Elisabetta Villa

GEOANALYSIS, FINDING THE ZIP CODES WHERE TRANSPORTATION IS ERODING MARGINS

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Knowing the average on-time delivery rate and the average cost per shipment is not enough to understand whether distribution operations are efficient. A 94% on-time delivery rate may hide regions with flawless performance alongside others where one in five deliveries arrives late. Similarly, transportation costs that are in line with the budget may include areas where costs are twice the average.

The interactive Geoanalysis dashboard in B-AI Semplice displays deliveries, volumes, costs, and on-time performance across three connected maps: global, national, and local, down to individual delivery destinations.

This allows transportation managers to identify where margins are being lost and why.

UNDERSTANDING WHERE INTERNATIONAL TRANSPORTATION ACTIVITY IS CONCENTRATED

In the global view, the dashboard color-codes each country based on deliveries and volumes and displays the cost per kilometer, allowing countries to be compared on an equal-distance basis.

Two countries may receive similar shipment volumes but generate very different costs. Shipments to France may travel on fully loaded vehicles with scheduled departures, while those to Germany may cost 30% more per kilometer because they are split into separate trips or assigned to a carrier with higher rates.

The dashboard makes it possible to identify countries where transportation costs are increasing faster than shipment volumes. On these routes, managers can consolidate shipments, renegotiate rates, or organize backhauls to prevent vehicles from returning empty.

COMPARING ON-TIME PERFORMANCE AND COSTS REGION BY REGION

In the national view, the dashboard displays each region’s on-time delivery percentage alongside its average cost per delivery, highlighting areas that exceed targets in red.

One region may have average transportation costs but poor on-time performance, while another may meet every delivery deadline at a cost significantly above target. In the first case, the problem concerns service reliability. In the second, it concerns financial sustainability.

Analyzing these two indicators together makes it possible to establish priorities, starting with regions that fall outside the acceptable thresholds for both. However, higher costs do not always indicate inefficiency, as islands and areas far from the warehouse naturally have higher structural transportation costs.

DRILLING DOWN TO INDIVIDUAL DELIVERY DESTINATIONS

The local view displays each delivery destination as a map pin, showing the number of pallets, weight, and volume delivered.

Two customers located just a few kilometers apart could be served on the same delivery route, yet they often receive their goods on different days or from different vehicles. Similarly, some areas have a high concentration of delivery destinations, while others require long journeys for only a few deliveries.

The dashboard makes it possible to identify these situations through the distribution of map pins and consolidate nearby delivery routes. Where an area has a high concentration of delivery points but remains highlighted in red, actual shipment volumes can help assess whether opening a mini-hub or drop-off point would be worthwhile.

FROM REGION TO ZIP CODE WITH SYNCHRONIZED MAPS

The three maps are connected. Clicking on a region automatically updates the local view to display only the delivery destinations within that area.

A region highlighted in red for poor on-time performance does not necessarily have a problem across its entire territory. Delays are often concentrated in just a few ZIP codes. In Lombardy, for example, they might affect only the metropolitan area served by vehicles departing around 5 PM and encountering rush-hour traffic.

With the dashboard, the problem can therefore be narrowed down from a regional issue to a local one. Moving the cut-off time earlier or reviewing pickup windows for those specific ZIP codes can increase successful first-attempt deliveries without changing the distribution strategy for the entire region.

FILTERING CARRIERS, LOCATIONS, AND DELAYS

Filters for location, carrier, and on-time performance narrow the map down to the information of interest. Selecting a carrier displays only the deliveries handled by that provider.

A carrier may provide reliable, on-time service across most of its assigned territory while consistently recording delays and above-average costs in just one small area.

The dashboard therefore makes it possible to evaluate carriers area by area and reassign only the problematic micro-area to a provider with a stronger local presence, without replacing a carrier that performs well almost everywhere else.

REVIEWING RATES AND SURCHARGES IN UNPROFITABLE AREAS

Some areas remain above cost targets even after optimizing delivery routes, carriers, and schedules due to distance, widely dispersed destinations, or accessibility challenges.

