Why do the chemical, pharmaceutical and process industries in Dortmund need an AI strategy?
Innovators at these companies trust us
Local challenge: complex processes, heavy regulation
The chemical, pharmaceutical and process industries in Dortmund face a double challenge: historically grown, complex production processes on the one hand and stringent regulatory requirements on the other. Without a clear AI strategy, many automation and efficiency potentials remain untapped.
Why we have local expertise
Reruption travels to Dortmund regularly and works on-site with clients: we are not remote-controlled consultants but Co-Preneurs who see the real friction points in production halls, laboratories and control rooms. This allows us to combine technical engineering with a realistic view of operations and compliance in NRW.
Our approach begins with a deep analysis of the existing data, process and IT landscape: from laboratory information management systems (LIMS) and MES to documentation workflows. Only in this way can use cases be identified that truly make a difference in practice — not in PowerPoint, but in active shifts.
Our references
Our work in manufacturing and technology environments is directly transferable to chemical and pharmaceutical processes. At Eberspächer we implemented AI-based solutions for noise reduction in manufacturing processes; the project demonstrates how sensor data, robust models and production knowledge have to come together to run reliably in production. These experiences are directly relevant for quality assurance and monitoring in process plants.
With STIHL we have accompanied several projects — from digital training platforms like saw training to ProTools implementations — developing industrial training and simulation solutions that can also be used in chemical laboratories for safe process training and skills development. For chemical technology we worked with TDK on PFAS removal technologies, which gave us a deep understanding of chemistry-specific process models and laboratory data.
About Reruption
Reruption was founded because companies should not only react but proactively shape the future. Our co-preneur mentality means: we take responsibility, operate within our clients' P&L and deliver not just strategies but prototypes and implementable roadmaps.
For Dortmund we bring, in addition to international AI expertise, experience in highly regulated environments. We combine rapid prototype development, technical depth and pragmatic governance models so that AI solutions in the chemical and pharmaceutical industries can be introduced safely, transparently and with commercial sense.
Want to know which AI use cases have the biggest leverage in your plant?
We conduct a targeted Use Case Discovery, analyze data sources on-site in Dortmund and deliver prioritized business cases and prototype recommendations.
What our Clients say
Hans Dohrmann
CEO at internetstores GmbH 2018-2021
This is the most systematic and transparent go-to-market strategy I have ever seen regarding corporate startups.
Kai Blisch
Director Venture Development at STIHL, 2018-2022
Extremely valuable is Reruption's strong focus on users, their needs, and the critical questioning of requirements. ... and last but not least, the collaboration is a great pleasure.
Marco Pfeiffer
Head of Business Center Digital & Smart Products at Festool, 2022-
Reruption systematically evaluated a new business model with us: we were particularly impressed by the ability to present even complex issues in a comprehensible way.
AI for the chemical, pharmaceutical and process industries in Dortmund: market, opportunities and implementation
Dortmund is part of an industrial ecosystem that has transformed from traditional steel and mechanical engineering to logistics, IT services and energy. For chemical, pharmaceutical and process companies, this shift means: stronger connectivity, greater data availability and at the same time increased demands on safety and compliance. A targeted AI strategy answers which initiatives create short-term value and which investments enable long-term scale effects.
Market analysis and regional reality
The demand for digital solutions in production facilities and laboratories is steadily growing in North Rhine-Westphalia. Dortmund, as a hub for logistics and IT, offers a dense network of service providers, suppliers and research institutions. For chemical and process companies this means access to software expertise—but also competition for skilled personnel. An AI strategy must therefore set not only technical goals but also consider personnel and partner landscapes.
It is important not to view the local market abstractly. Proximity to energy providers like RWE and industrial players like ThyssenKrupp creates synergies in energy optimization, predictive maintenance and supply chain digitalization. At the same time, closeness to logistics and IT hubs demands solutions that are interoperable and resilient.
Specific use cases for chemicals, pharma & process industries
Use cases can be grouped into three categories: laboratory and documentation automation, operational assistance systems (e.g. Safety Copilots) and enterprise-wide knowledge platforms. Laboratory-process documentation can be significantly accelerated and made audit-safe by AI-supported extraction, normalization and semantic linking of measurement data and experiment protocols.
