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Labialfarma

Making the shop floor visible to SAP in real time

Industry
Pharmaceutical and Nutraceutical CDMO
Headquarters
Mortágua, Portugal
Public information as of
January 2026

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

Strategic priorities

Labialfarma is a pharmaceutical and nutraceutical contract manufacturer operating from Mortágua, Portugal, as part of the Ferraz Group. Founded in 1981, the company differentiates itself through proprietary dosage forms including SmartPACKAGING, Nutrigummies, and Duolayer tablets, and is investing approximately EUR 4.5 million in a facility expansion designed to operate as a Born Digital production environment. It also holds a medicinal cannabis license for cultivation, manufacture, and export, with Infarmed as the national regulator.

The immediate operational challenge is an IT/OT air gap: despite significant investment in SAP S/4HANA Private Cloud, the shop floor remains disconnected from the ERP layer. Production orders are printed and executed manually, with results typed back into SAP eight to twelve hours later. The cannabis vertical adds a separate data complexity — Infarmed and importing country regulators require Seed-to-Sale documentation tracking mother plant genetics, clone lineage, light cycles, and cannabinoid potency, which standard pharmaceutical ERP data models cannot accommodate.

Capacity expansion is running alongside these digital challenges. The new multi-building campus will increase GMP floor area significantly, which means the cost of inheriting disconnected OT systems is higher than it was for the existing site. European clients are already demanding Scope 3 emission data and batch-specific Carbon Passports, which requires energy metering at the individual production line level — a capability that does not currently exist.

Challenges we see

  • Digital Integration

    Seeing production data in SAP only at shift end rather than in real time

    Despite significant investment in SAP S/4HANA, the shop floor remains disconnected from the ERP layer. Production orders are printed, executed manually, and results typed back into SAP hours later, creating an eight-to-twelve hour data latency.

    When the ERP system shows shift-end data rather than live data, a quality deviation that started at 10:00 is not visible to the production manager until 18:00. Bridging the PLC historians to SAP in real time changes the response window from hours to minutes.

  • Operations Manufacturing

    Managing frequent changeovers with spreadsheet-based scheduling

    Labialfarma's competitive advantage in versatile dosage forms creates significant operational friction through frequent changeovers between client batches on shared production lines. Spreadsheet-based scheduling is the operational norm.

    Where changeover sequences are not optimised, the downtime during mechanical re-tooling and recalibration is whatever the schedule allows, not whatever the physics allows. Optimising the sequence reduces lost time without changing the equipment.

  • Warehouse Operations

    Moving materials between buildings with manual warehouse processes

    Active recruitment for warehouse and factory operators indicates heavy reliance on manual labour for material movement between storage, weighing rooms, and production areas across the multi-building campus.

    Manual material movement scales with headcount rather than with production volume. As the new campus buildings come online, the labour requirement grows unless the movement is systematised and traceable.

  • Compliance Regulatory

    Building Seed-to-Sale records for medicinal cannabis that standard ERPs cannot model

    The medicinal cannabis vertical requires tracking agronomic variables including mother plant genetics, clone lineage, light cycles, and cannabinoid potency correlations, none of which standard pharmaceutical ERP data models are designed to capture.

    Where the cannabis tracking data model is separate from the rest of the ERP, every regulatory report requires a manual reconciliation between two systems that were not designed to speak to each other.

  • ESG Energy

    Providing batch-level carbon footprint data when energy is only metered at site level

    European clients increasingly demand Scope 3 emission data and batch-specific Carbon Passports, requiring granular energy consumption correlated to specific production orders rather than aggregate facility-level metrics.

    Site-level energy metering can report total consumption but cannot attribute it to a specific client batch. Without per-line metering, the carbon passport is an estimate rather than a measurement.

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. Connecting shop floor equipment to SAP in real time

    The SAP S/4HANA investment has not translated to real-time visibility because machine-level data from tablet presses, encapsulators, and packaging lines remains trapped in local PLC historians, requiring manual transcription that introduces hours of latency.

    Deploy IT/OT convergence middleware that pulls live cycle counts, error codes, and OEE metrics directly from PLCs and pushes them to SAP, enabling true real-time production intelligence and immediate deviation alerts.

    • Seidor/ITChannel press release on Labialfarma SAP S/4HANA implementation
    • Labialfarma Deep Research analysis

  2. Optimising changeover sequences across high-mix production lines

    High-mix CDMO operations require constant changeovers between client batches, but scheduling is managed through static spreadsheets that fail to optimise batch sequencing for minimal downtime.

