The impact of supply chain analytics on operational performance: a resource-based view

TLDR

The study investigates how supply chain analytics influence planning satisfaction and operational performance. The authors define SCA as an architecture integrating data management, IT‑enabled planning, and performance management resources, and test hypotheses using data from 537 manufacturing plants. Analysis shows that data management resources are a key building block, strongly predict performance management resources, and together with IT‑enabled planning resources drive supply chain planning satisfaction and operational performance, with advanced planning technologies following data acquisition. The paper concludes with limitations and future research directions.

Abstract

This study seeks to better understand the role of supply chain analytics (SCA) on supply chain planning satisfaction and operational performance. We define the architecture of SCA as the integration of three sets of resources, data management resources (DMR), IT-enabled planning resources and performance management resources (PMR), from the perspective of a resource-based view. Based on the data collected from 537 manufacturing plants, we test hypotheses exploring the relationships among these resources, supply chain planning satisfaction, and operational performance. Our analysis supports that DMR should be considered a key building block of manufacturers' business analytics initiatives for supply chains. The value of data is transmitted to outcome values through increasing supply chain planning and performance capabilities. Additionally, the deployment of advanced IT-enabled planning resources occurs after acquisition of DMR. Manufacturers with sophisticated planning technologies are likely to take advantage of data-driven processes and quality control practices. DMR are found to be a stronger predictor of PMR than IT planning resources. All three sets of resources are related to supply chain planning satisfaction and operational performance. The paper concludes by reviewing research limitations and suggesting further SCA research issues.