Take full account of existing systems—and turn them into a comprehensive whole
Working with the pharma, we capitalized on our experience managing vendor and indirect material master data and taxonomy design. We began by analyzing the existing:
- Data quality
- Data governance
- Target operating model
- Vendor data model
- Indirect material and taxonomy
- The vendor process and KPIs
The assessment revealed:
- Incomplete, often inaccurate and frequently duplicated vendor data
- Data-quality metrics that didn't coordinate with business needs
- Missing or incomplete data fields that affected supplier risk management
- Over 100 systems that held vendor data, with no integration between them
- No one had stewardship of MDM
- At 45-55 days, the vendor-onboarding process was too long
- The spend management tool categorized only 10% of indirect materials. Globally, the company made 93% of indirect material purchases through free-text entries rather than catalogs. That represented 65% of the organization's total spend.
- Multiple taxonomies and mismatching commodity codes. No one used commodity codes in 35% of global purchase orders.
We responded to these issues by recommending a comprehensive master data management framework—something that can seem daunting if you think of the process as limitless. So we were careful to align the road map to areas that would generate the greatest business impact.
Developing a powerful vendor data model
To create a robust vendor data model that met the company's needs and that positioned it for third-party oversight, we identified:
New attributes to improve supplier risk assessment, including site-level details and parent-child hierarchies
Attributes for better vendor matching, as well as tax codes, tax identification numbers, and banking detailsRedundant or unused attributes for removalPrinciples for determining whether attributes should reside in transactional or centralized master data systemsA road map of short- and long-term attribute changes.
Reshaping data quality and governance
We scrutinized the supplier data for completeness, accuracy, and uniqueness to benchmark the quality of the firm's vendor master data systems. Then we analyzed related business rules to measure the effectiveness of vendor master matching.
Finally, IT redesigned data governance and stewardship, giving people defined roles and responsibilities. It also proposed key metrics for the data quality dashboard.