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In mid-market transportation operations, the experienced dispatcher often remains the decision layer that connects systems, exceptions, and operating judgment.

Transportation planning is often treated as an optimization problem.

Give the system the orders, constraints, assets, drivers, delivery windows, and cost parameters, and the system should produce the plan. In stable operations, that approach can work.

But a recent conversation with Luuk Kuijpers, co-founder of Logicplan, pointed to a more practical issue.

In many mid-market transportation operations, the real planning system is still the experienced dispatcher.

The Dispatcher as the Decision Layer

Logicplan was founded after field research with dispatchers and planners. Kuijpers said the company spoke with more than 50 dispatchers to understand how they actually work.

What they found was fragmented information and judgment-intensive decision-making.

Dispatchers pull information from TMS screens, spreadsheets, emails, customer messages, phone calls, WhatsApp threads, and memory. They are not only building routes. They are coordinating transportation operations under time pressure.

An experienced dispatcher may know which customer can tolerate a shifted delivery window, which driver is best suited to a difficult stop, which exception requires escalation, and which constraint can be worked around.

Much of that knowledge is not documented. It sits in the head of someone who has seen thousands of exceptions over many years. That makes the dispatcher essential, but it also creates risk.

Why This Is Not Just a Solver Problem

One useful point from the conversation was Logicplan’s view that not every transportation planning challenge is a classical optimization problem.

Optimization tools can work well when operations are standardized and constraints are clear. Logicplan is focused on mid-sized transportation companies, including groupage, less-than-truckload, special transport, and construction-related transport, where planning depends heavily on company-specific judgment.

In these settings, planners may have access to automation tools but still do much of the work manually. That does not necessarily mean the tools are weak. It may mean the tools do not capture enough operating context.

The planner is not only calculating. The planner is recognizing patterns.

Above the TMS, Not Instead of It

Logicplan is not positioning itself as a TMS replacement.

Kuijpers was clear that the TMS remains the operational backbone. Logicplan’s role is to sit above and alongside the TMS as an intelligence and execution layer.

The system integrates with existing tools, builds operational context, and supports dispatchers in making decisions. In some cases, that may mean surfacing recommendations. In others, it may mean helping the planner work through a disruption interactively.

Over time, as the system captures more context and decision history, more automation may be possible. But the near-term value proposition is not to remove the dispatcher.

It is to make the dispatcher’s judgment more transferable and scalable.

The Knowledge Capture Challenge

The hard part is not only connecting to a TMS. It is capturing judgment.

A useful decision system needs to understand not only what decision was made, but why it made sense at the time.

What was the operating context? What options were considered? What did the planner know about the customer, driver, shipment, or prior exception? Which constraint was real, and which one was negotiable?

This matters because many transportation companies rely on planners whose experience is difficult to replace. When those planners retire, leave, or are unavailable, some of the operating logic leaves with them.

Bottom Line

The Logicplan conversation points to a gap in many transportation operations.

Systems of record hold data. Visibility platforms show events. Optimization engines solve defined problems. But many operational decisions still happen between those systems.

That is where planners use incomplete data, customer knowledge, exception history, and practical judgment.

Logicplan is early and is working with pilot customers. Its initial focus is mid-market transportation companies in the Netherlands, with European expansion planned. The larger issue is familiar across logistics: transportation planning is often a judgment-intensive process, not only a system calculation.

The post A Recent Conversation with Logicplan: Transportation Planning Beyond the TMS appeared first on Logistics Viewpoints.

Caterpillar as an Industrial Demand Signal

Caterpillar’s latest earnings report is useful because it tells a larger supply chain story. This is not only a story about heavy equipment demand. It is a story about how industrial capacity, energy infrastructure, construction activity, and AI-related investment are beginning to pull on the same supply base.

For the first quarter of 2026, Caterpillar reported sales and revenues of $17.4 billion, up 22 percent from the prior year. Adjusted profit per share rose to $5.54. The company also pointed to a record backlog, supported by strong order activity across its business.

Those numbers matter, but the more interesting question is what they signal.

