The Challenge

The North American truck OEM needed a single system which would show complete commodity spending information together with sourcing performance data for all manufacturing sites and their respective product lines.

The operational data existed in different storage systems with various file formats which forced commodity managers to perform extensive manual work while extending the duration of their sourcing activities.

Pain points

• The system encountered two primary issues because it processed extensive data collections and intricate models which decreased its operational speed and created problems with independent data verification.
• The organization faced difficulties when trying to evaluate different commodities because it lacked defined KPIs which would have measured cost performance and lead time and supplier evaluation metrics.

Objective

The system needs a ‘control tower’ layer which operates as a commodity management tool to deliver instant access to current data through its KPIs and advanced exploration functions.

Our Role and Approach

Build & deploy control-tower analytics

• The system combines all necessary data into one unified platform which includes spending information and supplier details and sourcing event records and uses a unified system to classify commodities.
• The system achieved two main benefits through its model optimization process which combined aggregation with star-schema design and calculated measures to decrease model dimensions and enhance refresh operations.
• The system used a KPI framework which established three essential performance indicators (cycle time and cost variance and supplier performance) together with their corresponding warning levels.
• The system includes Power BI application management with governance features which provide dashboards for different user roles and scheduled data updates and establish a regular review process (weekly reviews; KPI owners).

Key findings/ Suggestions

• The system needs to show its entire sourcing pipeline which should present all process stages and blocking points and all events that affect the process and standard work procedures to boost operational speed.
• Users must monitor their savings performance between validated and pipeline stages to link their work activities with their monetary results through the system.

The Result

• Commodity-level spend & performance control tower with daily refresh.
• Significant reduction in data model size enabling fast interaction.

Quantified impact

• Model size reduced from ~1-1.5 TB to ~40-60 MB.
• Sourcing/analysis lead-time reduced by ~80%.
• Benchmark: spend-control-tower governance can drive rapid indirect spend savings.