Safety Copilots — digital assistant systems that support operators in critical situations — are particularly valuable in processes with high safety requirements. They combine sensors, process models and contextual knowledge bases to provide real-time recommendations and reduce human error. Equally important are secure internal models: local, privacy-compliant variants that protect sensitive laboratory and production data from external access.
Implementation approaches and technical architecture
A pragmatic path starts with an AI Readiness Assessment and a broad Use Case Discovery across 20+ departments — exactly the modules we offer. The goal is to identify data sources, integration points (MES, LIMS, ERP) and governance risks early and to build prioritized prototypes that deliver results in weeks rather than months.
Technically, we recommend modular architectures: a robust Data Foundation with role-based access, feature stores for production and sensor data, and containerized models for local inference. Models should rely on explainable and verifiable methods, particularly in regulated environments like pharma, where audit trails and reproducibility are mandatory.
Success factors and common pitfalls
Successful AI programs are characterized by clear metrics, a pilot-first mindset and strong governance. A common mistake is over-optimizing technical metrics instead of economic levers: speed, quality and cost per run matter, but the impact on scrap, throughput or regulatory compliance determines the economic evaluation.
Governance must link regulatory requirements (e.g. GMP, ISO standards, data protection) with operational processes. Too little attention to data quality, ownership and change management leads to projects that may work technically but are not transferred into routine operations.
ROI, timeline and pilots
A typical approach starts with an AI PoC (our standard package) to validate technical feasibility — in many cases a PoC provides reliable performance data within a few weeks. For economic evaluation, companies should expect 6–18 months until the first measurable return, depending on complexity and integration effort.
It is important to build business cases not only on savings but also on risk reduction (e.g. fewer incidents), quality improvements and compliance benefits. Such holistic models convince decision-makers in both the CFO and quality management functions.
Team, skills and change management
An AI strategy requires interdisciplinary teams: data engineers, ML engineers, domain experts from laboratory and production as well as compliance officers. Local personnel development is essential; Dortmund's IT and logistics hubs provide good recruiting pools, but lasting success also requires internal enablement and clear ownership structures.
Change management is not an add-on: training, new shift procedures and visible quick wins lay the foundation for broader acceptance. Practical rollouts should be accompanied by champions in production and the lab so that insights quickly feed into processes.
Technology stack and integration issues
Proven stacks combine robust data warehousing solutions, message brokers for real-time telemetry, feature stores and MLOps pipelines for deployment. For pharmaceutical environments, on-prem or hybrid variants are often mandatory to meet regulatory requirements.
Integration efforts with MES, LIMS or SCADA systems are usually the most critical time and cost components. That is why we prioritize interfaces with high data value and build robust abstraction layers to ensure long-term maintainability.
Long-term scaling and roadmap
Scaling AI in the process industry follows a journey: from PoC to pilot to production and finally to portfolio optimization. A roadmap that identifies short-term value drivers, standardizes governance rules and lays the foundation for sustainable internal AI capabilities is crucial.
Our modules — from Use Case Discovery through business case modeling to AI governance — are precisely designed for this: rapid technical validation, clear economic assessment and an actionable path to scale.
Ready for the first PoC?
Book an AI Readiness Assessment and a fast proof of concept so you get reliable results and a clear production plan within weeks.
Key industries in Dortmund
Dortmund has undergone a remarkable transformation: from the center of the steel industry to a modern node for logistics, IT and energy. This transformation also affects the chemical, pharmaceutical and process industries, which are now more digitally connected than ever. Production sites are linked with IT service providers, logistics centers and energy suppliers, creating opportunities for data-driven optimizations.
Historically, Dortmund was shaped by the mining and heavy industries; from this emerged a competent manufacturing and engineering culture. This experience has transfer potential: process understanding, quality awareness and work organization are ideal prerequisites for introducing data-driven processes in chemicals and pharma.
The integration of logistics and IT expertise creates advantages for process companies: shorter supply chains, better traceability and optimized inventory management. For chemical and pharmaceutical firms this means: faster batch approvals, optimized material flows and improved compliance across the supply chain.
