Technical-economic cost modeling as a technology management tool
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it