Enhancing retail operations: Dynamic fulfilment strategies in Omnichannel retail

  • Hümeysa İskender Project Department Artbey International Project Import and Export Trade Co. Ltd. Orta, Yazlık, Adnan Menderes Cd. No:211 54050 Sakarya, Türkiye
  • Sinem Dayanıklı Industrial Engineering Department Sakarya University Sakarya Üniversitesi Endüstri Mühendisliği Bölümü M5 54050 Sakarya, Türkiye
  • Abdullah Hulusi Kökçam Industrial Engineering Department Sakarya University Sakarya Üniversitesi Endüstri Mühendisliği Bölümü M5 54050 Sakarya, Türkiye
  • Željko Stević Faculty of Transport and Traffic Engineering University of East Sarajevo Doboj, Bosnia and Herzegovina, School of Industrial Management Engineering, Korea University, Seongbuk-Gu, Seoul, Korea https://orcid.org/0000-0003-4452-5768
  • Safiye Turgay Industrial Engineering Department Sakarya University Sakarya Üniversitesi Endüstri Mühendisliği Bölümü M5 54050 Sakarya, Türkiye
  • Mahmut Baydaş Department of Accounting and Financial Management Necmettin Erbakan University Necmettin Erbakan University Yeni Meram Boulevard Kasım Halife Street 42090 Meram / KONYA
  • Jasmina Bunevska Talevska Faculty of Technical Sciences, University St.Kliment Ohridski Bitola 7000 Bitola, North Macedonia
DOI: https://doi.org/10.5937/sjm20-54499
Keywords: decision-making, dynamic programming, genetic algorithm, omnichannel marketing, order fulfillment

Abstract


This study explores the trend of in-store fulfillment in omnichannel retail, where nearby store inventories are utilized for online order fulfillment. This model presents advantages such as faster delivery, cost reduction, improved stock management, increased sales, and enhanced customer satisfaction. Operational challenges include determining the optimal fulfillment location for online orders. Investigating a retailer with online and brick-and-mortar stores, this paper addresses dynamic order fulfillment decisions considering customer demand and shipping cost uncertainties. The study employs Stochastic Dynamic Programming for shorter periods and the Genetic Algorithm for longer periods, revealing that the Genetic Algorithm provides solutions closely approximating optimal.

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Published
2025/06/04
Issue
Vol. 20 No. 1 (2025): Serbian Journal of Management
Section
Original Scientific Paper

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