KPI Design to Combat Rising Logistics Costs in Thailand: Tracking Dwell Time, Load Factor, and Redelivery Rate

Target audience: Branch managers, logistics department heads, and administrative managers at Japanese companies operating logistics hubs, 3PL operations, or distribution centers in Thailand and ASEAN, as well as operations managers at manufacturers and retailers who outsource their logistics functions.

Thailand’s logistics industry continues to face difficult conditions into 2026. The World Bank has issued a cautious growth outlook for Thailand in 2026, with significant uncertainty weighing on export-dependent supply chains. On top of that, structural changes — including continued increases in labor costs, fuel prices, and vehicle maintenance expenses; fluctuating delivery routes due to traffic congestion and road construction; and a rise in small-lot, high-frequency deliveries driven by e-commerce growth — are steadily pushing up logistics costs.

In response to this environment, many companies have embarked on “KPI redesign” as a means to maintain and improve operational performance. However, there is a large gap between simply setting KPIs and actually making them usable for management decisions. When field data is entered manually into Excel every day, losses tend to accumulate unnoticed until it is already too late.

This article focuses on three metrics that have a particularly significant impact on logistics operations in Thailand — dwell time, load factor, and redelivery rate — and explains in concrete terms why these three indicators directly affect costs, and how to measure, manage, and drive improvement. Whether you are someone who is “collecting numbers but not putting them to use,” or a manager who is “unsure what to measure in the first place,” you will find a practical, actionable framework here.

Why Logistics KPI Redesign Is Needed Now

Multiple overlapping factors are driving up logistics costs in Thailand. First is the ongoing rise in labor costs. As minimum wages continue to be revised upward, the difficulty of hiring drivers, sorting workers, and warehouse staff — along with rising wages — shows no sign of abating. Next is the volatility of fuel costs. Fluctuations in crude oil prices directly impact delivery costs, and when contracts are set at fixed freight rates, any increase hits the company’s own margin.

Furthermore, the increase in small-lot, high-frequency deliveries is depressing load factors and driving up the cost per delivery. As e-commerce demand expands, volumes that could previously be handled in a single delivery run are increasingly being split into multiple runs, making it a chronic inefficiency to cover the same distance with fewer goods loaded.

Then there is the redelivery problem. In Thailand, the ambiguity of address notation, security gate procedures at condominiums and apartment complexes, and failed deliveries due to recipient absence all create an environment more prone to redelivery than Japan. Redelivery doubles the cost of fuel, driver labor, and vehicle operating time — and if left unchecked, it can account for more than 10% of total delivery costs.

Against this backdrop, conventional management metrics (such as number of deliveries completed and sales target achievement rates) are increasingly inadequate. Designing KPIs that make field inefficiencies visible through data and link directly to corrective actions has become essential for protecting profit margins.

The KPI Design Trap: Are You Just Measuring Without Acting?

There is a scenario commonly seen at Japanese-affiliated logistics sites: daily delivery performance data is transcribed into Excel or spreadsheets, compiled into graphs on a weekly or monthly basis, and reported — and then management looks at the graph, confirms “We achieved a 95% rate this month,” and calls it done.

The problem with this management style is that KPIs have become “records” rather than “the basis for decision-making.” For example, even if you can see that the load factor is trending downward, you cannot take corrective action unless you can identify which routes, which days of the week, or which customers are particularly underperforming. If the data cannot be broken down, compared, or tracked over time, the effectiveness of your KPIs is cut in half.

There is also the problem of a disconnect between reports to Japan headquarters and the actual situation on the ground. When numbers compiled by local Thai staff are translated into Japanese and reported to headquarters, the definitions of figures and the methods of aggregation can subtly differ. If the definition of “load factor” differs between Japan and Thailand, or if the criteria for counting “redeliveries” vary by person, long-term trend analysis becomes unreliable.

The starting point for KPI design is not choosing what metrics to measure, but first deciding “when this number moves, who does what, and how?” KPIs that are not linked to action become costly decorations that serve no real purpose.

Metric 1: Dwell Time (Waiting Time)

Dwell time refers to the time a truck or delivery driver spends waiting at a loading or unloading location before the actual work begins. In Japan, the attitude tends to be “a little waiting is unavoidable,” but in Thai logistics operations, dwell times can become surprisingly long. Causes vary widely, including entry procedures at industrial estates, insufficient dock capacity at warehouses, and shift management issues for receiving staff.

Extended dwell times generate costs in several ways. First, there is increased driver tie-up time. Thailand has regulations for overtime pay, and extended waiting time will incur additional labor costs. Second is reduced vehicle turnover. The number of deliveries a driver can complete in a day depends heavily on dwell time. Adding just 30 minutes of waiting per stop can dramatically reduce the number of deliveries possible in a day. Additionally, there is fuel waste. In Thailand’s climate, vehicles often idle while keeping the cargo area cool during waits, and fuel consumption from idling cannot be ignored.

The first step in managing dwell time is automated collection of actual performance data. By automatically recording arrival and departure times from drivers’ smartphones or vehicle GPS terminals, you can eliminate the effort and input errors associated with manual aggregation. Next, you aggregate the average dwell time by receiving location to identify problem sites. When dwell times are disproportionately long at a specific warehouse or factory, you can focus on the cause — insufficient dock capacity, peak-hour congestion, or issues with the receiving setup — and pursue targeted negotiations and improvements.

Specific improvement measures include spreading delivery time slots (introducing a reservation system), standardizing advance communication with receiving locations, and sending real-time wait status notifications to drivers. Some of these measures can be started without an IT system, but as you scale to managing multiple sites and many drivers, the ability to automatically collect and visualize data makes a significant difference in results.

Metric 2: Load Factor (Utilization Rate)

Load factor is the ratio of the actual load on a truck to its maximum payload capacity. For example, if a truck with a maximum capacity of 10 tons is carrying 6 tons, the load factor is 60%. The lower the load factor, the higher the cost per item delivered.

Declining load factors are a structurally prone problem in Thai logistics operations. The main causes include shrinking order lot sizes from customers (driven by e-commerce and the move toward high-mix, low-volume production), route optimization that depends on the personal experience of individual drivers, and a failure to account for demand fluctuations by season or day of the week in advance.

Approaches for improving load factor include, first, route optimization. By consolidating customers in similar areas onto the same delivery run, you can improve both load volume and total distance traveled. Next is adjusting order lead times. By proposing and negotiating minimum lot sizes or order cycles with customers, it may be possible to steer orders toward lot sizes that are easier to load efficiently. Shared delivery services are also effective: by sharing deliveries heading in the same direction among multiple companies, you can improve low-margin routes where it would be difficult to raise load factors on your own.

The key caution in managing load factor is to look at both weight-based and volume-based measures. Heavy goods hit the weight limit first, while light goods are constrained by volume. If you only look at one or the other, you will get a load factor figure that does not reflect reality. This is especially relevant in Thai food logistics handling frozen and refrigerated goods, where volume constraints tend to be the binding factor first, making efficient management of chilled truck space utilization critical.

Metric 3: Redelivery Rate (Failed Delivery Rate)

Redelivery rate is the proportion of delivery attempts that were not successfully received on the first try. Even compared to Japan’s BtoC environment, redelivery rates in Thailand tend to be higher, influenced by infrastructure differences, address system characteristics, and cultural differences in handling absent-recipient situations. Even in business-to-business (BtoB) deliveries, redeliveries can occur because factory receiving windows have limited operating hours or cumbersome entry procedures prevent goods from being accepted.

The cost calculation for redelivery is straightforward: the cost of one redelivery is approximately equal to the cost of one standard delivery. This is because fuel, driver labor, and vehicle depreciation costs are all incurred twice. If the redelivery rate drops from 5% to 2%, the 3% difference translates directly into delivery cost savings. At large-scale sites, the annual savings from reducing redeliveries can reach several million yen.

Measures to reduce the redelivery rate include automating pre-delivery confirmation contacts (day-before notifications via SMS or LINE), offering a pre-specified delivery time slot service, and establishing contactless drop-off options and alternative pickup locations. For BtoB deliveries, real-time notifications to the receiving representative and a review of the factory’s receiving setup are effective.

From a data management perspective, it is essential to record and categorize the reasons for each redelivery. By aggregating by reason — “recipient absent,” “access denied,” “address unknown,” “recipient’s convenience” — you can see which countermeasures to prioritize. If you manage only the “number of redeliveries” without recording reasons, you cannot take targeted corrective action.

Managing the Three Metrics Together: View Them in Combination, Not in Isolation

Dwell time, load factor, and redelivery rate may appear to be independent metrics, but in the field they are closely interrelated. For example, consolidating routes to increase the load factor can increase the number of delivery stops per run, which in turn increases dwell time. Conversely, introducing time-specified deliveries to reduce dwell time can also reduce the load volume per run by spreading deliveries across time slots.

If you pursue improvement in a single metric without considering these trade-offs between indicators, you will find yourself in a “whack-a-mole” situation where improving one metric causes another to deteriorate. To manage the three metrics together, it is effective to place a unified metric — Cost per Delivery — at the center of your management framework, and to regularly review how much each KPI is contributing to that cost.

When reporting to Japan headquarters, it is more effective for investment decision-making to present the three metrics not as individual reports, but as a breakdown diagram showing “what proportion of total delivery costs is attributable to each factor.” Quantifying it in concrete terms — such as “reducing dwell time will cut monthly vehicle tie-up by X hours” and “a 2% reduction in redelivery rate will deliver annual cost savings of X baht” — is the key to gaining headquarters approval.

Field Implementation of KPI Management: A Phased Approach

To embed a KPI management system in field operations, it is important to proceed in stages rather than trying to implement everything at once. Thai logistics operations often involve mixed Japanese-Thai teams, and the system design must account for the time it takes to establish new systems and procedures.

The following table outlines a phased approach by scale and maturity.

Phase	Key Activities	Tools Used	Estimated Duration
Phase 1: Standardize Definitions and Data Collection	Unify definitions and aggregation standards for the three metrics across the organization. Begin weekly aggregation, even if manual.	Excel / Spreadsheets	1–2 months
Phase 2: Automated Collection and Visualization	Automatically pull data from GPS terminals, WMS, and dispatch systems. Begin daily monitoring via dashboard.	WMS / TMS / Route optimization tools	3–6 months
Phase 3: Analysis and Action Integration	Standardize procedures defining who takes what action when KPI deterioration is detected. Track improvement effects quantitatively.	Business system integrations / Alert functions	6 months+
Phase 4: Rollout and Headquarters Integration	Expand to multiple sites and teams. Standardize KPI report formats for Japan headquarters.	Integrated management systems / Reporting tools	1 year+

A common failure that occurs at many sites is getting stuck at Phase 2 — automated collection and visualization. “We built a dashboard, but nobody looks at it” and “the numbers are there, but they’re not being used for improvement” represent a near-zero return on investment. Transitioning to Phase 3 — designing the process that links KPIs to action — is the most important and most challenging step.

The Fragmentation Problem: WMS, TMS, and Dispatch Systems

A common challenge at logistics sites in Thailand is that the warehouse management system (WMS), transportation management system (TMS), dispatch system, and billing system all operate independently. Real-time warehouse inventory status is not reflected in dispatch planning; actual delivery results are not automatically reflected in invoices, requiring manual intervention. These inefficiencies are a daily reality.

This fragmentation has a significant impact on logistics cost management KPIs. For example, to calculate load factor accurately, WMS shipment data and TMS vehicle data must be linked — without that connection, accurate figures cannot be produced. To accurately reflect redelivery costs in billing, delivery performance data and the billing system must be synchronized.

Full system integration is costly and time-consuming, but it is possible to start with minimum viable data linkage. For example, even the analog approach of daily CSV exports of WMS shipment data imported into a dispatch management spreadsheet will improve the accuracy of KPI calculations. After confirming demand for more connectivity, API integration or integrated system implementation can then be considered — an approach that manages investment risk while building practical effectiveness.

Japan-Thailand Communication and the Risk of Knowledge Concentration

Another practical challenge facing Japanese companies managing logistics sites in Thailand is the communication gap between Japan and Thailand, and the concentration of knowledge in individual local staff members.

While experienced local logistics staff are invaluable, the “experiential knowledge” they hold — the shortcuts on certain routes, the names of receiving contacts at specific locations, seasonal traffic congestion patterns — is often not recorded in any system or document. When such a staff member transfers or resigns, operational quality suddenly deteriorates, and this problem recurs repeatedly.

In the process of developing KPIs, it is important to simultaneously advance the conversion of tacit knowledge into explicit knowledge. For example, if the experience that “this receiving location always has long dwell times on Monday mornings” is accumulated as data, it can be applied to dispatch planning. Likewise, recording the knowledge that “redelivery risk on this route increases in the rainy season” reduces the risk associated with knowledge concentration in individuals.

When reporting to Japan headquarters, it is also important to clearly state the definitions and aggregation methods behind each number. A report of “80% load factor” is meaningless if it is unclear whether it is weight-based or volume-based, or which routes it represents as an average. Standardizing the report format and making it a habit to attach a definitions document will improve the quality of communication between Japan and Thailand.

Combining BOI Incentives with Logistics DX Investment

Thailand’s BOI (Board of Investment) offers incentives for investment in automation, AI, data analytics, and enterprise management IT, with programs that can be utilized in the logistics industry. However, many companies have a perception that BOI is “something used when investing in manufacturing equipment,” causing them to overlook its applicability to logistics DX investments.

System investments for logistics KPI management — route optimization systems, WMS, and data analytics platforms — may qualify for BOI’s IT and digitalization-related preferential measures, provided they meet the relevant requirements. It is important to check BOI requirements at the planning stage of your investment and consciously design your investment with eligible items in mind, as this can significantly reduce your effective cost.

