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Valsoia

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

Strategic priorities

Valsoia operates across 4 stated priorities, with the most concrete near-term plan anchored on serravalle sesia smart factory.

Doubling production capacity by 2026 with Industry 4.0 infrastructure, interconnecting new machinery with management systems under Italian Transizione 4.0 tax incentives.

Transitioning from manual or older systems to durable, compliant frameworks through IT/OT convergence and ERP optimization to control purchasing costs and improve margins.

Achieving 2024-2026 targets including -15% water consumption, +10% self-produced energy, and <10 Kg/ton waste while meeting CSRD compliance requirements.

Challenges we see

  • Digital Integration

    IT/OT Convergence Gap

    The Serravalle Sesia plant expansion requires connecting isolated Industry 4.0 machines to a Manufacturing Execution System (MES). The transition from manual or older systems to durable, compliant frameworks is identified as a focus for 2024.

    Without IT/OT convergence, production data cannot inform financial forecasts in real-time, and the company risks not realizing the full value of its CAPEX investments in new machinery.

  • ESG Regulatory

    ESG Supply Chain Automation

    The need for progressive integration of ESG factors in supply chain management implies current vendor oversight is manual and labor-intensive. CSRD compliance requires automated double materiality assessment.

    Manual vendor audits drive compliance risks and administrative burden for the Sustainability Manager, potentially leaving the company facing CSRD compliance penalties.

  • Operations Logistics

    Distribution Network Inefficiency

    High fuel and CO2 consumption in the distribution network indicates a lack of advanced route optimization or fleet management software. The company targets -5% CO2 reduction.

    Without AI-driven logistics optimization, the company will struggle to meet its carbon reduction targets while simultaneously facing rising transportation costs.

  • Digital R&D

    Joining records across systems

    R&D operations managing complex biotechnological research (okara protein isolation) require sophisticated data capture across enzymatic screening with multiple solvent combinations and conditions. LCA evaluations need export to ESG reporting systems.

    Without LIMS and electronic lab notebooks, the 4-unit R&D team will struggle to scale to support 6 new projects per year or accelerate time-to-market for alternative protein products.

  • Operations Manufacturing

    Fragmented Value Chain Visibility

    The Mapping of the Value Chain listed as a 2024 highlight suggests end-to-end visibility was previously a significant blind spot. Real-time monitoring of water and energy consumption requires sensor integration.

    Without unified data platforms, the company cannot identify specific production steps responsible for waste, making the -15% water reduction and -10% extraction energy targets unachievable.

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 Floor Connectivity

    New Industry 4.0 machinery at Serravalle Sesia operates in isolation without connection to enterprise management systems, preventing real-time production visibility and automated quality control.

    Implementing an IT/OT integration architecture with MES would enable production data to flow from PLCs and sensors directly to business intelligence dashboards, supporting the methodical digital transformation leadership desires.

  2. Sustainability Data Intelligence

    Achieving -15% water consumption and +10% self-produced energy targets requires real-time monitoring, but current systems lack IoT sensor integration and centralized sustainability dashboards.

    Deploying smart metering with flow analytics across facilities would enable data-driven identification of waste sources, supporting both operational efficiency and CSRD compliance reporting.

  3. R&D Digitalization Gap

    Laboratory operations managing complex research like okara protein isolation rely on manual data recording across multiple enzyme screening conditions, limiting throughput and integration with ESG systems.

    Implementing LIMS and electronic lab notebooks would transition the lab from manual recording to high-throughput digital operations, accelerating the 6 new projects per year target and circular economy initiatives.

  4. M&A Digital Integration

    The KELE & KELE acquisition and future brave M&A activities require smooth integration of diverse, cross-border business models without creating new data silos.

    Developing a Digital Integration operating procedure with standardized ERP connectivity would enable rapid realization of synergies and unified reporting across the growing multi-brand conglomerate.

  5. Automated Quality Assurance

    Food safety management based on rigorous corporate principles relies on batch testing rather than real-time monitoring, creating delays in non-compliant product identification.

    Integrating automated chemical-physical and microbiological analysis with the production line would enable immediate rejection of non-compliant products, reducing waste and protecting brand quality.

What we'd propose

  • Digital CDMO

    Smart Factory IT/OT Integration

    Design and implement the IT/OT architecture for the Serravalle Sesia plant expansion, connecting Industry 4.0 machinery to enterprise systems for real-time production intelligence.

    • 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

    ESG Data Platform & Smart Metering

    Build an IoT-enabled sustainability monitoring platform with automated sensor integration for water, energy, and waste tracking to achieve 2024-2026 environmental targets.

    • 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

    Lab 4.0 Digital Transformation

    Implement Laboratory Information Management System (LIMS) and electronic lab notebooks to digitize R&D operations and accelerate alternative protein product development.

    • 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

    Distribution Optimization Platform

    Deploy AI-driven logistics and route optimization to reduce CO2 emissions and transportation costs across the distribution network.

    • 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
  • Enterprise AI

    M&A Digital Integration Framework

    Develop a standardized Digital Integration operating procedure for smooth onboarding of acquisitions like KELE & KELE with unified ERP connectivity and reporting.

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

Source: A4BEE analysis of public sources
IT/OT Integration 35 → 80
New Industry 4.0 machinery deployed but not yet connected to MES or enterprise systems; transition from manual to compliant frameworks identified as 2024 focus
Data Analytics 40 → 85
Value chain mapping completed as 2024 highlight indicates previous blind spots; sustainability monitoring requires real-time sensor integration
Laboratory Digitalization 30 → 75
R&D operates with manual data recording for complex research; LIMS and ELN needed to support 6 new projects per year
Supply Chain Visibility 45 → 85
ESG vendor audits remain manual; M&A integration (KELE & KELE) requires cross-border data connectivity
Automation & Robotics 50 → 80
Industry 4.0 equipment installed at Serravalle Sesia; integration with quality control systems needed for real-time monitoring
Cybersecurity & Compliance 55 → 85
Model 231 and internal controls in place but require ongoing digital adaptation; CSRD compliance automation needed

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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 Valsoia, 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].