An empirical investigation into intelligent cost analysis in purchasing

Purpose: Cost transparency is of central importance to reach a consensus between supply chain partners. The purpose of this paper is to contribute to the instrument of cost analysis which supports the link between buyers and suppliers. Design/methodology/approach: Based on a detailed literature review in the area of cost analysis and purchasing, intelligent decision support systems for cost estimation are identified. Subsequently, expert interviews are conducted to determine the application possibilities for managers. The application potential is derived from the synthesis of motivation, identified applications and challenges in the industry. Management recommendations are to be derived by bringing together scientific and practical approaches in the industry. Findings: On the one hand, the results of this study show that machine learning (ML) is a complex technology that poses many challenges for cost and purchasing managers. On the other hand, ML methods, especially in combination with expert knowledge and other analytical methods, offer immense added value for cost analysis in purchasing. Originality/value: Digital transformation allows to facilitate the cost calculation process in purchasing decisions. In this context, the application of ML approaches has gained increased attention. While such approaches can lead to high cost reductions on the side of both suppliers and buyers, an intelligent cost analysis is very demanding.