Resilient and sustainable supplier selection: an integration of SCOR 4.0 and machine learning approach

ABSTRACT

The purpose of this research paper is to implement a machine learning model with the integration of the supply chain occupational reference (SCOR) model to develop an artificial intelligence-based system for resilient and sustainable supplier selection for a pharmaceutical company. Initially, the SCOR 4.0 model with the integration of Best Worst Method (BWM) has been used to develop the framework of customer satisfaction and to identify the critical elements of the suppliers. Later, the gradient boosting machine learning model has been implemented to classify the supplier as well as rank the suppliers from best to worst based on the acceptability score. The result shows that the gradient boosting algorithm performs well as a classifier, where the supplier with the most acceptability score represents the best supplier and the supplier with the least acceptability score represents the worst supplier. This study contributes to our understanding of how and when integrated SCOR and machine learning models can help improve supplier selection.

KEYWORDS:

• Supplier selection
• resilience
• machine learning
• SCOR 4.0
• Pharmaceutical Supply Chain
• sustainability

Disclosure statement

The Coalition for Disaster Resilient Infrastructure (CDRI) reviewed the anonymised abstract of the article, but had no role in the peer review process nor the final editorial decision.

Additional information

Funding

The Article Publishing Charge (APC) for this article is funded by the Coalition for Disaster Resilient Infrastructure (CDRI).

Notes on contributors

Md Muzahid Khan

Md Muzahid Khan is a Lecturer in the Department of Military Institute of Science and Technology, Dhaka, Bangladesh. His research interests include supply chain management, sustainability quantification, healthcare and machine learning.

Imranul Bashar

Imranul Bashar completed his bachelor's in Industrial and Production Engineering from the Military Institute of Science and Technology, Dhaka, Bangladesh. His research interests include supply chain management, machine learning, and healthcare.

Golam Morshed Minhaj

Golam Morshed Minhaj completed his bachelor's in Industrial and Production Engineering from the Military Institute of Science and Technology, Dhaka, Bangladesh. His research interests include supply chain analytics and healthcare.

Absar Ishraq Wasi

Absar Ishraq Wasi completed his bachelor's in Industrial and Production Engineering from the Military Institute of Science and Technology, Dhaka, Bangladesh. His research interests include supplier management, data science, and optimization.

Niamat Ullah Ibne Hossain

Niamat Ullah Ibne Hossain is an assistant professor in the Department of Engineering Management at Arkansas State University, USA. His main research interests include machine learning, model-based systems engineering (MBSE)/SysML, data analytics, systems dynamics simulation, and systems resilience, risk & sustainability management.