Business Model Innovation for Additive Manufacturing
Bibliographic record
Abstract
Metal additive manufacturing is a rapidly emerging technology, with a growing number of companies interested in its implementation. The technology can have several positive effects, such as more efficient production, reduced transports, more focus on circular economy and reduced costs. However, the manufacturing process is still relatively slow, where components require extensive post-processing and new knowledge in the companies involved. For a broad implementation, support functions need to be developed for, amongst others, selection processes, order handling, design, and post-processing. To investigate possible uses for additive manufacturing, a case study was conducted at Alfa Laval. Their current conditions were mapped so that a new business model, specially designed for additive manufacturing, could be developed. Through interviews, future opportunities for the technology were identified, as well as problems related to these. Each possibility was examined related to how the company’s external and internal processes would be affected and changes that could occur in the value chain. The business model blocks that would need to be innovated to support this development, while simultaneously contributing to UN's sustainable development goals, were discussed. Furthermore, these scenarios were evaluated on the basis of viability, feasibility, and desirability. The report indicates that Alfa Laval should implement additive manufacturing for new products via R&D and for certain spare parts, and in line with this change, adjust their value creating processes, value proposition, and value capture. The two applications utilize different advantages of the technology, and which is best suited for Alfa Laval, and other manufacturing actors, needs to be evaluated during a longer test period within the business.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".