Technical-economic cost modeling as a technology management tool
Bibliographic record
Abstract
Purpose – The purpose of this paper is to show how technical-economical cost modeling can help in steering research and development to target key production cost elements of new products based on emerging technologies. Design/methodology/approach – The authors demonstrate the development and use of a technical-economic cost model (TCM) of the proton exchange membrane (PEM) in fuel cells to steer research to produce more economical and reliable products. A TCM is developed to depict how the production cost per unit varies depending on the different fabrication methods, production rate limitations, material selection, labor distribution, energy consumption, financial parameters and the target production volume. By using such an approach in the design, research time and resources can be saved by prioritizing R&D and production scale-up options at an early stage. Findings – The results of this study show the importance of applying technical-economic cost model (TCM) techniques on early stage research projects to steer the development for resolving key problematic figures. As a case study, a cost analysis platform has been established to apply this technique by analyzing different manufacturing and R&D options for producing durable PEM fuel cells. The projected manufacturing cost of the PEM is found to be lower than previously estimated and the enhanced durability does not significantly impact this production cost. Originality/value – Production is an important factor in informing NPD targets and R&D direction. And yet it is difficult to estimate scaled up production cost for prototype products and components in the R&D lab. Technical-economic cost models (TCM) are a tool to assist decision-making in technology portfolio management and NPD.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".