An anonymous cross-border digestive enterprise
Sell-in / Sell-through business analysis
Datawhale
Consumer goods/retail
Explicit FDE engagement
Standard case | Useful reference with remaining gaps
Evidence level A
Deployed / continuing iteration
A
Evidence level
Datawhale FDE Case 100 | No. 24: Using AI to connect overseas sell-in and sell-through data for consumer goods
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 context1 / 2
Transformation workflow1 / 2
Technical workflow2 / 2
Human roles & governance / 2
Measured outcomes1 / 2
Source traceability2 / 2
Remaining gaps: Business context, Transformation workflow, Human roles & governance, Measured outcomes
Business problem
Headquarters shipments, channel sales and end-of-life sales have broken calibres, and fair statements do not answer real business questions.
Solution
The data calibre is harmonized and each AI answer is required to be traced back to the original records, followed by business questions and insights.
Technical architecture & production workflow
Step 01
Business Data/Documents/System Inputs
→
Step 02
Data cleansing and business semantics
→
Step 03
AI/rules are understood, matched or generated
→
Step 04
Final check or business rule check
→
Step 05
Manual handling of low confidence/high-risk matters
→
Step 06
Turns out to write back the original business process and continue to settle the feedback.
Key technology & infrastructure components
Enterprise data/documentsLLM/Semantic UnderstandingOperational rulesHuman-in-the-loopOriginal operational system
Human roles & accountability
Operational personnel define calibres and validate unusual interpretations.
FDE delivery actions
- Take a full workflow with the first line of staff and confirm the real bottlenecks, not just the required documents.
- Inventory of available data, permissions, system interfaces and hidden operating rules
- Dismantling tasks into AI, rules/traditional software, three types of human responsibility
- We'll start with a narrow scene, PoC, and we'll use real samples to verify accuracy and business value.
- Embedded to the original system and designed for abnormal upgrades, feedback and follow-up iterative mechanisms
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
Establish an retrospective business analysis link, rather than simply a chat-type BI.
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.