Carrier and lane performance
Scorecards for on-time pickup and delivery, claims and cost per lane, so you know which carriers to give more freight.
Data Analytics · Logistics
Supply Chain Analytics & Logistics Data Analytics
Join your ERP, TMS, WMS and telematics data into dashboards and forecasts that show on-time performance, freight spend, fleet use and stock risk while there's still time to act.
Overview
Supply chain analytics that shows where time and money leak
Your supply chain already produces the data you need. Orders sit in the ERP, shipments in the TMS, stock in the WMS, truck locations and idle time in Samsara or Motive, and carrier costs in invoices and spreadsheets. None of it is joined up, so a simple question like cost per delivery by zone takes a week of exports. Supply chain analytics brings it together so you can see on-time performance, cost and stock in one place, and act before problems reach customers.
We build the whole stack. Pipelines pull data from your ERP, TMS, WMS, telematics and visibility tools into a secure warehouse. We clean and model it so a shipment, a customer and a lane mean the same thing in every report. Logistics data analytics dashboards then show on-time in-full, dwell and detention, carrier scorecards, freight spend by lane and the real reasons deliveries fail. A fleet analytics dashboard tracks utilization, empty kilometres, fuel use and maintenance per vehicle.
On top of clean data we add prediction. Demand forecasting tells procurement what to reorder and when. Inventory analytics flags slow movers and stockout risk by SKU and location. Anomaly detection highlights a lane whose costs are creeping up or a depot whose failed-delivery rate is rising. It's the same kind of work behind the predictive analytics in IWMCRM, our AI-powered CRM for pharma distribution, and the admin analytics panel we built for AA JoyLand's WhatsApp chatbot.
Access is role-based, so a depot manager sees their depot and finance sees the spend. Driver location data is handled with the controls GDPR and the UAE and Saudi PDPL call for.
Use cases
AI Data Analytics use cases in Logistics
Fleet analytics dashboard
Utilization, empty running, idle time, fuel and maintenance per vehicle and driver, from your telematics data.
Demand forecasting
Weekly forecasts by SKU and location that feed purchasing and replenishment plans.
Inventory analytics
Stock cover, slow movers, dead stock and stockout risk across warehouses and branches.
Freight spend and cost-to-serve
Breaks down freight cost by customer, lane, mode and order size so pricing reflects what each account really costs.
Last-mile delivery analytics
First-attempt success, failed-delivery reasons and COD collection by rider, zone and day.
How it works
How it works
- 01Connect your data sourcesSecure pipelines pull from ERP, TMS, WMS, telematics, visibility tools and spreadsheets.
- 02Clean and modelRecords are de-duplicated and standardized so shipments, lanes and customers match across systems.
- 03Build dashboardsRole-based views for leadership, operations, fleet, procurement and finance.
- 04Add predictionsForecasting and anomaly models run on top of the clean data.
- 05Deliver alertsScheduled reports and threshold alerts reach the people who need to act.
Capabilities
Key features
Unified logistics data model
One trusted source that joins orders, shipments, stock, vehicles and costs.
Role-based dashboards
Each team sees the metrics it owns, with drill-down to the individual shipment.
Forecasts and anomaly detection
Demand forecasts, ETA trends and cost anomalies built on your own history.
Map views
Delivery density, failed attempts and fleet movement shown on a map by zone and depot.
Natural-language questions
Ask "which carrier was late most often on the Jeddah lane last month?" and get an answer.
Automated reporting
Scheduled reports and alerts by email, Teams or WhatsApp.
Integrations
Works with your Logistics stack
- SAP S/4HANA
- Oracle NetSuite
- Odoo
- Microsoft Dynamics 365
- Samsara
- Motive
- project44
- Power BI
- Looker Studio
- BigQuery
- PostgreSQL
- Excel / Google Sheets
Using something else? If it has an API, a database or a webhook, we can connect to it.
Outcomes
Benefits for Logistics teams
Decisions from current data
Managers act on this week's numbers instead of waiting for month-end exports.
Freight spend under control
Clear cost by lane and carrier shows where to renegotiate, consolidate or switch.
Right stock in the right place
Forecasts and inventory views cut both stockouts and cash tied up in slow stock.
One version of the truth
Operations, fleet, procurement and finance work from the same numbers.
Our work
Related Work
FAQ
AI Data Analytics for Logistics: FAQs
What is supply chain analytics?
It's combining data from ordering, warehousing, transport and finance to understand performance and predict what happens next. Typical uses are on-time delivery tracking, freight cost analysis, demand forecasting and inventory planning.
Can you combine ERP, TMS and telematics data?
Yes. We build pipelines from systems like SAP, NetSuite, Odoo, your TMS and Samsara or Motive into one warehouse, so a shipment's order, route and cost finally sit in the same report.
How accurate is AI demand forecasting?
It depends on your data and how predictable demand is. We backtest forecasts against your own history before you rely on them, report accuracy openly and keep planners able to adjust the numbers.
Can we keep using Power BI or Excel?
Yes. We can deliver into Power BI, Looker Studio or Excel, or build custom dashboards if your team needs something more specific.
Do we need a data team to use this?
No. Dashboards and natural-language questions are built for operations managers, and we run the pipelines and models. If you have analysts, we work alongside them.
How do you protect driver location data?
We restrict who can see individual driver data, aggregate it where personal detail isn't needed, encrypt everything and log access. That's designed to support GDPR and the UAE and Saudi PDPL.
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