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Automated repricing and ordering strategies in competitive markets

  • Merchants on modern e-commerce platforms face a highly competitive environment. They compete against each other using automated dynamic pricing and ordering strategies. Successfully managing both inventory levels as well as offer prices is a challenging task as (i) demand is uncertain, (ii) competitors strategically interact, and (iii) optimized pricing and ordering decisions are mutually dependent. We show how to derive optimized data-driven pricing and ordering strategies which are based on demand learning techniques and efficient dynamic optimization models. We verify the superior performance of our self-adaptive strategies by comparing them to different rule-based as well as data-driven strategies in duopoly and oligopoly settings. Further, to study and to optimize joint dynamic ordering and pricing strategies on online marketplaces, we built an interactive simulation platform. To be both flexible and scalable, the platform has a microservice-based architecture and allows handling dozens of competing merchants and streams ofMerchants on modern e-commerce platforms face a highly competitive environment. They compete against each other using automated dynamic pricing and ordering strategies. Successfully managing both inventory levels as well as offer prices is a challenging task as (i) demand is uncertain, (ii) competitors strategically interact, and (iii) optimized pricing and ordering decisions are mutually dependent. We show how to derive optimized data-driven pricing and ordering strategies which are based on demand learning techniques and efficient dynamic optimization models. We verify the superior performance of our self-adaptive strategies by comparing them to different rule-based as well as data-driven strategies in duopoly and oligopoly settings. Further, to study and to optimize joint dynamic ordering and pricing strategies on online marketplaces, we built an interactive simulation platform. To be both flexible and scalable, the platform has a microservice-based architecture and allows handling dozens of competing merchants and streams of consumers with configurable characteristics.zeige mehrzeige weniger

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Metadaten
Verfasserangaben:Rainer SchlosserORCiDGND, Carsten WaltherGND, Martin BoissierORCiD, Matthias Uflacker
DOI:https://doi.org/10.3233/AIC-180603
ISSN:0921-7126
ISSN:1875-8452
Titel des übergeordneten Werks (Englisch):AI communications : AICOM ; the European journal on artificial intelligence
Verlag:IOS Press
Verlagsort:Amsterdam
Publikationstyp:Wissenschaftlicher Artikel
Sprache:Englisch
Datum der Erstveröffentlichung:13.03.2019
Erscheinungsjahr:2019
Datum der Freischaltung:19.05.2021
Freies Schlagwort / Tag:Dynamic pricing; demand learning; e-commerce; inventory management; oligopoly competition
Band:32
Ausgabe:1
Seitenanzahl:15
Erste Seite:15
Letzte Seite:29
Organisationseinheiten:Digital Engineering Fakultät / Hasso-Plattner-Institut für Digital Engineering GmbH
DDC-Klassifikation:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 000 Informatik, Informationswissenschaft, allgemeine Werke
Peer Review:Referiert
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