Bibliographic record
Abstract
In July 2007 Kemira Chemicals implemented a new ERP/MRP system in its North American Operations. The focus of this project is to compare inventory management practices at two Kemira plants both of which have long supply chains but elected to utilize the MRP system in different ways. The Prince George plant manages raw material inventories internally and the Bushy Park plant which is managed externally through the use of brokers. Due to the fact that forecasting is not customer order driven we can see considerable evidence of the bullwhip effect ...The four common bullwhipping behaviours: poor forecasting methods, order batching, price variations and the rationing game have all been observed in both supply chains throughout the research period ... Wherever possible visibility and control within the supply chain should be improved to mitigate risk ...The following recommendations should be implemented immediately. Bushy Park should fully employ all its purchasing activities in the ERP/MRP system. Communication should be improved between Kemira and its brokers to improve transparency of raw material inventory as it moves through the supply chain. A better methodology/discipline of forecasting should be executed immediately including a formal review process in cases where additional inventory is being ordered to take advantage of a price promotion. --P. 5.
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 | 0.003 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".