Integrated Quantitative Business Network Planning: Towards a New Understanding of Supply Chain Management
Bibliographic record
Abstract
In business management quantitative modelling is a core competency. It enables structuring various complex problems; reduce systems to their relevant elements and to make objective and clear decisions. This especially applies to production-distribution networks which are formed by multiple independent and globally active companies. Here, individual goals and collective tasks meet so that experienced-based knowledge is no longer satisfactory. A literature review showed that there is no satisfactory concept available for superior network and quantitative operational management in multi-tier business networks. Therefore, in this article we focus quantitative supply chain models as starting point for stipulations among independent network partners. First, we deduce the main elements of quantitative modelling for inter- and intra-organisational production-distribution planning. Thereby, we present an extension of the Two-Stage-Production-Distribution-Problem which can be used as starting point for iterative supply chain coordination. Based on a literature review we introduce a novel pivotal point supply chain management model. The approach induces ongoing alignment of the strategic, tactic and operative tasks. On each level of this hierarchical planning frame specific plans are computed for comparison and adjustment. The advantages of this approach can be found in the reliable objective basis for negotiations and the repeatable combined inter- and intra-organisational management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.012 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".