    Implement Digital Twin-based production scheduling that simulates alternative sequences, groups similar product runs together, and dynamically rebalances capacity across the expanded facility to recover hidden throughput.

    • Labialfarma Deep Research analysis

  3. Building a cannabis agronomic tracking system that feeds into SAP

    Standard pharmaceutical ERP data models cannot accommodate the agronomic tracking requirements of medicinal cannabis cultivation, including mother plant genetics, clone propagation, environmental conditions, and cannabinoid potency correlations.

    Build a specialised Track and Trace application that captures cannabis agronomic data and feeds sanitised batch information into SAP, ensuring complete Seed-to-Sale audit trails for Infarmed and importing country regulators.

    • Labialfarma Deep Research analysis
    • Infarmed and German regulator Seed-to-Sale documentation requirements

  4. Connecting R&D formulation data to manufacturing via a digital thread

    Formulation data resides in Excel or standalone instrument software in R&D laboratories, requiring manual transcription into Master Batch Records when products move to commercial manufacturing, slowing client launches and introducing transcription errors.

    Implement lab digitalization with a digital thread connecting R&D formulation parameters directly to manufacturing Bills of Materials in SAP, accelerating tech transfer for CDMO client products.

    • Labialfarma website positioning statement
    • Labialfarma Deep Research analysis

  5. Metering energy at production line level to support Carbon Passport reporting

    The company markets Bio-PET and environmental commitment but cannot provide batch-specific carbon footprint data because energy consumption is not metered at the individual production line level or correlated to specific client orders.

    Deploy an Energy Monitoring System with non-intrusive current transformers on key production assets, enabling automated Carbon Costing per unit that satisfies European client ESG requirements.

    • Labialfarma Deep Research analysis

What we'd propose

  • Digital CDMO

    IT/OT Convergence Platform

    Design and deploy a Manufacturing Service Bus that bridges the gap between SAP S/4HANA and shop floor equipment, enabling real-time bidirectional data flow for true OEE monitoring and automated production reporting.

    • OPC UA gateway deployment

      Machine-to-enterprise connectivity

      Install industrial gateways on tablet presses, encapsulators, and packaging lines using OPC UA (Open Platform Communications Unified Architecture) protocols to extract cycle counts, error codes, and speed data in real time from equipment that currently holds this data locally.

    • SAP integration layer

      Automated production order feedback

      Build middleware connectors that push live machine data directly into SAP S/4HANA production orders, eliminating manual end-of-shift data entry and reducing latency from hours to seconds.

    • OEE dashboard suite

      Grafana-based performance visualisation

      Design dashboards showing Availability, Performance, and Quality metrics per production line with automated alerts when deviation thresholds are breached, so the response window is minutes rather than hours.

    • A quality deviation is visible to the production manager during the shift it occurred, not at shift-end review.
    • SAP S/4HANA becomes the single system of record for both business and production data.
    • The OEE baseline established during commissioning quantifies the improvement opportunity for the expansion.

    We've done this before

    See how we do Digital CDMO
  • Enterprise AI

    Digital Twin Production Scheduling

    Implement a simulation-based scheduling system that models the high-mix production environment, optimising batch sequencing to minimise changeover downtime and maximise throughput across the expanded facility.

    • Production simulation engine

      Virtual factory modelling

      Create a digital replica of production lines including changeover matrices, cleaning requirements, and capacity constraints to test scheduling scenarios before committing to a production plan.

    • AI-driven sequencing

      Optimised batch order

      Train a scheduling model on historical changeover data to group similar product runs together and sequence changeovers to minimise total downtime across the week's production plan.

    • Dynamic rebalancing for the new campus

      Capacity across new buildings

      Extend the simulation model to cover the new multi-building campus as it comes online, so capacity allocation across buildings is optimised rather than estimated.

    • Changeover downtime is reduced without any change to the mechanical configuration of the lines.
    • The scheduling model improves continuously from historical data, compounding the benefit over time.
    • The new campus buildings come online with an optimised scheduling model rather than inheriting spreadsheet-based practices.

    We've done this before

    See how we do Enterprise AI
  • Digital CDMO

    Cannabis Seed-to-Sale Traceability System

    Design and build a specialised agronomic tracking application that captures the unique data requirements of medicinal cannabis cultivation and processing while feeding sanitised batch information into SAP for unified regulatory compliance reporting.

    • Cannabis agronomic data model

      Plant-to-batch lineage tracking

      Model mother plant genetics, clone propagation, light cycles, environmental conditions, and cannabinoid potency measurements as first-class entities, so each production batch carries a complete agronomic provenance record.