In Dortmund, collaborations between industry and software providers are increasingly being formed. These partnerships facilitate the development of solutions that are both domain-specific and technologically modern: from digital laboratory processes to condition monitoring in production lines.
Regulatory requirements are a constant factor: pharma projects must be GMP-compliant, and chemical production is subject to strict environmental and safety regulations. Therefore, robust data foundations, traceable model pipelines and audit trails are essential before AI is deployed at scale in routine operations.
Regional service providers — from energy suppliers to logistics companies and IT firms — offer a dense ecosystem for pilot projects. An AI strategy in Dortmund should leverage this network to develop cross-industry solutions that combine energy efficiency, process stability and supply chain resilience.
For mid-sized chemical and pharmaceutical companies, Dortmund offers a favorable environment to move quickly from prototypes to productive systems. Local networks reduce implementation risks because pilot partners, service providers and specialists are geographically reachable and enable rapid iterations.
In short: Dortmund's evolution from steel to software has created a unique combination of practical industrial competence and digital innovation capability — a fertile ground for AI strategies in the process industry.
Want to know which AI use cases have the biggest leverage in your plant?
We conduct a targeted Use Case Discovery, analyze data sources on-site in Dortmund and deliver prioritized business cases and prototype recommendations.
Key players in Dortmund
ThyssenKrupp has long been one of the region's defining employers. Today the company stands for industrial transformation, where digitization of manufacturing processes and predictive maintenance play a central role. Its innovation initiatives show how traditional industry can be linked with modern data solutions.
RWE, as a major energy provider, shapes the regional energy system and drives the energy transition. For chemical and process companies, RWE is an important partner in energy efficiency projects and the integration of renewables, which can benefit significantly from AI-supported load control and optimization.
Wilo, a globally active pump manufacturer, has its roots in the Ruhr region and combines mechanical engineering with smart products. The digitization of components and services — for example through condition monitoring and remote maintenance — demonstrates how product-proximate data enables new business models.
Signal Iduna is a major insurer in the region and represents the connection between financial services and regional industry. Insurers are playing an increasing role in assessing AI risks and insuring new digital business models, for example via specific policies for cyber or business interruption risks.
Materna is an example of local IT competence that supports companies in digitalization. As a service provider, Materna offers consulting and implementation services relevant for integrating AI solutions into existing enterprise landscapes, particularly in the areas of SAP, process and infrastructure projects.
In addition, there is a dual ecosystem of SMEs and specialized service providers that allows rapid iterations. Universities and research institutions in the area provide additional expertise so that companies in Dortmund can draw on a broad network for innovation projects.
Together, these players create a climate in which AI projects are not viewed in isolation: energy, logistics and IT are part of value creation, and successful AI strategies connect these areas to make processes more resilient and efficient.
For external partners like Reruption, the regional structure means: we can be on site quickly, leverage local partnerships and develop pragmatic, application-oriented solutions that meet the specific requirements of Dortmund's industry.
Ready for the first PoC?
Book an AI Readiness Assessment and a fast proof of concept so you get reliable results and a clear production plan within weeks.
Frequently Asked Questions
The entry point begins with an honest inventory: an AI Readiness Assessment clarifies data availability, the process landscape and compliance requirements. In Dortmund this often means analyzing interfaces to MES, LIMS and ERP and bringing stakeholders from production, QA and IT together. Only with this shared understanding can realistic use cases be identified.
The next step is a broad Use Case Discovery, ideally across 20+ departments, to find potentials that deliver immediate value. In established operations there are often low-hanging fruits in documentation processes and knowledge search that can be automated in the short term.
A pragmatic PoC (proof of concept) validates technical feasibility — our standard package is designed exactly for that. In many cases a PoC shows within a few weeks whether a use case is robust enough for production. It is important that the PoC already includes economic metrics: impact on scrap, cycle time or compliance costs.
Finally, the roadmap must include governance, scaling and change management. In Dortmund we leverage the local proximity to IT and service partners to quickly find implementation partners and support pilot projects on site. We travel to Dortmund regularly and work with clients directly in their facilities to keep critical learning cycles short.
