E-Bikes: How Best to Deploy Last-Mile Delivery Vehicles by Geographical Zoning and Topography

James Tan, the newly appointed manager at XDE’s micro-fulfilment centre (MFC) serving Sooseo district in Seoul city (South Korea), had been recruited to improve the firm’s operational performance and service level to customers. He had to propose the possible routing of electric cargo bikes (e-bikes) to be deployed such that it would minimise delivery lead-time.

By January 2024, battery-operated e-bikes were a promising alternative to trucks for last-mile delivery, as they offered higher accessibility, better last-mile delivery, and were environmentally friendly compared to conventional motorised vehicles that ran on fossil fuels.

Since e-bikes took a longer time to travel between delivery points, their use as a last-mile delivery option from MFCs involved careful route planning by delivery service provides, especially in areas that had uneven terrain or steep slopes.

Tan wanted to analyse the data to understand the factors that determined the number of e-bikes to be deployed to different districts of the city. Such factors included the difference between distance-based and time-based services, and the implication of MFC re-visiting frequencies based on a reduction in e-bike capacity (be it battery or load capacity). He also wanted to recommend an appropriate service boundary for running e-bikes.

Students will be able to use data analytics in operations management for route planning in an MFC, with a focus on operational performance and service level to customers. With an aim to reduce delivery lead-time, students will be able to: - Differentiate between distance- and time-based service - Understand the implication of revisiting MFCs due to reducing e-bike capacity - Determine the number of e-bikes deployed to different areas - Establish an appropriate service boundary for running e-bikes

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