A transportation management system collects data on every shipment you move: carrier performance, transit times, costs, accessorial charges, and exceptions. Supply chain optimization starts when you actually use that data to make decisions instead of letting it sit in spreadsheets or, worse, in someone's head. The difference between shippers who consistently reduce freight spend and those who keep throwing money at problems is usually not budget or headcount. It is whether they have the right data, in the right format, at the right time.

This post breaks down the specific categories of TMS data that matter most, how to turn them into operational decisions, and where most shippers leave value on the table.

What Is Supply Chain Optimization?

Supply chain optimization is the process of improving how goods move from origin to destination by reducing cost, shortening transit times, and increasing reliability. In transportation specifically, it means using data to make better decisions about carrier selection, mode choice, load planning, and network design. It is not a one-time project. It is an ongoing discipline that depends on consistent, accurate data flowing from a TMS into the hands of the people making daily shipping decisions.

Why Does TMS Data Matter More Than Gut Instinct?

Most logistics teams have experienced professionals who know their freight. That knowledge is valuable, but it does not scale. When you are managing hundreds or thousands of shipments per week across multiple modes, carriers, and lanes, no single person can hold all the variables in their head.

A TMS captures what actually happened on every shipment: what the carrier quoted versus what they invoiced, whether they picked up on time, how long the shipment sat at the dock, and whether it arrived within the delivery window. Over time, this creates a dataset that reveals patterns invisible to manual tracking.

For example, a logistics manager might have a "feeling" that Carrier A is reliable on the Chicago-to-Dallas lane. But TMS data might show that Carrier A's on-time delivery rate on that lane dropped from 94% to 81% over the last quarter, while their accessorial charges increased 12%. That is the kind of insight that changes a routing decision, and it only comes from systematic data collection.

What Types of TMS Data Drive Better Decisions?

Not all freight data is equally useful. Here are the categories that matter most for supply chain optimization, along with how they translate into action.

Carrier Performance Data

Carrier scorecards built from TMS data track on-time pickup rates, on-time delivery rates, claims ratios, tender acceptance rates, and invoice accuracy. This is the foundation for informed carrier negotiations and routing guide construction.

Practical application: If your TMS shows that a carrier accepts only 60% of tenders on a given lane, you know to either renegotiate that commitment or move them down in your routing guide. If their claims ratio is climbing, you can address it before it becomes a pattern that affects your customers.

Without this data, routing guide reviews become a guessing game, and you end up overpaying carriers who underperform while underleveraging ones who deliver consistently.

Freight Spend Analytics

Freight spend data includes cost per shipment, cost per pound, cost per mile, lane-level pricing trends, and accessorial charge breakdowns. A good TMS lets you slice this data by mode, carrier, origin, destination, customer, and time period.

This is where most shippers find their biggest savings opportunities. Common discoveries include lanes where spot rates have dropped below contract rates (signaling it is time to rebid), accessorial charges that make up 15-20% of total cost on certain lanes, and carriers whose fuel surcharge calculations do not match the agreed formula.

One concrete example: a shipper running freight spend analytics might discover that their LTL costs on eastbound lanes are 18% higher than the market benchmark. Digging deeper, they find that poor freight classification and inconsistent pallet configurations are driving the premium. That is a fixable problem, but only if the data surfaces it.

Real-Time Visibility and Exception Data

Shipment tracking data goes beyond knowing where a truck is right now. When captured systematically, it reveals dwell times at pickup and delivery locations, recurring bottlenecks on specific lanes, carriers or facilities with chronic delay patterns, and how often "on time" actually means on time versus technically within a loose window.

Exception data is especially valuable. Every late pickup, missed appointment, or damaged shipment is a data point. When you aggregate exceptions over weeks and months, you start seeing which problems are random and which are systemic. Systemic problems are fixable. Random problems are the cost of doing business. The TMS helps you tell the difference.

Lane and Network Analysis

Lane-level data shows volume, cost, transit time, and carrier performance for every origin-destination pair in your network. This is the basis for network optimization, which asks questions like whether you are shipping enough volume on a lane to justify switching from LTL to full truckload, whether a pool distribution strategy would reduce final-mile costs in a specific region, and whether consolidating shipments from multiple facilities could reduce total moves.

Network analysis also supports mode optimization. Your TMS data might reveal that intermodal service on a 1,500-mile lane delivers within one day of over-the-road at 20% lower cost. Without lane-level data, you would never test that hypothesis.

How Do You Turn TMS Data Into Action?

Collecting data is the easy part. The hard part is building processes that turn data into decisions on a regular cadence. Here is what that looks like in practice.

Weekly Operational Reviews

Use TMS dashboards to review the prior week's shipment exceptions, carrier performance against SLAs, and cost variances against budget. This is where you catch problems early, before a carrier's declining performance costs you a major customer.

Monthly Spend Analysis

Pull freight spend reports segmented by lane, carrier, and mode. Compare against benchmarks and prior periods. Identify the top five cost drivers and assign owners to investigate. This rhythm is what separates reactive freight management from proactive optimization.

Quarterly Carrier Reviews

Aggregate carrier scorecard data for quarterly business reviews. Use the data to have specific, evidence-based conversations about performance, pricing, and capacity commitments. Carriers respond better to data than to complaints, and the best ones will appreciate the transparency.

Annual Network Planning

Use a full year of TMS data to evaluate your distribution network. Are there new lanes that have grown enough to justify dedicated capacity? Are there facilities with consistently high dwell times that need operational changes? Annual planning with solid data prevents the slow drift toward inefficiency that happens when nobody is looking at the big picture.

What Happens When You Lack TMS Data?

The absence of centralized freight data creates a set of predictable problems. Carrier negotiations happen without performance evidence, so you end up negotiating on price alone and missing the total cost picture. Freight audits catch invoicing errors after payment, if they catch them at all. Routing guides go stale because nobody has the data to know which carriers are actually performing. And mode selection defaults to "what we have always done" instead of what the data supports.

These problems compound. A shipper without good data might overpay by 8-15% across their freight network without realizing it, simply because they cannot see where the waste is.

Frequently Asked Questions

What is supply chain optimization in transportation?

Supply chain optimization in transportation means using data and technology to reduce freight costs, improve delivery performance, and increase operational efficiency across your shipping network. It involves analyzing carrier performance, lane economics, and shipment patterns to make better routing and procurement decisions.

How does a TMS improve freight decision-making?

A TMS collects and organizes data from every shipment, including costs, transit times, carrier performance, and exceptions. This gives logistics teams the visibility to spot trends, compare carriers objectively, and make data-backed decisions instead of relying on institutional knowledge or manual tracking.

What freight KPIs should I track for supply chain optimization?

The most impactful KPIs include on-time delivery rate, on-time pickup rate, cost per shipment (by lane, mode, and carrier), tender acceptance rate, claims ratio, and freight spend as a percentage of revenue. A good TMS tracks these automatically and presents them in formats that support regular review cadences.

How often should I review TMS analytics?

Weekly reviews of exceptions and operational performance, monthly freight spend analysis, and quarterly carrier scorecards are a practical starting cadence. Annual network-level analysis rounds out the cycle. The key is consistency: data only drives optimization when someone is regularly looking at it and acting on what they find.