Reducing Transportation Costs: Why Your Network Is Your Greatest Lever
Transportation costs are rising, margins are under pressure, and at the same time, demands for delivery reliability and flexibility are growing. Most companies respond to this by issuing requests for proposals or negotiating freight rates. Yet a significant portion of the potential savings often lies elsewhere entirely: in the transportation network itself.
Studies of industrial, automotive, and retail networks consistently show that between 5 and 15% of transportation costs are not driven by higher freight rates, but rather by inefficient network structures, a lack of transparency, and historically established transportation patterns. Therefore, anyone seeking to reduce transportation costs sustainably must not only focus on prices but also optimize the entire transportation network. It is precisely this shift in perspective—away from individual trips and toward the network as a whole—that often determines whether savings remain one-time occurrences or are permanently reflected in the cost structure.
When the network structure itself becomes a cost driver
In many companies, the transportation network develops organically over the years. New suppliers are added, production sites change their requirements, customer bases grow, and ad-hoc solutions become the norm. What initially seemed pragmatic often leads to complex and inefficient transportation structures. The situation becomes particularly critical when transportation decisions are made decentralized and no one views the network as a whole. This results in costs that are barely visible on individual routes but add up significantly across the entire network.
A particularly common cost driver is the fragmentation of transport volumes. Instead of consolidating larger quantities into a few shipments, numerous small shipments are planned and processed. This leads to higher transport costs per unit, increased planning effort, reduced bargaining power with service providers, and overall greater complexity in transport planning. In transportation planning within the automotive sector in particular, such structures often arise due to varying call-off cycles, location-specific planning logic, or short-term production adjustments that are rarely made with the entire network in mind.
Closely related to this is truck utilization, which is one of the most important levers for reducing logistics costs. Nevertheless, in many networks, it is considered only on an ad hoc basis. Underutilized vehicles not only result in higher costs per load unit but also tie up transport capacity that may be lacking elsewhere. Networks with a high proportion of less-than-truckload (LTL) shipments—where shipments are not systematically consolidated—are particularly problematic. Even a few percentage points of increased utilization can yield significant savings, especially in networks with high transport volumes.
The situation is exacerbated by a lack of transparency regarding routes and volumes. Many companies know their annual transportation costs very precisely. However, it becomes significantly more difficult when it comes to questions such as: Which routes incur the highest costs? Where do less-than-truckload shipments regularly occur? Which suppliers or plants cause high transportation inefficiencies? On which routes do a particularly large number of special trips occur? Without a transparent view of volume flows, shipment structures, and routes, much potential for cost reduction remains hidden because no one in the company has a complete overview of the data.
Impact on Costs and Service Levels
Network inefficiencies affect not only costs but also the operational performance of the supply chain. High transportation costs directly reduce the contribution margin. At the same time, complicated transportation structures increase vulnerability to capacity bottlenecks, disruptions, and last-minute schedule changes.
The result is a classic conflict of objectives: Companies try to maintain high service levels but compensate for network problems with additional shipments, special runs, or safety stock. As a result, costs continue to rise instead of falling, and the actual cause—namely, the inefficient network structure—remains unaddressed. Over time, this solidifies a pattern in which operational symptoms are treated while the structural root of the problem remains undiscovered. That is why it is not enough to simply eliminate individual special trips or outliers. Only by examining the entire network can one determine whether these are isolated cases or recurring patterns that indicate deeper structural weaknesses.
The Hidden Potential Lies Between Locations
The greatest potential for savings often lies not within individual plants or warehouses, but at the interfaces between them. Anyone seeking to optimize their transportation network should therefore first analyze the actual material and goods flows, rather than relying solely on the experience of individual locations.
Many networks exhibit transportation patterns that have evolved over time. Suppliers deliver to multiple plants simultaneously, even though volumes could be consolidated. Individual sites are served directly, even though intermediate consolidation would be more cost-effective. A structured network analysis answers questions such as: Which transportation flows run parallel to one another? Where do supplier relationships overlap? Which volumes are suitable for consolidation strategies? Where do unnecessary transport kilometers occur? This often reveals surprising connections that are barely visible in day-to-day operations—for example, when two suppliers from the same region independently supply the same plants without the flows ever having been considered together.
Partial loads and empty runs also offer particularly high potential. In many transport networks, return trips are not systematically utilized, so that vehicles return with low load factors or even empty, while other shipments are planned in parallel. Such inefficiencies can only be identified by analyzing complete transport structures, as this involves not individual routes but recurring patterns across the entire network.
Consolidating shipments is one of the most effective measures for network optimization in logistics. Possible approaches include consolidation centers for supplier flows, multi-pickup strategies, milk run concepts, joint transport planning across multiple plants, and the regional bundling of customer deliveries. The key is striking a balance between cost reduction and service requirements, as not every form of bundling automatically improves overall performance. For example, if a consolidation center is located too far from the actual supplier sites, the additional kilometers traveled to reach it can offset the savings achieved on the main route.
Practical Tip: Before implementing a bundling concept, it’s worth simulating the effects on costs, capacity utilization, and service levels—ideally using a software solution rather than relying on assumptions.
