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

Core Workflows

1. Scope and problem framing

Clarify horizon, granularity, decision consumer, and operational KPI contract.

See references/predictivelogisticsscope.md.

2. Demand forecasting and features

Build SKU/location/lane demand models with logistics calendars, promotions, and hierarchy reconciliation.

See references/demandforecastingand_features.md.

3. Inventory and network positioning

Connect forecasts to positioning, safety stock interfaces, and multi-echelon handoffs.

See references/inventoryandnetwork_positioning.md.

4. ETA, lead time, and capacity

Model transit times, node congestion, and capacity signals for planning and execution.

See references/etaleadtimeand_capacity.md.

5. Evaluation and monitoring

Backtest against operational KPIs; track drift, bias, and forecast value.

See references/modelevaluationand_monitoring.md.

6. Operations integration

Wire scores to OMS/TMS/WMS, planning cycles, and human-in-the-loop overrides.

See references/integrationwithoperations.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

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