This paper proposes an adaptive local Distribution Network (DN) approach supported by real-time consumer preference and inventory data for dynamic order allocation.
An operational approach is outlined for DNs fulfilling omni-channel online orders, tailoring different delivery modes to cater to heterogeneous consumer demands. Since these combinations generate high task uncertainty, the approach draws on Organizational Information Processing Theory (OIPT), leveraging real-time data to enhance the retailer's information processing capacity. To evaluate its performance in a real-world scenario, a simulation was conducted implementing the proposed approach in a Brazilian retailer context.
Results highlight the importance of aligning delivery strategies with diverse consumer expectations, suggesting that relying on a single facility type may be insufficient to meet such diverse demands. Computational outcomes indicate that decentralizing the DN into an adaptive hybrid approach, combining facilities and postponing fulfillment decisions using real-time data can mitigate task uncertainty, reducing fulfillment time for quick commerce and minimizing delivery attempts.
This technology-driven solution empowers retailers to overcome the limitations of static networks. By aligning logistics operations with consumer preferences, it enhances fulfillment performance and balances operational trade-offs, preparing retailers for ever-evolving market trends.
This research addresses a gap in the holistic integration of multiple delivery modes, consumer logistics preferences and inventory locations by incorporating intelligent systems that enhance information processing capacity for postponed fulfillment decisions.
