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Record W2795593215

Optimization of a Dual-Channel Retailing System with Customer Returns

2018· article· en· W2795593215 on OpenAlexfundno aff
Mohannad Radhi

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Retail Behavior Studies
Canadian institutionsnot available
FundersUniversity of Windsor
KeywordsBusinessDual (grammatical number)Channel (broadcasting)MarketingComputer scienceTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.206
Teacher spread0.183 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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