Digital Transformation in Logistics
Full traceability, intelligent routing and automated warehouses with AI, cloud and specialized talent.
Los 3 retos tech de Logística
End-to-end real-time traceability
The customer expects to know where their package is by the minute. That means events from the driver's device, logistics partner API, and consolidation with business rules, all under a sub-second SLA.
Routing with real constraints
Delivery windows, vehicle capacity, urban traffic, hazardous zones, loading schedules. A real VRP isn't solved with Google Maps — it requires optimization with OR-tools or equivalent.
WMS that talks to OMS, TMS and ERP without breaking
Incorrect picking, warehouses with phantom inventory, and duplicate shipping labels stem from disjointed systems. Event sourcing and continuous reconciliation are table stakes.
Demand forecasting by zone, not global
Stock-out in Bogotá while surplus in Medellín. Requires forecasting by SKU × zone with weekly retraining and detection of short-tail vs. long-tail products.
Mobile operation apps with low connectivity
Drivers and warehouse workers operate on unstable 3G. Apps must work offline-first, sync when possible, and never lose scan data.
Cómo el marco I+C+S resuelve esto
AI for Logistics
Demand prediction, route optimization with ML and computer vision.
Cloud for Logistics
Cloud IoT platforms for real-time tracking and operational dashboards.
Staffing for Logistics
Engineers with experience in TMS, WMS and IoT integrations.
Industry challenges
End-to-end real-time traceability
The customer expects to know where their package is by the minute. That means events from the driver's device, logistics partner API, and consolidation with business rules, all under a sub-second SLA.
Routing with real constraints
Delivery windows, vehicle capacity, urban traffic, hazardous zones, loading schedules. A real VRP isn't solved with Google Maps — it requires optimization with OR-tools or equivalent.
WMS that talks to OMS, TMS and ERP without breaking
Incorrect picking, warehouses with phantom inventory, and duplicate shipping labels stem from disjointed systems. Event sourcing and continuous reconciliation are table stakes.
Demand forecasting by zone, not global
Stock-out in Bogotá while surplus in Medellín. Requires forecasting by SKU × zone with weekly retraining and detection of short-tail vs. long-tail products.
Mobile operation apps with low connectivity
Drivers and warehouse workers operate on unstable 3G. Apps must work offline-first, sync when possible, and never lose scan data.
Regulatory frameworks we operate under
Electronic invoicing and delivery notes
Issuance, validation, and storage of transport documents.
Logistics Quality Management
Applicable for operators that export or serve corporate clients.
Cargo transportation regulations
Vehicle and driver registration, licensing, and compliance.
Recipient and driver data
Consent, retention, and deletion rights.
How we implement in this industry
Real patterns we have delivered, not theoretical slides.
End-to-end traceability platform
Driver device events, logistics partners, and IoT sensors consolidated into a single timeline. Public API for corporate client integrations.
Outcome: Sub-2s visibility from pickup to confirmed delivery.
VRP Router with OR-Tools and Live Traffic
Multi-constraint optimization (capacity, time windows, zones, restocking) with periodic reoptimization throughout the day. Integration with urban traffic data.
Outcome: -28% fuel and +22% deliveries per route.
Cloud WMS with real-time reconciliation
Route-optimized picking in warehouse, automatic recount by ABC classification, continuous reconciliation against ERP with discrepancy dashboard.
Outcome: 60% reduction in phantom inventory.
SKU × zone forecasting with weekly retraining
Time series with causal factors (promotions, events, weather), short/long tail detection, warehouse-level replenishment recommendations.
Outcome: -35% in stock-outs in historically problematic zones.
Our playbook for this industry
A repeatable method refined across 13 years and 7 countries.
Flow mapping and pain points
We map physical flow (warehouse → last mile) and digital (WMS, TMS, OMS, ERP). We identify where time or margin is lost.
Event-driven architecture with traceability
Event sourcing of movements, single timeline per tracking number, actionable observability of failed messages.
Routing and forecasting with operational validation
Pilot in one city or warehouse, measure against baseline, adjust with operations before rollout.
Mobile offline-first apps and operations
Driver and warehouse app with deferred sync, never lose data. Live monitoring dashboards.
Industry signals you should know
Common tech stack
Questions from companies in this sector
Yes. We use Google OR-tools and commercial alternatives depending on volume. We model real constraints (capacity, time windows, restricted zones, intermediate refueling) and reoptimize throughout the day with traffic data.
Yes. Whatever you have — SAP TM, Manhattan, Oracle, proprietary — we integrate via REST/SOAP or with middleware if it doesn't expose an API. Anti-corruption layer pattern always, to avoid coupling release cycles.
Offline-first with deferred sync. Critical operations (scanning, delivery photo, signature) are stored locally and synchronized when network is available. Divergence monitoring between device and server to alert discrepancies.
Yes. Models by SKU × geographic zone, with causal variables (promotions, weather, events). Weekly retraining and detection of short-tail vs. long-tail products to avoid overfitting.
Target sub-2 seconds from the moment the event fires on the driver's device until it's reflected in the client's public API. With graceful degradation (eventual consistency) if a partner's backend fails.
Looking to optimize last-mile or traceability without replacing everything?
Conversemos sobre cómo la tecnología puede impulsar tus resultados.