Caterpillar sits at the intersection of several physical economy markets. Its machines support construction, mining, energy, infrastructure, and industrial operations. When demand rises across those categories at the same time, it is rarely isolated to one end market. It usually reflects broader capital spending, asset replacement, capacity expansion, or network reconfiguration.

That appears to be the case now.

The Power Demand Behind the Numbers

One notable driver is power demand. Caterpillar’s Power & Energy segment reported first-quarter sales of roughly $7.0 billion, up 22 percent from the prior year. Demand for large engines, power generation equipment, and related services is increasingly tied to data centers, grid constraints, industrial electrification, and backup power requirements.

The AI infrastructure buildout is especially important. Data centers do not exist only in software markets. They require land, construction, switchgear, generators, cooling systems, transformers, logistics capacity, and long-lead industrial equipment. As data center development expands, it pulls demand into parts of the industrial supply chain that many technology observers rarely track.

Caterpillar is one of those indicators.

Dealer Inventory and End-User Demand

The company’s construction segment also strengthened materially, with higher sales tied to equipment demand and dealer inventory dynamics. Caterpillar said the first-quarter revenue increase was driven primarily by higher sales volume and favorable price realization, with higher volume supported by changes in dealer inventories and higher sales to end users.

That distinction is important. In heavy equipment, reported sales can be influenced by both end-user demand and dealer inventory movement. When dealers rebuild inventory after a period of caution, manufacturers may see a sharp lift. But the quality of that lift depends on whether equipment is ultimately moving into productive use.

In Caterpillar’s case, the backlog and order activity suggest that this is not merely a channel restocking story. It reflects real demand across construction, power, and industrial markets.

What This Means for Supply Chain Leaders

For supply chain leaders, the implication is straightforward: the physical infrastructure cycle is becoming more complex. It is no longer enough to track construction as one market, energy as another, and technology infrastructure as a third. These markets are increasingly connected.

AI data centers require power generation. Power generation requires engines, components, control systems, and service networks. Construction requires machines, parts, labor, and transport capacity. Mining and resource industries feed many of the materials required for electrification and industrial buildout. A constraint in one area can quickly influence delivery schedules, pricing, and capital project timing in another.

That creates three practical supply chain issues.

First, long-lead industrial capacity is becoming more strategic. Equipment availability, component sourcing, and production slots can become limiting factors for major capital programs. Buyers that treat heavy equipment as a transactional procurement category may find themselves exposed when demand accelerates.

Second, aftermarket and service capacity matter more. Machines do not create value simply because they are delivered. They create value when they are operating. Parts availability, technician capacity, maintenance planning, and field service execution become essential to uptime.

Third, supplier visibility needs to extend beyond tier-one relationships. In power generation and heavy equipment, bottlenecks may come from engines, castings, electronics, controls, semiconductors, batteries, alternators, or logistics capacity. The risk is not always visible from the final assembly point.

The Physical Layer of the AI Economy

Caterpillar’s results also reinforce a broader point about AI infrastructure. The AI economy is not weightless. It depends on industrial supply chains. Chips, servers, and software receive most of the attention. But the buildout also depends on generators, switchgear, power systems, construction equipment, land development, and freight movement.

That is why Caterpillar’s backlog matters. It is a demand signal from the physical layer of digital infrastructure.

There are still risks. Tariffs, manufacturing costs, component constraints, and regional demand variation can all affect margin and delivery performance. Caterpillar noted unfavorable manufacturing costs in the quarter, even as higher volume and pricing supported the top line.

From Backlog to Execution

The lesson is not that demand solves every problem. It is that demand shifts the nature of the problem. When markets are weak, the issue is utilization. When markets accelerate, the issue becomes conversion: can backlog be turned into delivered, supported, profitable equipment?

For industrial supply chains, that is the operating question.

Caterpillar’s quarter should be read as more than a strong earnings result. It is a signal that the next phase of AI, infrastructure, and industrial growth will be constrained by physical capacity as much as by digital ambition. The companies that understand that connection will plan differently. They will look beyond software roadmaps and into supplier capacity, service networks, logistics lanes, and equipment availability.

The post Caterpillar and the Supply Chain Signal Behind Heavy Equipment Demand appeared first on Logistics Viewpoints.