Digitalization is a leadership responsibility
For a long time, the modernization of IT landscapes in the chemical industry was treated purely as an infrastructure issue. Today, however, it has evolved into a central issue of strategic corporate management. Without consistent data and integrated processes, fundamental business questions can only be answered to a limited extent:
- How profitable is a product at a specific location?
- What supplier and material dependencies exist?
- How high is energy consumption per production unit?
- Which batches are affected by a change in quality or specifications?
- Welche regulatorischen Informationen fehlen?
In many companies, legacy ERP systems, in-house local developments, ad-hoc Excel solutions, and fragmented interfaces make it difficult to achieve precisely this holistic view. This not only drives up IT costs but also significantly increases business, compliance, and transformation risks. Digitalization is therefore no longer an end in itself – it has become an essential prerequisite for reliably managing energy efficiency, sustainability, supply chain transparency, compliance, and modern AI applications.
The digital core of the chemical industry is more than just an ERP system
While SAP S/4HANA can form the transactional core of a chemical company, a pure ERP system alone is not sufficient to meet the industry’s specific requirements. The digital core also indispensably includes chemical-specific processes such as batch tracking, formulation management, quality assurance, hazardous substance management, product compliance, and seamless traceability.
This core is complemented by the seamless integration of production and laboratory operations – for example, via MES and LIMS systems – as well as by an integrated data and analytics layer for material, energy, emissions, quality, and supply chain data.
The SAP Business Suite for Chemical Industry and SAP Best Practices for Chemicals provide an industry-specific framework for this. In addition, SAP Cloud ERP in the chemical industry represents a potential target model for standardized, cloud-based core processes. Whether this model is suitable in a specific case, however, depends largely on process complexity, the level of regulation, integration requirements, and the individual differentiation needs of the respective company.
Standardization enables Scalability
While standardization is often mistakenly viewed as a limitation, it is in fact the fundamental prerequisite for the scalability of the digital core. Standardized processes facilitate plant-wide and regional comparisons, reduce complex interfaces, and lay the groundwork for automation, compliance, and AI.
This does not mean that all processes within the company must be identical. Rather, what is crucial is the deliberate differentiation between:
- standardizable core processes,
- necessary industry-specific functionality,
- genuine competitive differentiation,
- special solutions that have evolved over time without strategic added value.
Making this distinction is purely a management task – it should under no circumstances be derived solely from historically existing custom code.
Clean Core: Governance Instead of a One-Time Cleanup
Keeping technical debt under control and developing all extensions in a future-proof manner is a key objective of this transformation. The concept is called Clean Core. In practice, it is often equated with a one-time ban on modifications to the SAP core. However, this falls short: Clean Core primarily describes a sustainable architectural and governance framework. The ERP core remains as close to the standard as possible, while necessary extensions are implemented via approved APIs and defined extension points. Differentiating functions, in turn, are consistently operated outside the core. This decoupling makes future releases, tests, and functional enhancements significantly more predictable. You can read more about Clean Core here in another blog post.
The SAP Business Technology Platform serves as the central integration, extension, and innovation layer. Among other things, it supports data processing, analytics, automation, and flexible extensions without unnecessarily modifying the ERP core.
In this context, RISE with SAP must also be clearly defined, as these concepts are by no means interchangeable:
- RISE with SAP is the transformation and operations offering.S/4HANA bildet den eigentlichen ERP-Kern.
- BTP supports extension and integration.
- Clean Core describes the overarching architecture and governance principle.
To objectively assess the current state of an existing SAP landscape and prioritize modernization needs in a targeted manner, we use the adesso Clean Core Index. This proprietary adesso approach is not intended as an independent industry standard, but rather as a pragmatic consulting and management tool. Read more about it here.
Data Becomes a Management Tool
Ein moderner digitaler Kern verbindet nicht nur isolierte Systeme, sondern verknüpft die zugrundeliegenden Geschäftsobjekte. In der Chemieindustrie bedeutet dies, dass Material, Produkt, Charge, Anlage, Standort, Kunde und regulatorische Informationen stets im Zusammenhang betrachtet werden müssen.
These linkages are business-critical: A single quality deviation in a batch can have immediate cascading effects on production, warehousing, customer delivery, traceability, product safety, and regulatory documentation.
For data to fulfill this control function, clear data ownership, consistent master data, and a traceable data origin are required. Energy and emissions data must be directly linked to products, quantities, and locations, while supply chain risks must be systematically linked to materials, suppliers, and production planning. Only through this end-to-end consistency does data become a reliable basis for decision-making for the CFO, COO, and executive management.
