For many logistics providers, distribution is where cost accumulates in ways that are even harder to see — spread across carrier invoices, warehouse systems, and operational decisions that happen dozens of times a day without a clear cost attached to any of them.

What follows is a breakdown of 6 specific areas where that cost tends to hide and what addressing each one actually changes.

We’ve also identified 14 cost-saving opportunities across consolidated networks, full truck operations, and multi-carrier management. You can find those here.

When Your Data Isn’t Telling You Enough

Franjali Kinathi
LSP Expert & Sales Manager

When the Numbers Don’t Match Across Sites

What These 6 Areas Have in Common

The same thread runs through all of them: cost that exists in the operation but isn’t visible in the right place at the right time. Analytics that aren’t connected. Carrier rates that haven’t been compared. Network decisions made site-by-site instead of holistically. Dock time that’s never been measured as a cost line. None of these require a new warehouse or a new system to address—they require the right analytical lens applied to data that’s already there.

The cost problem in transport is rarely one big thing. It’s a dozen smaller ones — each individually manageable, collectively significant, and almost always invisible until someone goes looking at the right level of detail.

Rate compression and margin pressure get most of the attention. But the costs that actually compound are harder to see: lanes that haven’t been repriced because no one knew they were underwater, trucks running half-empty because the schedule wasn’t updated, manual processes that outgrew their original scale and were never revisited. The aggregate P&L absorbs all of it quietly.

What follows is a breakdown of 14 areas where that margin tends to hide. Most transport businesses don’t fit neatly into one segment, so several will apply regardless of how you operate. The goal isn’t to work through all 14 — it’s to find the two or three where the gap is largest, and start there.

IN CASE YOU WANT TO JUMP AHEAD

1. No P&L Visibility Below the Business Unit Level

2. Line-Haul Routes That Don’t Reflect Today’s Volumes

3. Treating All Freight as Equally Urgent at Consolidation

4. Pickup and Delivery Zones That Haven’t Been Revisited

5. Tour Planning That Hasn’t Kept Up With Network Complexity

6. No Visibility Into Which Runs Are Actually Profitable

7. Manual Dispatch at a Scale It Was Never Designed For

8. Empty Miles Treated as a Cost of Doing Business

9. Coordinating Carriers Without a Customer-Level P&L

10. A Network Design Built for Conditions That No Longer Exist

11. Store Distribution That Isn’t Treated as a Routing Problem

12. Inbound Freight Decisions Made Without Full Cost Visibility

13. The Gap Between Your Current Network and the Optimal One

14. No Visibility Into Product and Customer Profitability

When You Run a Consolidated Network

Franjali Kinathi
LSP expert & Sales Manager

When Every Kilometer Is Your Cost

07 Manual Dispatch at a Volume It Was Never Designed For

Manual dispatch is reasonable at a certain volume. As complexity grows, decision quality under time pressure declines — and the gap between what a person can evaluate and what an algorithm can evaluate widens with every variable added. Automated dispatch handles standard assignments algorithmically and surfaces exceptions for human review. The direct saving is fewer hours on routine planning; the indirect one is fewer poor decisions made on high-pressure days.

08 Empty Miles as a Cost of Doing Business

A truck running Hamburg to Warsaw and returning empty has covered 600km at cost with no revenue attached. If that’s a regular lane, the annual total is not a rounding error. Treating empty mile reduction as an optimization problem — combining loads, matching backhaul opportunities systematically, occasionally accepting longer dwell for a loaded return — produces a gap against sector benchmarks that is direct and quantifiable.

When You Manage Logistics on Behalf of Others

09 Managing Carrier Costs Without a Customer-Level P&L

When you coordinate multiple carriers on behalf of a customer, cost data lives in multiple systems — none of which connect natively. Even sophisticated operations often lack a real-time, customer-specific cost statement covering all flows. Without it, carrier cost drift is hard to catch and the network design question gets conflated with individual carrier performance. A continuous customer-level P&L across all modes turns the coordination role into a genuine analytical value-add.

10 Network Design That Was Optimized for Yesterday’s Conditions

Network redesign is traditionally a consulting project done every three to five years. By the time implementation begins, some of the assumptions have already shifted — and in the meantime the network drifts away from optimal, quietly, until it becomes a structural cost problem. Running design as a continuous, data-fed process means configuration stays current rather than calcifying between reviews.

11 Store Distribution Costs That Aren’t Treated as a Routing Problem

For operators managing retail distribution, last-mile delivery from DC to store is often the highest cost-per-unit element in the network — and the one managed with the least structure. Frequencies are set by replenishment logic, store groupings are inherited from historical territories, and route sequences run on driver habit. Treating it as a routing and network design problem — right frequencies, efficient tours, optimized sequencing — consistently recovers cost without touching the upstream network.

12 Inbound Freight Decisions Made Without Full Cost Visibility

For operators managing intercontinental supply chains, call-off timing, mode selection, and consolidation decisions are made repeatedly, under pressure, with incomplete cost information. The default bias is toward speed — air when ocean would have sufficed, partial containers when a week’s planning would have allowed full consolidation. Making the real cost of each decision explicit means expensive defaults get challenged more often, and the savings accumulate across hundreds of call-offs a year.

