Issue

Syensqo currently operates two separate SAP ECC systems, each housing material master data for spare parts. Over time these systems have accumulated inconsistencies, duplicate records and non-standardized data. These inconsistencies create challenges in spare parts identification, procurement and maintenance planning.

As Syensqo prepares to transition to a single S/4HANA system, it is critical to cleanse and harmonize spare parts data to prevent these inefficiencies from being carried forward. Without a structured cleansing approach issues such as duplicate materials, incomplete records and inconsistent descriptions will persist, resulting in difficulties in inventory management, inaccurate procurement planning and potential operational disruptions post-migration.

Key areas covered in this document include:

• Benefits and drawbacks of each product
• Overview & Background
• Product Options
• Evaluation
• Recommendation
• Business & Project Impacts

A decision is required on how to systematically cleanse and harmonize the spare parts data, ensuring accuracy, consistency and usability in the new S/4HANA environment while minimizing operational disruptions during the migration.

Recommendation

Given the complexity and importance of ensuring accurate and consistent spare parts data as Syensqo transitions to a single SAP S/4HANA platform a two-stage approach is recommended for material data cleansing and optimization. This approach will leverage both Syniti - Enterprise Data Matching and Sphera - MRO Data Quality Optimization Services to ensure data quality, streamline integration and optimize critical spare parts for better operational efficiency.

Stage 1: Initial Deduplication and Cleansing with Syniti - Enterprise Data Matching

The first stage focuses on addressing the fundamental issues of duplicate records and data inconsistencies. The goal is to use Syniti - Enterprise Data Matching to perform large-scale deduplication and data cleansing across all material records. Syniti’s AI-driven data matching tools will help identify and eliminate duplicate entries, standardize basic data elements and ensure the overall consistency of spare parts data. Additionally, the tool can be repurposed for cleansing other master data objects (e.g. Vendors) beyond just materials.

Stage 2: Standardization, Enrichment and Optimization with Sphera - MRO Data Quality Optimization Services

Once the initial cleansing is complete, Stage 2 focuses on optimizing critical and high-value spare parts data. For these materials, it is important to go beyond basic cleansing to ensure that the data is standardized, enriched and optimized to support operational and compliance needs. This stage will leverage Sphera’s MRO Data Quality Optimization Services, which specialize in improving data quality for Maintenance, Repair, and Operations (MRO) items.

Background & Context

Spare parts data is critical for effective asset management, procurement and maintenance planning. The transition to S/4HANA presents an opportunity to cleanse, standardize and harmonize spare parts data to enhance operational efficiency. The chosen tool must facilitate data validation, deduplication and enrichment while integrating seamlessly with the data migration strategy led by Syniti.

To address these challenges, the project aims to:

• Define and standardize foundational data elements such as manufacturer name, part number, model number, short/long descriptions and MRP type.
• Ensure compliance with industry standards by linking parts to specifications, regulatory requirements and maintenance best practices.
• Identify and eliminate duplicate spare parts records using advanced data matching and cleansing techniques.
• Leverage an automated, scalable data cleansing solution to process large volumes of spare parts data efficiently before migration to S/4HANA.

By conducting a thorough data cleansing exercise prior to migration, Syensqo can eliminate these inefficiencies and create a more streamlined, accurate, and efficient spare parts management system within S/4HANA. This will ultimately enable smoother procurement processes, better maintenance planning and more accurate inventory tracking contributing to greater overall operational efficiency.

Assumptions

• Syniti is the official data migration partner for the S/4HANA implementation.

• The cleansing process must be completed before the S/4HANA migration cutover.

• The selected tool must support large-scale data cleansing while ensuring data integrity and governance.

• Business users will be engaged to validate and approve the cleaned data.
• Standardized naming conventions and classifications will be adopted.
• All materials are reviewed and categorization with the correct Material Type to ensure accurate classification.

Constraints

• Differences in the data structures of the two ECC systems will require careful consideration when merging or harmonizing data.
• The cleansing process must not interfere with ongoing operations or delay other critical project activities.

Impacts

• Improved accuracy and consistency in spare parts data.
• Enhanced efficiency in procurement, maintenance planning, and inventory management.
• Reduced data redundancy and improved system performance in S/4HANA.
• Minimized risk of data migration errors and inconsistencies.

Business Rules

• Material descriptions must follow a standardized dictionary format to ensure consistency.
• Identified duplicates must be either merged or removed, with no duplicate records migrated to S/4HANA.
• Only validated, cleansed spare parts data will be migrated into the new S/4HANA system to ensure data integrity.

Options considered

Option A: Syniti - Enterprise Data Matching

Syniti’s Enterprise Data Matching tool provides advanced data matching, cleansing and migration capabilities. As Syniti is already the migration partner for the project, leveraging their data quality tools ensures alignment and efficiency.

Features & Benefits:

• AI-driven data matching for duplicates and inconsistencies.

• Seamless integration with SAP 

• Supports large-scale data cleansing for global operations.

• Alignment with the project’s migration strategy, reducing implementation complexity.

Limitations:

• Cleansing is performed solely within staging databases and is not validated against external databases.
• Requires customization for specific MRO data classification / material needs.

• May involve additional licensing costs depending on scope.

Option B: Sparetech - MRO Master Data Cleansing

Sparetech specializes in MRO (Maintenance, Repair and Operations) data quality improvement, focusing on spare parts classification, taxonomy and cleansing.

Features & Benefits:

• Industry-specific MRO data cleansing and classification.

• AI-based recommendations for standardization.

• Automated enrichment of missing attributes using external databases.

• Supports multiple languages for global deployments.

Limitations:

• Limited integration with Syniti, requiring additional effort to align with the migration approach.

Option C: Sphera - MRO Data Quality Optimization Services

Sphera provides a service-based approach to MRO data optimization, focusing on compliance, standardization and cataloging.

Features & Benefits:

• Expert-led data cleansing and classification services.

• Standardization of Material Descriptions and Attributes
• Automated Enrichment of Missing Data, Cross-Referencing with External Sources

• Regulatory compliance support for industry standards.

• Ensures alignment with corporate MRO strategies.

Limitations:

• Higher cost due to service-based pricing model.

• May require additional validation when integrating into SAP S/4HANA.

Evaluation

Outline why you selected a position. The best format could be a pro/con table (sample below), but is up to you as the author. You must consider complexity, feasibility, cost/effort to implement, but also ongoing operational impact and cost. You must consider the program principles and explain any deviations in detail. This is probably as important as the decision itself.

See also

Insert links and references to other documents which are relevant when trying to understand this decision and its implications. Other decisions are often impacted, so it's good to list them here with links. Attachments are also possible but dangerous as they are static documents and not updated by their authors.

Change log

Workflow history