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.

No abstract

Even though global supply chains are (usually unintentionally) tied to slave labor, research and practice have largely ignored the issue. This is expected to change as civil society activism and new legislation increase the risk of litigation and reputational damage to supply chain partners. To deal with and combat modern slavery in the supply chain, a theory inspired social supply chain management framework consisting of indicators and countermeasures of modern slavery in the supply chain is developed. The framework is refined in a qualitative expert interview study. The theoretical framework is then evaluated by a multimethod empirical analysis that includes a multicase study based on publicly available supply chain data from 6000 media articles and company websites as well as a quantitative empirical study based on survey data from 280 corporate sustainability experts operating in global supply networks. The results show that economic, political-legal, social, and environmental factors have a significant impact on contemporary slavery in the supply chain. The study also motivates supply chain partners to use preventative and detective measures to reduce the probability of encountering modern slavery actions in their supply chain. Theoretical and managerial implications are drawn from the findings, pointing to a holistic approach to combatting modern slavery in the supply chain.