Anonymous cross-border logistics enterprises · Optimization of container loading

A Evidence level

Datawhale FDE Case 100 | No. 6: From experience-based loading to intelligent cross-border logistics planning

Publisher: Datawhale FDE100 · Official case collection PDF · Direct sources at the case level

Claim origin: Disclosure by the author of the case · Independent verification: No · Accessed: 2026-09-19

Evidence level measures whether a source can be located and reviewed; it does not mean vendor-reported claims were independently audited.

Standard case | Useful reference with remaining gaps 7 / 12

Business context / 2

Transformation workflow1 / 2

Technical workflow2 / 2

Human roles & governance1 / 2

Measured outcomes1 / 2

Source traceability2 / 2

Remaining gaps: Business context, Transformation workflow, Human roles & governance, Measured outcomes

The key to this case is not the single-point use of AI, but the re-establishment of “optimal container loading” as an enforceable, verifiable production stream with boundaries of human responsibility.

Business problem

The loading rate directly determines the profit, but the loading is highly dependent on manual experience.

Solution

Convert space, goods attributes and business constraints into optimization issues, with AI-assisted interpretation and presentation.

Technical architecture & production workflow

Step 01

ERP/WMS/orders/inventory/supplier/constraint data

→

Step 02

Harmonization of entity and operational calibre

→

Step 03

Forecast/optimal/LLM recognition anomalies and candidate actions

→

Step 04

Promising options for rules and optimizer calculations

→

Step 05

Operations confirm high-impact movements

→

Step 06

Rewrite results and update inventory/order status

Key technology & infrastructure components

ERP/WMS dataSynonyms/substantial layersForecast/optimal algorithmLLM/AIPRules EngineOperations workstation

Human roles & accountability

Dispatchers handle unusual cargo, special restraint and final loading.

FDE delivery actions

Define true decision-making units and constraints with plan/procurement/movement control personnel
Harmonization of entity calibres such as SKU, plant, order, supplier, etc.
Replace the word "see report" with the word "identify anomalies for action-execution"
Sorting recommendations with financial impact/service level
Feedback to the system on the causes of manual adoption/rejection

Reusable delivery patterns

HF, high-cost, verifiable narrow-flow selection
Steps that can be validated with a certainty tool to prevent model self-assessment
Retain manual responsibility for high-risk actions
For each manual amendment to follow-up searchable context/rules

Business outcomes & delivery results

The case highlighted that loading thresholds such as 98 were directly related to profits; specific company indicators were not made public.

Evidence boundaries & verification notes

Public information confirming business processes; technical components are abstract architecture based on public description
The source can be directly located in the case; the value remains disclosed by the source and does not represent an independent audit.

Primary source: Datawhale FDE Case 100 | No. 6: From experience-based loading to intelligent cross-border logistics planning ↗ Additional sources: Datawhale FDE100 case webpage | No. 6 ↗ Additional sources: Secondary collation or aggregation source used in the old version ↗