Elisabetta Villa

Logistics is a data factory. On a typical working day, a logistics platform generates an impressive amount of information. The modern supply chain is, in fact, a massive data-producing machine. Data can come from:

• TMS (Transport Management System)
• WMS (Warehouse Management System)
• Carrier KPIs
• IoT sensors
• Delivery data
• Customer feedback

The paradox is that this data often remains unused.

Many companies already have the information needed to understand where inefficiencies, delays, or uncontrolled costs are generated. But this information remains scattered across different systems, manual reports, and Excel spreadsheets.

As a result, many logistics decisions are still made based on experience and intuition rather than on a structured reading of data.

The problem, therefore, is not having data.

The problem is making it readable.

For this reason, it is time for the logistics world to adopt Business Intelligence as well. Not as a new operating system, but as a tool capable of connecting existing information and transforming it into clear indicators on which decisions can be based.

Why many companies cannot read their own data

If the data already exists, why do so few companies manage to truly use it to govern logistics?

The answer is simpler than it may seem.

In most organizations, data is not organized to be read, but to make operational processes work.

The WMS records warehouse movements.
The TMS manages shipments and truck routes.
The ERP tracks orders and invoicing.

These systems are designed to execute operations, not to analyze them.

The result is that information remains fragmented. Each system knows a piece of the story, but rarely is there a place where this data is brought together to answer simple but essential questions.

Questions like these.

Which customers are truly profitable once logistics costs are considered.
Which transport routes are eroding margin.
Which carriers are actually the most reliable over time.
Which warehouse has the highest productivity per operator.

In many companies the answer arrives late, often at the end of the month, when someone exports data from different systems and tries to reconstruct the overall picture in an Excel sheet.

The problem is that by then operational decisions have already been made.

One of the main obstacles to using data in logistics is precisely the fragmentation of information across different systems and departments. Without a structure capable of integrating this data, even the most advanced organizations risk making decisions based on partial information.

As a result, something curious happens.

Companies have sophisticated systems to execute operations, but often lack equally effective tools to understand what those operations are actually producing.

This is why many logistics decisions continue to be based on the experience and intuition of operational managers.

Experience is valuable, but when margins are tight and complexity increases, it is not always enough.

When logistics data becomes readable, something interesting happens. Many problems that once seemed inevitable begin to become measurable, and therefore manageable.

According to several McKinsey analyses on supply chain analytics, the systematic use of data can improve overall supply chain productivity by 15 to 20 percent. At the same time, predictive models applied to demand management can reduce forecasting errors by 20 to 50 percent. Numbers that, in a sector where margins often remain below five percent, make an enormous difference.

The real cultural shift logistics must make

At this point it becomes clear that the issue is not simply technological.

The real shift logistics must make is first and foremost cultural.

For years, logistics has been managed as an operational function. The main objective was to ship orders on time, dispatch trucks, and maintain customer service levels. But the complexity of modern supply chains is changing the rules of the game.

Today logistics managers must deal with an increasing number of variables. Transport costs that constantly fluctuate, unstable demand, increasingly complex distribution networks, pressure on delivery times, and margins that are often very thin.

In such a context, a broader perspective is needed. What is required is the ability to continuously read a set of key indicators that truly describe how logistics is performing.

Indicators such as logistics cost per order, delivery service level, warehouse productivity per operator, vehicle capacity utilization, inventory turnover, or the impact of transport costs on revenue.

These are data points that allow companies to understand not only whether operations are functioning, but how much they are actually contributing to the company’s profitability.

When these indicators become visible and shared across the organization, the way decisions are made begins to change. Business Intelligence becomes a governance tool.

It does not replace the operational systems that manage warehouses and transport. It works alongside them, connecting the data they generate every day and transforming it into readable information for those who need to make decisions.

70% of Business Intelligence projects never make it into production (Source: Gartner – Big Data Project 2024). They do not slow down, they do not get postponed, they do not get resized. They die. And the most convenient explanation is always the same: the technology was not suitable, the data was messy, the integration too complex.

The problem is that this is not true. Or rather, it is not the real issue.

The reality is more uncomfortable to admit: Business Intelligence does not fail because of the platforms, but because of how it is treated by management. It is launched as a promising initiative, often sponsored at the highest levels, only to be abandoned halfway when it stops being a visible priority.

In recent years, technology has made enormous progress. BI platforms are mature, powerful, flexible. The problem is that they are chosen and evaluated as if they were finished solutions, when in fact they are empty infrastructures. They must be filled with models, KPIs, rules, coherent data.

When BI becomes an empty box that consumes time and budget

At this point, external consultants enter the scene. The project expands, workshops are organized, meetings multiply, working groups are created. Every department must contribute, provide data, explain processes, validate numbers. The problem is that the managers involved already have full agendas. BI becomes important, but never urgent. And anything that is not urgent in a company tends to slip.

Meanwhile, data is never truly “ready.” Every system has its own logic, every department its own KPI definition, every Excel file tells a slightly different story. More time is spent discussing whether the number is correct than using it to make decisions. The project slows down, costs increase, complexity grows. And the more complexity grows, the less desire there is to deal with it.

This is where many projects begin to lose internal support because they turn into a problem to manage. No one openly cancels them. They simply stop being defended. And this is the first real sign that the project is entering its slow terminal phase.

Time passes, the context changes, the project fades out

At this stage, the Business Intelligence project is formally still alive, but inside the company something else is already happening. New clients arrive, often larger and more complex. Volumes increase, operational urgencies grow, exceptions multiply. Management attention shifts to what has immediate impact.The problem is not that someone decides to stop the project. The problem is that no one has time to follow it anymore. Calls are postponed, data requests remain unanswered, KPI decisions are delayed. At this point, the project enters a sort of organizational limbo.This is how most Business Intelligence projects die. Not with a spectacular failure, but from lack of attention, lack of energy, lack of continuous sponsorship.

The collateral damage: Excel everywhere, decision silos

When a Business Intelligence project fades out, the company does not stop. It continues with alternative solutions. Each department keeps building its own reports, almost always in Excel. Everyone uses “their” data, “their” definitions, “their” numbers.

Logistics looks at volumes, finance looks at costs, transport looks at shipments. Everyone has numbers, but no one has the same numbers. Meetings turn into exercises in defending data, not moments of decision. More time is spent discussing how a KPI was calculated than deciding what to do tomorrow.

This is exactly the scenario from which B-AI Semplice was born. Not as a technological alternative to large platforms, but as a pragmatic response to a recurring organizational failure. The idea is simple and, for that very reason, unconventional. Drastically reduce the time between decision and use. Eliminate the endless building phase. Remove the excuse of “we are not ready yet.”

B-AI Semplice was created for those working in logistics and transport who need control, not another project to manage. A ready-to-use, vertical BI with preconfigured KPIs, capable of reading both physical and logical warehouses and transport flows wherever they are. Not in a few days, but in a few weeks. Long enough to be credible, short enough to remain a priority.

Because the real problem of Business Intelligence has never been knowing what to do. It has always been being able to do it before the context changes again.