Demand Planning Information Sharing: N ZAR
Bibliographic record
Abstract
Organisations are currently faced with difficulties in effectively aligning demand plans to the volatile environments in which they operate.While operating environments and consumer needs change, capacity capabilities often do not reflect the demand plans.The absence of alignment results in inaccurate forecasts, thus putting the longterm sustainability of a business at risk.The focus and aim of the study is to understand how demand planning information are shared at N ZAR for optimal performance.A quantitative explorative case study research design is being used and data was collected through a structured self-administered questionnaire in this study.The sample size was 86, which comprised of employees from Demand and Supply Planning, Finance and Control, Sales and Marketing divisions.The sample includes top management, middle management, first level management and non-management.Data analysis uses descriptive and multivariate statistics.The study findings show most of the participants responded positively to the statements that information sharing achieves demand chain coordination.This study recommended that top management should provide full support to information sharing initiatives to facilitate the demand planning process.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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".