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This research is about embedding service-based supply chain management (SCM) concepts in the education sector. Due to Canada's competitive education sector, the authors focus on Canadian universities.

The authors develop a framework for evaluating and forecasting university performance using data envelopment analysis (DEA) and artificial neural networks (ANNs) to assist education policymakers. The application of the proposed framework is illustrated based on information from 16 Canadian universities and by investigating their teaching and research performance.

The major findings are (1) applying the service SCM concept to develop a performance evaluation and prediction framework, (2) demonstrating the application of DEA-ANN for computing and predicting the efficiency of service SCM in Canadian universities, and (3) generating insights to enable universities to improve their research and teaching performances considering critical inputs and outputs.

This paper presents a new framework for universities' performance assessment and performance prediction. DEA and ANN are integrated to aid decision-makers in evaluating the performances of universities.

The findings suggest that higher education policymakers should monitor attrition rates at graduate and undergraduate levels and provide financial support to facilitate research and concentrate on Ph.D. programs. Additionally, the sensitivity analysis indicates that selecting inputs and outputs is critical in determining university rankings.

This research proposes a new integrated DEA and ANN framework to assess and forecast future teaching and research efficiencies applying the service supply chain concept. The findings offer policymakers insights such as paying close attention to the attrition rates of undergraduate and postgraduate programs. In addition, prioritizing internal research support and concentrating on Ph.D. programs is recommended.

Managing inventory continues to be a growing area of concern for many retailers due to the multitude of issues that arise from either an excess or shortage of inventory. This study aims to understand how a large-scale retail chain can improve its handling of excess seasonal inventory using three common strategies: information sharing, visibility, and collaboration.

This study has been designed utilizing a case study method focusing on one retail chain at three key levels: strategic (head office), warehouses, and retail stores. The data have been collected by conducting semi-structured interviews with senior-level employees at each of the three levels and employing a thematic analysis to examine the major themes.

The results show how three common strategies are being practiced by this retailer and how utilizing these strategies aids the retailer in improving its performance in regard to seasonal inventory. Among our research findings, some challenges were discovered in implementing the strategies, most notably: human errors, advanced forecasting deficiencies, and the handling of return merchandise authorizations.

This research takes a case study approach and focuses on one big-box retailer. The authors chose to study three levels (head office, warehouses, and retail stores) to gain a deeper understanding of the functions and processes of each level, and to understand the working relationships between them. Through the collection of primary data in a Canadian context, this study contributes to the literature by investigating supply chain strategies for managing inventory. The Canadian context is especially interesting due to the multi-cultural demographics of the country.