The Material Master in SAP is the foundation from which the entire supply chain, production, and sales processes originate. A single error in master data can trigger a domino effect that impacts multiple areas of the business. For instance, in regulated industries such as pharmaceuticals, a component with an incorrectly defined validity period could lead to an entire batch being discarded due to a false expiration.
These errors not only cause delays and product losses but can also result in regulatory non-compliance and traceability issues, ultimately affecting the company’s ability to meet quality standards and regulatory requirements.
In the following sections, we will address the major challenges encountered in SAP regarding data quality and governance for regulated industries. We will explore how the use of AI and complementary tools can empower businesses in this space and tackle the most common issues.
Challenges in Data Quality and Governance for Regulated Industries
Challenge 1 – Incomplete Materials or Critical Errors
The creation of material texts with missing views (such as plant, storage location, or sales) or with incorrect values in critical fields is a common issue that affects data quality and governance across all types of industries. This can cause delays in key processes (e.g., preventing invoicing or the creation of purchase orders) and lead to rework to correct the missing information.
- Example: A frequent case is omitting the classification view in a raw material, even though it is essential for batch management (storing characteristics such as manufacturing and expiration dates). Its absence leads to significant reprocessing efforts. Another critical error is defining an incorrect unit of measure, which may go unnoticed until it causes serious problems, such as miscalculated stock or incorrect pricing per unit.
- How to resolve it: Define solid business rules that enforce complete and correct data from the moment a material is created. In practice, this means setting up validations to ensure all required views are present according to the material type and organizational unit, and that all key fields contain valid values. Strategies range from marking required fields in Excel templates to custom validation programs, but the most effective approach is a systematic enforcement of business rules to eliminate incomplete or inaccurate data. In short, no material should enter SAP without meeting the minimum required and consistent data standards. Today, there are tools that natively integrate with SAP to facilitate this process, such as SiDM Materiales.
Challenge 2 – Duplicates and Lack of Standardization
In large organizations, multiple departments may create materials using different criteria, resulting in duplicates or inconsistencies in codes and descriptions. The issue is clear: a single SKU created more than once in SAP leads, at best, to wasted time identifying and blocking the duplicate, and at worst, to duplicated purchases and inventory. Lack of standardization in naming and coding complicates inventory management and can hinder version traceability, potentially causing obsolescence or unnecessary stock destruction.
- Example: Imagine a packaging material registered by Department A as “20×20 Cardboard Box” and by Department B as “20cm Cardboard Case.” Although referring to the same item, the differing description and code may go unnoticed as a duplicate. This could lead each department to manage orders and inventory separately, duplicating stock and potentially causing overages or expirations.
- How to resolve it: Implement data management tools with functionality to detect duplicates both during record creation and among existing records. These tools should be embedded in the creation process to provide immediate alerts for potential duplicates. Advanced solutions like SiDM Materials use Artificial Intelligence (AI) to enhance detection by analyzing complex data patterns. An AI engine can identify duplicate records and suggest how to consolidate them into a single master record — a capability that surpasses traditional methods. In addition, SiDM Materials integrates text standardization and multilingual description templates, further reducing inconsistencies across departments.
Challenge 3 – Lack of Governance and Traceability in Requests
Without proper master data governance for the creation and modification of materials, any user could enter changes without prior analysis or approval. The lack of a formal approval flow often results in disorganized manual processes (via emails, phone calls, or isolated forms) and limited visibility into who made what change and why.
In many companies, different departments are responsible for specific views of a material, and without coordination, involving all stakeholders becomes bureaucratic and inefficient — sometimes excluding key players or causing delays in the supply chain. In highly regulated industries like pharmaceuticals, this lack of control not only affects efficiency but also jeopardizes regulatory compliance: GMP/FDA standards require proof of master data control, and a creation process without traceability can undermine audit outcomes.
- Example: Consider a life sciences company during an audit. The auditor asks for the change history of a critical material (such as a product formula) and who approved the changes. If the company lacks a defined approval flow in SAP, that information may be scattered across emails — or worse, may not exist at all. This would be a negative compliance finding.
- How to resolve it: The solution lies in establishing structured workflows for material creation and modification, where each request is logged and passes through the appropriate stakeholders at each stage. Instead of relying on spreadsheets, a fully integrated tool (ideally within SAP itself) should be used to unify this process. Well-designed workflows allow for multiple approval levels based on material criticality or type of change and involve the responsible areas for each data set. These workflows are fully auditable in SAP and meet GMP/FDA traceability requirements by recording who requested, who approved, and when.
