Creating a Structured Technology Scouting Approach for Operations Development - A Case Study Within a Multifaceted Global Industrial Company
Bibliographic record
Abstract
The current fast paced technological innovation demands major attention from businesses across the globe. AI dominates discussions one day, and the next it is all about quantum computing. To remain competitive companies must constantly be on the watch for trend shifts and technological advancements. In order to stay ahead of the curve companies can utilize technology scouting to monitor the technological horizons. Alfa Laval has recently begun developing their technology scouting efforts within their manufacturing department, to leverage technological advancements to their advantage. The aim of this thesis is to identify barriers within Alfa Laval’s technology scouting function and deliver insights regarding potential improvements. The thesis is made in context of Alfa Laval’s manufacturing department but aims to be generally applicable to all forms of technology scouting within the organization. By conducting a literature review, several insights on best practices and theoretical optimal scouting organizations were found. To complement this information, a series of interviews were conducted with employees at Alfa Laval which gave insight into the current situation at Alfa Laval. Lastly interviews were conducted with employees at other global industrial companies which resulted in a broader knowledge of what practices are currently in use. The information received during interviews was then clustered and analyzed together with academic theory, which resulted in a plan of action in the form of a roadmap. The recommendations from this roadmap were to educate employees and formalize the technology scout role. This is closely followed by the implementation of standard documents, and internal databases. By implementing these steps, it will increase transparency within the organization, increase the amount of external scouting and improve the ability to align scouting efforts with business strategy.
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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 | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".