Abstract Healthcare providers are under increasing pressure to reduce waste, eliminate unnecessary costs and redundant efforts, thereby improving the quality and consistency of healthcare delivery. Lack of automation and the lack of use of global identifiers for products and locations, also known as supply chain data standards, are two critical factors that can help streamline providers operations and improve process efficiency. Despite widespread consensus among various stakeholders, healthcare providers lack a well-defined approach towards adopting and implementing data standards. Supply chain data standards can be defined as a set of product and location identifiers which are used in supply chain related processes and transactions. Healthcare providers willing to successfully adopt data standards in some or all of their operations need to invest in several process changes and technology installations or upgrades; however, they often struggle to justify returns on those investments and hence, find uncertain Return on Investment (ROI) as a critical barrier. In this article, we present a hierarchical comprehensive spreadsheet based decision support tool that helps potential healthcare providers to evaluate their readiness requirements and quantify the potential impacts of their decisions in terms of non-monetary performance measures, such as increased productivity, enhanced patient safety and reduction in errors resulting in decreased volume of transactions. This tool has undergone extensive testing with healthcare providers of different size, scope, and needs. We present numerical results showcased through practical examples in this article. The software is publicly available free of cost for download at http://cihl.uark.edu. Keywords: Healthcare supply chaindata standardsGS1return on investmentdecision support Acknowledgments The authors are grateful to the editor and the anonymous referees for their detailed comments, which greatly improved the clarity of presentation and readability of this article. A portion of the research was supported by the Center for Innovation in Healthcare Logistics (CIHL) at the University of Arkansas, Fayetteville. Findings and conclusions are those of the authors, and not necessarily those of either CIHL sponsors or collaborating healthcare organizations. The authors would like to acknowledge Ashraf Hajiyev, Nabil Lehlou and Jennifer Pazour for their contributions.