Architecture, Performance, and Investment in Product Development Networks
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
Firms engaging in product development (PD) face the imperative problem of allocating scarce development resources to a multitude of opportunities. In this paper, we propose a mathematical formulation to optimize PD investment or resource allocation decisions. The model maximizes the performance of a product under development, based on its architecture and the firm's available resource, by choosing the optimal resource allocation across product modules and design rules that govern the relationships between these modules. Results based on a comprehensive experiment (with various architectural patterns, escalating number of dependencies, and different problem sizes) shed light on three important hypotheses. First, product architecture affects resource allocation decisions and ultimately product performance. The second hypothesis tests whether modular or integral architectures can attain higher performance levels based on our formulation. A third hypothesis states that there is a shift in the temporal allocation of resources from design rules to individual modules, thus supporting the move from integral to modular architectures as the product evolves across multiple generations. Finally, the model and the experimental results provide design and managerial insights to both development engineers and managers. Specifically, for development engineers, the model and its analysis provide guidance for selecting the product architecture which leads to maximum performance. For development managers, the model and its analysis assist in deciding the optimal budget proportions to be allocated to modules and to design rules, given a fixed architecture and budget.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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