Elisabetta Villa

The problem isn’t BI. It’s where you start.

by

When a company decides to adopt Business Intelligence software, expectations are high—but projects often struggle to take off.
The reason isn’t the technology. It’s the starting point.

Many BI tools are empty shells. Powerful, yes—but empty. Faced with a blank dashboard to fill from scratch, even well-structured companies get stuck.
No one knows where to begin, which KPIs to choose, or which data to activate first.

You need a consultant, an IT team, weeks of integration. And so, the project drags on, slows down—or worse, stops altogether.

B-AI Semplice was created precisely to overcome this block.
It’s the first ready-to-use vertical Business Intelligence platform designed for logistics.
No blank sheets. No development from scratch.
Preconfigured dashboards, field-tested KPIs, and integrated artificial intelligence that lets you ask questions in natural language and get clear, visual, and immediately actionable answers.

You can ask:

  • Which customer has the highest profitability?
  • What’s the saturation level of your vehicles?
  • How much stock has left a specific warehouse in the past 30 days?

And if your data isn’t yet integrated with your ERP or WMS? No problem.
You can start even with a simple Excel file. The data is recognized, organized, and made available right away.

Behind the platform is the expertise of GEP Informatica, a company that has spent years working inside warehouses and operational flows—not in labs.

Everything you see in B-AI Semplice comes from real needs, encountered in hundreds of projects.
It’s designed for people who make decisions every day—under pressure, with little time and a lot of responsibility.

That’s why the impact is immediate. Because you don’t have to wait months to see something that works.
You start right away, customize as you go, and above all, stop wasting time reinventing what already exists.

In logistics, complexity is inevitable. But analysis doesn’t have to be.

With B-AI Semplice, Business Intelligence is no longer a project to plan.
It’s an ally to use. Now.

When BI stops being an IT project and becomes an operational tool

by

The scene is almost always the same.
The data is there—it comes from the WMS, the TMS, the ERP, and from Excel files no one dares to touch anymore.
Someone has even built dashboards—maybe nice to look at—but when a real answer is needed, that answer doesn’t arrive. Or it arrives too late.

Business Intelligence stays there, locked inside a technical perimeter, managed by IT or an external consultant, far from the people who have to make decisions every day.
The logistics manager looks at the numbers but can’t immediately understand where the problem is. Transport costs increase, but it’s not clear why. The warehouse slows down, but the charts don’t tell the story.

At that point, BI stops being a working tool and becomes just another IT project—technically correct, but disconnected from real operations.

The problem isn’t the data, but how it’s turned into charts

This is where the real issue emerges.
Data is not missing—if anything, there’s too much of it.
The problem is that it’s turned into charts designed for the people who build them, not for those who need to read them.

Crowded histograms, endless tables, KPIs all treated the same way, colors that don’t help distinguish what matters from what’s just noise.
The result is paradoxical: the more numbers there are, the less clarity you get.

People working in logistics and transport aren’t looking for elegant reports. They’re looking for fast answers.
Where am I losing time? Where am I overspending? What has changed compared to last week?

When charts don’t help answer these questions, BI stops guiding decisions and turns into a simple reporting exercise.

When BI becomes an operational tool

The turning point comes when Business Intelligence stops being built around data and starts being built around decisions.

Dashboards are no longer designed to show everything, but to immediately highlight what really matters in logistics and transport.

A few key indicators, readable at a glance, designed for day-to-day operations: order fulfillment times, transport costs, warehouse saturation, delivery punctuality.

Numbers that speak the language of people working in the field.

At this point, BI becomes a real working tool again—not a one-off project.
Dashboards are opened every day, not just at the end of the month, because they help understand what’s happening and where to act immediately.

This is where data visualization stops being aesthetic and becomes functional.

The creator user as a driver of autonomy

The real leap forward happens when BI no longer depends on someone who “builds the charts,” but on those who know the processes.

With the creator user, the platform isn’t just used to view predefined data—it allows dashboards to be expanded and adapted autonomously.
No code to write. No tickets to open. No weeks of waiting.

