☰

Evonik

Connected data for EBITDA growth

A specialty chemicals company building IT/OT integration, AI-powered batch records, and ESG data infrastructure

Industry
Specialty chemicals
Headquarters
Essen, Germany
Public information as of
January 2026

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

Strategic priorities

Evonik is a German specialty chemicals company undergoing a major corporate transformation under the Evonik Tailor Made programme, targeting EUR 1 billion EBITDA growth and EUR 400 million in annual cost savings by 2027. The company operates across two segments — Custom Solutions (innovation-driven, high-margin niche markets) and Advanced Technologies (efficiency-driven, cost-leading bulk chemicals) — with a combined manufacturing footprint spanning multiple continents. The transformation includes scaling Next Generation Solutions to more than 50% of group sales by 2030, achieving a 25% GHG reduction on Scope 1 and 2 emissions, and rebalancing geographic manufacturing footprint with new facilities in Singapore, Austria, Slovakia, Japan, and China.

The digital gap is in the plants. Decades-old production equipment with legacy PLCs and SCADA systems operates as isolated islands without standard communication protocols. Connecting that equipment to enterprise systems is the prerequisite for every other ambition on the roadmap — AI-driven yield optimisation, predictive maintenance, CSRD compliance, and digital procurement all depend on having factory-floor data in a usable form. The Tailor Made programme's EUR 400 million cost savings target is ultimately an OT data problem.

The window is now. The structural reorganisation into two segments is underway and the CAPEX allocation is being set for the next cycle. The equipment and network architecture chosen in 2025 and 2026 will determine whether Evonik's AI and sustainability ambitions have a data foundation to run on, or whether they remain strategic statements without operational grounding.

Challenges we see

  • Digital Integration

    Legacy OT equipment blocking the data foundation

    Evonik's global manufacturing footprint includes decades-old production equipment with legacy control systems (PLCs, SCADA) operating as isolated islands without standard communication protocols, while the digital transformation strategy requires real-time data integration for AI-driven optimisation.

    Where PLCs and SCADA systems from different equipment generations do not speak a common protocol, the data they produce stays inside the machine. A unified data acquisition layer that translates each protocol into a standard format means the data from every machine on the plant floor is available to the analytics platform simultaneously.

  • Operations Manufacturing

    IT and OT team misalignment slowing the Tailor Made programme

    The Evonik Tailor Made programme targets EUR 400 million in annual cost savings. IT teams prioritise data availability through frequent updates while OT teams prioritise 24/7 reliability, creating conflicting priorities and implementation friction.

    Where IT and OT teams have different priorities and incentive structures, the programmes they run in parallel can work against each other. A shared digital backbone with explicit reliability contracts between IT and OT means both teams are working from the same platform toward the same uptime targets.

  • ESG Regulatory

    Siloed ESG data across global sites

    CSRD and ESRS requirements mandate a transition from rolling quarter estimates to full-year real data using comprehensive environmental databases like Ecoinvent v3.10, but sustainability metrics are scattered across disparate systems and sites without a single source of truth.

    Where sustainability data is collected manually at each site and assembled into a group-level report, the result is an estimate built from fragments. An automated ESG data platform that pulls environmental data directly from site-level monitoring systems means the group-level report is a live reflection of actual consumption, not a reconstruction from spreadsheets.

  • Operations Operations

    No real-time visibility into upstream supply chains

    The end of multilateralism and escalating trade tensions between the US, EU, and China force geographic footprint reconfiguration, while volatile raw material and energy prices exposed the risks of traditional procurement models in 2023-2024.

    Where Key Starting Material suppliers and logistics routes are managed through periodic reporting rather than live data, the response to a disruption arrives after the shortage has already hit. A digital procurement platform with upstream visibility means the response window opens when the risk appears, not when the order fails.

  • Digital Operations

    Skills gap blocking adoption of modular industrial software

    The structural reorganisation requires multidisciplinary teams bridging process engineering and digital dexterity, while simultaneously executing workforce reductions in a socially responsible manner. Rapid adoption of modular SaaS-based industrial software requires skills that are scarce in traditional chemical manufacturing organisations.

    Where SaaS-based industrial software requires digital skills that the existing workforce does not have, the adoption curve is longer than the implementation timeline. A structured adoption programme with embedded training and clear role evolution means the workforce is ready to use the platform by the time the rollout is complete.

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 legacy OT equipment to a unified data platform

    Legacy SCADA and PLC systems at global production sites operate as isolated islands without standard communication protocols, preventing real-time data aggregation for AI-driven process optimisation and Advanced Process Control implementation.

    Deploy unified data acquisition infrastructure with OPC UA connectivity to enable IT/OT convergence, creating contextual data lakes that feed predictive analytics and yield optimisation models targeting 15-20% yield improvements.

    • Evonik Strategic Analysis Report, 2025

  2. Replacing paper batch records with AI-powered electronic batch records

    Traditional batch management at specialty chemical facilities relies on paper-based processes, causing slower batch releases, higher deviation rates, and limited data-driven quality oversight across global sites.