13 Not Knowing the Gap Between Your Current Network and the Optimal One

Every operation has a theoretical optimum — the configuration that, given current constraints, would produce the lowest cost at the required service level. Most don’t know what that looks like, so decisions about where to invest and what to change are made on intuition. A brownfield analysis — current state versus constrained optimum — makes the gap concrete, separates what’s recoverable through operational improvement from what requires structural change, and gives any network investment decision a clear starting point.

14 No Visibility Into Product and Customer Profitability

Fulfillment cost varies significantly by SKU, customer, and process, but most operations don’t have that breakdown. Pricing decisions, customer mix choices, and process investments are made without knowing which ones are margin-positive and which are quietly subsidized by the rest of the portfolio. A cost-to-serve analysis at the SKU level makes those numbers explicit: full fulfillment cost per product, per customer, and per process type. For operations managing complex multi-modal flows on behalf of shippers, this is often where the most actionable commercial findings sit.

What These 14 Areas Have in Common

Strip away the operational specifics, and a single thread connects all of them: you cannot optimize what you cannot see. Whether it’s lane profitability in a consolidated network, empty-mile rates in a full-truck fleet, or total logistics cost across a multi-carrier customer portfolio, the organizations that get ahead aren’t necessarily the ones with the best infrastructure. They’re the ones with the best operational visibility.

This is why the right starting point is almost always controlling and transparency rather than optimization algorithms. Getting the numbers right, broken down at the right level, is what makes everything else possible. Once you can see the P&L per lane, the load factor per depot, or the real cost per drop, the decisions become considerably clearer. And so does the savings opportunity.

A practical note

Not all 14 will be equally relevant to every operation. The right starting point is wherever the pain is most acute, and the highest-leverage areas are usually obvious once the data is in the right shape. Identifying the one or two where the gap is largest and building from there tends to produce faster and more durable results.

The logistics companies that will come out ahead over the next five years won’t necessarily be the largest. They’ll be the ones running with the highest operational intelligence, knowing where their margin is, where it’s leaking, and how to close the gap systematically. In most cases, the data to do that already exists. The work is in making it usable.

For years, supply chain teams were told the same story: collect more data, build better dashboards, and decisions will improve. So they did. More systems. More KPIs. More granularity. And yet, many teams quietly experience the opposite.

The problem isn’t a lack of data anymore. It’s what too much data does to human decision-making. Teams that expanded their dashboards and tracking infrastructure often found that decisions slowed down, confidence dropped, and internal debates multiplied.

In case you want to jump ahead

1. How More Data Actually Worsen Decisions

2. What “Accepting Uncertanity” Actually Means?

3. Deleting Metrics is a Stratregic Skill

4.From Data Obsession to Decisions Design

5. Clarity Beats Precision

How More Data Actually Worsens Decisions

Among the many supply chain myths that continue to shape decision-making, one stands out: the more data you collect, the better your decisions become. It sounds logical and for years, organizations have invested heavily in gathering more information and expanding visibility. But beyond a certain point, more data doesn’t create more insight. It creates noise. And that noise can make decisions slower, less confident, and ultimately worse.

Three dynamics are most responsible for this pattern.

Data doesn’t clarify when it overwhelms. It postpones action.

Dashboards fail fastest when definitions are not shared across teams. The same word can mean completely different things depending on who built the report.

CLASSIC EXAMPLES

“On-time delivery” defined differently by sales, logistics, and finance.
“Inventory” meaning booked stock in one report, physical stock in another.
“Demand” referring to forecasts, orders, or shipments, depending on which slide you’re looking at.

When numbers disagree, people stop trusting dashboards. Teams revert to gut feel. Side Excel files quietly take over. Paradoxically, the more data that is available, the less confident teams feel using it.

Modern analytics tools are extremely good at producing highly granular forecasts, long decimal places, and exact-looking cost numbers. But supply chains are probabilistic, volatile, and uncertain by nature. Pretending otherwise creates overconfidence, fragile

plans, and bigger surprises when reality deviates.

A forecast shown as 2.03 €/unit feels precise, even when it is fundamentally uncertain. That illusion of precision is often more dangerous than admitting uncertainty.

What “Accepting Uncertainty” Actually Means

Accepting uncertainty does not mean giving up control. It means being honest about what the data can and cannot tell you. In practice, it means using ranges instead of point estimates, planning scenarios instead of single outcomes, and being explicit about the limits of any forecast.

€2.03

per unit: false precision that feels safe but isn’t

Plans built on ranges survive volatility better than plans built on fake certainty. Saying “we don’t know exactly, but here’s what’s likely” leads to more robust decisions, not weaker ones.

Deleting Metrics is a Strategic Skill

The best analytics teams don’t just add KPIs. They actively remove them. Winning teams kill unused or redundant metrics, limit dashboards to what actually drives decisions, assign exactly one owner per metric, and review KPIs continuously, not once.

THE TEST

Ask this for every metric on your dashboard:
“If this metric moves tomorrow, would we actually do something different?”
If the answer is no, the metric doesn’t belong there.

