In today’s complex supply networks sharing information between buyers and suppliers is critical for sustainable competitive advantage. In particular, for both business partners, cost information is highly relevant in purchasing situations. According to empirical studies in literature, artificial neural networks (ANNs) are expected to have a great potential to reveal cost structures by machine learning (ML). In digitally enabled supply chains this information can contribute to cost reduction and operational excellence and lead to win-win situations in supplier relationship management. Nevertheless, authors do not thoroughly investigate how ANNs may support cost estimation for purchasing decisions. Based on a case study from the automotive industry, we evaluate ANNs regarding their capability to gain cost structure data. In an additional comparative study, we benchmark ANNs for cost estimation in purchasing against other promising ML algorithms. Thereby, we apply the cross-industry standard process model for data mining projects. The findings of the studies show that some ML algorithms outperform ANNs regarding accuracy. The research results give indications for choosing the ML approach that promises the best outcome for cost estimations and cost structure information to support decision-making in buyer–supplier relationships.

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.

Dynamic capabilities enable companies to react agilely to changing competitive or environmental conditions. However, there is a lack of understanding the influences of the technological transformation process. Therefore, in this article, we examine the interrelationships and influences that the technological transformation process and digital competencies have on dynamic capabilities. Following an empirically calibrated system dynamics approach, three simulation studies are conducted investigating the development of dynamic capabilities in technological change, the influence of digital competencies on dynamic capabilities, and the relationship between dynamic technological capabilities and the competitiveness of companies. The results show that the impact of technological transformation is changing the concept of dynamic skills and that digital competencies are becoming increasingly important in this context. Thereby, a positive relationship between the determinants of digital maturity and the concept of dynamic capabilities is discovered. In addition, competitive advantages for companies can be realized through the targeted training of dynamic skills.

Early cost estimation is a decisive value driver in the product development process in manufacturing industries. Machine learning offers new intelligent methods to support traditional cost calculation processes. While traditional research on intelligent cost estimation focuses on machine learning regression or classification models, we propose a new approach based on interlocking deep learning methods. In this paper we investigate the applicability of deep learning techniques, focusing on image recognition and deep learning regression as well as autoencoding to estimate product costs of circuit boards to be purchased. We create and evaluate deep learning models using real-world data from an original equipment manufacturer (OEM). Our findings suggest that deep learning models can streamline cost calculation and estimation processes while deep learning object recognition-based cost estimation outperforms autoencoding techniques. This research is designed to be transferable to other cost estimation projects.