Building a Global Supplier Performance Scorecard with BigQuery and Looker Studio

Note: The dashboard image shown is a synthetic/demo representation created for illustration.

BigQuery supplier scorecard

Supplier performance reporting with BigQuery supplier scorecard can quickly become complex when quality, purchasing and delivery information comes from different systems and business processes.

In a recent enterprise analytics project, the organization had implemented a new supplier quality application for recording non-compliance claims. However, the application did not provide the supplier performance scorecard required by procurement and supplier development teams.

The requirement went considerably beyond creating a dashboard.

The business needed a global supplier performance model combining rolling performance indicators for quality and delivery with several calculated performance indices and an overall supplier classification.

The calculation depended on information from multiple sources, including:

• Supplier quality and non-compliance data

• Purchased-parts volumes

• Delivery performance data

• Supplier master data

• Category-specific performance targets

Some of this information was collected independently across multiple countries before being consolidated for reporting.

The Challenge

The previous reporting environment relied on a legacy desktop BI solution. Reporting was relatively slow and required significant manual data preparation.

Different datasets also had different structures, ownership and update processes.

The main challenge was therefore not the dashboard itself.

The real challenge was establishing a reliable data foundation where supplier, purchasing, quality and delivery information could be brought together consistently before calculating supplier performance.

Building a common data foundation

BigQuery was used as the central analytics and transformation layer.

Data from the different operational sources was cleaned, standardised, validated and transformed into reporting-ready datasets. Business rules and KPI calculations were implemented centrally so that the reporting layer did not have to reproduce complex transformation logic independently.

Looker Studio was then used as the reporting interface.

This separation created a relatively clear architecture:

Operational data sources → BigQuery transformation and business logic → Looker Studio reporting

The objective was to keep the data preparation and calculation logic controlled in the data layer while providing business users with a simple reporting experience.

Moving toward one trusted view

The resulting scorecard provides supplier development, procurement, sourcing and supplier master-data users across multiple countries with a common view of supplier performance.

Instead of relying on fragmented reporting and locally interpreted calculations, users can work from a consistent set of KPIs and supplier-performance results.

For me, one of the main lessons from this project has been that successful BI reporting starts well before the visualisation layer.

A reliable BI solution starts with a reliable data foundation. Visualisation is only the final layer. Consistent data models, validated transformation logic and clearly defined business rules are what make reporting trustworthy.