Predictive Logistics Developer
When to Use
- Build demand forecasts at SKU, location, lane, or network-node granularity with logistics-aware features
- Design inventory positioning and safety stock model interfaces that feed planning and execution systems
- Predict ETA, lead time, and transit time distributions from operational and external signals
- Forecast capacity, congestion, and throughput for nodes, lanes, and facilities at integration level
- Integrate route and network flow predictions with TMS/WMS/OMS—not full VRP solver implementation
- Model cold chain, perishables, and shelf-life constraints in forecast and positioning logic
- Encode promotions, seasonality, and calendar effects for logistics demand and capacity
- Run backtests, monitor drift, and score models against fill rate, OTIF, WMAPE/MAPE, and service KPIs
- Define feature stores, inference contracts, and batch/real-time scoring pipelines for logistics ML
When NOT to Use
- Pure OR/MIP formulation and solver implementation without logistics prediction scope →
operations-research-algorithm-developer
- Supply chain strategy, RFQ, supplier scorecards, or inventory policy governance without ML build →
- WMS workflows—waves, pick paths, RF scanning, slotting application logic →
- Fleet telematics ingestion, map matching, or geospatial pipeline engineering →
geospatial-telematics-developer
- Generic ML experimentation, causal inference, or MLOps without logistics domain framing →
- EDI/X12 mapping, AS2, or partner document translation →
- Warehouse dimensional modeling or dbt mart design without prediction modeling →
Related skills
| Need | Skill |
|---|
| LP/MIP, VRP, scheduling optimization | operations-research-algorithm-developer
|
| SCM strategy, forecast process, supplier QBRs | |
| WMS application and ERP/WMS integration | |
| GPS/telematics streams and spatial ETL | geospatial-telematics-developer
|
| Partner EDI and order/shipment documents | |
| General ML, A/B tests, MLOps patterns | |
| BI dashboards and KPI storytelling | |
| Feature pipelines and warehouse modeling | |
Core Workflows
1. Scope and problem framing
Clarify horizon, granularity, decision consumer, and operational KPI contract.
See references/predictive_logistics_scope.md
2. Demand forecasting and features
Build SKU/location/lane demand models with logistics calendars, promotions, and hierarchy reconciliation.
See references/demand_forecasting_and_features.md
3. Inventory and network positioning
Connect forecasts to positioning, safety stock interfaces, and multi-echelon handoffs.
See references/inventory_and_network_positioning.md
4. ETA, lead time, and capacity
Model transit times, node congestion, and capacity signals for planning and execution.
See references/eta_leadtime_and_capacity.md
5. Evaluation and monitoring
Backtest against operational KPIs; track drift, bias, and forecast value.
See references/model_evaluation_and_monitoring.md
6. Operations integration
Wire scores to OMS/TMS/WMS, planning cycles, and human-in-the-loop overrides.
See references/integration_with_operations.md
Outputs
- Problem brief — granularity, horizon, consumers, KPI targets, and non-goals
- Feature catalog — definitions, freshness SLAs, leakage checks, and hierarchy keys
- Model card — training window, metrics (WMAPE/MAPE, bias), segments, and known failure modes
- Backtest report — rolling-origin results tied to fill rate, OTIF, or inventory service proxies
- Inference contract — schema, latency, batch cadence, fallback rules, and version pins
- Monitoring runbook — drift thresholds, retrain triggers, and escalation to planning ops
Principles
- Optimize for operational KPIs, not only statistical accuracy — tie WMAPE to service and inventory outcomes
- Respect logistics calendars — lead times, cutoffs, carrier schedules, and promotion lift are first-class features
- Prevent leakage — exclude post-decision signals; align train labels to information available at forecast origin
- Reconcile hierarchies — bottom-up vs top-down consistency for SKU × location × lane stacks
- Separate prediction from optimization — deliver distributions and interfaces; route MIP/VRP to OR peers
- Monitor in production — drift, bias by lane/node, and forecast value beat one-time offline accuracy
- Document override paths — planners and TMS rules may supersede scores; model serving must degrade safely
When to load references
| Topic | Reference |
|---|
| Role scope, boundaries, RACI | references/predictive_logistics_scope.md
|
| Demand features, seasonality, promotions | references/demand_forecasting_and_features.md
|
| Safety stock, positioning, multi-echelon | references/inventory_and_network_positioning.md
|
| ETA, lead time, capacity signals | references/eta_leadtime_and_capacity.md
|
| Backtesting, WMAPE, drift, KPIs | references/model_evaluation_and_monitoring.md
|
| OMS/TMS/WMS integration, cadence | references/integration_with_operations.md
|