Supply Chain Management: Increasing Performance and Coordination in a Sales & Operations Planning Context
Bibliographic record
Abstract
Alfa Laval’s business unit Gasketed Plate Heat Exchanger uses Sales and Operations Planning to balance supply with demand to ensure profitable growth. Currently, the business unit is experiencing inefficiencies as they fail to execute the plans from Sales and Operations Planning. Large inventories, high obsolescence, material availability, and “firefighting” are some problems mentioned. To support their strategy, the Sales and Operations Planning team wants to understand how they can address these inefficiencies and improve performance. The findings of this thesis were that a lack of knowledge and awareness impacts the business unit to not adhere to plans from the Sales and Operations Planning and that supply chain discontents have led to a large assortment and low inventory turnover. To raise the plan adherence ability in the business unit it is recommended that a knowledge development initiative is driven. Such an initiative should include increasing managerial and process knowledge and spreading general awareness of the process. The inventory turnover rate can be improved by driving modularization and incentivizing an alignment between functions to enable a phase-out of old products and indirectly obsolete inventory.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".