Those managing logistics and transport can add a view, drill into an anomaly, cross different datasets the moment a question arises.
BI becomes as flexible as the business it supports.

It’s no longer an advanced feature reserved for specialists—it’s an operational tool that grows with the company.
And that’s when data stops being just numbers and truly starts driving decisions.

Business Intelligence and AI in logistics.The real trends toward 2026, seen from the decision-making table

by

Monday morning meetings are no longer a formality

At exactly 9:00 a.m., the operations director opens the weekly meeting. On the table there are reports, Excel files, a few charts. Volumes are stable, deliveries are on track, complaints are under control. And yet, margins have dropped again. No one can say exactly why. Some mention overtime, others a few more urgent requests, others increasingly demanding customers. All plausible explanations, but none verifiable at that moment. The data exists, but it arrives late, or it does not arrive together.

Figures from the Contract Logistics Observatory help explain why this scene is happening more and more often. Real market growth is around +0.3%, while labor costs are increasing by +4.4%. This means that even a company that is “working well” can see its margins erode without clear warning signs. No crisis is needed, just a series of micro-decisions made without a clear understanding of their economic impact.

Looking toward 2026, the real risk is not working less, but working blind. In this context, Business Intelligence is not meant to prove what happened, but to understand what is happening while it is happening.

When complexity increases, data starts to contradict itself

After growth through acquisitions, the organization is larger, but not yet unified. Each warehouse has kept its own tools, each transport office its own habits. Definitions sound the same, but they are not truly aligned. “Order fulfilled”, “on-time delivery”, and “productivity” change slightly from one site to another. No one does it on purpose, it just happens. The problem emerges when those numbers need to be consolidated at management level.

Industry consolidation, with dozens of acquisition and merger operations every year, is multiplying this situation. More structures mean more data, but also more versions of the truth. The result is that meetings start by talking about numbers and end by talking about numbers, without reaching decisions. Operational Business Intelligence is born exactly here, not to add another reporting layer, but to create a shared language. When data is not comparable, complexity grows faster than the ability to manage it. And toward 2026, this asymmetry will become one of the main brakes on sustainable growth.

AI enters the scene when uncertainty becomes too expensive

When people talk about Artificial Intelligence, the most common idea is that of a system that makes decisions instead of people. In real operational contexts, the opposite happens. AI is adopted when people have to decide too often, with too many variables and too little time.

BI logistics trends 2026

It is no coincidence that today AI is mainly applied to order management and demand forecasting, both accounting for 14% of use cases. Here, mistakes are not abstract. They mean wrong shifts, underutilized vehicles, missed delivery windows.

The Observatory shows that 81% of companies that have adopted AI solutions report concrete benefits, and only 11% talk about replacing human labor. The interesting point is this. AI does not eliminate decision responsibility, it eliminates noise. It reduces unnecessary variables, highlights recurring patterns, and makes anomalies visible that would otherwise emerge too late. Toward 2026, AI will not be a differentiating element to talk about, but a silent component of everyday Business Intelligence. It will not make headlines, but it will make the difference.

The moment when data stops being numbers and becomes choices

At the end of the month, when final reports arrive, it is already too late to correct many decisions. Cost per order has increased, but shipments have already left. A customer is eroding margins, but service has been guaranteed for weeks without clear warning signs. This is where the limit of a BI designed only to look backward becomes evident.

With energy costs rising and margins under pressure, deciding late means losing value. Operational Business Intelligence exists for this reason, to make data readable while processes are still running. Not to control, but to choose. Knowing today that a route is going out of control, knowing now that a line is losing efficiency, knowing immediately where to intervene. Toward 2026, the most mature companies will no longer ask what data they have available, but which decisions they can make right now. At that point, BI and AI stop being tools and become part of the way of working.

