In logistics, data is everywhere but rarely in one place. The transport system knows when a truck left, the warehouse knows the picking status, telematics logs the route and the driver’s hours, and controlling spreadsheets count costs in their own way. The operations director ends up with four different truths and no certainty. The problem is not a shortage of data, but the absence of a single layer that turns it into comparable KPIs and concrete decisions.
In our experience, the hard part is not the visualisation but agreeing on what exactly we measure and where each number comes from. So on analytics projects we start with metric and source definitions, and only then with charts. Our data and BI competence draws on the whole RITS Group, including our Big Data and AI/data teams, and our experience in transport and travel tech was built in part on Data and Analytics work. Even if we haven’t run your exact scenario, we provide the people who have designed and maintained this kind of solution.
Why is logistics data so fragmented?
The fragmentation isn’t anyone’s fault; it comes from the history of the systems. Each tool was built to solve a different operational problem, and each keeps data in its own format, with its own definition of a “delivery”, an “order” or a “status”. A TMS thinks in transport orders, a WMS in picking lines, and telematics in GPS events every few seconds.
On top of that come signals from outside the organisation: carrier statuses, customer booking notifications, EDI files in several flavours. The result is that the same shipment carries a different identifier in three places, and its delivery time depends on who you ask. Before we calculate any metric, we have to marry these worlds together and agree on a shared vocabulary. It’s unglamorous but decisive work, because any KPI built on badly joined data is pure fiction.
How do you build operational KPI dashboards in logistics?
A good dashboard doesn’t show everything; it shows what the reader can act on. We design it around the role: a dispatcher’s screen looks nothing like an operations director’s cockpit. The set of transport KPIs that works most often is:
- on-time performance (OTD/OTIF) split by carrier, region and customer type,
- cost per shipment and per kilometre, separating own routes from subcontractors,
- fleet utilisation and vehicle fill rate (payload, empty mileage),
- order cycle time from intake to delivery,
- data quality as a KPI in its own right, meaning how many events arrive on time and complete.
Every number connects to a path for drilling in, from the aggregate value down to a single route, because without that a KPI is a curiosity rather than a decision tool.
How do you calculate the real cost per shipment?
Cost per shipment sounds simple until you try to compute it. The trouble is that costs sit in different systems and at different granularity: carrier invoices arrive in bulk, fuel is settled per vehicle, and warehouse labour per shift, not per parcel. To reach an honest number, we break total cost down into its drivers and allocate it back to shipments using sensible keys such as weight, volume or line count.
That surfaces things a bulk invoice hides: that one lane structurally runs at a loss, that small parcels erode the margin despite correct pricing, or that the cost of empty runs outweighs the savings from a cheap carrier. At that point cost stops being a line in a report and becomes an argument in negotiations and a basis for rerouting.
How do you report on-time performance (OTD)?
On-time reporting looks trivial, yet everything hinges on the definition of “on time”. Do we count from the booking or from confirmation? Is the tolerance a time window or an exact minute? Does a partial delivery count as met? Pinning down these rules matters more than the chart itself, because they decide whether OTD at 95% is a success or a problem swept under the rug.
Once the rules are clear, we design the on-time report to show not just the result but the cause:
- the OTD trend over time, split by carrier and branch,
- the reasons for delay, categorised consistently rather than in free text,
- at-risk shipments, flagged before the deadline passes rather than after.
That last element shifts reporting from an accounting mode into an operational one, because it lets the team react while something can still be saved.
How do you forecast throughput and utilisation?
Once the historical data is in order, looking forward is the natural next step. We build models that forecast shipment volume and warehouse load on a weekly and seasonal basis, so you can plan shift staffing and vehicle allocation before a bottleneck appears. This isn’t reading tea leaves; it’s grounding capacity decisions in the patterns that recur every year anyway.
We treat the forecasting layer as an extension of analytics, not a separate world. Only once the foundation of clean data and agreed metrics is working do we add predictive models and, where it makes sense, an AI component. That competence genuinely exists within the RITS Group, so we switch it on when it brings value, not because it sounds good.
How do you design the data warehouse and metrics layer?
Under every dashboard there has to be a repeatable foundation, or the numbers drift apart the moment a source changes. We build it as a data warehouse with an explicit metrics layer, fed by pipelines that regularly pull from the operational systems:
- pipelines from TMS, WMS and telematics, resilient to missing and duplicated events,
- a modelling layer that turns raw tables into coherent entities (shipment, route, order),
- a central metric definition, so OTD or cost per shipment is computed identically in every report,
- data-quality checks built into the pipeline, with an alert when a source stops delivering a full set.
This architecture means a new metric lands in days rather than weeks, and everyone in the company looks at the same figures.
Which skills do we provide for logistics analytics?
We shape the team around the specific stage and scope of work, not “just in case”. On data projects we most often staff these roles:
- a data engineer building and maintaining the pipelines and warehouse,
- a BI analyst / dashboard developer translating metrics into readable screens,
- a data architect owning the model and the metrics layer,
- an ML specialist / data scientist where forecasts and an AI component come in.
Such a team can work as an extension of your data department or as a dedicated team running the topic end to end. We laid out the differences between these models in our article on IT engagement models.
How do we do it at RITS?
We work as a nearshore partner from Poland that combines data competence with experience in transport projects:
- our Data and BI skills draw on the RITS Group, including our Big Data and AI/data teams,
- in the transport and travel tech sector we have delivered Data and Analytics work,
- we start from metric definitions and data quality, not from a pretty chart,
- we verify skills before the start and scale the team as the project matures.
Most often we do this as custom analytics software built by our team and, where gaps need filling fast, as team augmentation.
Where do you start with logistics analytics?
Start from one decision you want to make better, for example which carrier to scale back or which lane to renegotiate. That decision defines the metrics you need, and the metrics define the sources and the architecture. It’s a far healthier starting point than trying to measure everything at once, because it delivers a result you can see in operations straight away.
Want to turn data from TMS, WMS and telematics into KPIs and decisions? Let’s talk - we’ll match a team and an architecture to your logistics.