Logistics analytics that connects TMS, WMS, ERP, and telematics inside your own environment. AI text-to-SQL for OTIF, fleet utilization, and freight cost, with no per-seat pricing and real-time data. Book a demo.
By Anusha Maduri, Marketing & Content Specialist, Analytify AI · Updated June 10, 2026
Logistics analytics is business intelligence for the systems that plan, move, and store goods, where the data is scattered across a dozen platforms and the answer you need is almost always real-time. Analytify gives supply-chain teams, 3PL operators, and fleet managers an AI-powered platform that connects TMS, WMS, ERP, and telematics inside their own environment, so route, warehouse, and freight decisions happen on live data that never leaves their perimeter. It is the rare combination of generative BI and full self-hosting that an operations team can actually deploy across a sprawling network.
Every large BI vendor will sell a logistics operation a cloud dashboard. Far fewer will let that operation keep carrier rates, customer volumes, and route data on its own infrastructure, run open-source software it controls, and still get plain-English, AI-driven analysis across every node. That gap, between what logistics teams are offered and what their operations and security functions can approve, is exactly what a self-hosted supply chain BI platform closes.
The distinction from ordinary self-service analytics is the integration burden and the clock. A logistics dashboard is not a quarterly report; it is an operational instrument that drivers, dispatchers, and dock managers act on within the hour. That raises the bar on three things at once: how many source systems you can connect, how fresh the numbers are, and whether the people on the floor can ask their own questions without waiting for a central team.
Three forces make logistics analytics its own discipline. The data is fragmented across systems that were never designed to talk to each other: a TMS for transportation, a WMS for the warehouse, an ERP for orders and inventory, and telematics for the vehicles. The cadence is operational, not strategic, so a daily refresh is too slow for route and dock decisions. And the investment is accelerating: Gartner forecasts that supply chain management software with agentic AI capabilities will grow from less than 2 billion dollars in 2025 to 53 billion dollars in spend by 2030, and predicts that 70 percent of large organizations will adopt AI-based supply chain forecasting by 2030.
The takeaway for a COO or supply-chain leader: the upside of AI across the network is real, but only if it can be deployed on data that is unified and current. An analytics platform that forces a cloud upload, or that refreshes once a day, cannot keep pace with how logistics actually runs.
Vehicle utilization, route adherence, idle time, fuel burn, and driver behavior pulled from telematics, the core of fleet analytics. Live data lets dispatch reroute around a closure or a late dock before the cost compounds, rather than reading about it the next morning. This benefits directly from real-time analytics on streaming vehicle data.
Pick, pack, and ship rates, dock-to-stock time, labor productivity, and slotting efficiency from the WMS, so a distribution center manager sees bottlenecks as they form. The win is catching a throughput slump during the shift, not in a week-end report.
On-time, in-full performance by carrier, lane, customer, and facility, the number retailers penalize and customers remember. Tracking OTIF against its components shows whether a miss came from the warehouse, the carrier, or the order itself.
Cost per mile, freight cost per unit, accessorial charges, and the value of inventory sitting in transit, the financial side of logistics analytics that funds the rest. Tie spend to service level and the trade-offs become visible instead of assumed.
On-time inbound, fill rates, lead-time variance, and damage rates by supplier and carrier, so sourcing and transportation decisions rest on the same governed numbers the operations team sees. This is where predictive analytics flags a supplier trending toward late before it breaks a promise date.
A strong logistics dashboard tracks the metrics operations, finance, and customers all watch. These are the core ones.
| KPI | What it measures | Why it matters |
|---|---|---|
| OTIF (on-time in-full) | Orders delivered on time and complete | Service level and retailer compliance |
| Perfect order rate | Orders on time, complete, undamaged, accurate | End-to-end execution quality |
| Fleet utilization | Active vehicle time versus available | Asset efficiency and capacity |
| Cost per mile | Total cost divided by miles driven | Transportation profitability |
| Dwell time | Time vehicles wait at a facility | Detention cost and throughput drag |
| Dock-to-stock time | Receipt to put-away duration | Inbound and warehouse efficiency |
| Order cycle time | Order placed to order delivered | Customer experience and velocity |
| Freight cost per unit | Shipping cost per item moved | Unit economics of distribution |
Logistics analytics earns its budget the moment it stops being four disconnected reports. Instead of analysts stitching transportation, warehouse, order, and vehicle data together by hand each week, a connected platform reads from each source and unifies them on a defined cadence, with the lineage attached. Analytify connects directly to the databases behind your operational stack, including Oracle, SAP HANA, Microsoft SQL Server, and PostgreSQL, so OTIF, fleet, and freight numbers all trace back to a single governed semantic layer rather than four versions of the truth. A clean data pipeline keeps the network view current without a nightly export.
