Market segmentation using predictive technology and shared-service contracts
Bibliographic record
Abstract
‘Shared-service contracts’ provided by Original Equipment Manufacturers (OEMs) are a standard feature of capital-intensive, long-life products. In such contracts, OEMs assume a predetermined portion of the products’ long-term failure opportunity costs by conducting routine and breakdown maintenance tasks for their customers. Recently, OEMs have been utilizing predictive technology to improve service productivity. It enables OEMs to reduce failure opportunity costs mentioned while using the data generated to develop products at lower costs. We extend the adverse selection framework to model the market segmentation of products offered with shared-service contracts and predictive technology. Our model includes: (i) the cost of product development and operation, (ii) the failure opportunity cost driven by long-term product failures, (iii) the cost of designing product reliability, and (iv) the cost of incorporating predictive technology, which reduces the first two cost components. Here, the OEM targets a basic and a premium offering at their respective customer types. We find that using predictive technology improves the quality and price of OEM products, thereby increasing their profitability. However, predictive technology enables the OEM to design a product line with lower reliability. In addition, the OEM is more likely to choose a ‘premium-only’ strategy when predictive technology is present.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.007 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".