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Record W4416744892 · doi:10.1108/tcj-03-2025-0073

Reinventing supply strategies in community organizations post-pandemic: a case study of <i>La Ruche Vanier</i>

2025· article· en· W4416744892 on OpenAlexaffabout
Tiloux Soundja, Karima Afif

Bibliographic record

VenueThe CASE Journal · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsConfidentialityData collectionFood supplyCase study researchNonprofit organizationStrategic planning

Abstract

fetched live from OpenAlex

Research methodology This case study draws on both primary and secondary data sources. Primary data includes six hours of interviews and informal discussions with Pascale Bouffard, Deputy Director and Coordinator at La Ruche Vanier. This interview was conducted as part of a project approved by the Research Ethics Committee of Université Laval: Approval No. 2024–286 / 17-07-2024. Secondary data collected from websites of the Québec Food Banks, Moisson Québec, Statistics Québec reports and La Ruche Vanier’s reports, as well as academic publications and news articles on post-pandemic food aid dynamics. The case incorporates real-world organizational challenges. To ensure the confidentiality of other individuals involved in the data collection of this case, fictitious names have been assigned. The donor organization is referred to as “Tout Aliment en Express,” “Charbel (Sales Manager)” and “Angélique (Volunteer).” The reported events and the details about organizational activities, industry operations and statistics are authentic. Case overview/synopsis This case study presents a real-world example of how La Ruche Vanier, a community organization in Quebec City dedicated to distributing food donations to vulnerable individuals in the Vanier neighborhood, navigated food supply disruptions in the post-pandemic era (2023–2024). In May 2024, Bouffard, the Deputy Director and Coordinator, recognized a worsening decline in food donations, which severely strained the organization’s operational capacity. Faced with these challenges, Bouffard posed a critical question: How can supply strategies be adjusted to effectively address the growing demand while managing limited resources? To stabilize the supply, Bouffard needed to make strategic decisions regarding food procurement. This case is decision-driven, focusing on one key supply strategy − purchasing − within a nonprofit context. Students will step into Bouffard’s shoes to analyze the data provided by Angélique, a volunteer, and make strategic decisions to address the challenges. Additionally, students will take on the role of a strategic advisor, like Angélique, to propose recommendations and solutions for procurement strategies. Complexity academic level This case study can be incorporated into courses on procurement management, operations and logistics management, strategy, supply chain management and distribution management. It is appropriate for both advanced courses and foundational ones focused on nonprofit organization management or post-crisis management. Relevant topics include managing complex procurement processes in a nonprofit context, or the challenges surrounding procurement decision-making in a dynamic, complex and uncertain post-crisis environment. Targeted for second- and third-year undergraduate students (business administration) as well as graduate students (Master’s and MBA programs), this case is best suited for those who have already been introduced to procurement strategy concepts in their coursework. The case is of medium difficulty, requiring more than a simple copy-paste approach. While Figures 1–3 and Table 1 provide valuable data, clear prompts are included with the questions to encourage critical thinking and guide students through the analysis process.This case study is suitable for a hybrid learning environment. It integrates both in-seat and online components to facilitate dynamic discussions and collaborative analysis. Its design supports synchronous and asynchronous engagement through digital exhibits and guided prompts, making it adaptable to blended classroom settings.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.763
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.045
GPT teacher head0.304
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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