{"id":2066,"date":"2026-07-15T11:33:23","date_gmt":"2026-07-15T11:33:23","guid":{"rendered":"https:\/\/www.scmchamps.com\/blog\/?p=2066"},"modified":"2026-07-15T11:35:23","modified_gmt":"2026-07-15T11:35:23","slug":"sap-tm-automation-reduce-logistics-costs","status":"publish","type":"post","link":"https:\/\/www.scmchamps.com\/blog\/sap-tm-automation-reduce-logistics-costs\/","title":{"rendered":"How SAP TM Automation Reduces Logistics Costs by 10\u201325%"},"content":{"rendered":"

Introduction<\/h2>\n

You’ve probably seen the claim before: SAP TM automation can cut transportation costs by 10\u201325%. It shows up in vendor decks, consulting proposals, and articles like this one. Fair question \u2014 where does that number actually come from?<\/p>\n

Most content states it and moves on. This article does the opposite. I’m going to break the number down lever by lever, give you a simple way to estimate where your own organization would land in that range, and show you what it looked like on a real project.<\/p>\n

This is written for logistics directors, supply chain leaders, and SAP program owners\u00a0 anyone who needs to put a defensible number in front of a CFO, not just a slogan.<\/p>\n

One note before we start: if you’re still at the earlier stage SAP is live, costs are high, and you’re not sure why \u2014 start with [the 5 hidden gaps that keep transportation costs high after SAP implementation]. That article covers the diagnosis. This one covers what fixing it is worth.<\/p>\n

Where the 10\u201325% Actually Comes From<\/h2>\n

There’s no single switch that delivers a 20% saving. The number is a stack \u2014 five levers, each contributing its share. In the SAP TM optimization projects I’ve worked on, the contributions typically look like this:<\/p>\n

\n\n\n\n\n\n\n\n\n\n
Savings Lever<\/th>\nWhat Automation Changes<\/th>\nTypical Contribution*<\/th>\n<\/tr>\n<\/thead>\n
Load consolidation<\/strong><\/td>\nThe optimizer merges compatible shipments; trucks stop leaving below capacity and needless LTL moves disappear<\/td>\n4\u201310 percentage points<\/td>\n<\/tr>\n
Route optimization<\/strong><\/td>\nSystem-proposed routes instead of planner habit<\/td>\n2\u20135 percentage points<\/td>\n<\/tr>\n
Carrier rate optimization \/ tendering<\/strong><\/td>\nRule-based carrier selection and cost ranking instead of habitual assignment<\/td>\n2\u20136 percentage points<\/td>\n<\/tr>\n
Reduced planning errors & rework<\/strong><\/td>\nFewer manual touches means fewer re-plans, expedites, and penalties<\/td>\n1\u20132 percentage points<\/td>\n<\/tr>\n
Settlement leakage recovery<\/strong><\/td>\nAutomated freight settlement catches billing discrepancies before they’re paid<\/td>\n1\u20133 percentage points<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

*Illustrative ranges based on our SAP TM<\/strong><\/a> optimization project experience. Actual results vary with freight spend, network structure, planning maturity, and system configuration.<\/p>\n

Two things are worth noticing in this table. First, consolidation is almost always the largest lever \u2014 which is why organizations shipping heavy LTL volumes tend to sit at the upper end of the overall range. Second, the smaller levers are not decoration. One or two percentage points of settlement leakage on a large freight spend is real money, recovered quietly, every single month.<\/p>\n

Now, the insight that separates a good optimization project from a mediocre one: the order of these fixes matters.<\/strong> Consolidation savings depend on freight unit building rules being fixed first. Freight units created too early, with broad default rules, lock shipments into shapes the optimizer can’t recombine later. Automate carrier selection on top of badly built freight units and you’ve just optimized the wrong thing very efficiently.<\/p>\n

Why this doesn’t happen on its own:<\/strong> identifying the levers is the easy part; pulling them is where organizations get stuck. The typical story is one we examined in depth in [the previous article] \u2014 settings that never moved past their go-live state, an optimization roadmap that ended when the project team disbanded, and old planning routines that quietly outlived the new system.<\/p>\n

Estimate Your Own Number<\/h2>\n

Industry ranges are a starting point, not an answer. Here’s the simple framework I use in first conversations to estimate where an organization will land \u2014 three inputs, in this order:<\/p>\n

1. Annual freight spend.<\/strong> This is the base everything is calculated on. A 12% saving means very different things at $5 million and at $50 million of freight spend.<\/p>\n

2. How manual is planning today?<\/strong> Be honest here. If the working day begins with shipment data being pulled out of SAP and into spreadsheets, you’re realistically at the high end of the 10\u201325% range. If it’s partially automated, the middle. If the optimizer is already doing most of the work, the low end; your remaining gains come from tuning, not transformation.<\/p>\n

