Use the current tools
We connect around what already runs before replacing anything.
Logistics and operations
AI transport planning and logistics software for orders, routes, capacity, and exceptions
Build an owned planning layer that connects orders, stock, routes, drivers, capacity, ERP, WMS, or TMS data and gives planners explainable AI suggestions.
How we keep it useful
Industry pages work best when they stay close to the daily operation. We start with the messy workflow, not with a generic AI demo.
Design the exception path
The workflow must know when to stop and hand over to a person.
Measure one outcome
First proof is practical: fewer interruptions, faster follow-up, cleaner data, or lower cost.
Planning problems that do not fit simple SaaS
The best first step is often not a full transport management system, but a planning layer that connects what already exists.
Capacity changes faster than the planning sheet can keep up.
Dispatch decisions depend on local knowledge in a few people's heads.
Exception handling is hidden in phone calls and chat threads.
Management sees delays after they happen, not while they can still be prevented.
Planning SaaS vs owned planning layer
The best first step is often not a full transport management system, but a planning layer that connects what already exists.
| Planning SaaS vs owned planning layer | Rented SaaS | Owned workflow |
|---|---|---|
| Inputs | Works best when data already fits the tool. | Connects orders, capacity, calendars, fleet data, and manual exceptions. |
| Decisions | Rules are often generic. | Rules reflect routes, customers, drivers, SLAs, and local constraints. |
| Exceptions | Handled outside the system. | Captured, prioritized, and visible before they break the day. |
| AI | Feature add-on. | Planning assistant trained around your constraints and approval rules. |
Roadmap for AI-assisted planning
Give planners better visibility first, then automate suggestions once the data is trustworthy.
01
Unify planning inputs
Bring orders, capacity, drivers, locations, timeslots, and constraints into one model.
02
Make exceptions visible
Create a live exception queue for delays, missing capacity, urgent orders, and conflicts.
03
Add suggestion logic
Let AI suggest route swaps, priority changes, and customer updates with reasons.
04
Close the feedback loop
Compare planned vs actual performance and tune the rules weekly.
Example workflow
- 1
Step 1: Orders and capacity are imported each morning.
- 2
Step 2: System highlights conflicts, missing drivers, and risky routes.
- 3
Step 3: AI proposes changes with impact on SLA and cost.
- 4
Step 4: Planner approves, edits, or rejects suggestions.
- 5
Step 5: Customers or internal teams receive reviewed updates.
Relevant solutions
Use these proposition pages when you want to turn the industry example into a concrete buying path.
Operational automationAI workflow automation services for SMEsHave AI workflow automation designed and built for intake, routing, documents, follow-up, reporting, and internal handovers with human control.Strategic starting pointAI Roadmap for SMEsTurn scattered AI ideas into a ranked execution plan with quick wins, data requirements, risks, and a 90-day build sequence.
Practical guides
Read the implementation choices behind this workflow before you define scope.
AI for workforce planning and schedulingHow AI can improve staffing, shifts, and capacity planning.Read guideCustom ERP for SMEs: when does it make sense?When to build an ERP-like internal system instead of buying a large suite.Read guideAI document processing for quotes, contracts, and invoicesClassify, extract, validate, route, and approve business documents with AI while keeping exceptions under human review.Read guide
FAQ
Do we need to replace our TMS?
Not first. Many teams start with an owned planning layer on top of existing systems.
Can AI make planning decisions automatically?
It can suggest decisions. Automatic dispatch should only come after clear rules, logging, and approval confidence.
What data is required?
At minimum: orders, time windows, resources, locations, constraints, and actual outcomes.
Can this work as an AI logistics operations assistant?
Yes. The same layer can monitor incoming orders, missing information, capacity conflicts, delays, and customer updates, while planners approve consequential changes.
How do you measure the return on AI transport planning?
Measure planner hours, late changes, empty capacity, avoidable kilometres, SLA misses, and time spent updating customers before and after the pilot.