Years in Logistics Operations
Real warehouse expertise, made testable for AI.
I bring 12+ years across warehouse operations, fulfillment, cold chain, WMS, and logistics data—ready to review scenarios, validate process logic, and explain where an AI answer succeeds or fails in real operations.
m² Ambient + Cold Chain Site
Pallets/Day Managed
Parcels/Day at Peak Scale
Useful AI evaluation starts with knowing what breaks on a real warehouse floor.
My judgment is grounded in throughput constraints, inventory movement, cold-chain zones, WMS behavior, service levels, and the people executing the process.
Requirements mapped to operating evidence.
This is a transfer of proven domain judgment into AI evaluation—not a claim of prior paid model-training work.
RequirementsReview and validate logistics data, documents, and workflows
Evidence
BI & Data Analyst at Lazada; SOP design, KPI governance, dashboards, and operational-data validation across warehouse roles.
RequirementsEvaluate AI answers against warehouse best practice
Evidence
Led WMS recovery, high-volume sortation, cold-chain operations, controlled ramp-up, and SLA continuity in live environments.
RequirementsCreate realistic queries, cases, and operational hypotheticals
Evidence
Hands-on cases include a 2,000+ returns backlog, a 10,000-letter backlog, 300,000 parcels/day, and a 60,000 m² mixed-temperature site.
RequirementsExplain complex domain decisions clearly
Evidence
Translated operating requirements into SOPs, business logic, KPIs, ownership, dashboards, and cross-functional execution plans.
The experience behind the judgment.
Warehouse depth first, supported by analytics, systems recovery, fulfillment, sorting, and frontline flow control.
Jan 2023 — Sep 2026
New Land VJ Long AnDomain depth · Integration
Warehouse Manager
All prior layers converged: people, process, data, and systems applied to an ERP/WMS-enabled Ambient and Cold Chain operation.
~60,000 m²NLVJLA warehouse site
- Designed SOPs and the operating model.
- Delivered the Central Retail Vietnam transition with controlled ramp-up and SLA continuity.
- Directed 50+ staff across sites at up to 18,000 pallets/day and ~800 tons/day.
Knowledge integratedEnd-to-end logistics transformation
Oct 2021 — Aug 2022
Boxme FulfillmentDomain depth · Systems
Warehouse Manager
Data visibility met WMS reality: standardizing SOPs and KPI governance, then leading a cross-functional recovery.
10,000 m²Boxme warehouse scale
- Cleared a WMS-related returns backlog of 2,000+ orders.
- Established an operational review cadence for visibility and control.
Knowledge addedWMS recovery · Multi-site governance
2020 — 2021
LazadaDomain depth · Data
BI & Data Analyst
Operational experience moved behind the dashboard—turning SLA and throughput data into clearer decisions for logistics teams.
Knowledge addedData validation · Decision visibility
2017 — 2020
Lazada E-LogisticsDomain depth · Scale
Sorting Center Supervisor
At e-commerce speed, warehouse discipline expanded into high-volume flow control and Process Stabilization.
~7,000 m²Lazada Sorting Center
- Managed approximately 300,000 parcels/day.
- Kept backlog below 5% and reprocessing below 0.05%.
Knowledge addedThroughput control · Stabilization at scale
2016 — 2017
Organic DaLat JSCDomain depth · Warehouse floor
Warehouse Supervisor
Postal flow became physical warehouse practice: supervising people, goods, storage, and daily execution close to the floor.
Knowledge addedWarehouse discipline · Team supervision
2012 — 2016
ViettelPostDomain depth · Foundation
Hub Specialist
The journey began inside postal flow—where bottlenecks were visible, urgent, and solved through disciplined workflow redesign.
- Reduced a 10,000-letter backlog from three months to one week.
Knowledge addedFlow visibility · Backlog recovery
Evidence accumulated
Domain judgment for AI evaluation
Areas I can review.
My practical scope covers warehousing and fulfillment, where process logic, data, systems, and execution meet.
Warehouse & Fulfillment
Inventory flow · Cold Chain · Cross-docking · 3PL · E-commerce Fulfillment · Sorting Center
Systems & Process Logic
ERP/WMS · Process Design · Returns Recovery · Operational Ramp-up
Operational Quality
SOP review · SLA/KPI Governance · Bottleneck analysis · Cross-functional execution
Data & Evidence
Python · BI and Reporting · Operational Analytics · Dashboards · Data validation
Sample domain review.
Illustrative evaluation—not a claim about a Micro1 model or customer dataset.
Hypothetical AI recommendation
“Place every fast-moving SKU in the locations closest to dispatch to minimize travel time.”
Expert review
The direction is plausible but incomplete. A valid answer must also test replenishment travel, picker and MHE congestion, order profiles, slot capacity, FEFO/FIFO rules, temperature segregation, dock cut-off times, and the cost of relocating stock.
- What is correct: velocity-based slotting can reduce pick travel.
- What is missing: constraints that can move the bottleneck upstream or create safety and service risks.
- Better conclusion: simulate candidate slotting by zone and shift, then compare total touches, replenishment load, congestion, and SLA impact.
A practical review loop
for logistics AI.
- 01Ground the scenario
Identify the real process, objective, inputs, and service commitment.
- 02Check constraints
Test inventory, labor, MHE, WMS, temperature, timing, and capacity assumptions.
- 03Trace consequences
Look for shifted bottlenecks, edge cases, safety risks, and KPI trade-offs.
- 04Write the verdict
Separate what is correct, missing, and operationally preferable.
Available for remote domain-expert work.
Relevant assignments include warehouse and fulfillment scenarios, SOP and workflow review, WMS/process logic, operational data validation, and AI-output evaluation.