为京东物流的下一步而来：在首个加拿大站点成形之际，我带来它最需要的从零到一能力——在这块大陆上从零启动过仓库，也从零架构过一整套物流生态（OMS · TMS · WMS · 智能柜 · 承运商 API）。
Built for JD Logistics' next chapter: as the first Canadian operation takes shape, I bring the exact zero-to-one muscle it needs — a greenfield warehouse launched on this continent, and a full logistics ecosystem (OMS · TMS · WMS · lockers · carrier APIs) architected from scratch.
京东物流正在开启第一个加拿大站点——仓储、履约与运输从零搭建，第一批客户方案将由这个岗位亲手设计。这不是我研究出来的岗位描述，而是我已经做过两次的工作：在蒙特利尔约两个月内从零建仓上线；在香港从零架构最大的最后一公里生态——OMS、TMS、柜机操作系统、微型仓 WMS、IoT 硬件与承运商 API——做到年处理 1,100 万件包裹。
我的工作方式正是这场建设所需要的：数据为先（Tableau/Metabase KPI 体系直接给出行动建议，而不止于看板）、流程为先（先重设拣货路径与工作流，再谈自动化），并精通这个岗位所需的两种语言——用英语服务加拿大客户，用母语普通话对接总部。
JD Logistics is opening its first Canadian operation — warehousing, fulfillment and transportation stood up from zero, with the first customer solutions designed by whoever holds this seat. That is not a job description I studied; it is work I have already done twice: a greenfield warehouse launched in Montréal in about two months, and Hong Kong's largest last-mile ecosystem — OMS, TMS, locker OS, micro-warehouse WMS, IoT hardware and carrier APIs — architected from zero to 11 million parcels a year.
I work the way this build will demand: data-first (Tableau/Metabase KPI systems that prescribe actions, not just dashboards), process-first (pick-path and workflow redesign before automation), and fluent in both of this role's languages — English with Canadian customers, native Mandarin with HQ.
香港最大的最后一公里网络：1,000+ 智能柜、800 家便利店、年处理 1,100 万件包裹——OMS、TMS、硬件与承运商 API 一体化设计。从零到市场第一，5 年。
Hong Kong's largest last-mile network: 1,000+ lockers, 800 convenience stores, 11M parcels/year — OMS, TMS, hardware and carrier APIs designed as one system. Zero to market leader in 5 years.
仓储运营 · AIWarehouse Ops · AI多智能体系统覆盖入库、出库、库存、人力与成本 KPI——实时异常预警并直接给出纠正动作。从看板到行动的闭环。
Multi-agent intelligence over inbound, outbound, inventory, labor and cost KPIs — real-time anomaly flags with prescribed corrective actions. Closing the loop from dashboards to action.
履约 · 多站点Fulfillment · Multi-site多站点（多伦多 + 蒙特利尔）任务编排：自动化排程、员工绩效追踪、动态费用配置。多站点履约的任务底座。
Multi-site (Toronto + Montréal) task orchestration: automated planning, worker performance tracking, dynamic fee configuration. The task backbone of multi-site fulfillment.
运输 · AITransportation · AI面向末端配送的 AI 路径优化——聚类 + 约束求解，覆盖两个枢纽、每月 48K 订单。运输侧的算法实践。
AI-orchestrated route optimization for last-mile delivery — clustering + constraint solving over 48K orders/month across two hubs. Algorithms applied to the transportation side.