Challenge 4 – Incomplete Production Data
Beyond the material’s own attributes, in manufacturing industries it is essential to link production data to the material master — primarily the Bill of Materials (BOM) and Routing Sheets (or manufacturing recipes in food processing).
A common challenge is that, due to role separation or urgency in launching a product, the material is created in SAP but the BOM and routings are not loaded in time. These components are often out of sight of the master data team, as they are treated as separate objects, which can result in no one taking responsibility until it’s too late.
- Example: A food & beverage company creates a new flavored product in the system but forgets to associate the ingredient list and mixing procedure (its BOM and routing). When the production order is launched, the system throws errors because the recipe is missing; the order is delayed while engineers scramble to build the missing BOM. This seemingly minor oversight causes production delays and potential market shortages — all due to incomplete master data.
- How to resolve it: Production data should be considered an integral part of the material creation process. That means including the creation or linkage of BOMs, production routings, or other recipes from the initial master data request. This ensures that when a new product is created, its technical manufacturing data is also handled. Data governance tools like SiDM Materials allow for this setup — for example, indicating in the request whether a material requires additional master data (BOMs, routings, formulas) and assigning responsible parties to complete them within the workflow. This not only provides visibility into pending data, but also automatically notifies the relevant person to enter it at the right time.
How to Solve Material Master Challenges in Regulated Industries: AI-Driven Data Quality and Governance Solutions
Overcoming the challenges described above requires a combination of data governance tools (to manage processes with roles and approvals) and data quality mechanisms (to validate correct values and structures). In practice, this means deploying applications that enforce checks during the creation or maintenance of materials (e.g., mandatory fields, valid value lists, approval workflows based on material type), ensuring that only consistent data enters the system. These tools should also offer the following capabilities:
- Rules aligned with real business logic: The key lies in identifying and configuring the right business rules. A strong strategy is to extract these rules from existing data — analyzing the current material master to detect typical values or logical relationships (e.g., if a material is of type X, then field Y must always be “ABC”).
- AI beyond the chatbot concept: Not every use of Artificial Intelligence must take the form of a conversational bot. In this context, a powerful AI application is the induction of rules and predictions based on historical data. That is, using machine learning algorithms to analyze existing material master data and identify which values should populate each field of a new material based on prior patterns.
- From know-how to automated rules: Instead of relying on an individual’s memory or experience to input data correctly, structured processes can verify that materials are being entered accurately — and AI can learn from those processes to establish automatic rules.
SiDM Materials: Master Data Quality and Governance in SAP for Regulated Industries
To comprehensively address the challenges outlined above, Innova developed SiDM Materials, a solution focused on master data quality and governance within SAP. Below is a summary of how SiDM Materials addresses each of the mentioned needs:
- Configurable business rules for complete and accurate data: SiDM Materials allows configuration of field-level and organizational-view-level rules without coding, ensuring that every new material includes all required views and valid values in each field. For example, it can prevent the creation of a material if it is not extended to a mandatory plant, or automatically populate default values based on material type. These rules apply not only to new entries but also help identify and correct incomplete existing records.
- Approval workflows with full traceability: The solution includes customizable workflows, ensuring that any request to create, extend, or modify a material follows a defined and auditable process. Different workflows can be set up for different scenarios, with multiple approval levels or parallel steps when needed. Each responsible user only sees and completes the fields under their scope. All actions are recorded in SAP — who requested, who approved, and when — ensuring compliance with internal policies and facilitating audits.
- AI for prediction and auto-completion of master data: SiDM Materials offers an optional AI service that analyzes the company’s historical data to establish auto-completion rules. The system learns from existing materials and can predict a large portion of the values for a new material based on similar past cases. In many businesses, this has enabled the automatic completion of 70% to 100% of fields, significantly speeding up the master data creation process, reducing human error, and standardizing information from the outset.
- Extensibility to other processes and advanced analytics: SiDM Materials is part of a broader master data application suite. The same platform can be adapted or extended with additional services for data analytics in other SAP business scenarios (e.g., mass data cleansing, S/4HANA migration, data quality analysis in other modules). Thanks to its native SAP integration, the solution leverages the full richness of corporate data to deliver actionable insights.
- Multilingual support: English, Spanish, and Portuguese by default, with the ability to translate to other languages in approximately one week.
