Customer:
As a leading international manufacturer of commercial vehicles with an annual turnover of around 2.5 billion euros and over 5,500 employees, the company was faced with the challenge of managing over 5,000 different drawing parts with an annual purchasing volume of around 15 million euros.
Initial situation: Complexity and challenges in procurement
Procurement proved to be a complex undertaking, as more than 70 suppliers were listed for some product groups and there was no clear allocation of purchased parts to the suppliers’ production strengths. Moreover, price levels were not systematically reviewed and renegotiated in the past. This led to the need to identify potential savings of 10% and to increase the efficiency of procurement processes.
Measures: Strategy and implementation steps
To overcome these challenges, the company developed a comprehensive strategy. This ranged from the analysis of ERP procurement data and the pre-selection of suitable articles to the application of machine learning for CAD model analysis and the consolidation of ERP and CAD data with the help of data mining. Missing and incorrect article information was processed using Data Extraction Services. The analysis of purchase prices per item was based on the suppliers’ inventory prices, supported by data analytics. With the help of predictive costing, logical target prices were calculated and best-case purchasing scenarios were determined. At the same time, supplier and machine park information was obtained in order to create a detailed “parts-machine-supplier matrix”, which served as the basis for targeted market price inquiries. Negotiations with suppliers ultimately led to the realization of the identified savings potential.
Results: Significant cost savings and efficient supplier structure
The successful implementation of these measures led to impressive results. Procurement costs for sheet metal parts were reduced by an average of 20% on a weighted average, while costs for turned, drilled and milled parts were reduced by an average of 15%. The supplier pool was consolidated by an impressive 50%, depending on the product group. This success story not only underlines the efficiency of AI tools in procurement, but also the strategic foresight of the company to strengthen its competitiveness and achieve sustainable cost optimization.
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