An integrated two-dimensional warranty framework for second-hand equipment considering condition-based maintenance, upgrades, and past life
Bibliographic record
Abstract
Purpose This study aims to investigate the impact of a two-dimensional warranty policy for secondhand equipment, incorporating prior age and usage, condition-based preventive maintenance (PM) and upgrade activities, on minimizing the total expected cost (TEC) incurred by dealers under warranty coverage. The research addresses the critical challenge of ensuring reliability and cost-effectiveness for secondhand equipment dealers, while fostering trust in reliability improved secondhand products. Design/methodology/approach Based on a quantitative modelling approach, this work develops a reliability-based framework that integrates sequential condition-based maintenance (CBM) policy governed by a reliability threshold, with efficiency levels and upgrade intensity as decision variables. PM actions are modeled with dynamic age-reduction effects, while upgrade actions enhance the equipment’s reliability prior to market reintroduction. The model is validated through numerical experiments and sensitivity analyses to explore the influence of key parameters on warranty servicing costs and reliability. Findings The results reveal that the TECs associated with warranty coverage are highly sensitive to the equipment’s effective age, usage history and reliability thresholds, emphasizing the necessity for high-efficiency PM and targeted upgrade strategies. The findings highlight that higher upgrade levels are more cost-effective for older equipment, while optimal PM scheduling minimizes degradation and warranty costs. These insights underscore the importance of aligning warranty constraints with cost-reliability tradeoffs in designing effective warranty policies. Research limitations/implications This study assumes a fixed warranty coverage policy and a known degradation process, focusing on the dealer’s perspective in warranty servicing. Future research could explore dynamic warranty policies that adjust based on product condition and market demand, as well as incorporate customer behavior and preferences to provide a more comprehensive understanding of warranty optimization strategies. Practical implications The proposed framework helps secondhand equipment dealers optimize maintenance and warranty decisions by balancing upgrade intensity and PM strategies. By integrating a reliability-driven approach, dealers can minimize warranty servicing costs while improving equipment performance and customer satisfaction. The findings offer valuable decision-support tools for designing cost-effective and competitive warranty policies in the secondhand equipment market. Social implications This study contributes to the circular economy by encouraging the refurbishment and extended use of secondhand equipment, reducing waste and promoting sustainability. By improving the reliability of pre-owned products, the proposed warranty framework enhances consumer trust in the secondhand market, ultimately increasing the adoption of sustainable consumption practices. Originality/value This research introduces an innovative two-dimensional warranty framework for secondhand equipment, integrating past life considerations, a reliability improvement program and a CBM policy. The proposed framework strategically balances maintenance efficiency and upgrade intensity, providing dealers with actionable insights to design optimal warranty policies, reduce TECs and enhance customer confidence in refurbished equipment.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".