Legacy systems driven logistics dispatch runs on documents and processes taken care of by your employees. But in high-volume B2B supply chain environments operating on Dynamics 365 Supply Chain Management, the cost of a single document error, which can be anything like;
- Mismatched quantity
- Unvalidated load
- Missing compliance record
- Micounted Spools
- Failure to capture vehicle details
Issues like these are not caught until the shipment is already in transit and intelligent document processing closes that gap.
DynaTech's Logistics Dispatch Automation connects directly to your D365 SCM environment through AI-powered computer vision, configured APIs, applying OCR-based document extraction, real-time dispatch validation, automated documentation generation, and end-to-end automation across every shipment event. Errors are surfaced before trucks leave the dock, not after customers call to report them.
What Makes Logistics Dispatch Automation Different from Built-In Copilot?
Microsoft Copilot is built to manage the upper conversational layer and help logistics teams summarize documents, navigate workflows, and surface shipment information quickly.
Microsoft Copilot is built to manage the upper conversational layer – summarizing documents or navigating workflows. It’s not designed to run a Machine Learning based object detection model on spool images, automatically compute net weight from tare and gross images, or trigger an automated email dispatch via Power Automate.
DynaTech's Logistics Dispatch Automation system is purpose-built for execution, not conversation. It applies;
- OCR-based AI document processing for incoming shipping documents
- Auto-generates dispatch documentation
- Captures proof-of-loading
- Machine-learning-based spool detection and counting
- Azure Document Intelligence OCR for weight extraction and plate reading
- Automated PDF report assembly
- Direct integration with Power Automate for hands-free email delivery
It does all these tasks while following a structured automated workflow thus showing the real difference between AI assistance and AI execution.
Key Capabilities of Logistics Dispatch Automation Process
1. OCR Processing
Our AI logistics solutions applies OCR-based intelligent document processing to extract structured data from incoming shipping documents, including;
- Purchase orders
- Bills of lading
- Delivery notes
- Packing lists
- Number plate reading (Saudi format supported)
More document types can be added as per your requirements. This capability eliminates manual data entry at the point of dispatch and reduces transcription errors across high-volume shipment cycles.
2. Spool Detection and Counting
Using a trained ML-based object detection model, it identifies and counts spools in any uploaded image. This provides visual confirmation of load composition before dispatch, directly addressing the “mismatched quantity” problem.
3. D365 Supply Chain Integration
The data our logistics document automation process extracts is cross-validated against your established D365 SCM records. The data is extracted through configured APIs, OData endpoints, and integration connectors.
4. Automated Documentation
Based on validated dispatch data, it generates required shipping and compliance documentation automatically, applying configured templates and workflow rules, without requiring manual document assembly at each dispatch event. It also generates reports combining app images and data into a single PDF file and sends it to specified recipients using Power Automate.
5. Real-time Dispatch Validation
DynaTech’s smart AI logistics automation system has access to dispatch data of every shipment. Using this access, it evaluates order configurations and applies entity-aware validation logic to determine field-level discrepancies on time before it affects the supply chain or passes incorrect information to the customers or carriers.
6. Proof-of-Loading
It captures and records loading confirmation of the ruc at the document and item level, creating a structured proof-of-loading record linked to each dispatch event, and shares the information downstream for validation, sharing to clients, carriers, and while doing so prevent dispute resolution.
7. Compliance Audit Trail
Every extraction, validation, documentation event, and loading confirmation is logged within the system using a structured and retrievable audit trail. This data is needed for compliance review without asking your team to manually track every dispatch and logged entry from fragmented source records.
Logistics Dispatch Automation Process by DynaTech
The Problem Our Dispatch Management Automation System Solves
Dispatch teams in B2B supply chain environments manage a continuous flow of shipping documents as they have to;
- Manually enter data into D365 SCM
- Cross-check load details against open orders
- Generate dispatch records under time pressure
The manual approach isn’t fool proof as errors slip through, loads depart with mismatched documentation, which you must have experienced in the past.
When dispatch volume increases, document variability gets more complex especially in enterprise supply chain environments. By the time a discrepancy is reported, the shipment is in transit and correction costs more than prevention would have.
Hence, you need a smart logistics document automation system as traditional approaches to logistics document processing are not built for scale.
What the Automation System Actually Does?
DynaTech's Logistics Dispatch Automation system takes care of three core functions of every dispatch, logging load information, sharing accurate information with the clients, and customers, and creating a data log for every dispatch.
Our solution connects to your Dynamics 365 Supply Chain Management environment through configured API and integration layers where the DispatchSense dashboard offers the following key functions;
- Upload Images: The AI automation system clicks pictures when connected to the cameras, uploads the required photos, blank truck weight, loaded truck weight, spool images, number plate, and any spool slips.
- Automated Processing: The machine learning model instantly counts spools and Azure Document Intelligence OCR extracts tare, gross, and net weights, reads the number plate, and captures text from spool slips.
- Validation & Assembly: The system calculates net weight, verifies data completeness, and surfaces any anomalies (e.g., unreadable plate, missing weight image) in real time.
- Report & Dispatch: One click generates a consolidated PDF with all extracted data and images. Power Automate then sends the report to the designated distribution list.
This ensures your dispatch and operations teams can act on specific findings before the shipment is released.
Intelligent Document Processing Use Cases
Scenario 1: OCR Mismatch on Outbound Freight
A distribution company processes high-volume outbound shipments daily, no doubt here. Let’s say a lading bill submitted by the carrier contains a quantity figure that doesn't match the confirmed D365 sales order line.
