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Data-driven customer acceptance for attended home delivery.

Charlotte Köhler1, Ann Melissa Campbell2, Jan Fabian Ehmke3

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Summary
This summary is machine-generated.

This study introduces a data-driven approach to optimize home delivery scheduling. By analyzing historical order data, retailers can improve delivery efficiency and customer acceptance rates, managing scarce delivery capacities effectively.

Keywords:
Attended home deliveryData-driven customer acceptanceHistorical dataSamplingVehicle routing with time windows

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Area of Science:

  • Operations Research
  • Supply Chain Management
  • Data Science

Background:

  • Home delivery services necessitate customer presence, leading to agreed-upon delivery time windows.
  • The impact of accepting a current delivery request on future customer availability and route efficiency is often unclear for retailers.

Purpose of the Study:

  • To develop and evaluate a data-driven method for managing scarce delivery capacities in home delivery services.
  • To assess how historical order data can inform customer acceptance decisions to optimize route efficiency and future availability.

Main Methods:

  • A sampling-based customer acceptance approach utilizing historical order data.
  • A data-science process to determine optimal data recency and sampling volume for decision-making.
  • Identification of key features influencing acceptance decisions and retailer revenue.

Main Results:

  • The proposed approach effectively uses historical data to manage delivery capacity.
  • Identified features significantly improve the accuracy of delivery acceptance decisions.
  • Demonstrated improved revenue potential for retailers through optimized scheduling.

Conclusions:

  • Historical order data is crucial for efficient management of delivery capacities and customer scheduling.
  • The developed data-science approach enhances decision-making for home delivery services, balancing current and future customer needs.
  • Retailers can improve operational efficiency and revenue by leveraging data analytics for delivery management.