Bibliographic record
Abstract
Strategic cost management in supply chains is not a new concept. Coordinated actions between companies of the same chain, in order to reduce costs and end consumer price, offer opportunities for improved results. Interorganizational Cost Management (IOCM) is a structured approach with a broad vision, beyond the borders of the organization, which aims to reduce costs at the internal and external levels. Indeed, cost management is a complex issue that permeates all areas of the organization and may pose a number of difficulties to be implemented and sustained. Thus, this work has the overall goal of identifying, in the literature, the factors and conditions that inhibit the applicability of the Interorganizational Cost Management approach. To achieve these goals, an analysis was made of 35 academic research studies available in the literature that reported the difficulties faced by companies in cooperative cost management. The analysis of the studies showed the perceptions of different companies, and described the difficulties they face; therefore, the present research is qualitative and exploratory. Factors that inhibit IOMC were grouped into: (i) corporate strategy; (ii) integration of companies; (iii) people; (iv) intra- and interorganizational processes; (v) corporate training and education; (vi) disputes between companies; and (vii) lack of trust between companies.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".