Business Intelligence and AI in logistics. The real trends toward 2026 seen from the decision-making table

by

1. Monday morning meetings are no longer a formality

At exactly nine o’clock, the operations director opens the weekly meeting. On the table there are reports, Excel files, a few charts. Volumes are stable, deliveries are on track, complaints are under control. Yet margins have dropped again. No one can say exactly why. Some mention overtime, others a few more urgent requests, others increasingly demanding customers. All plausible explanations, but none verifiable at that moment. The data exists, but it arrives late, or it does not arrive together.

Figures from the Contract Logistics Observatory help explain why this scene is repeating more and more often. Real market growth is around +0.3%, while labor costs are rising by +4.4%. This means that even a company that is “doing a good job” can see its margins erode without clear warning signs. No crisis is needed, just a series of micro-decisions made without a clear understanding of their economic impact. Looking toward 2026, the real risk is not working less, but working blind. In a context like this, Business Intelligence is not meant to prove what happened, but to understand what is happening while it happens.

2. When complexity increases, data starts to contradict itself

After growth through acquisitions, the organization is larger, but not yet unified. Each warehouse has kept its own tools, each transport office its own habits. Definitions sound the same, but they are not truly the same. “Order fulfilled”, “on-time delivery”, and “productivity” change slightly from one site to another. No one does it on purpose, it just happens. The problem emerges when those numbers need to move up to a management level.

Industry consolidation, with dozens of acquisition and merger operations every year, is multiplying this situation. More structures mean more data, but also more versions of the truth. The result is that meetings start by talking about numbers and end by talking about numbers, without reaching decisions. Operational Business Intelligence is born right here, not to add another layer of reporting, but to create a shared language. When data is not comparable, complexity grows faster than the ability to manage it. Toward 2026, this asymmetry will become one of the main obstacles to sustainable growth.

3. AI enters the scene when uncertainty becomes too expensive

When people talk about Artificial Intelligence, the most common idea is that of a system that makes decisions instead of people. In real operational contexts, the opposite happens. AI is adopted when people have to make decisions too often, with too many variables and too little time. It is no coincidence that today AI is mainly applied to order management and demand forecasting, both accounting for 14% of use cases. Here, mistakes are not abstract. They mean wrong shifts, underutilized vehicles, missed delivery windows.

The Observatory shows that 81% of companies that have adopted AI solutions report concrete benefits, and only 11% talk about replacing human labor. The interesting point is this: AI does not remove decision responsibility, it removes noise.

BI e AI nella logistica operativa

It reduces unnecessary variables, highlights recurring patterns, and makes anomalies visible that would otherwise emerge too late. Toward 2026, AI will not be a distinctive element to talk about, but a silent component of everyday Business Intelligence. It will not make headlines, but it will make the difference.

4. The moment when data stops being numbers and becomes choices

At the end of the month, when final reports arrive, it is already too late to correct many decisions. Cost per order has increased, but shipments have already left. A customer is eroding margins, but service has been guaranteed for weeks without clear warning signs. This is where the limits of a BI designed only to look backward become clear.

With energy costs rising and margins under pressure, deciding late means losing value. Operational Business Intelligence exists for this reason: to make data readable while processes are still running. Not to control, but to choose. Knowing today that a route is going out of control, knowing now that a line is losing efficiency, knowing immediately where to intervene. Toward 2026, the most mature companies will no longer ask what data they have available, but which decisions they can make right now. At that point, BI and AI stop being tools and become part of the way of working.

The 6 KPIs That Actually Tell You If Your Logistics Are Under Control

by

In most logistics companies today, data is not missing.
What is missing is silence and priority.

Dashboards, reports, indicators, weekly, monthly and yearly comparisons. Every function measures something. Every piece of software promises visibility. Every meeting brings a new number to the table. The result is paradoxical. The more KPIs there are, the less useful they become for decision making.

This happens because in logistics data is often added, rarely chosen.
What gets measured is what is easy to extract, not what truly governs the system. The outcome is dozens of disconnected indicators, read after the fact, useful to explain what already happened but weak when it comes to anticipating what is about to happen.

Key operational logistics KPIs

The problem is not technical. It is cognitive.
When everything looks important, nothing really is. Weak signals get lost in the noise, priorities blur, and decisions always arrive one step after the operational effect.

