An integrated performance measurement framework for enhancing public health care supply chains

ABSTRACT

Performance improvement is a key step in quality management standards and awards such as ISO 9001, MBQA and EFQM. It is also indispensable in process improvement programs relying on PDCA or DMAIC. From a Supply Chain perspective, performance management is necessary to enhance its quality by improving both effectiveness and efficiency. Generally, the scope of performance management involves activities, such as defining, planning, measuring, monitoring and improving a system’s performance. This paper introduces a framework based on the Balanced scorecard (BSC) and the (SCOR) model to engineer an integrated performance measurement system for managing public healthcare supply chains. This generic performance measurement system which is meant to monitor the supply chain’s performance against its strategic objectives, is then customized to include the most relevant indicators according to the Decision-Maker’s preferences. As an illustration, the suggested framework is applied to develop a performance measurement system for the pharmaceutical products supply chain for the Moroccan public sector.

KEYWORDS:

• Performance improvement
• Performance measurement system
• Public healthcare supply chain
• Balanced Scorecard (BSC)
• SCOR model

Acknowledgments

This work was conducted within the research project RSCM2015-2018. The authors would like to thank the Moroccan MS, MESRSFC and CNRST for their support.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Z. Chorfi

Z. Chorfi is a PhD student in the Department of Industrial Engineering, at Ecole Mohammadia D’Ingénieurs (EMI) at Mohamed V University, Rabat, Morocco. She received her Dipl-Ing degree in Industrial Engineering from Ecole Mohammadia D’ingénieurs (EMI), Rabat, Morocco, in 2011. Her areas of interest include supply chain management, performance measurement, multi criteria decision analysis, design of experiments etc…

L. Benabbou

L. Benabbou is an Associate Professor of Industrial Engineering at Ecole Mohammadia d’Ingénieurs (EMI) at Mohamed V University. She earned MBA and PhD in Management and Decision sciences from Laval University. Her areas of interest include decision/management sciences, machine learning, Data valorisation and Operations Management. Several of her research paper related to these fields has been published in international scientific journals and conferences’ proceedings.

A. Berrado

A. Berrado is an Associate Professor of Industrial Engineering at EMI School of Engineering at Mohamed V University. He earned MS/BS in Industrial Engineering from same institution, an MS in Industrial and Systems Engineering from San Jose State University, and a PhD in Decision Systems and Industrial Engineering from Arizona State University. His areas of interest include Data Science, Industrial Statistics, Operations and Supply Chain Modelling, Planning and Control. He published several papers in international scientific journals and conferences’ proceedings. He is member of INFORMS, IEOM and IEEE.