Procurement Analysis Solution for a Global Wind Turbine Manufacturer

About Client

World’s largest wind turbine manufacturer headquartered in Germany.
Caters to multiple regions across the globe.
Specializes in the development, manufacturing, project management and servicing of offshore wind turbines.

The client wanted to get consolidated, real-time and effective insights of their procurement processes in one place.
Some concerns were as follows:

Inability to detect and track minute-level issues in order management, vendor management, free stock and out-of-stock items, and more.
Absence of a system to track key level indicators such as the granular bifurcation of the number of purchase orders as per the production plant, the month, the year, etc.
Difficulties in managing and calculating the costs and savings of the suppliers from various countries.
Lack of a solution that can measure the increase or decrease of the cost of raw materials over a period of time.
No visual dashboard to get instant insights from the organization’s data.
Low-cost solution to smoothen the operations and address the issues.

Here’s what we did to tackle the problem:

Requirement Gathering
– Understanding the procurement processes model from the stakeholders.
– Data sources and KPIs were identified.

Data Sourcing
Data stored in multiple formats such as SharePoint files, flat files (Excel and JSON), Microsoft OneDrive files, legacy on-premise SQL, etc., were acquired.

Data Ingestion
The data was stored in Azure Blob storage, converted to the structured format and fed to the data warehousing layer.

Data Visualization and Optimization
– A customized dashboard was built using Power BI for tracking the KPIs and streamlining the processes.
– Complex data querying and analysis were performed to get in-depth insights.
– Optimization of the solution for faster loading of reports using Premium capacity workspaces.

Full transparency and management of sales, costs and credit control.
Better tracking of the material’s lifecycle from the warehouses to the respective inventories. This helped reduce operational costs by 29% by the end of FY.
Cumulated savings per plant increased by 1/4th as compared to the previous FY.