Customer work · Food services

Compass Group standardized SAP master data, reporting, and supply-chain measures

Compass Group worked with inconsistent records and conflicting reports across SAP and related systems. BluePi integrated inventory, sales, and operational data on AWS Redshift, established master-data workflows and governance, and applied field-level quality controls.

Compass Group standardized SAP master data, reporting, and supply-chain measures system diagram

SAP procurement reporting moved from daily Excel work to Redshift and Power BI

Compass Group teams exported SAP procurement data and spent two to three hours preparing a daily Excel report. BluePi introduced a scheduled path from SAP through custom ETL on Amazon EC2 into Amazon Redshift data marts and Power BI.

01 · Daily SAP extraction

An initial load and daily end-of-day deltas moved SAP procurement records into raw Redshift tables.

02 · Redshift facts and data marts

Aggregation jobs organized raw records by dimensions such as date, region, site, and vendor before loading report-ready facts.

03 · PR-to-PO failure analysis

Power BI showed purchase-order status and highlighted reasons a purchase requisition did not convert automatically.

From SAP purchase requisition to Power BI failure analysis

SAP data entered the EC2 ETL path as an initial load and daily delta. Raw records landed in Redshift, aggregation jobs generated facts and data marts, and Power BI presented purchase-order status and failed-conversion reasons.

Keep raw SAP records available

Initial and daily delta loads entered raw Redshift tables before reporting logic changed the data.

Build facts from business dimensions

Aggregation jobs organized procurement data by dimensions including date, region, site, and vendor.

Expose failed-conversion reasons

Power BI connected purchase-order status to the conditions that prevented automatic conversion so teams could correct the source issue.

Where this pattern fits

This case fits procurement teams that still export ERP data into spreadsheets to investigate purchase-requisition and purchase-order performance. BluePi can start with one daily report, its SAP inputs, transformation rules, and the decisions it supports.

Review your procurement reporting path

Case details

Open a section to review the customer problem, implementation, business change, and architecture.

01The starting point

Duplicate records, hard mappings, and disconnected sources caused departments to report different numbers. The inconsistencies weakened analytics and made supply-chain and food-cost reporting difficult to trust.

  • Data inconsistency issues: Compass Group worked with inaccurate and inconsistent data across the organization, which weakened analytics and slowed business decisions.
  • Multiple sources, conflicting reports: Reports drew on many disconnected sources, so different teams produced conflicting numbers.
  • Data duplication challenges: Duplicate records and hard mappings to new structures made reporting slow and unreliable for individual departments.
02System delivered

BluePi integrated SAP inventory, SAP sales, and related operational sources, established a central master-data repository and workflows, defined governance standards, and applied field-level validation and quality rules for AWS Redshift reporting.

  • Complete data integration: BluePi integrated diverse data sources, including SAP inventory and sales modules, into one unified data environment.
  • Master data management and workflows: The solution established master data management (MDM) alongside automated workflows, standardizing how data is handled across the organization.
  • Single source of master data: BluePi created a central repository for all master data, ending conflicts between different versions of the same information.
  • Governance and data quality standards: Formal data governance structures and quality standards give the organization clear guidelines for maintaining data integrity.
  • Data quality rules: Validation and quality control rules applied to each master data element keep data consistent and accurate through its lifecycle.
03Outcomes

Compass Group gained standardized master records and more reliable reporting. Supply-chain activity and food costs could be reviewed from shared definitions instead of department-specific mappings and duplicate records.

  • Standardized masters: Master data now uses uniform definitions and formats across the organization, ending conflicts over how data is structured and read.
  • Improved data quality: Data accuracy and reliability improved across the organization, so reporting and analysis can be trusted.
  • Improved supply chain monitoring: Compass Group can now track supply chain operations and food costs precisely, with better control over critical operating expenses.
04Delivered procurement analytics path

SAP procurement data was extracted each day and loaded through custom ETL on Amazon EC2. Raw SAP tables entered Amazon Redshift before aggregation jobs organized facts by dimensions such as date, region, site, and vendor. Power BI used the resulting Redshift data marts to report purchase-order status and the reasons purchase requisitions did not convert automatically.

05What changed for procurement reporting

Teams moved from daily SAP dumps and repeated Excel work to scheduled reporting from Redshift data marts. Power BI made purchase-order status and failed-conversion reasons visible so departments could correct contract, quantity, and process issues.

System diagram