In these cases, the dashboard provides an objective basis for updating transportation rates and surcharges, introducing minimum order quantities, or agreeing on different delivery frequencies with customers.

QUERYING THE MAP WITH ARTIFICIAL INTELLIGENCE

The Geoanalysis dashboard can also be queried using natural language. Transportation managers can ask how much each kilometer served in Northern Italy costs or which carrier records the most delays in a specific province, receiving the answer as a KPI or chart.

The AI Analyzer reads the dashboard data and highlights key findings, such as a region performing below target or a micro-area where on-time delivery performance is deteriorating, reducing the time needed to identify areas that require further investigation.

FROM MAPS TO OPERATIONAL DECISIONS

The Geoanalysis dashboard connects transportation costs and on-time performance to the geographical areas where they occur, making it possible to distinguish problems caused by carriers, schedules, or geography.

Based on this information, transportation managers can consolidate delivery routes, adjust cut-off times, reassign problematic micro-areas, and review rates for areas that are structurally unprofitable.

Because in distribution, margins are not lost in the national average. They are lost in a specific ZIP code.

Why Does Logistics Respond Too Late? Watch the Webinar Again

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How Much Is It Costing Us?

Anyone who manages a warehouse or organizes transportation will eventually be asked the same question, whether during a meeting or in an email from management. How much is this customer, this route, or this department costing us? It sounds like a simple question, but providing an accurate figure is another matter entirely.

The data needed to answer it is already there, but it is scattered across different systems, each storing it in its own way. The WMS knows how many order lines and packages have been prepared for that customer, the TMS knows the trips and rates applied, and the ERP stores carrier invoices and working hours. But none of these systems was designed to connect all this information. So the answer ends up being built manually in an Excel spreadsheet, and while everyone waits, decisions rely on experience, just as they always have. By the time the figure is finally ready, the person who asked for it has often already had to make a decision without it.

The Webinar Where the Question Finally Found Its Numbers

We started the September 25 webinar with the question “How much is it costing us?” because behind it there are almost always other questions, less explicit but more uncomfortable. How much does each shipped order really cost? Which customers consume more resources than they generate? On which routes are margins being lost without anyone noticing?

In the first part, we explained why WMS, TMS, and ERP systems, despite recording everything, cannot answer these questions on their own. Each system knows one part of the story, but none of them tells the whole story.

In the second part, we opened B-AI Semplice and let the platform speak for itself. On screen, data from Excel, WMS, TMS, and ERP systems took shape as ready-to-use logistics and transportation KPIs, the same indicators that many managers still reconstruct manually every time someone asks for them.

And that is where the difference becomes clear between an Excel spreadsheet that takes an entire afternoon to prepare and having the same information available exactly when it is needed.

There is another detail that changes the way we think about Business Intelligence. Getting started does not require launching a project that takes months before producing its first useful figure. B-AI Semplice already speaks the language of warehouse and transportation professionals and does not need to be built from scratch for every company.

The Cost of Time That No One Measures

Every day that passes before an answer arrives has a cost that never appears in the reports. An increase in transportation costs discovered at the end of the month has already affected every shipment made in the meantime, day after day, with no opportunity to stop it.

The same increase identified as it is happening is still something you can act on, whether that means changing carriers, reviewing a delivery route, or renegotiating terms with a customer.

The question “How much is it costing us?” will always remain the same. What can change is when the answer arrives.

If You Missed the Webinar

You can find the full webinar recording below.

Watch the Recording Now
Richiesta video Webinar 25/09/26

If I can give you one piece of advice, watch it while thinking about the last time someone asked you for a number and how long it took you to find it.

Warehouse Capacity Utilization, Understanding Where Space Is Really Running Out

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Knowing that a warehouse is 70% full is useful, but it is not enough to make informed decisions. That percentage does not show which areas are under pressure, which products are taking up the most capacity, or whether the available space is being used efficiently.

The B-AI Semplice Capacity Utilization dashboard makes it possible to drill down to individual areas, aisles, or bays, showing occupied and available locations and connecting space utilization with the characteristics of the items stored.

The goal is to understand not only how much space is being used, but how it is being used.

