Integrated Use of Market Information in Supply Chain Management through Price Regression Analysis

With the growing globalization of the market, it has become more important for companies to improve their competitiveness.
The integration of Market Information (MI) along multiple dimensions can aid a purchasing team’s ability to reduce purchasing costs within their Supply Chain (SC). In order to realize this type of strategic advantage, purchasing managers need transparency and knowledge about their purchased components and suppliers. Therefore, the possession of appropriate methods that integrate MI to knowledge is crucial. Price regression analysis (PRA) creates knowledge and transparency in purchasing (one business function of Supply Chain Management (SCM)) by integrating high quantities of Market Information. PRA is a linear regression methodology that identifies cost reduction potential through derivation of dependencies of component performance to its price. The results of PRA support purchasing in negotiations and sourcing decisions. This knowledge enables decision makers to improve their Supply Chain.

Considering this, the main question throughout the thesis is:

- How can PRA support SCM in terms of reducing cost and providing transparency by integrating Market Information?

To answer this question, four distinctive objectives are to be fulfilled:

1.Provide insight into MI and SCM
2. Exemplify the need to improve SC performance in the automotive industry by
means of cost reduction

3. Provide insight in PRA as a MI integration methodology that improves SC
performance in the automotive industry

4. Estimate the cost reduction potential through PRA for an OEM

Thus, MI and SCM were explained to show that integration of provides transparency and is an opportunity to reduce costs. In this context, PRA serves as enabler for supporting cost reduction efforts in the Supply Chain and thus increases SC performance. A sample commodity was analyzed to underline the potential of PRA and revealed a cost reduction potential of ca. 11%. To derive the impact of PRA for an OEM, the calculated average cost reductions through PRA were used to calculate the savings potential for purchased material on an OEMs Return On Sales (ROS). The ROS increased absolutely 0.71% on average per annum. Consequently, PRA is a powerful methodology for SC improvement.