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Enregistrement W4412735999 · doi:10.1108/jm2-01-2025-0003

An integrated two-dimensional warranty framework for second-hand equipment considering condition-based maintenance, upgrades, and past life

2025· article· en· W4412735999 sur OpenAlex

Pourquoi ce travail est dans la base

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Notice bibliographique

RevueJournal of Modelling in Management · 2025
Typearticle
Langueen
DomaineEngineering
ThématiqueReliability and Maintenance Optimization
Établissements canadiensUniversité Laval
Organismes subventionnairesnon disponible
Mots-clésWarrantyComputer scienceBusinessOperations managementProcess managementRisk analysis (engineering)Reliability engineeringEconomicsEngineering

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Méthodes · Signal consensuel: aucune
Score de désaccord entre enseignants0,726
Score d'incertitude au seuil0,576

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,013
Tête enseignante GPT0,248
Écart entre enseignants0,236 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle