Visualizing Gross Profit for Logistics Companies: Profitability Management by Job, Vehicle, and Customer

Target Readers: Executives, branch managers, and administrative staff at Japanese-affiliated logistics companies and 3PL operators based in Thailand and ASEAN. This article is written primarily for those who manage on-the-ground operations in delivery, warehousing, and customs clearance, and who face challenges such as “not knowing where profit is made and where it disappears” or “reporting only revenue figures to headquarters with no visibility into the cost structure.”

The logistics business in Thailand is at a crossroads. Rising fuel costs, labor costs, and road tolls, combined with the growth of e-commerce driving an increase in small-lot deliveries, and shippers demanding that freight rates remain unchanged — these factors are compounding, and many branches are experiencing a situation where “revenue is being maintained but nothing is left over.” The problem is not simply rising costs; it is the inability to identify which jobs, which vehicles, and which customer relationships are actually generating profit.

The revenue structure of the logistics industry is inherently more difficult to visualize than that of manufacturing or retail. Mileage, cargo volume, and working hours differ from job to job, making the allocation of fixed and variable costs complex. While a Japanese headquarters’ accounting system may aggregate figures under line items such as “transportation revenue,” “fuel costs,” and “labor costs,” those figures alone cannot answer the question: “How much profit is the scheduled service between Bangkok and Ayutthaya actually generating for a specific customer?”

This article provides a concrete framework for what Thailand-based logistics companies need to do in order to visualize gross profit by job, vehicle, and customer — where to start, and how to establish profitability management in a way that minimizes disruption at the operational level. We draw on cases and failure patterns that TOMAS TECH has observed firsthand at sites across Thailand to deliver content that is directly applicable to real-world operations.

1. Why “Revenue Is Up but Profit Is Invisible”

The primary reason P&L in the logistics industry is hard to see lies in the structural tendency for the relationship between revenue and costs to break down naturally. In a manufacturing and sales business, linking production costs to sales revenue is not particularly difficult. In logistics, however, a single truck may carry cargo from multiple shippers on a single day using a consolidated load, driver overtime spans multiple jobs, and warehouse fixed costs are indirectly spread across dozens of customers.

At Japanese-affiliated logistics branches in Thailand, it is still common to manage revenue in Excel or a core system, while field operation records are kept on paper daily reports, driver activity is recorded on handwritten duty logs, and fuel costs are compiled from monthly receipts — all in siloed systems. In such an environment, attempting to calculate “how much was earned and how much cost was incurred” for each individual job requires staff to manually reconcile data, which itself becomes a cost.

A further challenge specific to Thailand is the communication gap between Japanese managers and Thai staff. When field input/output data is managed in Thai, the actual situation remains invisible to the Japanese side until monthly aggregation occurs. To track performance in real time on a weekly or daily basis, the entire workflow from data entry to aggregation must be redesigned.

2. Defining the “Unit” of Profitability Management: Three Axes — Job, Vehicle, Customer

When advancing gross profit visibility, the first decision to make is “what unit to use for profitability measurement.” In the logistics industry, three primary axes are commonly used.

By Job (Delivery Route / Lot): This approach treats a specific run (e.g., a scheduled weekly Tuesday/Thursday Bangkok–Rayong service) as a single profitability unit. Fuel costs, tolls, and driver costs for the route can be directly linked, and comparisons with utilization rates become clear. However, cost allocation is required for consolidated loads.

By Vehicle: This method aggregates revenue earned and costs incurred for each individual truck. Vehicle utilization, fuel efficiency, repair costs, insurance premiums, and driver assignments can all be viewed at a glance. Comparisons such as “this 10-ton truck is profitable, but that 4-ton truck has high maintenance costs relative to its utilization” become possible.

By Customer: This method consolidates revenue and costs for each shipper or consignor. Facts such as “Customer A has high volume but also makes many discount requests, so the actual gross margin is lower than Customer B’s” become visible. This data can also be used to review the customer portfolio and as supporting material for freight rate negotiations.

Attempting to manage all three axes simultaneously creates a heavy initial burden. In practice, it is more realistic to begin with the axis that is easiest to control for your organization — typically by vehicle or by major customer — and then drill down to the job level once data is in place.

3. Data Required for Gross Profit Calculation, and Why It Is Hard to Gather in the Field

To calculate gross profit by job, the following data is required at a minimum.

Data Item	Revenue or Cost	Typical Management Status at Thailand Branches
Freight rate / invoiced amount per job	Revenue	Entered into Excel or a core system on a per-transaction basis. Relatively well-maintained in most cases.
Fuel costs (refueling records / mileage)	Cost	Fuel slips are stored on paper and entered in monthly batches. Difficult to break down by vehicle and date.
Driver labor costs (including overtime)	Cost	Payroll is finalized monthly, but allocation data showing how many hours were spent on which job or route is unavailable.
Highway tolls / port charges	Cost	If ETC/e-Money records exist, data can be extracted by vehicle; paper receipts make aggregation cumbersome.
Vehicle repair and maintenance costs	Cost	If a vehicle register exists, costs can be linked by vehicle. However, confirmation is often not completed until after month-end.
Warehouse and cargo-handling costs	Cost	Often recorded as overall warehouse fixed costs rather than being linked directly to individual jobs.
Waiting time / delay occurrence records	Cost (opportunity loss)	Even if daily reports note “X hours waiting,” the data is not converted into a monetary value and is rarely used for improvement.

Structuring the data this way makes it clear that the core problem is not a lack of data per se, but the absence of a mechanism to link data to individual jobs. Revenue-side data tends to be relatively well-maintained, anchored by invoices, but cost-side data typically stops at monthly aggregation by account code, with no allocation to jobs, vehicles, or customers.

4. The First Step: Visualizing Fuel and Utilization

As a first step toward profitability management, the measure that proves most immediately effective at most operations is “visualizing fuel costs and vehicle utilization.” There are two reasons for this. First, fuel costs are the largest variable expense in logistics operations, and tracking them alone begins to reveal the contours of the cost structure. Second, vehicle activity records (mileage, load volume, number of runs) are relatively easy to obtain from drivers or GPS devices, making visualization achievable with a comparatively modest investment.

In practice, the following initiatives are commonly used as starting points.

Digitizing driver daily reports: Replace paper daily reports with tablet entry, recording departure time, return time, mileage, fuel volume, and job number on a daily basis. This alone enables subsequent aggregation of fuel costs and operating hours by vehicle and by job.
Introducing fuel management cards: Assign a fuel card to each vehicle to digitize refueling data. This shifts from monthly receipt aggregation to daily automatic aggregation.
Recording load factor: Building the habit of recording loaded weight and volume for each delivery makes it possible to identify “routes with high empty-haul rates.” This leads to improvements in consolidated loads and acquisition of return cargo.

What matters at this stage is not demanding a perfect system from day one. Even simply compiling one week of data manually begins to generate hypotheses such as “this vehicle is not cost-effective on this route.” The start of a cycle of testing and improving such hypotheses is the launching point for embedding profitability management.

5. Pitfalls of Customer-Level Profitability: “Large Customers ≠ Good Customers”

When logistics companies visualize profitability by customer, they frequently encounter the reality that “customers with high volume have low gross margins.” This is a pattern specific to the logistics industry, arising from the following structural dynamics.

Large shippers have strong negotiating power and, over the course of long-term relationships, have often accumulated successive freight rate reductions. At the same time, it is not uncommon for additional work disproportionate to the volume — dedicated vehicle arrangements, special packaging, priority handling, and time-specific deliveries required by the shipper — to have increased. The result is a situation where “revenue is large but the gross margin is low, and the operational burden is heavy.”

Conversely, even relatively smaller customers can achieve high gross margins if their delivery routes are standardized, cargo volumes are stable, special handling is minimal, and freight rate negotiations are conducted on reasonable terms. Cultivating such customers as “semi-premium customers” contributes to long-term improvement of the revenue structure.

An important caution when visualizing customer-level profitability is not to judge based solely on direct costs. Comparing on the basis of “full cost” — which accounts for indirect costs that arise per customer, including sales costs, billing administration, claims handling, and inventory storage — provides a view of profitability that more closely reflects reality. Starting with direct costs only and gradually increasing the precision of indirect cost allocation is a realistic approach.

6. How the Disconnect Between WMS, Dispatch, and Billing Blocks Profitability Management

At Japanese-affiliated logistics branches in Thailand, warehouse management (WMS), dispatch management, and invoicing frequently operate on separate systems or forms, and this is the single greatest barrier to profitability management.

For example, when goods are received or dispatched from the warehouse, the information may reach the dispatch team only through a verbal briefing the following morning; driver daily reports are submitted on paper; and invoices are created by the accounting department in a separate Excel file at month-end — when these disconnects exist, there is no single screen on which to view “which delivery incurred what costs and how much was invoiced.”

Resolving this problem does not necessarily require the upfront introduction of an expensive integrated system. Starting with “unifying job numbers (Order IDs)” is an effective first move. Simply assigning a common job number to warehouse inbound/outbound records, dispatch instructions, driver daily reports, and invoices makes after-the-fact reconciliation possible. Even in an Excel-based environment, having a shared key enables calculation of revenue and costs by job.

As a next step, introducing a simple tool that links billing data with dispatch and activity data — or a tablet-based field input solution — significantly reduces the time required for monthly aggregation. When all data is connected, it becomes possible to detect anomalies early, such as “this job’s invoice is two days late” or “this customer’s waiting time at the dock is longer than average.”

7. Turning Waiting Time, Delays, and Load Factor Losses into “Visible Costs”

The most representative losses that occur daily in logistics operations but are never converted into monetary terms are waiting time, delays, and declining load factors. These items are rarely recognized as “costs being incurred,” and it is difficult to raise their improvement priority. However, when converted into monetary figures, the management impact proves to be significant.

For example, calculating the cost per hour of one 10-ton truck (driver labor cost + vehicle depreciation + proportional fuel cost) reveals that one hour of waiting in front of a shipper’s factory represents a substantial cost. Converting this to a monthly or annual figure and visualizing which shipper relationships generate the most waiting time creates grounds for “negotiating waiting compensation” or “proposing adjustments to delivery time windows.”

The same logic applies to declining load factors. Identifying routes with high empty-haul rates and combining them with return-trip jobs makes it possible to increase revenue with almost no increase in additional fuel costs. Optimizing consolidated loads without data means relying on intuition and experience, but once load records accumulate, data-driven decision-making becomes possible.

Recording delays and analyzing their causes is equally important. By continuously logging “when, which job, and what caused the delay,” patterns emerge: “a specific shipper’s loading time consistently runs longer than scheduled” or “traffic congestion occurs frequently on a specific road segment on certain days and times.” This data can be used not only for improvement proposals but also as a delivery quality report to customers, enhancing trust.

8. Billing Omissions and Discount Management Directly Impact Gross Profit

When advancing profitability management in the logistics industry, two factors that are easy to overlook but have a major impact on gross profit are “billing omissions” and “discount management.”

Billing omissions occur when additional services (waiting time compensation, special packaging, in-warehouse sorting, document preparation on behalf of the shipper, etc.) are not reflected in invoices even though they have been provided. In Thailand, there are cases where “goodwill-based additional services” with Japanese shippers have become customary, and work that should properly be charged for is provided free of charge until it becomes “standard expectation.” When such omissions accumulate month after month, the total over a year becomes a figure that cannot be ignored.

As for discounts, there are cases where individual discount approvals made by sales staff are not shared with management, resulting in a situation where “freight unit prices have declined without management realizing it.” Simply establishing the habit of monitoring the trend in invoiced unit prices by customer on a monthly basis makes it possible to detect the accumulation of such discounts early.

Digitizing the invoicing process is also important. When invoices are created manually in Excel, input errors leading to amount discrepancies and delays of several days in issuance leading to payment delays are common occurrences. Transitioning to a system that links billing data with actual performance data reduces invoicing errors and also contributes to improved cash flow.

9. Investment Decision for Profitability Management: How to Build a 3-Year Payback Scenario

When a system investment is required to establish a profitability management framework, explanations to Japanese headquarters must be framed not as “this will be more convenient” but as “this will pay back within 3 years.” Below is a framework for building a typical payback scenario.

Cost Reduction Perspective: Can labor costs currently spent on monthly aggregation and reporting (e.g., 2 staff × X hours per month × hourly rate) be reduced? How much can be recovered annually through reduced billing omissions? Can improved fuel management yield fuel efficiency gains?

Revenue Growth Perspective: Can improved load factors generate additional revenue through return-trip cargo acquisition? Can visibility into delivery quality serve as a tool for acquiring new customers or negotiating rate improvements?

Risk Reduction Perspective: Can early detection of deteriorating profitability jobs enable corrective action before losses accumulate? Can improved management precision by customer and vehicle reduce the workload for audit and tax compliance?

By building up these factors and presenting the case in the form of “an estimated annual benefit of XX baht against an initial investment of YY baht, achieving payback in X years,” it becomes easier to obtain headquarters approval. Where Thailand’s BOI (Board of Investment) incentives for automation, data management, and enterprise IT system investment are applicable, the effective investment cost can be reduced further.

10. Phased Implementation: Start Small and Roll Out Broadly

Attempting a company-wide rollout of a profitability management framework all at once creates a risk of project failure due to the compounding of field resistance, system implementation delays, and data quality issues. TOMAS TECH recommends starting with a small unit, confirming results, and then rolling the approach out broadly.

Phase	Estimated Duration	Activities	Outcomes to Confirm
Phase 1: Current State Assessment	1–2 months	Manually estimate job-level gross profit for 3–5 major routes using existing data.	Form a hypothesis about which routes are profitable and which are not. Quantify the scope of improvement in monetary terms.
Phase 2: Systematizing Data Collection	2–3 months	Build out field data entry: digitize daily reports, unify job numbers, introduce fuel management cards, etc.	Achieve the ability to link fuel costs, operating hours, and invoiced amounts via job number.
Phase 3: Weekly Monitoring	3–6 months	Establish a weekly profitability review meeting to regularly review gross profit by vehicle and by customer.	Increase response speed to anomalies. Confirm the effects of improvement initiatives in a timely manner.
Phase 4: Broad Rollout and Automation	6–12+ months	Roll out successful management practices to other branches and all routes. Introduce systems as needed to automate aggregation.	Reduction in management workload. Improvement in the quality of headquarters reporting materials and reduction in preparation time.

The key across all phases is “not placing excessive burden on the field.” When new data entry tasks are added, field resistance emerges. In particular, when asking Thai staff to take on new forms or input tasks, carefully explaining “why this input is necessary” and ensuring that they can see how the results contribute to improving their own work is the key to making the practice stick.

11. Failure Patterns and How to Avoid Them: Lessons from Thailand Operations

From cases where profitability management initiatives failed, here are several typical failure patterns.

Failure Pattern 1: Seeking a Perfect System from the Start
There are cases where an attempt to implement a fully integrated dispatch, WMS, and accounting system all at once led to ballooning requirements, an implementation period exceeding one year, and the operation being abandoned before it ever took root in the field. The countermeasure is to follow the sequence of “first build the framework using paper and Excel, then use tools to improve efficiency once it has stabilized.”

Failure Pattern 2: The System Is Used Only by Japanese Managers
A management dashboard was built out, but Thai staff on the ground did not sustain their data entry, and data never accumulated. Profitability management only works when both field data entry and management analysis are functioning together. Providing a Thai-language UI, simplifying input, and giving feedback on the results of input are critical.

Failure Pattern 3: Monthly Aggregation Alone Cannot Keep Up with Improvement
Looking at monthly aggregated results and seeing “last month’s profitability was poor” provides information about a month that has already ended. Without a mechanism to detect anomalies on a weekly — ideally daily — basis, by the time a problem is recognized, losses have already accumulated.

Failure Pattern 4: Immediately Cutting Unprofitable Customers
When profitability visualization reveals that a specific customer relationship appears to be unprofitable, the decision to immediately end that relationship requires careful consideration. If that customer underpins other profitable jobs, or if future volume growth is expected, making decisions based solely on short-term profitability is dangerous. The basic approach is to use profitability data as “negotiating material” and first attempt an improvement negotiation.

12. BOI Incentives and Utilizing Logistics IT Investment

Thailand’s BOI (Board of Investment) continues to offer incentive programs targeting investment in automation, AI, data management, and enterprise IT systems. In the logistics sector, systems such as dispatch management systems, WMS, IoT sensor-based vehicle utilization management, and AI-driven route optimization can qualify for these incentives.

By leveraging BOI privileges, companies can benefit from corporate income tax exemptions, duty exemptions on imported machinery, and streamlined work permit processing for foreign technicians. When planning logistics IT investment, rather than considering BOI applications as an afterthought, it is important to confirm incentive eligibility requirements at the investment planning stage and incorporate the application schedule from the outset.

While Japan’s “Monodzukuri Subsidy” and similar programs do not apply directly to overseas branches, there are cases where the Japanese headquarters obtains a subsidy for advancing domestic production and logistics management, and then rolls out the resulting knowledge and tools to Thailand branches. Positioning the investment as a collaborative headquarters–branch initiative makes it easier to obtain support from the Japan side.

13. TOMAS TECH’s Perspective

TOMAS TECH provides IT solutions that connect field data to management decision-making for Japanese-affiliated manufacturers, logistics operators, and food companies throughout Thailand and across ASEAN. We introduce — without any hard sell — how our solutions can contribute to addressing the challenges of profitability management.

Inventory Management System PEGASUS: Records inbound/outbound activity, inventory movements, and lot management within the warehouse in real time. For logistics operators, it accurately accumulates the data — “what was stored, when, in what quantity, and where” — that is necessary for allocating warehouse storage costs by job and by customer. Visualizing each shipper’s storage space utilization leads to improved invoicing precision for warehousing charges and better storage efficiency.

Paperless Application i-Reporter: Enables field forms such as driver daily reports, inspection records, delivery completion reports, and load records to be entered on tablets and smartphones. Digitalizing paper daily reports dramatically shortens the lead time from data entry to aggregation and analysis. With support for both Thai and Japanese, it reduces the input burden on local staff while enabling managers to quickly obtain the data they need.

Operations Management System: A framework for tracking the real-time operating status of vehicles, equipment, and personnel. For logistics operators, centrally managing each vehicle’s travel records, operating hours, stoppage time, and waiting time through the system improves the precision of cost allocation required for profitability management.

Smartwatch System: Tracks the work progress of warehouse cargo-handling staff and delivery staff through a system integrated with smartwatches. By recording who spent how much time on which task, the data needed for per-job labor cost allocation is obtained.

TOMAS TECH recommends starting with an interview about current data management and operational workflows, and then beginning with a single point where impact is most likely to be felt quickly. We support Thailand branches at a pace that suits their field conditions, with the philosophy of “growing” profitability management “incrementally” rather than implementing it “all at once.” Please feel free to reach out via our contact form.

Summary

Visualizing gross profit by job, vehicle, and customer for a logistics company is the foundation for understanding “where profit is being generated and where it is being lost” and improving the precision of management decisions. The starting point is building a system that records field data — fuel costs, labor costs, waiting time, load factors — and links it to revenue data.

Rather than attempting to build a perfect system all at once, a phased approach that begins with estimates for major routes, builds a data collection framework, embeds weekly monitoring, and then rolls out broadly is what raises the success rate in Thailand operations.

To change the situation of “revenue is being maintained but nothing is left over,” it is essential to recognize the small losses in the field — waiting time, empty hauls, billing omissions, and the accumulation of discounts — as monetary figures, and to keep the improvement cycle turning. Not DX as a buzzword, but DX that changes the numbers on the ground is what will sustain the long-term competitiveness of Thailand-based logistics operations.

Profitability management is becoming not “nice to have” but “impossible to make management decisions without” in today’s business environment. As Thailand’s cost environment, talent environment, and customer demands continue to evolve, the gap between branches that have data-driven profitability management and those that do not will continue to widen.