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

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Show simple item record Chae, Bongsug K. Olson, David Sheu, Chwen 2014-11-06T15:23:54Z 2014-11-06T15:23:54Z 2014-11-06
dc.description.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. en_US
dc.language.iso en_US en_US
dc.relation.uri en_US
dc.rights This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research, 2014, available online at: en_US
dc.subject Manufacturing management en_US
dc.subject Data mining en_US
dc.subject Supply chain management en_US
dc.title The impact of supply chain analytics on operational performance: a resource-based view en_US
dc.type Article (author version) en_US 2014 en_US
dc.citation.doi doi:10.1080/00207543.2013.861616 en_US
dc.citation.epage 4710 en_US
dc.citation.issue 16 en_US
dc.citation.jtitle International Journal of Production Research en_US
dc.citation.spage 4695 en_US
dc.citation.volume 52 en_US
dc.contributor.authoreid bchae en_US
dc.contributor.authoreid csheu en_US

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