Optimize MRO data quality for S/4HANA migration

With SAP ending mainstream support for SAP ECC in 2027, manufacturing companies are speeding up their transition to S/4HANA. However, while organizations prioritize financial, procurement, and production data during this transformation, MRO (Maintenance, Repair and Operations) data often takes a backseat. This oversight can lead to major operational inefficiencies during and post-migration.

This article explores why MRO data readiness is not just crucial for a successful S/4HANA migration, but also represents a strategic opportunity for organizations to elevate their data quality standards. While migration creates urgency around data cleansing, it should be viewed as a catalyst for establishing long-term data excellence.

We will look at:

• MRO data today: Navigating complexities
• Impact of poor MRO data quality on S/4 HANA and beyond
• SPARETECH's structured approach to ensure MRO data quality
  
  

The current state of MRO data: A complex challenge

The legacy of fragmented systems and subpar data quality

Manufacturing companies face significant challenges with material master data management. Years of operations have created a complex web of data inconsistencies. Organizations often work with fragmented ERP systems where critical information is scattered across SAP ECC, Oracle, and custom-built databases. Mergers and acquisitions further contribute to this problem, as companies inherit diverse systems and data silos from acquired entities. This fragmentation prevents companies from maintaining a single source of truth and making it difficult to maintain data accuracy.

Duplicate records, where identical materials exist under different descriptions and part numbers, further complicate data management efforts. Incomplete or outdated information presents another challenge. Critical details like manufacturers, descriptions, and article numbers are often missing or incorrect. Obsolete materials in the system also create additional complexity.

Moreover, standardization issues plague many organizations. Manufacturing companies either lack standardized naming conventions, or have established standards that people fail to follow consistently. Different facilities, suppliers, and departments end up using varying terminology and classification systems. It's not unusual to see material master records with manufacturer part numbers stored within type codes, making it difficult to identify specific components.

When everyone's responsibility becomes no one's priority

Organizational dynamics further complicate the situation. Many companies lack comprehensive data governance strategies, with responsibilities often split between different departments. This creates a situation where data quality issues are widely acknowledged, but no single entity takes ownership of the cleanup process.

“Even when governance strategies exist, they're typically designed for production materials, while historically, spare parts data has received little attention despite its critical role in maintenance operations. Without clear ownership or dedicated resources for MRO data management, data quality issues persist and multiply, hampering S/4HANA migration efforts.”

Karina Ziskel
Account Executive at SPARETECH

The hidden cost of poor MRO data quality on S/4HANA migration and beyond

Poor MRO data quality already creates significant operational challenges in manufacturing companies. Now, as companies prepare for S/4HANA migration, these existing data quality issues persist and become even more critical. Poor data quality will not only complicate the migration process itself but will also limit the future benefits of S/4HANA's advanced capabilities.

Mapping challenges and resource strain during migration

Duplicate and inconsistent spare part records create significant mapping challenges. The same part often exists under different names, descriptions, and formats across systems—for example, a single bearing might be named "BRG-6203-2RS" in one plant but "6203.2RS" in another. Without proper harmonization before migration, S/4HANA will treat these variations as separate parts, leading to duplicate inventory records and procurement inefficiencies. Incorrect mapping can also cause parts to be misclassified, affecting procurement, maintenance, and reporting.

Additionally, resolving these data inconsistencies puts considerable strain on human resources during migration, when multiple teams are already stretched thin. With SAP consultants, IT teams, and business users heavily involved in core migration tasks, addressing data quality issues will create an additional burden as teams must verify and correct data while managing their regular responsibilities. These overlapping demands can significantly extend the project timeline and increase migration costs.

Limiting S/4HANA capabilities

S/4HANA's advanced features require clean, structured data to function effectively. With its lightning-fast processing capabilities, S/4HANA promises real-time insights through intuitive Fiori dashboards and seamlessly integrated processes. However, this speed and sophistication can become a liability when working with poor-quality MRO data.

Organizations cannot fully leverage S/4HANA's process automation capabilities if the underlying data is unreliable. For example, SAP S/4HANA's embedded analytics and machine learning features enable automated demand forecasting, procurement, and inventory management. However, when spare parts are duplicated or misclassified, the system fails to accurately forecast demand or optimize inventory levels. This forces teams to manually verify stock levels and maintenance schedules, effectively negating the transformative potential of S/4HANA's advanced supply chain and planning capabilities.

