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 Vox Clamantis in DesertoLatin expression meaning ‗the voice of one crying in the wilderness'.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)
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".