In this context, adding new KPIs does not improve control.
Often, it makes it worse.

Logistics does not need more numbers. It needs a few right indicators, capable of describing the real state of the system before a problem becomes visible to everyone. Indicators that bring order, not complexity.

This is where the right question comes from, the one almost nobody asks.
If you had to choose only a few, which KPIs actually matter?

In logistics, a few indicators drive everything else

A warehouse does not work as a sum of independent activities.
It works as a system.

Inbound, storage, picking and outbound are not separate blocks you can optimize one by one. They are interconnected parts, where every imbalance quickly propagates to the others. When something goes wrong, the effect is always visible downstream, but the cause is almost always upstream.

This is where many measurement systems fail.
They focus on where the problem explodes, not where it originates.

A slowdown in picking is almost never just a productivity issue. It is often the result of irregular inbound, growing saturation or unbalanced inventory. A declining service level rarely depends only on outbound. It is the final point of a chain of decisions made days or weeks earlier.

That is why not all logistics KPIs carry the same weight.
Some indicators describe local symptoms. Others tell the story of the entire system.

The truly relevant KPIs are those that allow you to understand whether the warehouse is working in balance or constantly compensating. Whether it is absorbing variability in a healthy way or accumulating operational tension.

When these indicators hold, everything else is manageable.
When they break, even the most “good looking” KPIs stop making sense.

This is why it makes sense to talk about a small set of key KPIs. Not because the others are wrong, but because without a clear hierarchy the risk is measuring everything and controlling very little.

The 6 KPIs that describe the real behavior of logistics

If you look at a logistics system from above, without diving into operational details, the same balance points always emerge. These are what determine whether a warehouse runs smoothly or spends the day chasing problems.

They do not depend on the industry, order volume or layout complexity.
They depend on how flows behave.

There are six of them.

Inbound performance
Because most logistics problems do not start where they explode, but in how goods enter the system. Irregular inbound creates instability before the warehouse even realizes it.

Key operational logistics KPIs
Key operational logistics KPIs

Warehouse saturation
Space is not just a physical constraint. It is a decision margin. When saturation grows without control, every activity becomes more expensive, even if volumes appear stable.

Inventory and rotation
Stock is never neutral. Some inventory works for the business. Some silently slows it down. Understanding how and where goods accumulate is essential to avoid structural inefficiencies.

Picking productivity
Picking is where all inefficiencies become visible. Measuring it is not about finding someone to blame, but about understanding where the system creates operational friction.

ABC analysis
In every warehouse, a small number of items generates most of the work. Ignoring them means optimizing marginal details while leaving the real workload untouched.

Key operational logistics KPIs

Outbound and service level
This is the final point of the system. It shows whether the promises made to customers are sustainable or whether the warehouse is simply catching up late.

These six KPIs do not explain every single activity.
They explain whether the logistics system, as a whole, is working in balance or in constant compensation.

If only one of them is out of control, the warehouse can still function.
If more than one starts sending signals, the problems are not episodic. They are structural.

The same KPIs, three roles, one shared advantage

One of the most common misunderstandings about logistics KPIs is thinking they are only for people who “do analysis”. In reality, the same indicators take on different meanings depending on who reads them.

The same six KPIs can support operational, managerial or executive decisions, without changing the numbers, only the perspective.

People on the floor use them to anticipate issues, prepare work and reduce daily urgency.
Warehouse managers use them to understand whether processes, layout and organization are coping with real variability.
Operations and supply chain leaders use them to connect logistics, costs and service level, and decide where to intervene and where to invest.

It is the same set of KPIs, read on three different levels.

This is exactly why the manual is not designed for a single profile, but for anyone who holds responsibility over logistics, from operations to executive level. It does not explain how to build indicators, but which ones truly matter and why.

