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 →

  supply-chain-manager

• WMS workflows—waves, pick paths, RF scanning, slotting application logic →

  wms-developer

• Fleet telematics ingestion, map matching, or geospatial pipeline engineering →

  geospatial-telematics-developer

• Generic ML experimentation, causal inference, or MLOps without logistics domain framing →

  data-scientist

• EDI/X12 mapping, AS2, or partner document translation →

  edi-engineer

• Warehouse dimensional modeling or dbt mart design without prediction modeling →

  analytics-data-engineer

Related skills

operations-research-algorithm-developer

supply-chain-manager

wms-developer

geospatial-telematics-developer

edi-engineer

data-scientist

bi-analyst

analytics-data-engineer

Core Workflows

1. Scope and problem framing

references/predictive_logistics_scope.md

2. Demand forecasting and features

references/demand_forecasting_and_features.md

3. Inventory and network positioning

references/inventory_and_network_positioning.md

4. ETA, lead time, and capacity

references/eta_leadtime_and_capacity.md

5. Evaluation and monitoring

references/model_evaluation_and_monitoring.md

6. Operations integration

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

references/predictive_logistics_scope.md

references/demand_forecasting_and_features.md

references/inventory_and_network_positioning.md

references/eta_leadtime_and_capacity.md

references/model_evaluation_and_monitoring.md

references/integration_with_operations.md