Table of Contents
The ideal number for Master Data errors is Zero.
Jump to the details for the “Pursuit of Perfect Master Data” approach.
Highlights
Industry: CPG/Personal Healthcare
Geography (Global): HQ in North America, Plants/Sales Regions in EU, Asia-Pacific, and China
Scope: Costing, Materials Management, PLM, Packaging, Data Quality
Cross-functional Teams: Finance, Inventory Management, Marketing, Transportation, R&D, Packaging, PLM, and Master Data Management
Systems Supported: SAP S/4HANA, SAP PLM, Custom App based on SQL Server, SQL Server
Total number of stakeholders involved in Master Data workflows: 100+ globally
Key Master Data Issues before ZMDM adoption
- Cost synchronization between different SAP systems (Manufacturing and Order-to-cash SAP systems) was time-consuming and an obstacle to financial and operational excellence
- Annual cost updates used to take upwards of 3 months, using up valuable time of finance teams
- The manual approach to resolving data quality issues was long and tedious, leaving a lot of data quality issues to persist
- Maintaining consistent dimensional and packaging related data for SKUs across multiple systems to support New Product introduction launches
Results
| Before ZMDM | After ZMDM |
|---|---|
| Cost synchronization between different SAP systems time was time-consuming and took significant coordination and effort to update | Process simplified by allowing finance teams to review data to be updated as part of the workflow, and updates in multiple SAP systems automated after review activity |
| Annual cost updates were a 3-month effort with multiple teams involved | It is minimized to less than a month and requires minimal effort |
| Packaging and dimensions related data for SKUs required many teams to be involved and update of several systems | Cross-functional workflow simplified coordination and automated most of the data capture and updating processes |
| Data quality issues were many and handled manually with data loads | Data quality issues are automatically captured and resolved using cross-functional and automated workflows |
| Expensive legacy Master Data Management platform doing very little in terms of Master Data process and data quality improvement | Saving $400-500K in license/subscription/operations costs yearly by decommissioning the legacy Master Data Platform |
Case Study Details
The Pursuit of Perfect Master Data
The ideal number for Master Data errors is Zero.
There are two ways to achieve zero master data errors. Error-proofing and error correction. Error-proofing is infinitely better than error correction.
The company’s intricate landscape included multiple SAP environments for different functions and additional solutions for PLM and Go-to-market. Over time, disparate systems dealing with shared master data (Materials, Finance—costing/Pricing, etc.) have led to data quality issues. To correct the data quality issues, the Master Data team adopted a solution and process as shown below.
Error-proofing relies on incorporating Poka-Yoke principles in Master data workflows.
The Error Correction process is supported by data quality rules that run within and across all the systems that require consistent master data using SAP InfoSteward and ZFlow. ZFlow is generally used to address complex data quality rules spanning multiple systems. Master data records that fail the data quality rules in these systems are assigned to appropriate correction workflows in ZFlow. The correction workflows include a combination of automated data corrections and manual review steps by cross-functional teams from Finance, R&D, and Supply Chain teams.
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