Consolidating Master Data for a Water Treatment Company with SAP MDG

SAP Master Data Governance – Streamlining Business Processes

Introduction:

The client is a leading global provider of water and wastewater treatment solutions for its industrial and recreational customers. Based in the USA, the client has a broad portfolio of products and services, they have more than 40k+ customers located in more than 100 locations across several countries. 

They faced significant challenges in managing their master data across multiple applications. Stridely Solutions partnered with them to streamline their master data management using SAP Master Data Governance (MDG).

Challenges

Data Management Issues

  • Managed master data across various applications (HANA, ECC, CPI), leading to time-consuming, repetitive processes.
  • Prone to data duplication, resulting in inefficiencies.
  • Manual governance of data, leads to unproductive use of resources.

System and Process Inefficiencies

  • Depended on inefficient manual processes and antiquated systems.
  • Lack of data accuracy and insufficient process documentation.
  • Workarounds and reliance on institutional knowledge exacerbated inefficiencies.

Data Entry and Accuracy Problems

  • Data for each material had to be entered into multiple systems, increasing the risk of errors and redundancy.
  • The same issue extended to customers, supplier, functional locations, and BOM data.
  • Lack of flexibility and speed in processes hindered innovation and delayed decision-making and reporting.

Technical Debt and Data Verification

  • Data is often duplicated and inaccurate, leading to significant technical debt.
  • Manual verification of data is required, consuming countless hours.
  • Inefficiencies affected data accuracy across all functions, making it difficult to retrieve accurate historical data.
  • These issues impeded the company’s ability to scale for growth.

Governance and Workflow Deficiencies

  • Lacked a governance workflow model and process for managing master data.
  • No system in place to track or approve new data entries, resulting in inconsistent master data structures and reporting across business systems.
  • The absence of process documentation and understanding led to a reliance on institutional knowledge.

The client managed master data across various applications, including HANA, ECC, and CPI. This process was time-consuming, repetitive, and prone to data duplication, leading to inefficiencies. Governance of data was manual, resulting in unproductive use of resources.

Additionally, the company was dealing with inefficient manual processes, antiquated systems, and a lack of data accuracy. Workarounds and dependence on institutional knowledge due to insufficient process documentation further exacerbated these issues.

Data for each material had to be entered into multiple systems. This increased the risk of errors and data redundancy. The same issue extended to customers, suppliers, functional locations, and BOM data. The lack of flexibility and speed in these processes made it difficult to deliver innovation and delayed decision-making and reporting.

The data was often duplicated and inaccurate, leading to significant technical debt. Users had to manually verify the data, spending countless hours reviewing requests. This issue affected data related to all the functions, making it difficult to retrieve accurate historical data. These inefficiencies impacted the company’s ability to scale for growth.

The client lacked a governance workflow model and process for managing master data. There was no system in place to track or approve new data entries, leading to inconsistent master data structures and reporting across business systems. This absence of process documentation and understanding led to a reliance on institutional knowledge.

Solution and Results

Centralized Master Data Management with SAP S/4HANA MDG

Stridely implemented SAP MDG to centralize the client’s master data management. SAP MDG provided the data governance and workflow processes to serve as the central framework to create, manage, approve, and distribute master data across five different ERP systems (SAP and non-SAP) and other applications.

This solution enabled users to gather data from a single application, reducing duplication and ensuring data accuracy.

Single System Data Entry

Post-MDG implementation, data entry for materials, customers, suppliers, and BOMs was streamlined into one system. This approach eliminated redundancy, improved data accuracy, and enhanced efficiency by reducing the need to enter data into multiple systems. The consolidation of master data into MDG facilitated a more flexible and scalable process, improving speed and innovation delivery.

Data Consolidation and Cleansing

Stridely consolidated and cleaned up the client’s business data, reducing technical debt. By merging data for required functions into one system, resulting in improved data accuracy and eliminating redundant information. This cleanup effort also enhanced the ability to scale for growth.

Enhanced Data Governance and Visibility

The implementation of SAP MDG introduced a governance model and workflow, enabling the client to track and approve new data entries. This provided complete data visibility and ensured a consistent master data structure across current and future business systems.

The new governance framework reduced dependence on institutional knowledge by providing clear process documentation.

Value Add-ons

  1. Key Mapping: Stridely developed a key mapping function to generate unique serial numbers consistent across ECC and S4HANA applications. This reduced enhance data management across all systems, consistent data integration, Centralized control, and improved system interoperability.
  2. Custom UI Development A custom user interface was created to simplify data search. Previously, managing 1,000 fields for a single material or product was complex. The new interface prioritized 10 key fields out of 100 relevant ones, It will help the client users to fill data more efficiently by providing only main key fields as per business requirements.
  3. Alphanumeric Solution A duplicate check functionality was implemented to validate and align different number ranges (alpha, check, incremental) for materials, customers, and suppliers. This ensured data validity and consistency, reducing errors and improving data quality.
  4. Enhanced Data Validation and Integrity Advanced validation mechanisms were incorporated to ensure data entered into the system adhered to predefined standards. This helped in maintaining data integrity and reducing the need for manual corrections.
  5. Automated Workflow Integration The introduction of automated workflows within SAP MDG streamlined data approval processes. This integration allowed for real-time tracking and governance, ensuring that data entries were promptly reviewed and approved. Additionally, we customized the approver determination to accommodate the complex approver assignment scenarios required by the business, enabling precise and flexible approval workflows for various combinations.
  6. Reporting and Analytics Enhancements The reporting and analytics capabilities of the MDG system were enhanced, providing the client with better insights into their data. This improvement enabled more informed decision-making and strategic planning.

Key Statistics

  • 60% Improvement in Cycle Time for fulfillment and procurement processes.
  • 100% Improvement in Data Quality
  • 75% Reduction in Manual Data Input
  • 100% Visibility Across the Data Lifecycle
  • 75% Reduction in Data Duplication
  • Significant improvement in Data Governance Compliance, ensuring data integrity and accuracy.
  • 100% Reduction in Technical Debt

Business Outcome

The implementation of SAP MDG significantly improved the client’s master data management. The client now enjoys greater data accuracy and consistency, better visibility into requests and trends, and more streamlined invoicing and procurement processes.Users have gained a deeper understanding of the importance of master data management, contributing to a culture of data integrity and efficiency.

Overall, the centralized and automated data management processes have led to improved decision-making, operational efficiency, and strategic advantages for the company in their market.

 

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