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Case study 06 / Logistics

Intelligent Supply Chain

A shared data layer across orders, fleet telemetry and warehouse events that powers demand forecasting and exception management.

Concept Architecture

Illustrative reference architecture, not a client engagement. Outcomes described here are design goals, not measured results.

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Reference flow

05 stages

Reference flow for Intelligent Supply Chain: Orders & IoT, then Event stream, then Forecasting, then Optimization, then Exceptions.

01Challenge

Why this problem is hard.

Orders, warehouse operations and transport each run on their own systems, with their own identifiers and update cycles. Planners reconcile them in spreadsheets, forecasts are built on stale snapshots, and exceptions such as a late trailer or a short pick are discovered when a customer calls.

  • 01

    Disconnected systems

    ERP, WMS, TMS and telematics each hold part of the picture, with different identifiers for the same shipment.

  • 02

    Batch latency

    Nightly extracts mean decisions are made on yesterday's state.

  • 03

    Reactive exceptions

    Problems surface late, when fewer recovery options remain.

  • 04

    Forecasts without context

    Demand forecasts ignore operational signals such as stock positions, promotions or lane disruptions.

02Business context

Assumptions and constraints.

The concept assumes a distributor or third-party logistics operator with several warehouses, a mixed own and contracted fleet, and established ERP, WMS and TMS platforms that will remain in place.

Constraints the design must respect

01Integrate, don't replace
Core platforms stay. The data layer works with their APIs and change feeds.
02Variable partner data
Carrier and partner data arrives in mixed formats and quality, and is validated at the platform edge.
03Planner control
Optimization proposes and planners approve. Automation is limited to well-understood, reversible cases.
04Cost discipline
Telemetry volumes can be large. Retention and aggregation are designed deliberately.

03Architecture

Reference architecture.

Six stages that turn source-system changes into shared business events, then into forecasts, plans and routed exceptions. Select a stage to see what it does and the components involved.

Order, inventory and shipment changes come from ERP, WMS and TMS through change data capture or APIs. Fleet telematics and scanner events arrive as device streams.

  • Change data capture
  • Partner APIs and EDI
  • Telematics streams
  • Scanner events

Key patterns

  • Canonical business events
  • Identity resolution
  • Probabilistic forecasting
  • Exception routing

04Technology

Representative technology.

Technologies we would consider for this concept. Final selection follows the organization's existing standards, skills and estate. Listing a technology does not imply a vendor relationship.

  • 01

    04 items

    Integration

    • Change data capture
    • API gateway
    • EDI translation
    • Device telemetry ingestion

  • 02

    04 items

    Data platform

    • Kafka-compatible event streaming
    • Lakehouse with open table format
    • Low-latency state store
    • Data contracts and lineage

  • 03

    04 items

    Analytics & ML

    • Forecasting models
    • Optimization solvers
    • ETA prediction
    • Backtesting framework

  • 04

    04 items

    Applications

    • Planner workbench
    • Exception queue
    • Notifications
    • Role-based access control

05Engineering approach

Principles behind the design.

The decisions that shape every stage of the architecture, and why we would hold to them under delivery pressure.
  1. 01

    Events as the shared language

    A canonical event model gives every team the same view of an order's state, whatever the source system.

  2. 02

    Current state and history

    Operations need the latest state in seconds; forecasting needs complete history. Both are served from the same events.

  3. 03

    Uncertainty made visible

    Forecasts and ETAs come with ranges, so planners judge risk instead of trusting a single number.

  4. 04

    Automation by exception

    Routine cases flow through. People focus on exceptions, with clear ownership and recovery options.

06Implementation

A phased delivery path.

Each phase ends with a working, reviewable increment and an explicit decision on whether and how to continue.

Delivery path

04 phases

Delivery phases: Event model & sources, then Visibility, then Exceptions, then Forecasting & optimization
  1. 01Event model & sources
  2. 02Visibility
  3. 03Exceptions
  4. 04Forecasting & optimization
  1. Phase 0101

    Event model & sources

    Define canonical events and identifiers, then connect the first sources with change data capture.

    Deliverables

    • Canonical event model
    • Source connectors
    • Identity resolution rules
    • Data contracts
  2. Phase 0202

    Visibility

    Current-state views of orders and shipments across systems, with data quality monitoring.

    Deliverables

    • Current-state store
    • Visibility views
    • Data quality checks
    • Lineage
  3. Phase 0303

    Exceptions

    Detect and route at-risk orders with rules first, then ETA models as history accumulates.

    Deliverables

    • Exception rules
    • ETA model
    • Exception queue
    • Recovery playbooks
  4. Phase 0404

    Forecasting & optimization

    Introduce forecasting and optimization for selected decisions, backtested against history before planners rely on them.

    Deliverables

    • Forecast models
    • Backtests
    • Optimization pilots
    • Planner workbench

07Intended outcomes

Design goals, not results.

What this architecture is designed to achieve. These are intended outcomes for a concept, not measured results from a client engagement.

  • Design goal 01Intended

    One operational picture

    Orders, inventory and transport share an event model and identity, so teams work from the same facts.

  • Design goal 02Intended

    Earlier exception detection

    At-risk orders become visible while recovery options still exist.

  • Design goal 03Intended

    Honest uncertainty

    Planners see forecast ranges and their drivers, not single numbers presented as certain.

  • Design goal 04Intended

    Integration without replacement

    Existing ERP, WMS and TMS platforms stay in place; the data layer connects them.

Concept Architecture

No figures are attached to these goals. Actual results depend on the environment, data and delivery, and would be measured against baselines agreed at the start of a real engagement.

08Lessons & risks

Engineering lessons and risks to manage.

Where designs like this tend to run into trouble, and what we would plan for from the start.

  • Risk 01

    Identity resolution is the foundation

    Matching the same shipment across systems is unglamorous and decisive. Budget time for it.

  • Risk 02

    Partner data quality varies

    Validate at ingestion and show quality per source, or poor feeds will quietly distort forecasts.

  • Risk 03

    Optimization needs real constraints

    A solver that ignores dock capacity or driver hours produces plans nobody can run.

  • Risk 04

    Start with exceptions

    Exception management delivers usable value from visibility alone, before forecasting models mature.

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