Abstract

This article consolidates current knowledge of methods that optimize data flows in the supplier chain and associate them with an improvement in the verification of the supplier's efficiency. Drawing fifteen sources of precipitation collection, mapping four family-WMS-C methods, mapping of flow values ​​4.0 with EDI/ASN integration, logging process and KPI and SLAS sharing through cross orgastular administration and suppliers. Small empirical component includes: We reduce standardized effects from reported messages, combine them with random effect models and run meta-ragging for mediators testing (automation levels, depth of management, supplier level). Results show that data standardization plus visibility of close time realized permanently works better than different analysts; The management system increases technical benefits. Asymmetry must still explain; Publishing distortion of control shows limited deformity. The design of the synthesis, presents the verification and sam-dualog book for setting up the warehouse: diagnose data delay, perfection and agreement; Verify the supplier against the KPI; And adjust through targeted tasks (eg ASN mandate, Master-Dedaa, verification, combined control panel). The contribution is practical: Trackable structure and matrix of decision -making, which aligns the intervention of the data flow with the average supplier results and supports replication in practice in different contexts.

References

1. [1.] Burkhart, M., & Bode, C. (2024). On supplier resilience: How supplier performance, disruption frequency, and disruption duration are interrelated. Journal of Purchasing and Supply Management, 30(2), 100921. https://doi.org/10.1016/j.pursup.2024.100921
2. [2.] O’Connor, N. G., & Schloetzer, J. D. (2023). Aligning performance measurement systems across the supply chain: Evidence from electronic components suppliers. Journal of Management Accounting Research, 35(3), 77–98. https://doi.org/10.2308/JMAR-2022-003
3. [3.] Zafarzadeh, M., Zeike, S., & Glock, C. H. (2023). Capturing value through data-driven internal logistics: Case studies on enhancing managerial capacity. Production & Manufacturing Research, 11(1), 213–236. https://doi.org/10.1080/21693277.2023.2214799
4. [4.] Wuennenberg, M., Muehlbauer, K., Meissner, S., & Fottner, J. (2023). Towards predictive analytics in internal logistics – An approach for the data-driven determination of key performance indicators. CIRP Journal of Manufacturing Science and Technology, 41, 76–86. https://doi.org/10.1016/j.cirpj.2023.05.005
5. [5.] Muehlbauer, K., Wuennenberg, M., Meissner, S., & Fottner, J. (2022). Data driven logistics-oriented value stream mapping 4.0: A guideline for practitioners. IFAC-PapersOnLine, 55(16), 364–369. https://doi.org/10.1016/j.ifacol.2022.09.051
6. [6.] Baihaqi, I., & Sohal, A. S. (2013). The impact of information sharing in supply chains on organisational performance: An empirical study. Production Planning & Control, 24(8–9), 743–758. https://doi.org/10.1080/09537287.2012.666865
7. [7.] Vanpoucke, E., Boyer, K. K., & Vereecke, A. (2009). Supply chain information flow strategies: An empirical taxonomy. International Journal of Operations & Production Management, 29(12), 1213–1241. https://doi.org/10.1108/01443570911005974
8. [8.] Gzara, F., Pochet, Y., & Rardin, R. L. (2020). Data-driven modeling and optimization of the order fulfillment process in e-commerce warehouses. INFORMS Journal on Optimization, 2(3), 208–229. https://doi.org/10.1287/ijoo.2019.0039
9. [9.] Shou, Y., Li, Y., Park, Y., & Kang, M. (2018). Supply chain integration and operational performance: The contingency effects of production systems. Journal of Purchasing and Supply Management, 24(4), 352–360. https://doi.org/10.1016/j.pursup.2017.11.004
10. [10.] Molinaro, M., Danese, P., Romano, P., & Swink, M. (2022). Implementing supplier integration practices to improve performance: The contingency effects of supply base concentration. Journal of Business Logistics, 43(3), 369–391. https://doi.org/10.1111/jbl.12316
11. [11.] Lee, C.-H., Son, B.-G., & Roden, S. (2023). Supply chain disruption response and recovery: The role of power and governance. Journal of Purchasing and Supply Management, 29(3), 100866. https://doi.org/10.1016/j.pursup.2023.100866
12. [12.] Wu, Q., Zhu, J., & Cheng, Y. (2023). The effect of cross-organizational governance on supply chain resilience: A mediating and moderating model. Journal of Purchasing and Supply Management, 29(1), 100817. https://doi.org/10.1016/j.pursup.2023.100817
13. [13.] Huo, B., Zhao, X., Shou, Y., & Ye, Y. (2016). The impact of human capital on supply chain integration and competitive performance. International Journal of Production Economics, 178, 132–143. https://doi.org/10.1016/j.ijpe.2016.05.009
14. [14.] Yamada, S., Matsumoto, Y., & Yoshida, T. (2024). The optimization of picking in logistics warehouses in the event of sudden picking order changes and picking route blockages. Mathematics, 12(16), 2580. https://doi.org/10.3390/math12162580
15. [15.] Meudt, T., Metternich, J., & Abele, E. (2017). Value stream mapping 4.0: Holistic examination of value stream and information logistics in production. CIRP Annals, 66(1), 413–416. https://doi.org/10.1016/j.cirp.2017.04.005