Where Capacity Utilization Is Concentrated

Average capacity utilization can hide very different situations within the same warehouse.

A warehouse that is 65% occupied overall may have some aisles that are completely full and others with plenty of available locations. If the most congested areas are also those where most warehouse movements take place, the problem becomes operational before it becomes a physical space issue.

The dashboard helps identify these imbalances and adjust product distribution before they lead to longer travel distances, interference between picking and replenishment activities, or difficulties in managing storage locations.

Which Products Are Taking Up the Most Space

Once the most congested areas have been identified, it is possible to understand which products are responsible for that space utilization and evaluate them according to their characteristics.

A bulky, fast-moving item may justify the space it occupies, while an equally bulky product that is rarely handled represents a very different situation.

The dashboard therefore makes it possible to distinguish between items that occupy a large amount of space but contribute significantly to warehouse operations and those that tie up capacity without generating adequate turnover.

This information can be used to improve slotting, keeping the most important and frequently handled items in the best locations while moving slower-moving products to deeper or less strategic areas.

Volume, Weight, and Value Provide Different Perspectives

Space utilization can be analyzed using volume, weight, inventory value, or other parameters that are relevant to the company.

This avoids relying solely on the number of occupied storage locations.

One area may contain many small, lightweight items, while another may hold only a few SKUs that consume a much larger share of physical capacity. Similarly, a relatively small section of the warehouse may contain a significant portion of the total financial value of the inventory.

Changing the analysis parameter therefore makes it possible to better understand what is driving capacity utilization and choose the most appropriate action.

Understanding How Capacity Utilization Changes Over Time

The dashboard shows capacity utilization trends by day, month, and year, making it possible to distinguish seasonal peaks from structural changes.

If a specific area reaches high utilization levels every year before peak season, the manager can prepare it in advance by relocating certain items and freeing up the necessary storage locations.

If occupancy continues to increase over several months without subsequently decreasing, the data may indicate a sustained increase in inventory, lower turnover, or a change in the mix of goods being stored.

Historical data therefore becomes a useful tool for planning future capacity requirements as well.

Before Looking for More Space, Make Better Use of the Space You Already Have

The dashboard can be particularly useful when a company begins considering additional space, external warehouses, or facility expansions.

Before making an investment, it is possible to determine whether the problem is really a lack of available capacity or simply how that capacity is being distributed.

The simultaneous presence of congested aisles, underutilized areas, and slow-moving products occupying the most favorable locations may indicate that there is still room for optimization within the existing facility.

Relocating goods, consolidating certain areas, and freeing up prime locations for the most frequently handled items can recover capacity while also reducing the distances traveled by operators.

Analyzing Capacity Utilization With AI

The Capacity Utilization dashboard can also be queried using B-AI Semplice’s Artificial Intelligence through natural language.

For example, users can ask which aisles have the highest occupancy levels, which products take up the most volume, or which areas have experienced the greatest increase in capacity utilization over the most recent period.

The AI Analyzer can also automatically analyze the dashboard data and provide a written summary of the main findings, helping managers identify the situations that require further investigation more quickly.

From a Percentage to Effective Space Management

The Capacity Utilization dashboard makes it possible to move beyond a simple occupancy percentage toward a more informed approach to warehouse capacity management.

Knowing where capacity utilization is concentrated, which items are driving it, and how it is changing over time makes it possible to improve slotting, prepare for peak periods, reduce congestion, and assess more accurately whether additional space is actually needed.

Because before asking how large the next warehouse needs to be, it is worth understanding how well we are using the one we already have.

Find Out Where Your Warehouse and Transportation Operations Are Eroding Margins

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Free webinar on Friday, September 25 at 11:00 AM

The data is already in your company. It is in your WMS, TMS, ERP, Excel spreadsheets, and reports from different departments.

The challenge is being able to analyze it all together.

A slowdown in picking can delay vehicle loading. A missed departure can result in an urgent shipment. The cost appears in transportation, but the cause originated hours earlier in the warehouse.

To protect margins, you need to reconstruct this chain.

Business Intelligence Already Built for Logistics

During the webinar, we will introduce B-AI Semplice, the first Business Intelligence solution already built for logistics and transportation.

