An empirical study of South African business forecasting practices in the context of Western benchmarks
Bibliographic record
Abstract
Effective and productive forecasting management has evolved from a tactical to a strategic weapon for firms attempting to stay ahead of adverse economic, industry and market environments.With the advent of increased domestic and global competition this evolution has become more widespread and acute.Firms leveraging their forecasting management skills into their profit statements are indeed staying one step ahead of their competitors while those failing in this management discipline are falling behind their competitors both on tactical and strategic levels.Naturally following these events numerous issues and questions are raised.Where do firms, both from a national and international perspective, rate in terms of forecasting management competency and effectiveness?Do the forecasting practices of a particular firm lead the pack, lag the pack or just go along with the pack or does the firm deserve the moniker of best in class', world class or the best of the best?This study attempts to answer these questions for South African firms in the context of Western' standards or benchmarks.The Western benchmarks reflect forecasting management standards of certain firms primarily domiciled in the United States, Europe, Canada and Mexico although most are multinational, operating globally.The study utilises a qualitative multi-method approach.An ethnographic Long Interview" strategy is used to obtain, in sutu, face to face practice evidence from 30 respondents holding high level forecasting positions at 20 South African firms.The interview evidence At the beginning of each forecasting period, how does the sales forecasting process begin?(Example: sales forecasts developed by Computer System, Sales Force, Both Computer System and Sales Force, Marketing, Forecasting/Planning Group)
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".