Toward servitization: Optimal design of uptime‐guarantee maintenance contracts
Bibliographic record
Abstract
This paper analyzes the contracting of maintenance services provided by an original equipment manufacturer (OEM) to an operator for a device. The service provider can exert different levels of maintenance effort during the course of the contract and the device's reliability (uptime) is influenced by these levels. However, the service provider's effort level is noncontractible. Our research seeks to find the optimal structures, as well as parameters, of performance‐based maintenance contracts. We single out a unique uptime‐guarantee contract structure that contains profit‐maximizing contracts in many situations. Complete servitization is the essence of such optimal contract structures. With this contract structure, the service provider simply guarantees 100% uptime and compensates the operator's for any occurred downtime at a higher unit rate than it charges for maintenance services. Our findings show that some of the well‐known performance‐based contract structures used in practice (e.g., pay‐for‐performance contracts) can be suboptimal for the OEMs. We incorporate the customer's ability to affect the uptime and show that the optimal contract structures can also coordinate the customer's effort. We demonstrate the advantages and limitations of offering menus of contracts to increase the service provider's expected profits. Finally, through simulations using a sample data set, we find that a contract designed using the key ideas in our paper shows very promising results for practitioners.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 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".