B-AI Semplice is based on GEP Informatica’s forty years of experience and includes dashboards, indicators, and analysis criteria designed around the needs of those managing warehouses, shipments, and distribution networks.

It is not a generic platform that needs to be configured from a blank page. It organizes data from the systems already used by the company and transforms it into a unified view of logistics operations.

Ten Connected Operational Areas

Logistics does not operate in silos.

Inventory, capacity utilization, warehouse operations, picking, shipments, and transportation constantly influence one another. B-AI Semplice connects 10 operational areas to show how an issue can turn into delays, higher costs, or reduced service levels.

This makes it possible to start from the final outcome and trace it back to the operational causes that generated it.

Over 50 Ready-to-Use Dashboards

B-AI Semplice provides over 50 dashboards covering the main warehouse and transportation processes.

Each analysis helps you understand:

  • what is happening;
  • which deviations require attention;
  • where further investigation is needed;
  • which decisions should be considered.

Artificial Intelligence features support dashboard analysis by highlighting critical issues, trends, and recurring patterns. Users can also query data using natural language and generate text that can serve as a starting point for reports and meetings.

Three Decision-Making Levels

B-AI Semplice is designed for three different levels of use:

  • the operational level, to monitor activities and take action;
  • the management level, to compare performance, costs, and resources;
  • the executive level, to assess profitability and investments.

Logistics Specialists, Warehouse Managers, Transport Planners, Logistics and Transport Managers, Supply Chain Managers, and Operations Managers can therefore start from the indicators most relevant to their roles while working from the same data foundation.

What We Will Cover During the Webinar

During the webinar, we will show:

  • how B-AI Semplice organizes data from Excel, WMS, TMS, and ERP systems;
  • how the six operational areas are structured;
  • what information the dashboards provide;
  • how to connect warehouse performance with transportation costs;
  • how Artificial Intelligence supports data interpretation.

Join our free webinar on Friday, September 25 at 11:00 AM and find out where your logistics operations are eroding margins..

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Picking. How to Measure Warehouse Productivity, Time, and Costs

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Picking is one of the most time-consuming and resource-intensive activities in a non-automated warehouse because it involves not only the physical picking of items, but also operator travel, location searches, task management, any required checks, movement to the packing area, and all the waiting time caused by tools, equipment, or processes that are not perfectly coordinated.

To assess the efficiency of this activity, it is therefore not enough to know the total number of packages prepared, because the same result can be achieved with very different levels of labor, time, and space utilization. Two shifts may prepare the same number of packages, but one may require more hours, more operators, or a significantly higher number of tasks and movements.

The Picking dashboard in B-AI Semplice makes it possible to analyze this data in a structured way, connecting operator productivity with task characteristics, picking methods, customers served, work shifts, and the warehouse areas involved.

The goal is not simply to determine whether the warehouse is operating quickly or slowly, but to understand which factors are driving performance and where improvements can be made to increase productivity without compromising service quality.

Productivity per Operator

One of the main indicators available in the dashboard is productivity per operator, which can be measured through the number of packages prepared per working hour and the related hourly cost.

This data makes it possible to compare operator performance, but it must be interpreted according to the type of activity performed. An operator preparing simple orders, with many items concentrated in the same area, works under very different conditions from someone handling fragmented orders, bulky items, or specific packaging requirements.

For this reason, productivity should not be analyzed in isolation, but together with the number of tasks completed, the average number of picks included in each task, the picking method used, and the customer being served.

If an operator records a lower-than-average number of packages per hour, the dashboard helps determine whether the cause is actually related to individual performance or instead to the complexity of the assigned work, the characteristics of the area in which they operate, or the presence of external delays.

This distinction is also important for workforce management, because it prevents inefficiencies caused by warehouse layout, task configuration, or the availability of work equipment from being automatically attributed to the operator.

Hourly Cost and the Cost of Productivity

The number of packages prepared per hour measures physical productivity, while hourly cost makes it possible to connect operational performance with its financial impact.

When costs remain constant but the number of packages prepared decreases, the unit cost of each package increases. Conversely, improved productivity allows labor costs to be spread across a greater number of completed activities.