Compromised AI and Machine Learning potential

As manufacturers are increasingly incorporating the use of artificial intelligence (AI), data quality becomes crucial. AI-driven procurement decisions and cost optimization efforts fail when working with unreliable MRO part and supplier data. This can lead to suboptimal purchasing strategies, inaccurate cost predictions, and missed savings opportunities. For example, AI algorithms might fail to identify bulk purchase opportunities, miss potential supplier consolidation benefits, or make incorrect recommendations for reorder points due to inconsistent or incomplete data.

Achieving MRO data readiness: SPARETECH’s structured approach

“A successful migration journey begins long before the actual technical implementation, requiring organizations to take a holistic view of their current systems and data landscape. This preparatory phase is crucial for identifying potential challenges and establishing the right approach to minimize friction during transition.”

Karina Ziskel
Account Executive at SPARETECH

Data health check

Preparing your MRO data for S/4HANA migration requires a comprehensive approach. The process should begin with a thorough data health check to assess the current state of your material master data. This evaluation provides crucial insights into specific areas requiring attention and helps develop a targeted improvement strategy.

Based on the data health check findings, organizations typically need to implement a combination of the following initiatives to achieve optimal data readiness for S/4HANA migration:

Data cleansing and enrichment

Data cleansing and enrichment form the core of the preparation process. This involves identifying and removing duplicates, correcting inaccuracies, and filling gaps in mandatory fields. Beyond basic cleanup, data enrichment adds value by incorporating missing attributes and technical details that enhance data utility. This includes completing records with crucial manufacturer information such as product names, EAN, article numbers, and ECLASS IDs.

Through access to the SPARETECH database, organizations also gain transparency into product life cycle information (active or obsolete status and potential successors) and available suppliers, supporting effective obsolescence management and procurement capabilities.

Data standardization

Standardizing data structure ensures consistency across all data entries by establishing uniform formats for data attributes (e.g., plant data, part numbers) and using consistent terminology (e.g., article number, manufacturer) throughout the system. This eliminates discrepancies such as variations in naming conventions, inconsistent data entry, and mismatched formats, enhancing data quality and making it easier to search, analyze, and share.

Simultaneously, restructuring data may be necessary to organize and properly arrange information, such as moving misplaced entries to the appropriate column and ensuring each data field contains accurate information.

Data harmonization

Data harmonization is essential for organizations with multiple ERP systems. This complex process requires careful identification and mapping of equivalent materials across different systems while resolving conflicts between varying naming conventions and data structures.

"Organizations must meticulously consolidate supplier information, part numbers and technical specifications from all sources. The goal is to create a single source of truth, where each material has one leading record. This master record becomes the standard reference across all facilities, ensuring consistency in ordering, maintenance, and inventory management."

Jeffrey Loh
Product Manager at SPARETECH

SPARETECH as an interim solution during migration

During the S/4HANA migration process, SPARETECH serves as a centralized platform for managing MRO data before transferring it to the new system. Teams can efficiently search, create, and update spare parts records in a structured environment, ensuring data accuracy and completeness before going live. Once migration is complete, the validated and standardized data transfers seamlessly into S/4HANA, enabling a smooth transition with minimal operational disruption.

Maintaining data quality: A continuous journey

Achieving MRO data readiness for S/4HANA migration is not a one-time project but the beginning of a continuous journey. Organizations must implement strong data governance processes and maintain data quality through digital workflows. This sustained commitment ensures that clean, standardized data continues to drive operational excellence long after the initial migration.

SPARETECH supports this data governance through configurable procedures and intelligent quality checks, ensuring consistent data quality while streamlining material creation and maintaining compliance with company standards.

Taking the first step

Ready to ensure a smooth and successful S/4HANA migration? The journey to optimal MRO data readiness starts with understanding where you stand. Contact SPARETECH for a quick complimentary data health assessment to give you a brief overview of your current data quality status.

Our team of experts will help you identify potential challenges, develop a tailored strategy, and guide you through every step of your data preparation journey. Don't let poor data quality compromise your S/4HANA migration – take the first step toward data excellence today.