AI Readiness begins before the AI Use Case
Artificial intelligence offers enormous potential in the chemical industry – for example, to support demand forecasting, supply chain planning, quality management, maintenance, document processing, and energy optimization. However, the key prerequisite for success is not the preferred AI model, but rather the quality of the underlying processes and data.
For scalable AI applications, companies need, among other things:
- consistent business objects,
- up-to-date master and transaction data,
- clearly defined data ownership,
- traceable data provenance,
- appropriate access rights,
- reliable monitoring, and human-in-the-loop controls.
The Business AI strategy described by SAP and the AI Assistant Joule serve as good examples of functional AI applications in the SAP environment. However, they should not be misunderstood as a universal, automated solution for a company’s AI readiness. Data quality, process integration, IT security, and governance remain crucial factors.
Specifically, the following application areas arise primarily for chemical companies:
- Demand and sales forecasts,
- Identification of supplier and material risks,
- Prediction of quality deviations,
- Predictive maintenance,
- Optimization of energy consumption and production sequencing,
- Automated support for regulatory and quality-related documentation.
AI should by no means be reduced to generative models. Depending on the use case, machine learning, classical statistical methods, mathematical optimization, or rule-based automation often prove to be the far more suitable solution.
Governance and Auditability
AI applications in the areas of production, quality, safety, or compliance, in particular, require complete traceability. Companies should therefore clarify fundamental guiding questions in advance:
- Exactly which decision does the system support or automate?
- What data is used as the basis for calculations?
- Who verifies the output results?
- When and how must a human intervene manually?
- How are deviations and model errors systematically detected?
- How are usage, process changes, and results documented in an audit-proof manner?
The EU AI Act and the NIST AI Risk Management Framework serve as guidelines and reference frameworks in this regard. However, the specific regulatory classification depends heavily on the respective use case, as by no means does every internal AI application automatically fall into a high-risk category. In addition to AI-specific considerations, established industry standards such as REACH, hazardous substance requirements, quality standards, and CO₂ reporting obligations must also be processed systematically and transparently within the digital core.
The Transformation Flywheel
The true value of the digital core does not unfold in isolated instances but through a self-reinforcing cycle:
Standardized processes → better data quality → more reliable analyses → automated decisions → more efficient processes → room for innovation
Both the SAPinsider example on transformation and strategic growth and SAP’s best-practice documentation impressively illustrate how a standardized core must be positioned as a stable foundation for all further transformation steps.
What Must C-Level Executives Manage?
For this transformation to succeed, the executive board and management must focus on five key strategic decisions:
- Target State: Which processes will be standardized globally?
- Data:Who is responsible for master data related to materials, products, batches, assets, locations, energy, and emissions?
- Clean Core: Which extensions are permitted, and where exactly are they technically implemented?
- AI:Which use cases offer demonstrable business value based on reliable data?
- Operations: Who assumes operational responsibility for releases, interfaces, AI models, security, and user acceptance?
Key metrics such as the percentage of standardized core processes, data quality KPIs, the number and criticality of remaining in-house developments, the ongoing release and testing effort, and the measurable value contribution of productive AI applications serve as reliable performance indicators for management.
A Realistic Roadmap
A pragmatic and low-risk transformation path begins with a comprehensive assessment of existing systems, processes, in-house developments, interfaces, and data structures. Building on this, core processes are consistently standardized, and a binding Clean Core governance framework is established.
In the next phase, data accountability is strengthened, data quality is sustainably improved, and the first, highly prioritized AI use cases are implemented. Only once these models have proven themselves in operation should they be gradually scaled to additional plants, regions, and business units.
Conclusion and Outlook
The digital core of the chemical industry is not merely an IT or ERP project. It serves as the central link that connects business processes, production and laboratory data, regulatory requirements, supply chain management, sustainability, and AI.
Within this architecture, SAP S/4HANA forms the transactional core. SAP Cloud ERP represents a potential target state for standardized cloud processes – though not necessarily for every chemical company. SAP BTP serves as the integration and innovation platform, while RISE with SAP provides the appropriate transformation and operational framework, and Clean Core ensures the necessary architectural logic.
However, true business value is created only through bold process decisions, consistent data, robust governance, and clearly defined business objectives. adesso business consulting supports companies in systematically establishing these foundations – from process and system analyses, through Clean Core assessments and SAP architecture consulting, to the identification and implementation of value-adding automation and AI applications.
After all, one thing is certain: AI, resilience, and sustainability cannot be built on a fragmented data and process landscape in the long term.
The next articles in this series will therefore focus on specific operational levers: a more resilient supply chain, the direct link between sustainability and cost control, and the strategic growth areas of the chemical industry. The series concludes with the question of how all these building blocks can be orchestrated within an integrated transformation architecture.