    Implement AI-powered Electronic Batch Record solutions enabling rapid digitisation in weeks rather than years, achieving review by exception with fewer deviations, quicker batch releases, and up to 30% deviation reduction.

    • Evonik Strategic Analysis Report, 2025

  3. Predicting equipment failures before they disrupt production

    Unplanned equipment failures and yield variations caused by raw material inconsistencies disrupt production continuity at high-performance chemical manufacturing plants critical to the Advanced Technologies segment's cash generation mandate.

    Deploy AI/ML-based predictive maintenance systems integrated with the contextualised manufacturing data lake to anticipate equipment failures, reduce unplanned downtime, and improve yields through proactive intervention.

    • Evonik Strategic Analysis Report, 2025

  4. Building the ESG data platform for CSRD and ESRS compliance

    CSRD/ESRS compliance requires transitioning from quarterly estimates to full-year real data with comprehensive environmental database integration, but sustainability metrics are scattered across disparate systems and sites without a single source of truth.

    Build an integrated ESG data platform with automated data pipelines connecting environmental monitoring systems across global sites, enabling precise Scope 1 and 2 emissions tracking and streamlined sustainability reporting.

    • Evonik Strategic Analysis Report, 2025
    • CSRD requirements documentation

  5. Gaining real-time visibility across the upstream supply chain

    Traditional procurement models lack real-time visibility into upstream value chains, preventing agile response to price fluctuations, supply disruptions, and identification of potential bottlenecks for Key Starting Materials across Evonik's global manufacturing network.

    Implement digital sourcing platforms with AI-powered supply chain mapping tools to provide real-time upstream visibility, predictive sourcing analytics, and total spend optimisation across the Custom Solutions and Advanced Technologies segments.

    • Evonik Strategic Analysis Report, 2025

What we'd propose

  • Enterprise AI

    Unified IT/OT data platform for global manufacturing operations

    We deploy a unified industrial data platform connecting legacy manufacturing equipment — PLCs, SCADA, sensors — to cloud-based analytics through OPC UA protocols, creating a secure and scalable foundation for Evonik's IT/OT convergence and AI-driven manufacturing transformation.

    • OPC UA connectivity layer

      Every machine on the plant floor speaking one protocol

      Deploy an OPC UA server infrastructure that translates legacy PLC and SCADA protocols from Evonik's diverse equipment fleet into a standard data format, so that the analytics platform receives contextualised data rather than raw hex values.

    • Contextualised data lake

      Manufacturing data with asset context attached

      Build a contextualised data lake that tags every data point with equipment identity, operating mode, material batch, and shift information, so that analytics models are working with data that is ready to interpret rather than raw time series.

    • IT/OT reliability contract

      An explicit agreement between IT and OT on what the platform delivers

      Establish a formal operating level agreement between IT and OT teams that defines update windows, uptime guarantees, and rollback procedures, so that both teams have clear commitments that remove the source of conflicting priorities.

    • The EUR 400 million Tailor Made cost savings target has a data foundation to be achieved on.
    • Every subsequent AI and analytics initiative draws from the same data platform rather than building its own data pipeline.
    • IT and OT teams have a shared platform with explicit commitments, removing the source of implementation friction.

    We've done this before

    See how we do Enterprise AI
  • Digital Lab

    AI-powered electronic batch record system for specialty chemical production

    We implement a lightweight, AI-powered Electronic Batch Record solution that rapidly digitises paper-based manufacturing processes at specialty chemical facilities, enabling review-by-exception quality oversight and accelerated batch releases.

    • Rapid eBR deployment

      From paper to electronic batch records in weeks

      Deploy a configurable eBR platform that maps to Evonik's existing batch record templates and quality specifications, enabling a fast-track implementation that produces electronic batch records without requiring a full MES replacement.

    • AI-powered deviation detection

      Finding the batch that needs attention before it becomes an excursion

      Train machine learning models on historical batch data to identify patterns that precede deviations, so that the system flags batches that need human review rather than requiring every batch to be reviewed manually.

    • Accelerated batch release

      Review by exception instead of review by routine

      Configure the eBR system to route only exceptional batches to the quality team for manual review, releasing compliant batches automatically and reducing the quality team's administrative burden while shortening the batch release cycle.

    • Batch release cycles shorten because compliant batches are released automatically.
    • The quality team focuses on the batches that need attention rather than reviewing every batch by rote.
    • Deviations are caught earlier because the AI model identifies the pattern that precedes them.

    We've done this before

    See how we do Digital Lab
  • Digital CDMO

    Predictive maintenance and yield optimisation platform

    We deploy an AI/ML-based predictive analytics system integrated with the manufacturing data platform to anticipate equipment failures, optimise process parameters, and improve yields across Evonik's high-performance chemical production plants.

    • Equipment health monitoring

      Every critical asset tracked from the control room

      Deploy equipment health monitoring on critical assets — pumps, compressors, heat exchangers — using the contextualised data lake to feed anomaly detection models that identify emerging failure conditions before they cause unplanned downtime.

