Foresight, Competitive Intelligence and Business Analytics — Tools for Making Industrial Programmes More Efficient
Bibliographic record
Abstract
Creating industrial programmes, especially in technology, is fraught with high levels of uncertainty. These programmes target the development of products that will not be sold for several years; therefore, one of the risks is that the products will no longer be in demand due to the emergence of more advanced technologies. The paper proposes an integrated approach involving the complementary functions of foresight, intelligence and business analytics. The tools of foresight and intelligence are focused on the external environment and enable industry and researchers to, among other things, understand the direction in which markets and technologies are evolving, and profile local industries to determine which policy instruments may be effective in these industries. Signals picked up today through externally focused intelligence studies can be used to confirm conclusions from longer term foresight initiatives such as scenarios, roadmaps and scans, thereby providing the information needed to establish the long-term industrial policy that science and technology related industries require. The authors propose a dashboard for monitoring an industrial programme’s use so that any problems can be corrected early on. The dashboard relies on both information available in open sources and that accessible to a government. Combining foresight, intelligence and business analytics is believed to not only decrease uncertainty and risk but also make it more likely that the policy is implemented by its intended audience and that industry opportunities are identified at an early stage. To illustrate how this approach works in practice, the paper discusses a hypothetical case of a state programme to develop the nutraceuticals industry in Canada.
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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 | 0.023 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.022 | 0.027 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".