If you want a clear view of the 6 fundamental logistics KPIs and understand how to read them based on your role,

What is BI and why a supply chain needs Business Intelligence

by

Imagine a warehouse manager starting the day with three windows open on their screen: the Excel file with stock levels, the ERP dashboard, and a monthly report that arrived last night from the administration department.
Three worlds that don’t talk to each other, three different versions of the same reality.
He needs to understand whether there is actually enough space today to receive the incoming load from the Asian supplier or whether production is at risk of stopping.
He looks around, sighs, and does what everyone does in these situations: he makes a guess.

Why logistics needs BI

It’s a recurring scene. In warehouses, transport offices, supply chain departments.
There is no shortage of data—if anything, there is too much. What’s missing is a way to make it all communicate, to turn “numbers” into a readable story.
In those moments, the supply chain resembles a dashboard with all warning lights on… but without labels. Something is happening, but you don’t know what.
And so companies drift toward “intuitive management,” based on experience and memory.
It works as long as the market is stable—until unexpected peaks arrive, customers become more demanding, or transport costs spiral out of control.

Why generic BI tools are no longer enough in complex sectors

At some point, almost every company follows the same path.
They buy a general-purpose BI platform, start the project with enthusiasm, configure a few dashboards—and for a while, it works. Then comes the moment when logistics reality knocks on the door with hands dirty from day-to-day operations.
And that’s when the magic fades. The Logistics Manager wants to understand why saturation shot up to 85% last month, but the BI offers a pie chart that looks like it came from a school textbook. The Transport Manager requests a comparison of costs by route, carrier, and average shipped weight—what he gets is a perfect dashboard… except that no one has ever configured the calculation logic typical of transport.

The Supply Chain Director wants to see profitability per customer, including actual logistics costs, not the “estimated” ones—and discovers that the platform can’t do it without months of development.

Why logistics needs BI

The problem isn’t technology. General BI tools are excellent platforms—but they don’t know the industry. For them, a pallet is a “record,” a shipment is a “table row,” saturation is a “derived metric.” But in logistics and transport, every piece of data has a precise operational meaning. If you don’t teach it, it won’t understand it. And teaching it costs time, energy, and budget most companies don’t have.
The result? Everyone starts from scratch. Each company builds its own version of logistics KPIs, its own cost formulas, its own filters and dashboards. It’s like asking every restaurant to grow its own wheat just to have flour. Possible, yes. Logical, no.

Because the speed at which a company turns data into decisions doesn’t depend on how many dashboards it has—
but on how well those dashboards speak its language.

A vertical BI that speaks the language of logistics

Then something simple but decisive happens. Someone turns on a BI system that already comes with the vocabulary of logistics and transport built in. You no longer need to explain to a consultant what saturation is, or how the true average transport cost per customer is calculated. You no longer need to invent KPIs, filters, or formulas from scratch. You open a tool that instantly understands your world—because it was designed for it. And the difference is immediate. A Logistics Manager opens the dashboard and sees a map of space waste, updated daily, showing which items are consuming capacity without generating value.
He no longer needs to guess where to look—the BI shows it.

Why logistics needs BI

A Transport Manager finally gets a serious performance comparison between carriers, complete with punctuality, costs, vehicle saturation, SLA compliance.
Instead of generic charts, he has a picture that allows him to negotiate contracts with real arguments—not impressions.

A vertical BI does exactly this:
it shortens the distance between data and decision.
It doesn’t force you to build the structure—it gives it to you, ready-made, tested, and meaningful.

And this creates something far more valuable than technology: trust.
When a dashboard is built on KPIs that professionals recognize, they use it.
They browse it.
They build meetings around it.
They make decisions with it.

A generic BI tool asks you to adapt to it.
A vertical BI adapts to your way of working.

At that point, logistics stops being a puzzle to solve at the end of each month and becomes a system you can read in real time.
This is where industry-specific BI becomes a competitive lever—because it gives time, clarity, and speed to those steering the company through a supply chain that waits for no one.

This is the moment when BI stops being software.
It becomes an advantage.
And the companies that adopt it don’t just “look at data”—
they understand it.
And when you understand data, you finally start changing the game.