Introduction
In freight logistics, load consolidation combines multiple smaller shipments into one larger load. For most companies is a key strategy to cut costs and improve efficiency. However, successful consolidation depends on reliable, clean carrier performance data. Without accurate data on how carriers perform (on-time delivery, capacity usage, etc.), consolidation efforts can have the opposite effect. Unreliable carriers or incorrect data can lead to delays, empty truck space, and higher costs. For example, global on-time delivery rates dropped to about 52% in 2024 (from over 75% pre-pandemic), and up to 35% of truck miles are driven empty due to inefficiencies. Clean performance data enables shippers to consolidate loads confidently and avoid costly mistakes.
What Load Consolidation Actually Means
Load consolidation is the process of combining multiple shipments (often from different shippers) into a single full load to optimize space and reduce waste. Instead of sending half-empty trucks on the same route, consolidation creates a “communal shipping” system where unused truck capacity is shared. This approach improves cost-effectiveness and sustainability in logistics. For instance, recent research found that 58% of truckloads moved partially empty in 2024, up from 43% in 2023. In other words, over half of truck capacity went underutilized, leaving on average 34 feet of trailer space unused, equivalent to 1 in 3 trucks effectively moving completely empty. This highlights a huge opportunity: by consolidating compatible loads, shippers can fill that empty space and achieve economies of scale. Consolidation can lower shipping costs, reduce trucking activity, and reduce carbon emissions by eliminating wasted trips. But realizing these benefits is only possible when you can coordinate and plan shipments with reliable information, which is where carrier performance data is needed.
The Role of Carrier Performance Data in Logistics
Carrier performance data shows how well freight carriers meet important service standards. This includes delivery performance (on-time delivery rates, transit times), quality metrics (damage or loss rates, claims), capacity reliability (equipment availability, tender acceptance), and cost measures (cost per shipment, fuel usage). In a logistics network, these data points are vital for decision-making. They let shippers identify which carriers are dependable and efficient versus which ones cause delays or extra costs. For example, on-time delivery and transit time data help planners ensure shipments will arrive as scheduled, critical when combining loads with multiple delivery stops.
Poor carrier performance directly impacts costs: late deliveries often incur fees or customer penalties, and a single rejected or missed pickup can increase overall trucking costs by nearly 15% due to last-minute rebooking. Moreover, unreliable carriers tend to drive up empty miles and waste capacity, as noted earlier. High-quality performance data enables shippers to avoid these pitfalls by choosing the right carriers and routes for consolidated loads. With clear data, a shipper can proactively route freight through more reliable carriers or lanes and bundle shipments without risking service failures. Using historical performance data to match carriers to lanes yields higher on-time rates and more consistent service.
In short, carrier performance data is the compass that guides effective load consolidation.
The Data Challenges in Carrier Performance Monitoring
1. Siloed and inconsistent data.
Carriers use different systems and formats, so performance info often comes in incompatible forms. Data may be spread across spreadsheets, emails, and various TMS platforms, leading to inconsistencies. In 2022, 50% of shippers said getting accurate data was their top challenge, and even in 2024, one-third still struggle with data accuracy.
2. Manual tracking errors.
Without integration, many rely on manual data entry or tracking, which is prone to human error. Typos or delays in updating spreadsheets can skew carrier scorecards.
3. Lack of standard metrics.
Different carriers or 3PLs might define “on-time” differently (e.g., within 1 hour vs. 24 hours of schedule). Without standard KPIs, it’s hard to compare or consolidate performance data.
4. Time-consuming reconciliation.
Logistics analysts spend considerable time cleaning and reconciling data, matching loads to invoices, and verifying transit times. This administrative burden slows down decision-making.
5. Limited visibility.
Real-time performance visibility can be lacking. If you cannot see delays or issues as they happen, you can’t adjust consolidated shipments proactively. A lack of real-time tracking data leads to unpredictable deliveries and hinders efficient planning.
So, fragmented data can mislead shippers. Poor data might make a carrier look better (or worse) than reality, resulting in bad choices for consolidation. It’s clear that to consolidate loads successfully, shippers must first overcome these data hurdles.
How to Get to Clean Carrier Data
Achieving clean, reliable carrier performance data requires a strategic approach. Here are key steps and best practices logistics teams are using:
1. Integrate and consolidate data sources. Break down data silos by feeding all carrier data into a single platform or database. Ensure your Transportation Management System (TMS), warehouse system, and carrier feeds are connected so data flows seamlessly. Leading companies unify information from multiple sources into one “single source of truth”, implementing automated checks for consistency and completeness.
2. Standardize performance metrics. Develop a clear set of KPIs (on-time percentage, OTIF, damage rate, etc.) with standard definitions for all carriers. This way, every carrier is measured by the same yardstick. Industry groups and programs provide frameworks, but shippers should also align internally on what “on-time” means for their operations.
3. Automate data capture and validation. Whenever possible, use EDI/API integrations or tracking tools that automatically collect performance data (e.g., status updates, delivery timestamps) directly from carriers. This reduces manual errors. Implement software that can flag anomalies, for instance, if a delivery timestamp seems off, or if a data field is missing. Investing in tools that validate and clean data automatically pays off, as it ensures accuracy at the source.
4. Continuous data auditing and improvement. Cleaning data isn’t a one-time project. Set up ongoing audits and dashboards to continuously monitor data quality. Some organizations set up automated alerts for data issues, allowing carrier reps to log in to fix records. The goal is ongoing discipline in data management, so new errors don’t accumulate.
5. Collaborate with carriers. Finally, share performance reports with your carriers and involve them in the process. When carriers see that you’re tracking on-time performance and other metrics, they’re often motivated to improve. Also, by communicating your data needs (e.g., requiring timely electronic updates for each milestone), you set expectations that help keep the data clean from the start.
Firms that invested in data infrastructure and quality saw a 3× higher ROI from advanced supply chain initiatives than those that didn’t. Clean data not only enables load consolidation, but it also powers analytics, AI tools, and better decision-making across the supply chain.
Conclusion
So, you can’t effectively consolidate freight loads without clean carrier performance data. Load consolidation promises significant benefits: cost savings, higher efficiency, and greener operations, but those benefits only materialize when each link in the chain performs as expected. Clean data on carrier performance acts as the glue that holds a consolidated shipping strategy together. It allows logistics managers to choose the right carriers, anticipate issues, and coordinate multiple shipments with confidence.
On the flip side, bad data or unknown carrier performance can cause consolidated shipments to suffer delays, customer dissatisfaction, and erode any cost savings. Modern supply chains recognize this: nearly 87% of shippers consider strong logistics KPIs essential in today’s data-driven environment. Investing in data quality, integrating systems, standardizing metrics, and continuously cleansing data are prerequisites for advanced logistics strategies such as load consolidation.
Poor data quality can cost organizations millions and even cause high-tech supply chain projects to fail. Conversely, a solid data foundation empowers better decisions. By cleaning up carrier performance data, shippers position themselves to consolidate loads successfully, unlocking the full efficiency gains and cost reductions that consolidation delivers.