Optimization of a Dual-Channel Retailing System with Customer Returns
Notice bibliographique
Résumé
A plethora of retailers have begun to embrace a dual-channel retailing strategy wherein items are provided to consumers through both an online store and a physical store. As a result of standards and competitive measures, many retailers provide buyers who are unhappy with their purchases with the ability to achieve a full refund. In a dualchannel retailing system, full reimbursements can be done through what is called a crosschannel return, when a buyer purchases a product from an online store and returns it to a physical store. They can also be done through what is called a same-channel return, when a buyer purchases a product from a physical store and returns it back to the physical store, or purchases a product from an online store and returns it back to the online store. No existing research has examined all common types of customer returns in the context of a dual-channel retailing system. Be notified that the practice of cross-returning an item purchased from the physical store back to the online store is not common. Thus, it is not considered in this dissertation.\nWe first study the optimal pricing policies for a centralized and decentralized dual-channel retailer (DCR) with same- and cross-channel returns. We consider two factors: the dual-channel retailer’s performance under centralization with unified and differential pricing schemes, and the dual-channel retailer’s performance under decentralization with the Stackelberg and Nash games. How dual-channel pricing behaviour is impacted by customer preference and rates of customer returns is discussed. In this study, a channel’s sales requests is a linear function of a channel’s own pricing strategy and a cross-channel’s pricing strategy.\nThe second problem is an extension of the first problem. The optimal pricing policies and online channel’s responsiveness level for a centralized and decentralized dual-channel retailer with same- and cross-channel returns are studied. Indeed, the online store is normally the distribution centre of the enterprise and is not limited to the customers in its neighbourhood. Also, the online store experiences a much higher return rate compared to the physical store. Thus, it has the capability and the need to optimize its responsiveness to customer returns along with its pricing strategy. A channel’s sales requests, in the second problem, is a linear function of a channel’s own price, a crosschannel’s price, and the online store’s responsiveness level.\nThe third problem studies the dilemma of whether or not to allow unsatisfactory online purchases to be cross-returned to the physical store. If not properly considered, those returns may create havoc to the system and a retailer might overestimate or underestimate a channel’s order quantity. Therefore, we study and compare between four vi different strategies, and propose models to determine optimal order quantities for each strategy when a dual-channel retailer offers both same and cross-channel returns. Several decision making insights on choosing between the different cross-channel return strategies and some properties of the optimal solutions are presented.\nFrom the retailer’s perspective of outsourcing the e-channel’s management to a third party logistics and service provider, we finally study three different inventory strategies, namely transaction-based fee, flat-based fee, and gain sharing. For each strategy, we find both channels’ optimal inventory policies and expected profits. The performances of the different strategies are compared and the managerial insights are given using analytical and numerical analysis.\nMethodologies, insights, comparative analysis, and computational results are delivered in this dissertation for the above aforementioned problems.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».