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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.

This study focuses on a specific method of meat production that involves carcass purchase and meat production by packing facilities with a novel two-stage model that simultaneously considers location-routing and inventory-production operating decisions. The considered problem aims to reduce variable and fixed transportation and production costs, inventory holding cost and the cost of opening cold storage facilities.

The proposed model encompasses a two-stage model consisting of a single-echelon and a three-echelon many-to-many network with deterministic demand. The proposed model is a mixed-integer linear programming (MILP) model which was tested with the general algebraic modelling system (GAMS) software for a real-world case study in Iran. A sensitivity analysis was performed to examine the effect of retailers' holding capacity and supply capacity at carcass suppliers.

In this research, the number of products transferred at each level, the number of products held, the quantity of red meat produced, the required cold storage facilities and the required vehicles were optimally specified. The outcomes indicated a two percent (2%) decrease in cost per kg of red meat. Eventually, the outcomes of the first and second sensitivity analysis indicated that reduced retailers' holding capacity and supply capacity at carcass suppliers leads to higher total costs.

This research proposes a novel multi-period location-inventory-routing problem for the red meat supply chain in an emerging economy with a heterogeneous vehicle fleet and logistics decisions. The proposed model is presented in two stages and four-echelon including carcass suppliers, packing facilities, cold storage facilities and retailers.

In recent years, and especially during the coronavirus disease 2019 (COVID-19) pandemic, the significant role of agriculture, specifically red meat, in household consumption has been increased. On the other hand, the lack of proper policymaking in the production and pricing of red meat and the lack of a comprehensive study on the beef supply chain has led to a reduction in the role of this protein product in the household food basket. Thus, in this research, comprehensive strategic planning considering the effect of the COVID-19 pandemic has been illustrated to overcome the aforementioned problems.

To study the intended objectives, first, using qualitative methods, the strengths, weaknesses, opportunities and threats (SWOT) to the studied company's supply chain in Iran were identified and then using the SWOT-Quantitative Strategic Planning Matrix (QSPM) technique, the surrounding strategies have been analysed.

The results indicate that the most important strength of the studied company is the “access to the red meat market of the retirement plan”; the most important weakness is the “lack of required and on-time funding, especially in the condition of the COVID-19 pandemic”; the highest-ranked opportunity is the “access to banking facilities” and the main threat to the company is the “COVID-19 pandemic limitations and health protocols”. In the same vein, by examining the attractiveness score of internal and external factors, it was observed that diversity and competitive strategies would have a higher priority. Finally, the QSPM illustrated that activating the full capacity of existing infrastructure has the highest priority.

According to the red meat supply chain and the link amongst different market levels, identifying, analysing and improving the beef supply chain is of particular importance. One of the threats facing the international community is the emergence of events such as the COVID-19 pandemic, which requires businesses to choose the right strategy to deal with the issue. Therefore, the main distinction of this study is to identify, analyse and improve the red meat supply chain of a real case due to the condition of the COVID-19 pandemic.