Analysis and Optimization Plan Design of Hoobuy Agent Purchase Logistics Cost Data in Spreadsheets-CNfans spreadsheet-The best and new spreadsheet

Jordan 4 Retro (40 color)-0001

$34.25

AirPods 3-0102

$7.53

casabianca(40 styles)-1146

$25.89

Prada Casual Sneakers (22 color)-0015

Dior B30 Series (20 style)-0416

$45.21

2024 New Jordan 4 (12 colors)-0010

$38.36

New Balance 9060 series (20 style)-0011

$38.36

Denim tears T-shirt -0930

$9.32

Rolex watches (3 qualities 14 styles)-0396

$52.05

GOYARD Fashion bags-0182

$9.45

OG Balenciaga speed-1047

$44.93

dunk low （30 color）-0003

$19.18

Amiri Cap-0662

$13.56

Louis Vuitton fashion bag-0185

$47.95

24-25 Football Jerseys (40+ Colors)-0948

$9.32

Balenciaga Triples (7 styles) -0515

$82.19

LV bucket bag（10 styles）-0187

$54.66

LV Louis vuitton belt（36 style）-0246

$11.64

Introduction

In today's globalized market, cross-border agent purchase businesses like Hoobuy heavily rely on efficient logistics management to maintain profitability. This paper explores the analysis of Hoobuy's logistics cost data in spreadsheets and proposes an optimized combination strategy to minimize expenses while ensuring delivery timelines.

Data Collection and Spreadsheet Organization

The foundational step involves compiling comprehensive logistics data into structured spreadsheets with the following columns:

Shipping Channels
Base Freight Costs
Insurance Fees
Customs Duties/Taxes
Transit Time
Weight/Volume Ratios
Destination Zones/Tiers

Historical shipment data should be categorized by product type and destination region for pattern analysis.

Key Analytical Approaches

1. Cost Breakdown Analysis

Utilize spreadsheet functions (PivotTables, SUMIFS) to:

Calculate average cost per kilogram by shipping channel

Identify tax thresholds in high-tariff destinations

Compare volumetric vs. actual weight pricing

2. Scenario Modeling

Create simulation templates that automatically calculate total costs when adjusting:

Declared value (affecting insurance and tax liability)

Package dimensions (optimizing volumetric weight)

Multi-package shipments vs. consolidated shipments

3. Transport Mode Matrix

Develop a decision matrix that ranks shipping methods based on:

Priority Criteria Weightage

1 Cost per unit weight 40%

2 Delivery time 30%

3 Reliability score 20%

4 Customs clearance rate 10%

Optimization Strategy Design

1. Tiered Shipping Rules

Based on spreadsheet analysis, implement rules like:

Items < 2kg to USA: Priority Mail (5-day) vs. Express (3-day) when profit margin >$50

EU shipments valued <€150: Always split to avoid VAT registration thresholds

Bulky items to Australia: Sea freight consolidation for packages exceeding volumetric thresholds

2. Smart Combine/Uncombine Algorithms

Develop spreadsheet formulas that:

Automatically flag when multi-package shipments would reduce per-unit costs

Calculate optimal declared values balancing insurance and duty costs

Suggest repackaging when dimensional weight exceeds actual weight by >15%

Implementation and Verification

Pilot Testing:
KPI Tracking:
Spreadsheet Iteration:

Projected Savings

Preliminary modeling suggests potential savings of:

12-18% on North American light-package routes

22-30% on European bulk shipments

9-14% overall through insurance declaration optimization

Conclusion

Through systematic analysis of logistics data in spreadsheets, Hoobuy can implement dynamic shipping strategies that automatically select optimal combinations of shipping channels, insurance coverage, and packaging methods. This data-driven approach creates measurable cost reductions while meeting customer delivery expectations, directly enhancing the competitiveness of the agent purchase business.

Next Steps:

Priority	Criteria	Weightage
1	Cost per unit weight	40%
2	Delivery time	30%
3	Reliability score	20%
4	Customs clearance rate	10%

```