Bodendorf F, Merkl P, Franke J (2021)
Publication Type: Journal article, Original article
Publication year: 2021
Original Authors: Frank Bodendorf, Philipp Merkl, Jörg Franke
Pages Range: 1-22
DOI: 10.1080/00207543.2021.1998697
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.
APA:
Bodendorf, F., Merkl, P., & Franke, J. (2021). Artificial neural networks for intelligent cost estimation – a contribution to strategic cost management in the manufacturing supply chain. International Journal of Production Research, 1-22. https://dx.doi.org/10.1080/00207543.2021.1998697
MLA:
Bodendorf, Frank, Philipp Merkl, and Jörg Franke. "Artificial neural networks for intelligent cost estimation – a contribution to strategic cost management in the manufacturing supply chain." International Journal of Production Research (2021): 1-22.
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