Why Excel and Empirical Knowledge Have Reached Their Limits
Many transportation networks are still managed using spreadsheets, manual analyses, and years of experience. These approaches work surprisingly well up to a certain level of complexity. But requirements have changed: supply chains are becoming more global, product portfolios more diverse, and customer demands more dynamic. At the same time, expectations for cost efficiency and resilience are rising.
Even medium-sized networks can encompass thousands of routes, supplier locations, plants, warehouses, and customer relationships. The number of possible optimization combinations grows exponentially. Even experienced planners can no longer reliably assess which network structure delivers the best results, because the interactions between individual relationships, capacities, and order cycles have simply become too complex to map out mentally or in a spreadsheet.
Another key weakness of traditional analytical approaches is that they primarily focus on the past. Strategic decisions, however, require answers to questions such as: What happens when a new plant is added? What impact does an additional supplier have? How does the cost structure change with different order cycles? What effects result from consolidation strategies? Such questions can only be answered reliably through scenarios and simulations, because a mere analysis of historical data shows only what has happened, not what could happen.
Added to this are the operational risks of manual planning. Data sets quickly become inconsistent, important interdependencies remain hidden, and decisions are often based on assumptions rather than reliable facts. Especially in strategic transportation networks, such misguided decisions can lead to long-term cost consequences that only become fully apparent years later, when contracts, location decisions, or capacities have already been finalized.
Data-Driven Network Optimization in Practice
Modern, AI-powered transportation planning does not replace experience; rather, it complements it with data-driven decision support. The goal is to derive concrete courses of action from large volumes of data and to assess their impacts even before implementation.
In many cases, existing company data—including shipment data, transportation costs, transportation routes, volume information, supplier and customer locations, plant structures, as well as loading units and capacities—is sufficient for a well-founded network analysis. What matters most is not so much the volume of data as the quality and consistency of the information. Even with relatively manageable but clean datasets, reliable conclusions can be drawn.
In the next step, different network variants are simulated. This allows for the examination of questions such as: What are the effects of consolidation centers? How does capacity utilization change? Which mileage figures can be reduced? Which service levels remain achievable? How do total costs evolve? The advantage of this approach is that decisions are not based on assumptions but on quantifiable effects that can be directly compared with one another.
The true strength of data-driven network optimization lies in the systematic evaluation of alternatives. Instead of examining individual routes in isolation, transportation costs, capacity utilization, service levels, and network stability are evaluated collectively. This results in a significantly more well-founded basis for decision-making than traditional individual analyses. This not only provides companies with transparency regarding existing weaknesses but also offers concrete starting points for improving their transportation structures and permanently reducing transportation costs.
Keep a constant eye on cost drivers with the right KPIs
A one-time analysis is not enough. Transportation networks are constantly changing—for example, due to new suppliers, shifting demand, or seasonal fluctuations—and should therefore be reviewed regularly. A streamlined set of KPIs provides the necessary transparency without overburdening planning with too many metrics.
Transport costs per route show which transport routes account for the largest share of costs and where optimization measures have the greatest impact.
Utilization rate illustrates how efficiently existing transport capacities are being used and where there is potential for consolidation.
Proportion of less-than-truckload shipments often indicates a lack of synchronization between demand or untapped consolidation opportunities.
Empty-run rate reveals the extent to which transport capacities remain unused and where return-load concepts should be developed.
Cost per unit enables comparability between different routes, plants, and product groups.
Special-run rate: Special runs are often an indicator of planning problems, a lack of transparency, or insufficiently coordinated processes between production, procurement, and logistics.
FAQs About Reducing Transportation Costs
How much savings potential does network optimization offer?
Studies and real-world projects in industrial, automotive, and retail networks consistently show savings potential of 5 to 15% of transportation costs without the need for new freight rate negotiations.
What is the difference between freight rate negotiations and network optimization?
Freight rate negotiations focus on the price per shipment. Network optimization operates at a higher level: it reduces the number, length, and inefficiency of the shipments themselves—for example, through consolidation, better capacity utilization, and fewer empty runs.
What data is needed for a transportation network analysis?
As a rule, existing shipment, cost, volume, and location data from ERP or TMS systems is sufficient. Data quality is key, not data volume.
At what company size does software-supported network optimization become worthwhile?
Even medium-sized networks with multiple plants, suppliers, and customer locations quickly reach a level of complexity that is virtually impossible to assess reliably by hand. This is where a software-supported, data-driven analysis delivers the greatest value.
Conclusion: Those who focus solely on optimizing freight rates overlook the greatest lever for improvement
As logistics costs rise, many companies focus on price negotiations with transportation service providers. However, the significantly greater leverage often lies within the network itself.
Fragmented shipments, low load factors, empty runs, and a lack of transparency generate costs that often remain hidden when viewed in isolation. Systematic network optimization makes these opportunities visible and allows them to be leveraged effectively.
Therefore, anyone looking to reduce transportation costs should not just look at individual shipments but analyze the entire transportation network. Only the combination of transparency, data-driven evaluation, and strategic network design enables permanently lower logistics costs, stable service levels, and a more resilient supply chain.
Want to know how much savings potential your transportation network holds? Schedule a no-obligation consultation with our experts and learn how the S2data Platform analyzes, simulates, and optimizes your network.