An evidence-based management framework for business analytics

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.

KEYWORDS:

• Evidence-based management
• equipment replacement analysis
• simulation

Acknowledgement

The authors would like to sincerely thank the Iowa Department of Transportation (Iowa DOT) for sponsoring research project 90-00-RB14-014, “A Decision Support System for Optimized Equipment Turnover.” The authors would specifically like to thank Susan Brekke, Matthew Haubrich, David May, David Putz, and Kathy Skogerboe of the Iowa DOT for their engagement and feedback during the research project. Lastly, we thank the anonymous reviewers and editorial team for providing helpful guidance towards publication.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Federal Highway Administration [SPR RB14-014].