Pharma and process companies are subject to stringent regulatory requirements: GMP, 21 CFR Part 11 (where applicable), ISO standards and national environmental and safety regulations. These regulations require traceable data pipelines, audit trails and controlled access rights. An AI strategy must take these requirements into account from the outset in architecture and model processes.
Data protection is also central: laboratory and batch data often contain sensitive information that must be protected internally. An on-prem or hybrid architecture is often necessary to ensure data sovereignty while still benefiting from modern ML methods.
Another point is model validateability. In pharma environments, explainable models and traceable decisions are indispensable. Black-box models may deliver performance but make certification and acceptance by auditors and quality officers more difficult.
Practically this means: governance frameworks that define roles, responsibilities, data quality assurance and regular model review cycles. We implement these frameworks so they can be understood by internal audits and external auditors, creating the foundation for scalable AI solutions.
In many process plants use cases in three areas show particularly high value: laboratory and process documentation, Safety Copilots and knowledge search. Automated documentation reduces error sources and accelerates approval processes; Safety Copilots improve operational safety and reduce downtime risk; knowledge search makes expert knowledge immediately available and shortens onboarding times.
For example, AI-supported extraction of LIMS data and experiment protocols can significantly reduce batch testing time. This not only leads to faster delivery times but also to less storage and lower capital costs.
Safety Copilots combine sensor data, process history and operating procedures to provide real-time action recommendations. This is especially relevant in plants with high safety requirements, as commonly found in the chemical and process industries.
It is important that each use case has clearly quantified metrics: how many minutes are saved per batch, how much scrap can be avoided, or how much faster are approvals? Without these figures, a project remains a technical experiment rather than an economic decision.
Timelines vary greatly depending on complexity. A first technical PoC can often deliver results in 4–8 weeks, especially if data sources are clear and access is available. The transition from PoC to pilot typically requires 3–6 months, including integration, governance and user acceptance testing.
Many initiatives reach production readiness within 6–18 months. This phase includes robust MLOps pipelines, validation and often also an adjustment of operational processes. For highly regulated pharma environments, additional validation and documentation steps can extend the timeline.
Iteration speed is crucial: short development cycles, fast feedback loops and clear prioritization of use cases shorten time-to-value. That is why we focus on pilot projects with clear KPIs and a binding rollout plan.
For Dortmund-based operations, another advantage is proximity to IT and service partners, enabling faster organization of implementation steps and on-site support. Reruption accompanies the entire journey — from Use Case Discovery to handover into routine operations.
Integration begins with a clear mapping of data flows: which data is generated where, who owns it, and how is quality ensured? An important first step is defining interfaces and introducing data contracts that bind responsibilities and formats.
Technically, API-first approaches, robust message brokers for real-time data and ETL processes for historical data are recommended. Models should be operated in containerized environments so deployments are reproducible and rollbacks remain possible. Feature stores help standardize feature engineering and ensure consistency between training and inference.
Another focus is monitoring: production models need metrics for performance, drift detection and data quality. Only in this way can deviations be detected early and countermeasures initiated. In regulated environments, audit logs and versioning of models and data are additionally essential.
In Dortmund we work on-site with IT and automation teams to implement integrations pragmatically. Close collaboration reduces misunderstandings and makes it possible to overcome technical hurdles quickly.
Change management should run in parallel with technical development. Training for operators, lab staff and managers is just as important as technical deployments. Early success stories and visible quick wins help reduce skepticism and create acceptance.
Skill development happens in stages: basic knowledge about AI and data use for broad employee groups, advanced training for domain champions and technical training for data engineers and ML operators. Mentoring and on-the-job learning accelerate the transfer curve.
An important building block is interdisciplinary squads that bring together domain experts, data scientists and developers. Such teams are easy to network in Dortmund because local IT service providers and universities serve as talent sources.
Practically, we recommend a mix of external coaches for initial enablement and internal 'AI champions' who take long-term responsibility. Reruption supports both: we accompany pilot projects on site and transfer knowledge so that teams can continue independently after project completion.
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Philipp M. W. Hoffmann
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