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Transport & Logistics · Regional Logistics Operator — 200+ vehicle fleet, 14 warehouses

From Scattered Data to a Live Decision Layer

How we built an operational intelligence layer aggregating 7 data sources and converting signals into decision-ready outputs — reducing incident resolution time by 42%.

8 min read

Problem

The client operated a 200+ vehicle fleet across 14 warehouses with 7 different operational systems that didn't communicate with each other. Operational decisions were made manually by operations managers based on reports delayed by 4–8 hours. The result: excess operational costs from wrong routing decisions, recurring unpredictable delays, and inability to identify root causes until after the fact. The real challenge wasn't a lack of data — it was an abundance of data with no structure to make it actionable.

What we built

We chose a "Decision Layer First" approach — starting from the question: what decision must a manager make in three hours? Then we worked backwards to identify the required data. We built a unified data pipeline aggregating GPS signals, warehouse data, order schedules, and weather conditions into a single data model. On top of it, we deployed an anomaly detection and delay prediction ML model that fires before issues occur. The interface was a single dashboard giving managers "the top 3 things that need attention now" — not reports, decisions.

Outcome

Within 14 weeks: a unified decision dashboard, ~42% faster incident resolution in the pilot environment, and the ability to predict delays before they occur — with clear operational documentation for field managers.

Architectural decisions

  • Event-Driven over Batch Processing

    Batch reports arrived 4–8 hours late — we shifted to Kafka + streaming so every signal is available within seconds of occurring.

  • Centralized ML Feature Store

    Instead of building separate models for each system, we built a Feature Store shared across all models — cutting new model training time from weeks to days.

  • Explainable AI as a Non-Negotiable Standard

    Field managers won't trust a decision they don't understand the reason for. Every recommendation is accompanied by a 'why' written in natural language — not just a probability score.

Technical challenges

  • 7 systems with 7 different schemas and no shared standard

    We built a Canonical Data Model layer that translates each system to a unified model — with extensible connectors for any new system without touching the core.

  • ML models losing accuracy after seasonal changes in operational patterns

    We deployed a Model Drift Detection system that monitors model drift and automatically retrains when accuracy crosses a threshold — without manual intervention.

Architecture

PythonApache KafkaApache FlinkFastAPIPostgreSQLRedisScikit-learnMLflowKubernetesGrafanaOpenTelemetry

Results

−42%

Incident Resolution Time Reduced

7

Data Sources Unified

89%

Delay Prediction Accuracy

< 30s

Data to Decision Latency

“For the first time, our field manager knows what's going to happen before it happens. This wasn't just a system — it was a shift in how the team thinks.”
Chief Operating Officer — Regional Logistics Operator

Representative quote for discussion — composite scenario aligned with this archetype, not a named client endorsement unless stated otherwise.

These case studies are illustrative summaries for discussion. They are not guarantees of results for your organization unless confirmed in a separate agreement.

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