Not‐for‐profit supply chains in interrupted environments
Bibliographic record
Abstract
Purpose This paper primarily aims to address the following research question: Are techniques and practices developed for uninterrupted, for‐profit supply chains adaptable to the not‐for‐profit (NFP), interrupted context? In other words, can the managerial tools of business logistics be used in humanitarian relief logistics? Design/methodology/approach A combination of grounded research and case‐based research methods is used. Grounded research methods involve coding interview data to enable constant comparison of the data with emerging categories. Three managerial representatives of a single case organisation, the Mennonite Central Committee (MCC), are interviewed. Findings MCC is a small NFP organisation operating in interrupted environments. As a matter of strategy, it pursues economic and social objectives. Strategic partnerships with “like‐minded” organisations are critical to achieve these objectives. To assess its achievements, MCC needs a wide range of performance measures. Research limitations/implications Since the paper is based on a single case, it is difficult to generalise the results beyond MCC, a small, faith‐based NFP providing humanitarian assistance. There are future research opportunities to study more cases and search for additional themes. Practical implications Partnerships and performance measurement are important elements of supply chain management (SCM) in humanitarian relief, characterised by NFP operations in interrupted environments. SCM tools and techniques created in the for‐profit or business context, for partnership formation (e.g. “how to commit”) and performance measurement (e.g. balanced scorecard), should be adapted for the humanitarian context. Originality/value There is a growing literature on supply chain interruptions, but very little research on NFP sector supply chains, despite pressing needs for effective SCM within the NFP context. Also, there are relatively few studies on adapting business logistics practices for the humanitarian logistics context. The paper addresses these issues.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".