What separates strong analytics organizations from weak ones isn’t data volume. It’s discipline. Strong teams design analytics around decisions, not data availability. They prefer clarity over completeness. And they accept uncertainty instead of hiding it behind decimals.

Clarity Beats Precision

The teams that win aren’t the ones with the most KPIs or the most detailed dashboards. They’re the ones willing to simplify, standardize definitions, and admit uncertainty where it exists. In the end, analytics isn’t about knowing everything. It’s about deciding well and deciding in time.

To do that consistently, organizations need analytics that create clarity rather than add complexity. They need systems that help teams focus on the signals that matter, reduce noise, and turn information into action.

That’s exactly where our Data, Analytics and AI Services come in, helping organizations transform overwhelming amounts of data into clear insights that support faster and more confident decision-making.

163M

viewers reached in 2024

🔹​17 may 2025

Grand Final

163M viewers. Fixed. The deadline that never moves.

Every year on April 22, Earth Day reminds us of a simple truth: the future of our planet depends on the choices we make today.

In 2026, the theme “Our Power, Our Planet” puts a spotlight on one of the most transformative shifts of our time, the move toward clean energy. And within logistics and supply chains, that shift is becoming increasingly tangible through the rise of electric vehicles (EVs).

But while the conversation around EVs often focuses on technology, the real question for businesses is:
How do we actually make the transition and measure its impact?

In practice, the transition to electric trucks is less about replacing fleets overnight and more about optimizing where electrification makes sense first. Supply chain teams typically start by mapping their transport network to identify high-density, repeatable routes, then evaluate constraints such as range, charging availability, and payload requirements. From there, scenario-based analysis becomes critical, comparing diesel and electric options at shipment level to quantify both operational impact and CO₂ reduction. This step-by-step approach allows companies to move beyond ambition and implement electrification where it delivers measurable results.

1. Industry Context: Freight Emissions & EV Trends

2. Case Studies: Electric Trucks in Action

3. Modelling Emissions with CO2 App

4. Diesel vs. Electric Trucks: Key Metrics

5. Conclusion: Powering Our Planet via Greener Logistics

Transportation is one of the fastest-growing GHG sources. Trucks emitted about 6 GtCO₂ in 2024. Logistics overall is roughly 7% of global emissions. Until recently, battery-electric trucks were <2% of sales, but adoption is accelerating. In 2024 ~90,000 electric trucks were sold worldwide. Policy and market signals are key: China’s purchase subsidies, scrappage schemes and new efficiency standards (2023) have spurred much of the growth. The US and Canada are expanding incentives (up to $40k tax credits per truck and ~$1B for charging infrastructure). Europe has few direct subsidies but has strict CO₂ standards for trucks (–15% by 2025, –43% by 2030) and expanding low-emission zones.

Electric trucks are highest-value for predictable, high-mileage routes. For example, pre-ordered Mercedes eActros and Tesla Semi units are aimed at regional middle-haul corridors. In the US, short urban routes have adopted EVs early. Modern battery packs give ~200–500 km range and can be recharged by 80% in ~1 hour on 360–450 kW chargers. By contrast, diesel semis can refuel 1000+ km in 10–15 min. However, EVs are far more energy-efficient, around 55% higher efficiency than diesel on the same routes, meaning energy costs and CO₂ per km are dramatically lower.

Each case underscores that route characteristics matter: predictable, shorter routes with return-to-base charging see the biggest benefits. Fleets also learn operational lessons (e.g. planning charger use, driver training, maintenance of new systems). In all cases, operators report large fuel (and CO₂) savings, supporting the transition to Net Zero freight.

For supply chain managers, the real value of electrification lies in quantifying its impact before making investment decisions. Tools such as the CO₂ Emissions App (GLEC-compliant) enable this by translating transport activities into measurable emissions. Users can define shipment parameters, including vehicle type distance, load, and fuel type (diesel vs. electricity) and run scenario comparisons at shipment level.

This allows teams to evaluate “what-if” scenarios, such as replacing diesel trucks with electric alternatives on specific lanes. Instead of relying on averages or assumptions, planners can identify where electrification delivers the highest CO₂ reduction with minimal operational disruption. The ability to compare scenarios side by side turns sustainability from a reporting exercise into a decision-making tool.

Earth Day challenges us to turn awareness into action. In supply chains, that means leveraging our power to decarbonize operations. Electrifying freight, where it makes logistical sense, is a powerful lever for reducing carbon. But it requires careful planning, supported by data and policy. As we have seen, industry case studies prove EV trucks can slash emissions and even cut costs on key routes. Governments are aligning incentives and regulations to accelerate the shift. And analytical tools (like our CO₂ App) help managers measure the impact of each change.

Above all, Earth Day reminds us that every decision counts: choosing an electric truck or installing a charger is an act of stewardship for our planet. By integrating EVs into logistics in a strategic, data-driven way, companies can make meaningful progress on Net Zero while continuing to move goods efficiently. In the end, protecting the environment is not separate from doing business, it is a core part of it. And that is a future we can all power together.