This type of analysis can be used to compare different shifts, teams, or periods and determine whether organizational changes have produced measurable benefits.

For example, after changing the slotting of a warehouse area or the way picking tasks are structured, the manager can check whether the number of packages prepared per hour has increased and whether the operating cost per unit of work has decreased.

The dashboard therefore makes it possible to move beyond general assessments such as “the shift went well” or “we worked slowly today,” translating performance into indicators that can be compared over time.

The Number and Structure of Picking Tasks

Picking productivity depends significantly on how picking tasks are organized.

The dashboard shows how many tasks were completed by each operator, the average number of picks included in each task, and the total number of packages handled.

This data makes it possible to understand whether the warehouse is generating many tasks with only a few picks or a smaller number of larger tasks.

In the first case, operators may be forced to repeat the same routes frequently, increasing travel distances and the time required for each package. In the second case, fewer trips are required, but excessively long or complex tasks can increase the risk of errors, make container management more difficult, and slow down subsequent activities.

There is therefore no optimal number of picks per task that applies to every warehouse, because the result depends on the layout, item dimensions, order structure, and picking method used.

However, the dashboard makes it possible to compare different configurations and identify the one that provides the best balance between the quantity prepared, time required, and number of movements.

Picking Times by Customer

Not all customers generate the same workload, even when their order volumes are similar.

A customer may require customized packaging, specific labels, additional checks, separation by store location, or specific documentation, increasing the time required to prepare each package.

The dashboard makes it possible to analyze picking times for different customers, revealing operational complexity that often does not emerge when looking only at the number of orders or revenue.

This information can be used to assess the actual resources required to serve each customer, compare different management approaches, and determine whether specific requirements generate disproportionate costs compared with the volumes handled.

The data can also be useful when defining commercial terms, because it helps distinguish between a high-volume customer with standard requirements and a customer with similar volumes but a much higher level of customization.

The dashboard does not, of course, replace a complete profitability analysis, but it introduces an often-overlooked factor into the evaluation: the actual logistics time required to serve each customer.

Picking Times by Picking Method

B-AI Semplice makes it possible to compare the times recorded for different operating methods, such as multi-order picking, pick and pack, put to light, or stock picking.

This comparison helps determine whether the method being used is appropriate for the characteristics of the orders being processed.

Multi-order picking, for example, can reduce travel distances by allowing operators to pick items for several orders at the same time. However, it can also create additional sorting activities and increase the risk of errors when the process is not properly organized.

Pick and pack can eliminate subsequent handling steps because the item is picked and placed directly into the shipping container. However, it may be less efficient when orders are particularly complex or when packaging requires additional operations.

Put to light can speed up the sorting of items across different orders, but its effectiveness depends on volume, process repeatability, and the correct replenishment of workstations.

By analyzing time, packages, and tasks for each method, managers can determine which approach delivers the best results under different operating conditions, avoiding the use of a single procedure for orders with very different characteristics.

Performance by Day, Shift, and Time Slot

Another key feature of the dashboard is the ability to analyze picking performance by day, shift, and time slot.

This analysis makes it possible to identify when activities are most concentrated, when productivity decreases, and when available resources are either higher or lower than the actual workload.

If the number of picking tasks consistently increases during certain hours while the number of operators remains unchanged, the warehouse may accumulate delays that are later recovered through overtime or unplanned staff reallocation.

Conversely, a time slot with a limited number of tasks and a high number of operators may indicate that resources are oversized compared with the actual workload.

The dashboard makes it possible to determine whether these situations are occasional or recurring, providing a more reliable basis for shift planning.

Managers can decide to bring some employees in earlier, postpone other shifts, reinforce a critical time slot, or temporarily assign part of the workforce to other activities such as receiving, replenishment, inventory counting, or workstation organization.

In this way, workforce planning is based on actual flow data rather than solely on experience or established organizational habits.

Analyzing Travel Paths and Slow Areas

In manual picking, a significant amount of time is spent moving between storage locations.

