Compliance

    Luba adheres to Good Manufacturing Practice (GMP) and BRC standards through manual paper logs for batch records, cleaning cycles, and temperature checks. Quality managers spend excessive time on manual audit trails.

    Paper-based compliance creates ALCOA+ data integrity gaps, slows audit preparation, and holds back quality managers from focusing on process improvement initiatives.

  • Digital Integration

    IT/OT Infrastructure Fragmentation

    The company's 20.73% revenue growth has outpaced IT infrastructure development. Financial systems (top floor) remain disconnected from PLCs and packaging controllers (shop floor), creating visibility gaps.

    Without real-time cost-per-unit visibility for complex private label runs, leadership cannot make data-driven decisions on production optimization and margin protection.

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. Production Downtime and OEE Visibility

    With 250,000 packages daily capacity and 150+ formulations, unplanned downtime and hidden inefficiencies erode the ROI of the 2021 production expansion. No unified dashboard exists to track Overall Equipment Effectiveness across high-speed lines.

    Implement IoT-based OEE monitoring through OPC UA data acquisition from existing PLCs, providing real-time visibility into changeover times, micro-stoppages, and equipment availability to open hidden capacity.

  2. Formulation Data Silos

    R&D develops 150+ formulations annually but data on viscosity, absorption rates, and chemical properties is stored separately from production SCADA data. This disconnect causes trial-and-error during scale-up, wasting materials and time.

    Deploy a Digital Lab platform that integrates lab equipment data with production systems, creating a digital thread from formulation development to manufacturing parameters for first-time-right production runs.

  3. Manual Compliance Documentation

    GMP and BRC compliance relies on manual paper logs for batch records, cleaning cycles, and temperature checks. Quality managers spend hours auditing paper trails instead of driving process improvement.

    Implement paperless manufacturing with automated, sensor-driven batch records that capture data directly from equipment, ensuring ALCOA+ compliance while freeing quality staff for value-added activities.

  4. Foam Management in Lotion Mixing

    Chemical mixing of wipe lotions, especially antibacterials and detergents for Comfort and Med lines, generates foam that causes inaccurate volume filling and messy packaging. Current anti-foam chemicals are expensive and affect Natural branding.

    Deploy computer vision-based foam detection as a non-invasive watchdog system to autonomously manage antifoam dosing, reducing chemical costs while increasing production speed and maintaining eco-friendly positioning.

  5. Substrate Tension Control for Biodegradables

    Transitioning to biodegradable airlaid paper and cotton nonwovens requires precise tension control at high speeds. These materials have variable loft and absorbency between rolls, and web breakage causes hours of downtime.

    Implement AI-driven closed-loop tension control using digital twin modeling of substrate behavior, combined with real-time sensors for roll weight and moisture, enabling reliable high-speed processing of eco-friendly materials.

What we'd propose

  • Enterprise AI

    Industrial Data Platform for OEE Optimization

    Deploy a unified data acquisition and visualization platform that connects high-speed production lines to real-time OEE dashboards, enabling data-driven identification of downtime sources and capacity optimization opportunities.

    • 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 Lab for Formulation Lifecycle Management

    Implement a digital platform that captures R&D formulation data and integrates it with production systems, eliminating the black box between laboratory development and factory-scale manufacturing.

    • 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

    Paperless Manufacturing for GMP/BRC Compliance

    Replace manual paper-based compliance documentation with automated sensor-driven batch records, ensuring ALCOA+ data integrity while streamlining audit preparation and quality management workflows.

    • 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

    Computer Vision Foam Detection System

    Deploy a non-invasive computer vision watchdog system for lotion mixing tanks that autonomously monitors and controls foam levels, reducing antifoam chemical usage while preventing packaging quality issues.

    • 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

    AI-Driven Substrate Tension Control

    Implement closed-loop tension control system using digital twin modeling and AI algorithms to enable reliable high-speed processing of biodegradable airlaid paper and cotton nonwovens without web breakage.

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

Source: A4BEE analysis of public sources
Data Integration 25 → 75
IT/OT fragmentation between financial systems and shop floor; R&D data siloed from production SCADA; no unified data platform
Process Automation 45 → 80
High-speed machinery installed in 2021 but changeover management and quality checks remain manual; no closed-loop control
Real-Time Visibility 30 → 85
Leadership lacks cost-per-unit visibility; no OEE dashboards; reactive rather than predictive maintenance approach
Compliance Digitalization 20 → 75
Paper-based GMP/BRC documentation; manual batch records and audit trails; high risk of transcription errors
Advanced Analytics 15 → 70
No predictive analytics for downtime or quality; formulation optimization relies on trial-and-error; no digital twin capabilities
Workforce Enablement 35 → 75
No CIO/CTO role; digital decisions centralized under leadership; scientists manage data manually; need for digital operator training

Check this yourself

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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 Luba, 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].