- Machine Learning
The digital supply chain is rapidly evolving, putting greater pressure on suppliers to adopt to the dynamic demands of the buyers. This paper explores how supply chain complexities and buyer-supplier relationships as a complex adaptive system interact with an integrated and enacted external environment and drive the key supply chain performance of the supplier. Econometric modeling through fixed effects panel data analysis and seemingly unrelated regression analysis were conducted to analyze the impact between supply chain complexities, buyer-supplier relationship attributes, and supply chain performance through time. Consequently, moderation analysis and sensitivity analysis were conducted to determine the effects of the interpreted and enacted environment perspective. Results show supply chain complexities and buyer-supplier relationships have different impact on service level and sales that changes in degree and significance through time. Moreover, uncertainties and external attributes as the model representation of the interpreted and enacted environment have notable influences on the emergent system properties of the system.