An evidence-based management framework for business analytics. (2nd January 2019)

• Record Type: Journal Article
• Authors: Scheibe, Kevin P.; Nilakanta, Sree; Ragsdale, Cliff T.; Younie, Bob
• Is Part Of: Journal of Business Analytics. Volume 2:Number 1(2019)
• Page Start: 47
• Page End: 62
• Publication Date: 2019-01-02
• Subjects: Evidence-based management -- equipment replacement analysis -- simulation; Business intelligence -- Periodicals; Management -- Statistical methods -- Periodicals; Decision making -- Statistical methods -- Periodicals; 658.403
• DOI: 10.1080/2573234X.2019.1609341
• Languages: English
• ISSNs: 2573-234X

Abstract

ABSTRACT: It is said that knowledge is power, yet often, decision makers ignore information that ought to be considered. The phenomenon known as Semmelweis reflex occurs when new knowledge is rejected because it contradicts established norms. The goal of evidence-based management (EBMgt) is to help overcome Semmelweis reflex by integrating evaluated external evidence with stakeholder preference, practitioner experiences, and context. This evaluated external evidence is the product of scientific research. In this paper, we demonstrate an EBMgt business analytics model that uses computer simulation to provide scientific evidence to help decision makers evaluate equipment replacement problems, specifically the parallel machine replacement problem. The business analytics application is demonstrated in the form of a fleet management problem for a state transportation agency. The resulting analysis uses real-world data allowing decision makers to unfreeze their current system, move to a new state, and re-freeze a new system.