Operations · Supply Chain Visibility

Talk to Your
Operations & Logistics Data

Eliminate the "Where is my shipment?" chaos. Connect your Chatbot directly to your TMS, WMS, and GPS tracking systems to provide real-time updates to customers, drivers, and operations teams instantly.

Operational Value

Efficiency at Every Mile

Move from reactive "fire-fighting" to proactive supply chain management with AI-driven communication.

Total Visibility

Stakeholders can self-serve shipment status via LINE/Web 24/7, reducing dispatch center call volume by up to 60%.

Faster Exception Handling

When a truck breaks down or traffic hits, the bot proactively alerts the receiving team, rescheduling slots instantly.

Streamlined POD

Drivers upload photos of Proof of Delivery (POD) directly to the chat. AI verifies the image and updates the ERP immediately.

Automated Workflow

The Journey of a Smart Shipment

Order Placed

Customer requests delivery via Portal.

Dispatch & Alert

Bot notifies driver via LINE: "New Job Assigned".

In Transit

Customer asks Bot: "Where is it?"
Bot queries GPS + TMS.

[Image of Supply Chain Logistics map]

Exception?

If delayed, Bot proactively alerts customer with new ETA.

POD & Done

Driver uploads photo. Bot closes order.

Capabilities

Connected Logistics Ecosystem

TMS/WMS Integration

Deep binding with platforms like SAP, Oracle, and Inno Supply Chain Biz TMS to read status and write job updates.

Driver Chat Channels

Reach drivers where they are: LINE, WhatsApp, or SMS. No heavy app installs required for simple status reporting.

IoT & Sensor Data

"Is the cargo too hot?" The chatbot can query IoT sensors on the truck and alert if temperature thresholds are breached.

function: check_shipment.py Prod
def track_shipment(tracking_id):
    # 1. Query TMS for basic info
    tms_data = tms_client.get_order(tracking_id)
    
    # 2. Get Real-time GPS
    truck_id = tms_data['assigned_truck']
    gps_data = telematics.get_location(truck_id)
    
    # 3. Calculate ETA Deviation
    planned_arrival = tms_data['planned_eta']
    live_eta = google_maps.predict(gps_data.coords)
    
    delay_minutes = (live_eta - planned_arrival).minutes
    
    if delay_minutes > 30:
        return {
            "status": "DELAYED",
            "new_eta": live_eta,
            "reason": "Traffic detected"
        }
        
    return { "status": "ON_TIME" }

System Adapters:

Optimize Your Supply Chain Communication

Deploy CKN Chatbot Technology to bridge the gap between your systems, your drivers, and your customers.