The dashboard helps identify warehouse areas where picking tasks take longer and the stages of the process where delays are concentrated, such as entering the picking area, reaching the storage location, picking the item, moving it to packing, or closing the order.

An area may be slow because it contains high-demand items stored in hard-to-reach locations, because aisles are congested, because storage locations are difficult to identify, or because operators are forced to follow inefficient routes.

The analysis makes it possible to determine whether picking sequences force operators to move in a zigzag pattern, repeatedly returning to the same aisles, or whether more linear or U-shaped routes could be introduced.

This information can be used to improve slotting by moving the most frequently handled items to more accessible areas and assigning more distant locations to slow-moving products.

The connection with Inventory ABC Analysis is clear: Class A items should be stored in locations that minimize access time, while Class C items can be placed in less central areas without significantly affecting overall productivity.

Identifying Bottlenecks

An increase in picking times is not always caused by the picking activity itself or by operator speed.

Slowdowns may be caused by having too few scales for the number of workstations, a printer shared by too many operators, a handheld terminal that frequently loses connection, a shortage of available picking carts, or a packing station that cannot handle the incoming workload.

The dashboard makes it possible to identify the stages and areas where processing times increase, allowing managers to investigate the causes directly on the warehouse floor.

If picking is completed within the expected time but orders accumulate in front of the packing station, adding more picking operators could make the problem even worse by increasing the flow toward an activity that is already at capacity.

In this case, the right decision might be to add another workstation, relocate a printer, provide a second scale, or change the way activities are distributed.

The value of the dashboard lies precisely in its ability to identify the actual constraint within the process before investing in additional staff, equipment, or organizational changes.

Training and Performance Management

Operator-level data can also be used to design more targeted training activities.

When an operator performs below average, the manager can determine whether the difference is concentrated in specific warehouse areas, particular picking methods, or certain types of orders.

This analysis makes it possible to organize targeted coaching rather than relying on generic training programs.

One operator may struggle with multi-order picking tasks, another may not be sufficiently familiar with certain warehouse areas, while a third may lose time when using handheld terminals or completing closing activities.

Similarly, KPIs can be used to recognize top performers and potentially introduce incentive systems, provided that performance is not assessed solely on speed.

To avoid counterproductive behavior, productivity should be evaluated together with work quality, the number of errors, compliance with procedures, and the complexity of the tasks assigned.

Rewarding operators exclusively on the basis of packages per hour could encourage them to prioritize speed, increasing picking errors, product damage, or rework that may ultimately cost more than the productivity gains achieved.

From Measurement to Operational Improvement

The B-AI Semplice Picking dashboard connects productivity with the factors that drive it, bringing together operators, picking tasks, picking methods, customers, shifts, time slots, and warehouse areas.

Logistics managers can use this information to optimize shift staffing, improve slotting, reduce travel distances, choose the most appropriate picking method, eliminate bottlenecks, and implement targeted training activities.

The value of the dashboard therefore lies not only in knowing how many packages have been prepared, but in understanding how they were prepared, how much they cost, and which actions can improve performance.

Transportation Costs, Where They Come From and How to Reduce Them

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Knowing the total cost of transportation is not enough to understand whether distribution operations are efficient. The overall figure shows how much has been spent, but it does not reveal which customers, regions, carriers, or operational choices generated those costs.

The Transportation Costs dashboard in B-AI Semplice breaks down transportation expenses and makes it possible to quickly identify where costs exceed the company average or target. This allows transportation managers to address specific issues instead of simply discovering at the end of the month that the budget has been exceeded.

Understanding Which Areas Are More Expensive

The dashboard displays the average cost per delivery across different geographic areas, making it possible to compare regions, provinces, and individual ZIP codes.

For example, if deliveries in Apulia cost, on average, 40 percent more than the national average, managers can investigate the number of shipments, departure frequency, vehicle utilization, and the carrier involved.

Higher costs may be justified by longer distances, but they may also result from overly frequent trips carrying only a small number of orders. In this case, consolidating deliveries on fixed days can reduce the number of departures and lower the unit cost.

The cost map by ZIP code immediately highlights areas that exceed targets, showing where distribution is eroding margins and where service is being managed more efficiently.