3. Network complexity.<\/strong> A simple network \u2014 single site, a handful of carriers \u2014 offers less consolidation upside. A complex one \u2014 multiple plants or distribution centers, a mixed LTL\/FTL profile, many contracted carriers \u2014 offers more, because there are simply more combinations the optimizer can exploit that a human planner can’t evaluate under time pressure.<\/p>\n

Put those together and the math gets concrete quickly. Take an organization spending $20 million annually on freight, planning primarily in spreadsheets, across multiple distribution centers with a heavy LTL mix. That profile would realistically sit toward the upper end of the overall 10\u201325% range. And here’s the part worth pausing on: even the very bottom of that range is $2 million a year \u2014 comfortably more than what fixing the gaps typically costs, because most of the fixes are configuration work inside a system that’s already licensed and running.<\/p>\n

This is an estimate framework, not a guarantee. Our assessment exists precisely to replace this estimate with your actual numbers.<\/p>\n

What Waiting Another Year Actually Costs<\/h2>\n

Here’s the uncomfortable flip side of the same math. If your organization sits somewhere in that 10\u201325% range, then every quarter of manual planning has a price tag \u2014 you just never see the invoice.<\/p>\n

Unrealized savings don’t show up in any budget review. The freight bill gets paid, the planners stay busy, and everything looks like normal operations. Nobody approves a line item called “money we chose not to save.” That’s precisely why manual planning survives year after year: its cost is invisible while its familiarity is comfortable.<\/p>\n

Run your own freight spend against even the bottom of the range, divide by four, and that’s roughly what one more quarter of deferral costs. For most organizations I’ve worked with, that number ends the “is this worth looking at?” debate on its own.<\/p>\n

What Is SAP TM Automation and How Does It Work?<\/h2>\n

SAP TM automation is the use of intelligent rules and optimization logic within SAP Transportation Management to plan shipments, select carriers, and manage execution and settlement automatically \u2014 reserving planner involvement for the exceptions that genuinely require a decision.<\/p>\n

It works across three layers.<\/p>\n

Planning automation<\/strong> starts with freight unit building rules, which convert orders or deliveries into plannable freight units automatically. The VSR optimizer then proposes consolidated loads and routes, guided by planning profiles tuned to your network, fleet constraints, and delivery priorities.<\/p>\n

Execution automation<\/strong> covers carrier selection and tendering \u2014 using configured business rules such as allocations, business shares, or cost-based ranking \u2014 plus event management for real-time shipment tracking.<\/p>\n

Settlement automation<\/strong> calculates expected freight costs against contracted rates and flags discrepancies before invoices are paid.<\/p>\n

A concrete example of the difference this makes: in a manually planned operation, a planner assigning 100 shipments picks carriers and builds loads one at a time, based on experience. An automated setup evaluates all 100 together, finds the consolidation opportunities across them, and assigns carriers by contract logic \u2014 in minutes, repeatably, without depending on who’s planning that day.<\/p>\n

For the deeper explanation of each capability, see [the detailed automation section in our previous article<\/strong><\/a>]. For this piece, the essential point is simpler: the technology is standard SAP TM. The savings come from configuring and actually using it.<\/p>\n

A Scenario From Project Experience<\/h2>\n

A mid-sized consumer goods distributor, supplying retail chains and wholesale customers through three distribution centers, was working with around 18 contracted carriers on a mixed LTL and FTL network \u2014 several thousand shipments a month, primarily domestic.<\/p>\n

SAP TM was live. But planning relied heavily on individual planner decisions. Carrier assignment followed planner preference rather than cost or contract rules. Consolidation opportunities were missed week after week, producing a steady stream of unnecessary LTL shipments. And underneath it all, the freight unit building parameters had never been optimized \u2014 which meant the planning optimizer couldn’t do much even when it was used.<\/p>\n

Notice the pattern: this is the fix-ordering point from earlier, playing out in practice. The consolidation problem couldn’t be solved by better carrier selection alone, because the freight units themselves were the constraint.<\/p>\n

So that’s where the work started. We reviewed and optimized the freight unit building rules, refined the planning profiles, implemented rule-based carrier selection aligned with contracted rates, improved the tendering configuration, and adjusted optimization parameters to favor consolidation.<\/p>\n

The result was approximately 14% transportation cost reduction within nine months<\/strong>. Just as importantly, transportation planning became far more consistent because planners no longer relied on manual carrier selection and individual judgment for routine decisions. No new software was purchased. The system they already owned simply started doing the job it had been configured to do \u2014 this time with planning rules, carrier selection, and optimization settings aligned to the business’s transportation network.<\/p>\n

Against the framework above, this outcome is exactly where you’d expect it: a complex multi-DC network with heavy manual planning, landing solidly in the middle of the 10\u201325% range.<\/p>\n

Is This Relevant for Your Organization?<\/h2>\n

A quick way to qualify whether this math deserves your attention:<\/p>\n