Abstract
AbstractUncertainty becomes the new normal for organizations worldwide. Many organizations are dependent on complex global supply chains. COVID-19, but also environmental disasters or the war in Ukraine, demonstrate the volatility of supply chains. Procurement departments are the central interface between internal and external stakeholders and must manage the supply chain stability what requires fast and accurate decision-making. External shocks and sudden disruptions of central supply chains illustrated that data analytics could not prevent disruptions, although sound research on competitive advantages and numerous investments should have enabled organizations to data-driven decision-making. Rather, it became transparent, that there are numerous data deficits in organizations. We did an interview-based study with 23 procurement and supply chain experts about relevant data sets and the status of its usability. We contribute to theory and practice by uncovering relevant aspects of data and provide theoretical propositions on how decision-making can be improved in automotive procurement departments.
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Information Systems,Theoretical Computer Science,Software
Reference50 articles.
1. Ahuja, T. S. A., Ngai, Y. (2019). Shifting the dial in procurement. McKinsey. https://www.mckinsey.com/business-functions/operations/our-insights/shifting-the-dial-in-procurement. Accessed 21 May 2022.
2. Ambulkar, S., Blackhurst, J., & Grawe, S. (2015). Firm’s resilience to supply chain disruptions: Scale development and empirical examination. Journal of Operations Management, 33–34, 111–122. https://doi.org/10.1016/j.jom.2014.11.002
3. Bag, S., Sabbir Rahman, M., Choi, T. M., Srivastava, G., Kilbourn, P., & Pisa, N. (2023). How COVID-19 pandemic has shaped buyer-supplier relationships in engineering companies with ethical perception considerations: A multi-methodological study. Journal of Business Research, 158, 113598. https://doi.org/10.1016/j.jbusres.2022.113598
4. Baryannis, G., Dani, S., & Antoniou, G. (2019). Predicting supply chain risks using machine learning: The trade-off between performance and interpretability. Future Generation Computer Systems, 101, 993–1004. https://doi.org/10.1016/j.future.2019.07.059
5. Bellatreche, L., Ordonez, C., Mèry, D., Golfarelli, M., & Abdelwahed, E. H. (2022). The central role of data repositories and data models in Data Science and Advanced Analytics. Future Generation Computer Systems, 129, 13–17. https://doi.org/10.1016/j.future.2021.11.027