Measuring the Real Cost of Each Customer

Customers with similar volumes can generate very different transportation costs.

A customer who consolidates orders into a single weekly delivery may be more efficient than one who requires multiple small shipments, mandatory delivery appointments, or urgent deliveries.

The dashboard makes it possible to analyze the cost per delivery for each customer and measure its impact on overall performance. If a single customer in Milan increases monthly transportation costs by 15 percent, the transportation manager can determine whether the increase is driven by higher volumes or by fragmented orders and unscheduled departures.

This analysis can lead to concrete decisions, such as establishing fixed delivery days, introducing minimum order quantities, modifying cut-off times, or assigning an economic value to additional services requested by customers.

Comparing Carriers on the Same Routes

The total amount invoiced by a carrier is not enough to evaluate its competitiveness, because it may handle a larger number of shipments or more distant destinations.

Meaningful comparisons can only be made when carriers are evaluated on the same routes, ZIP codes, vehicle types, and service requirements.

The dashboard displays the cost per kilometer for different carriers and highlights significant differences within the same areas. If two carriers serve the same ZIP codes but one consistently shows a higher cost per kilometer, the transportation manager has concrete data to renegotiate rates or reassign part of the shipment volume.

Price should not be the only criterion, of course, because punctuality, reliability, and service quality must also be considered. However, the dashboard makes it possible to determine whether any cost difference is genuinely justified.

Distinguishing a Peak From a Structural Problem

Analyzing costs by day, week, and month makes it possible to distinguish a one-off increase from a trend that is becoming structural.

A daily spike may be caused by an urgent shipment or an exceptional trip. An increase that continues for several weeks, on the other hand, may indicate lower vehicle utilization, changing destinations, or gradually increasing transportation rates.

This analysis allows managers to intervene before the end of the month and measure the impact of their decisions by comparing performance before and after operational changes.

Increasing Vehicle Utilization and Reducing Empty Trips

When a vehicle operates partially loaded, transportation costs are spread across a smaller number of deliveries, increasing the cost per shipment.

The dashboard helps identify expensive routes characterized by low volumes or very frequent departures. Managers can then evaluate order consolidation, the use of more suitable vehicles, or the rescheduling of deliveries to different days.

They can also identify backhaul opportunities by organizing pickups in delivery areas and avoiding empty return trips. In this way, a return journey that has already been paid for can be transformed into a productive activity.

Reducing Urgent Shipments by Adjusting Cut-Off Times and Pickup Windows

Part of transportation costs arises before vehicles even leave the warehouse, when orders arrive too late to be consolidated and must therefore be assigned to urgent or dedicated shipments.

The dashboard helps identify customers, days, and time slots in which these situations occur most frequently. Adjusting the cut-off time by even a few hours can increase the time available to consolidate shipments heading to the same area and reduce extraordinary departures.

The same analysis can also be used to review pickup windows agreed upon with carriers, avoiding overly rigid constraints that prevent more efficient planning.

Analyzing Costs With Artificial Intelligence

The dashboard can also be queried in natural language through the Artificial Intelligence integrated into B-AI Semplice.

Managers can ask which ZIP codes exceeded the average cost, which customers contributed most to monthly cost increases, or which carrier has the highest cost per kilometer in a specific area.

It is also possible to activate the AI Analyzer, which reads the data displayed in the dashboard and generates a textual summary of the main anomalies, highlighting, for example, an area exceeding targets, a gradual increase in delivery costs, or a carrier consistently operating above average.

Decision-making remains the responsibility of the transportation manager, but AI significantly reduces the time required to identify the issues that deserve further investigation.

From Total Cost to Operational Decisions

The real value of the dashboard does not lie in simply showing how much has been spent, but in connecting costs to the factors that generated them.

Knowing that a specific area is too expensive makes it possible to consolidate delivery routes. Knowing that a carrier is operating above market levels makes it possible to renegotiate rates. Knowing that a customer places fragmented orders makes it possible to adjust cut-off times and service conditions.

In this way, transportation costs stop being just a number discovered at the end of the month and become a variable that can be monitored and controlled during day-to-day operations.