Under dispatch time pressure, manual review would have cleared it but the automated process flags the data mismatch at the OCR validation stage, as it can identify loaded truck weight and stops the dispatch, and surfaces the error before the load is released and in turn, preventing any future dispute with the carrier.
Scenario 2: Documentation Failure in Cross-Border Dispatch
After a configuration change in D365 SCM, one required document template stops generating correctly and this sends several issues downstream.
Our automation-enabled AI system prevents this as it identifies the documentation gap during its automated dispatch run and surfaces the failure before the shipment reaches customs, avoiding a border delay that would have gone undetected until the driver called.
Scenario 3: Spool Load Verification
A cable manufacturer loads multiple spools on a truck and the driver submits pictures of the loaded spools through the pre-connected dashboard.
The machine learning model counts the spools and compares them against the dispatch order count. Any discrepancies are flagged and shared with the team to ensure they can cross-check the dispatch order before the truck leaves the dock.
Operational Impact of Our Automated Dispatch Management Solution Using DispatchSense
| Business Challenge | Automation Process |
| Manual weight recording leads to incorrect net weight calculations and billing disputes. | OCR reads blank and loaded weight images automatically; net weight is calculated instantly with no manual intervention. |
| Spool counts rely on visual checks, leading to miscounts and load discrepancies. Shortage of time and pressure to complete the work can delay deliveries and trigger compliance issues. | Evaluates dispatch data against D365 SCM records through configured APIs in real time, while machine learning object detection counts spools in uploaded images and provides visual confirmation, flagging mismatches before departure. |
| Proof-of-loading records are incomplete, completed manually, or missing, creating accountability gaps that increase exposure during customer disputes. | Captures proof-of-loading at the document and item level, automatically generating structured records linked to each dispatch event. |
| Vehicle identification is typed manually, causing tracking errors. | Number plate OCR (Saudi format) extracts registration numbers accurately and embeds them in the dispatch report. |
| Document generation errors go undetected when D365 configurations change, producing incorrect or incomplete dispatch paperwork. | Automated documentation generation is validated against configured dispatch rules, and any output deviation is surfaced before the document is issued to carriers or customers. |
How the Logistics Workflow Automation Works Technically?
DynaTech's Logistics Dispatch Automation system is structured across defined, separated layers and every layer has a specific operational responsibility.
It accesses Dynamics 365 Supply Chain Management through APIs, OData endpoints, and integration connectors. This ensures we don’t need to make any core ERP customization or schema changes.
In addition to this, Azure OpenAI provides;
- Reasoning layer
- Evaluates extracted document data against configured dispatch rules
- Validation parameters
Lastly, Azure AI Foundry manages orchestration, execution sequencing, and output evaluation. Azure AI Vision helps identify objects loaded on the truck and extract text, while analyzing spatial data.
Security and identity related to the AI process are governed through Entra ID app registrations and service principal configuration.
Who Benefits from Logistics Dispatch Automation System?
- Logistics and Dispatch Operations: Dispatch coordinators no longer manually process incoming shipping documents. It handles extraction, validation, and documentation generation, allowing teams to focus on exception review rather than data entry.
- Supply Chain and Warehouse Management: Operations teams receive validated confirmation that every dispatched shipment aligns with D365 SCM order records before it leaves the facility, reducing downstream corrections and carrier disputes.
- Compliance and Audit Functions: Compliance teams receive structured, automatically generated audit trails for every dispatch event, eliminating the need to reconstruct records from fragmented sources during review cycles.
- IT and Implementation: It integrates with existing D365 SCM environments through configured APIs and integration layers, maintaining reliable logistics document automation across every release cycle without additional QA headcount or manual script maintenance.
What Does Deployment Look Like?
DynaTech configures the Logistics Dispatch Automation process to integrate with your existing Dynamics 365 Supply Chain Management environment. For deployment we provider the following;
- Entra ID app registrations
- Service principal configuration
- API permission scoping
- Integration layer setup
We align everything to your dispatch workflows and document types as the experts manage configuration and establish dispatch scenario libraries based on your existing order configurations, carrier document formats, and compliance requirements.
Once live, it operates without disrupting active dispatch schedules. Architecture, deployment timeline, and integration scope are confirmed during the scoping engagement.
The Return is Measurable and Not Theoretical
With automation of dispatch errors resolved after shipment cost significantly more than those caught at the loading dock. DynaTech's AI-enabled solution eliminates manual document processing cycles, compresses dispatch validation timelines, and surfaces field-level discrepancies before they reach carriers, delivering measurable operational returns from the first deployment cycle.
Frequently Asked Questions
Can logistics dispatch automation system process multiple document types from different carriers and suppliers?
Our AI-based automation process is configured to handle the document formats and templates specific to your dispatch environment. OCR extraction parameters and validation rules are scoped during deployment based on your carrier and supplier document structures.
Does deploying the automation process require changes to our Dynamics 365 SCM configuration?
No core ERP customization or schema changes are required. Deployment involves Entra ID app registrations, service principal setup, and API permission scoping managed by the DynaTech team. Integration specifics are confirmed during the scoping engagement.
How does your automation process handle exceptions when document data doesn't match D365 records?
When extracted data falls outside configured validation thresholds, it surfaces the discrepancy with complete context, document source, specific field reference, and the validation rule that failed, for your dispatch team to review and act on.
What does the compliance audit trail capture, and how is it accessed?
The audit trail logs every OCR extraction, validation run, document generation event, and loading confirmation, all linked to the corresponding dispatch event. Records are structured and retrievable for compliance reviews without requiring manual reconstruction from multiple source systems.