    • Yield optimisation models

      Process parameters tuned by the model, not by trial batches

      Train yield optimisation models on historical batch data to identify the operating window that maximises yield for each product grade, feeding recommended setpoints back to the APC system so that the process runs closer to optimal on every batch.

    • APC integration

      Advanced Process Control that actually advances

      Integrate the yield optimisation models with Evonik's existing Advanced Process Control infrastructure so that model recommendations are translated into actual setpoint changes without manual intervention, closing the loop between insight and operation.

    • Unplanned downtime drops because failures are anticipated rather than reacted to.
    • Yield improves because the operating window is defined by the model rather than by the last successful batch.
    • APC investments justify themselves through measurable yield gains that flow directly to EBITDA.

    We've done this before

    See how we do Digital CDMO
  • Enterprise AI

    Integrated ESG data platform for CSRD and ESRS compliance

    We build a unified sustainability data infrastructure connecting environmental monitoring systems across Evonik's global sites, enabling precise Scope 1 and 2 emissions tracking, automated CSRD/ESRS compliance reporting, and ESG performance management.

    • Automated environmental data pipelines

      Site-level monitoring data flowing to the group ESG platform

      Build automated data pipelines from site-level environmental monitoring systems — energy meters, emissions sensors, water consumption logs — to the central ESG data platform, replacing manual quarterly reporting with continuous data capture.

    • GHG emissions calculation engine

      Scope 1 and 2 emissions computed from actual consumption data

      Implement an emissions calculation engine that applies the GHG Protocol and Ecoinvent v3.10 environmental database to calculate Scope 1 and 2 emissions from metered consumption data, so that the emissions figure is a measurement rather than an estimate.

    • Automated CSRD/ESRS disclosure reports

      Reports produced by the platform, not assembled by the sustainability team

      Configure the ESG platform to generate CSRD and ESRS-compliant disclosure reports directly from the calculated emissions data, so that the sustainability team reviews and approves rather than assembles.

    • The CSRD compliance deadline is met because the data is flowing automatically rather than being collected manually.
    • Sustainability-linked financing is secured because the emissions data is verifiable from the platform.
    • The 25% GHG reduction target is trackable in real time rather than assessed annually from estimates.

    We've done this before

    See how we do Enterprise AI
  • Enterprise AI

    Digital procurement platform for upstream supply chain visibility

    We implement a digital sourcing platform that provides real-time visibility into Evonik's upstream value chains, predictive analytics for supply risk, and total spend optimisation across the Custom Solutions and Advanced Technologies segments.

    • Supplier risk mapping

      The Key Starting Material supply chain mapped in real time

      Build a digital map of the upstream supply chain for critical Key Starting Materials, linking each material to its supplier, geography, and logistics route, so that disruption risk is visible before the disruption materialises.

    • Predictive sourcing analytics

      Price movement predicted from market signals before the invoice arrives

      Train machine learning models on commodity pricing data, supplier delivery performance, and geopolitical signals to predict price movements and supply disruptions for critical materials, so that procurement can respond proactively rather than reactively.

    • Total spend optimisation

      Cross-segment spend visibility to identify procurement savings

      Aggregate spend data across the Custom Solutions and Advanced Technologies segments to identify consolidation opportunities, preferred supplier agreements, and volume-based pricing that would not be visible from a single segment perspective.

    • Supply disruptions are anticipated rather than reacted to after they have already affected production.
    • Spend across segments is optimised because the visibility exists to see the opportunity.
    • The local-for-local rebalancing strategy is supported by real data on supplier concentration risk.

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

Source: A4BEE analysis of public sources
IT/OT Integration 45 → 85
Legacy PLCs and SCADA systems from different equipment generations do not speak a common protocol. The OPC UA connectivity layer is the prerequisite for every other AI and analytics initiative in the transformation programme.
Data Analytics & AI 50 → 90
AI-driven yield optimisation and predictive maintenance require a contextualised data lake as their foundation. The AI use cases are defined; the data foundation is being built. The maturity gap is in the OT data layer.
Manufacturing Digitization 55 → 85
Paper-based batch records and manual quality processes create a ceiling on how fast batches can be released and how small deviations can be before they are noticed. Electronic batch records remove that ceiling.
Cybersecurity Maturity 50 → 80
Connecting OT equipment to IP networks expands the attack surface. The OPC UA platform must include explicit security architecture — device authentication, network segmentation, and intrusion detection — designed for OT environments.
ESG Data Infrastructure 40 → 80
CSRD and ESRS compliance requires continuous measurement rather than quarterly estimates. The current infrastructure cannot produce the real-data disclosures that regulators and investors are beginning to demand.
Supply Chain Visibility 45 → 80
Volatile raw material and energy prices in 2023-2024 exposed the limits of traditional procurement models. Real-time upstream visibility is a prerequisite for the regional manufacturing rebalancing strategy.

Check this yourself

Our Service Portal has free self-assessments and market comparisons. These are the ones that line up with what we've read above — no sales call required.

Think we've read this right?

Talk to us

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 Evonik, 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].