Modeling and Optimization of JD Logistics Delivery Time Data in Spreadsheets-Lovegobuy spreadsheet

Modeling and Optimization of JD Logistics Delivery Time Data in Spreadsheets

2025-04-27

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Introduction

Efficient logistics distribution is vital for JD.com's operations, where timely deliveries directly impact customer satisfaction. However, delivery times are influenced by multiple factors, including regional distance, weather conditions, traffic congestion, and operational workflows. To address these challenges, a data-driven approach using spreadsheet-based modeling and optimization can help improve efficiency and service quality.

Methodology

The study involved the following steps:

Data Collection:
Spreadsheet Model:
Distance (calculated via APIs like Google Maps)

Weather conditions (fetched from historical weather databases)

Traffic index (using real-time congestion data)

Current transportation routes and vehicle types

Linear Regression Analysis:
Monte Carlo Simulations:
Optimization a Approach:

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IntroductionWith the rapid growth of e-commerce, logistics efficiency has become a key competitive advantage. This study explores how spreadsheet-based modeling can optimize JD Logistics’ delivery timelines by analyzing factors like regional distance, weather disruptions, and traffic patterns. The goal is to propose data-driven improvements for route planning and resource allocation.1. Data Collection and PreparationWe sourced JD Logistics’ historical delivery performance datasets (2020–2023) covering:Delivery timestamps (order dispatch → customer receipt).Regional warehouses and destination coordinates.Ancillary data: Weather records (via OpenWeatherMap API), holiday calendars, and traffic incident reports.Data was cleaned in Google Sheets using FILTER()QUERY()2. Spreadsheet Modeling ApproachA two-phase model was constructed:a) Baseline Performance AnalysisCalculated mean delivery times per region using pivot tables, identifying underperforming zones (e.g., Central China’s +18% delay rate).b) Multivariate RegressionEmployed Google Sheets’ LINEST()VariableImpact CoefficientDistance (km)+1.2h per 100kmRainfall >20mm+2.5h delayUrban Traffic Index+0.8h per 10-point increase3. Proposed Optimization StrategiesSimulations suggested three improvements:⏱️ Dynamic SchedulingUse SCRIPT_TIMER_TRIGGERS