    • SAP integration for cannabis

      Sanitised batch data to ERP

      Feed sanitised batch information from the cannabis tracking application into SAP S/4HANA in a format the ERP can consume, so regulatory data and commercial data are in one system rather than two.

    • Regulatory reporting interface

      Infarmed and EU export submissions

      Produce Seed-to-Sale documentation packages in the format Infarmed and importing country regulators require, triggered by harvest, processing, or export events rather than assembled manually for each submission.

    • Seed-to-Sale documentation is generated as a byproduct of cultivation and processing, not assembled for each regulatory submission.
    • The cannabis tracking application and SAP together satisfy both the Infarmed licence requirements and the commercial batch record requirements in one workflow.
    • Future expansion into additional cannabis product lines attaches to the same data model rather than requiring a new system.

    We've done this before

    See how we do Digital CDMO
  • Digital Lab

    R&D to Manufacturing Digital Thread

    Implement an integrated laboratory digitalization solution that creates a data pathway from R&D formulation development through to manufacturing Bill of Materials in SAP, accelerating tech transfer for CDMO client products.

    • Formulation data capture from instruments

      Lab instrument to structured record

      Connect formulation instruments so that development parameters — dissolution profiles, compression forces, ingredient concentrations — are captured as structured data rather than transcribed from screen to spreadsheet.

    • Digital Master Batch Record linkage

      R&D to manufacturing Bill of Materials

      Map the formulation parameters captured during R&D directly onto the manufacturing Bill of Materials in SAP, so the tech transfer from lab to production is a data transfer rather than a re-keying exercise.

    • Tech transfer package automation

      CDMO client handoff acceleration

      Generate the formulation and process sections of the tech transfer package automatically from the structured records, so client handoffs are faster and free of transcription errors.

    • Formulation data moves from R&D to manufacturing as structured data, not as a PDF assembled manually.
    • Client tech transfer timelines shorten because the data is already structured for the recipient's systems.
    • Transcription errors that currently surface during scale-up are eliminated before they occur.

    We've done this before

    See how we do Digital Lab
  • Enterprise AI

    Energy Monitoring and Carbon Costing System

    Deploy a granular energy monitoring system that correlates production line consumption with specific client orders, enabling automated Carbon Passport generation and batch-level sustainability reporting for ESG-conscious pharmaceutical brands.

    • Non-intrusive energy metering

      Current transformers on production assets

      Install non-intrusive current transformers on tablet presses, encapsulators, and packaging lines to measure real power consumption per machine per production run, correlated to the SAP production order.

    • Carbon costing per client batch

      Batch-level Carbon Passport

      Calculate energy-derived carbon intensity per client batch using the metered production data, so the Carbon Passport is a measurement rather than an estimate allocated from site totals.

    • Scope 3 ESG reporting interface

      European client sustainability requirements

      Expose carbon intensity data through a reporting interface that satisfies Scope 3 emission data requests from European pharmaceutical and nutraceutical clients in the format they require.

    • Batch-level Carbon Passports differentiate Labialfarma in CDMO contract negotiations with sustainability-focused clients.
    • Energy metering identifies which production runs are most efficient, providing a data basis for continuous improvement.
    • Scope 3 reporting becomes automated rather than estimated at the end of each reporting period.

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

Source: A4BEE analysis of public sources
IT/OT Integration 30 → 80
SAP S/4HANA is in place but production data from tablet presses, encapsulators, and packaging lines remains in local PLC historians. The eight-to-twelve hour manual transcription cycle means production decisions are made on yesterday's data.
Data Analytics 35 → 75
Spreadsheet-based scheduling and manual OEE collection leave analytical capability concentrated in individual operators' knowledge rather than in a system the management team can query.
Process Automation 40 → 85
The multi-building campus expansion is being designed as a Born Digital environment, which means now is the last point at which the automation architecture can be set correctly before commissioning.
Regulatory Digitalization 45 → 90
Medicinal cannabis Seed-to-Sale requirements and GMP compliance across multiple client product types require a data infrastructure that can produce audit-ready documentation automatically.
Lab Connectivity 25 → 70
Formulation development instruments produce data that currently requires manual transcription into Master Batch Records, which is the primary source of the R&D to manufacturing tech transfer bottleneck.
Sustainability Tracking 20 → 75
Site-level energy metering cannot attribute consumption to specific client batches. Without per-line metering, Carbon Passport reporting is estimated rather than measured.

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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 Labialfarma, 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].