Bibliographic record
Abstract
ABSTRACT This case, based on a real-life situation of how logistics costs function in daily operations, aims to provide students with the opportunity to understand how logistics costs are calculated and how the inter-organizational nature of these costs affects the profitability of two companies. The case hinges on understanding cost behavior (fixed and variable) and on management control systems design. Although logistics costs represent a small fraction of total costs in manufacturing companies, they can negatively affect the bottom line if left unattended. Students are presented with data relating to a three-year project in the automotive industry that shows that the project has been experiencing a sustained increase in costs that has eroded its profit margin. While it appears that logistics costs are the problem, it cannot be verified until the contracts are studied. In addition, the financial- and contract-related data provided are sufficient to extend the profitability analysis to the provider of logistics services. This case is suitable for management accounting courses at the master's or advanced undergraduate level; it has been tested and well received by students who want to gain a greater understanding of logistics costs—their nature, behavior, possible containment strategies, and inter-organizational effects. Data Availability: Some of the data are from public sources, but the logistics contracts and cost schedules are private; the confidentiality agreement with the two companies requires masking certain details and modifying the numeric data.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".