This is the section every other supply chain BI page skips, and it is the one that decides the deal. Analytify is a self-hosted BI tool. It runs on-premises, in your private cloud account, or in an air-gapped environment, so carrier rates, customer volumes, and route data never leave your perimeter and never transit a vendor's cloud.
Because it is an open-source BI tool, your own security and operations teams can examine exactly what the software does. For a logistics network that treats its lane economics and customer mix as competitive advantage, an auditable system you control is materially easier to approve than a black box that ships your data offsite. Combined with the option of cloud BI when you want it, this is the deployment flexibility that lets an operation adopt AI-driven analytics without inheriting egress risk. The same approach already underpins our work in manufacturing and retail.
Self-hosting does not mean giving up modern AI. Analytify brings generative BI inside your environment, so a dispatcher or operations analyst can ask a question in plain English and get a governed SQL query in return, all without the data leaving the network.
Pairing AI with self-hosting and a per-platform license is the combination no large incumbent leads with, and it is the most defensible thing a logistics analytics stack can offer in 2026. With AI-powered business intelligence running where your data already lives, the people who run the network get answers without a ticket to a central analytics team.
The incumbents are capable and well known, but they are cloud-first and priced per seat. For a logistics operation with hundreds of dispatchers, dock leads, and managers who all need a view, the deciding factors are hosting, integration, and cost.
| Capability | Tableau / Power BI / Qlik | Analytify |
|---|---|---|
| Self-hosted, on-prem, or air-gapped | Limited or cloud-first | Yes, by default |
| Open source and inspectable code | No | Yes |
| Data stays in your environment | Often requires vendor cloud | No cloud egress |
| Connects TMS, WMS, ERP, telematics | Connectors vary, add-ons | Direct to your databases |
| AI text-to-SQL inside your network | Cloud-based AI | Runs in your environment |
| Licensing | Per seat, scales with headcount | Platform license, unlimited internal users |
For specific side-by-sides, see Analytify vs Tableau, Analytify vs Power BI, and Analytify vs Qlik Sense, or review pricing. Fleet telematics leaders such as Geotab and Samsara handle the vehicle data well, and Analytify reads from that telematics alongside your TMS and WMS so fleet analytics sits in the same governed view as the rest of the network.
It is software supply-chain and logistics teams use to optimize routes and fleets, measure warehouse throughput, track on-time delivery, and control freight cost. It must unify transportation, warehouse, ERP, and telematics data in real time because logistics decisions are operational.
Yes. Analytify is self-hosted and can run on-premises, in a private cloud account, or air-gapped, so carrier rates, customer volumes, and route data never leave your environment or transit a vendor cloud.
It connects directly to the databases behind each system, including Oracle, SAP HANA, SQL Server, and PostgreSQL, and unifies them in one governed semantic layer so OTIF, fleet, and freight numbers all trace back to a single source.
OTIF, perfect order rate, fleet utilization, cost per mile, dwell time, dock-to-stock time, order cycle time, and freight cost per unit, so operations, finance, and customers see the same numbers.
An OTIF above 95 percent is generally considered excellent, though targets vary by sector. Fast-moving consumer goods often aim for 95 to 98 percent to major retailers, while perfect order rate benchmarks commonly sit near a 90 percent median.
Yes, and the open code is an advantage. Your security and operations teams can inspect exactly what the software does, while self-hosting keeps lane economics and customer data inside your network rather than on a vendor’s cloud.
A dispatcher or analyst asks a question in plain English, such as OTIF by carrier and lane, and the platform writes the governed SQL against your operational data, returns the result, and keeps the query visible for review.
Tableau, Qlik, and similar tools are typically per-seat, which scales painfully across a large logistics workforce. Analytify uses a platform license with unlimited internal users on infrastructure you already run, usually far lower in total cost.
Book a walkthrough and we will show Analytify against a stack like yours, self-hosted, with no per-seat pricing.