One critical point regarding BOI applications is that retroactive applications are often not accepted. The approach of “let’s implement the system and then apply for BOI” will frequently not work. It is strongly recommended to consult with a BOI consultant or specialist organization before finalizing system selection and investment decisions, to confirm application timing and requirements.

Furthermore, leveraging BOI can also serve as persuasive material when seeking investment approval from Japan headquarters. If you can present a calculation showing that “by utilizing BOI tax incentives, the effective payback period can be kept within three years,” even cautious headquarters decision-makers are more likely to approve.

Failure Patterns in Logistics KPI Improvement and How to Avoid Them

The following table organizes common failure patterns seen in the field and how to avoid them, focusing particularly on problems that tend to repeat themselves at Japanese-affiliated logistics sites.

Failure Pattern	Why It Happens	How to Avoid It
Too many KPIs	The mindset of “let’s measure everything we can” leads to an excessive proliferation of metrics.	Start by limiting to 3–5 metrics, and design action linkages first.
Inconsistent definitions	Different individuals calculate figures differently; interpretations differ between Japan and Thailand.	Create and share a KPI definitions document in both Japanese and Thai.
Data collection remains manual	System investment is continually deferred, leaving drivers and staff to keep entering data manually.	Prioritize implementing automated collection mechanisms, even on a small scale.
Stopping at the dashboard	Creating a visualization tool becomes the end goal.	Document escalation procedures and responsible parties when KPIs deteriorate.
Field staff do not use the system	The system or metrics do not match the language or usability needs of the field.	Include Thai-language UI availability and ease of operation in your selection criteria.
Excessive focus on a single metric	Trying to maximize load factor alone causes trade-offs such as increased redeliveries.	Center management around a unified metric such as cost per delivery.

Demonstrating a 3-Year Payback: A Framework for Explaining to Japan Headquarters

To gain approval from Japan headquarters for a KPI management system investment at a Thailand site, it is essential to present specific cost reduction effects and a payback period calculation, not abstract benefits like “it will be more convenient” or “we’ll gain visibility.”

Use the following components to structure your calculation.

Current annual costs: Current values for redelivery rate, dwell time, and load factor, and the costs linked to them (driver labor, fuel, vehicle depreciation)
Projected post-improvement values: Assumptions about how much each metric will improve with enhanced KPI management (use conservative figures based on industry benchmarks or comparable cases)
Cost reduction effect: The difference between before and after, converted to an annual cost figure
Investment amount: Total of system implementation costs, operating costs, and training costs (effective amount after BOI incentive application)
Payback period: Investment amount ÷ annual cost reduction effect

The critical point is to use conservative assumptions. Even if there is a case study showing “another company achieved 30% improvement,” it is better to assume 10–15% improvement in your own projections — this builds more trust with headquarters decision-makers. Getting approval with optimistic numbers and then falling short of targets is worse than meeting or exceeding conservative projections, as the latter lowers the bar for the next investment request.

In addition to cost reduction effects, it is also recommended to try to quantify risk reduction. For example, stating that “without redelivery data on record, responding to customer complaints takes X hours per month” or “without dwell time data, we have no evidence base for negotiating costs with receiving locations” — by including management costs and risk costs in your calculations, the case for investment becomes considerably more compelling.

TOMAS TECH’s Perspective

TOMAS TECH CO., LTD. has provided systems and services to Japanese manufacturers, logistics operations, and food industry sites in Thailand and ASEAN to connect field data with management decision-making. Here we introduce the areas in which we can support your logistics KPI management.

Inventory Management System PEGASUS manages in-warehouse inventory and inbound/outbound shipment data in real time, functioning as a WMS. It resolves the problem of “we loaded the truck, but we can only find out afterward what was actually loaded,” and improves the integration accuracy between shipment data and dispatch planning. Having accurate inventory data also forms the calculation basis for load factor. PEGASUS supports a Thai-language interface suited to Thailand’s field environment, designed to be easy for local staff to operate on a daily basis.

Paperless Application i-Reporter is a tool that digitizes paper forms, daily reports, and checklists on tablets and smartphones. Drivers’ delivery reports, delivery confirmation records, and redelivery reason logs can all be entered and transmitted in real time from the field. This frees the recording and categorization of redelivery reasons from manual, person-dependent work, improving both the accuracy and immediacy of the data needed for KPI analysis.

The Operations Monitoring System provides the foundation for tracking the operating status of vehicles, equipment, and personnel in real time. It can be used for automated measurement of dwell time and for aggregating utilization rates by vehicle and by route. By integrating with smartphones and GPS terminals, it minimizes the data entry burden on local staff while enabling continuous data accumulation.

The Smartwatch System supports hands-free operations in warehouse and logistics environments. During cargo sorting, picking, and inspection work, workers can confirm instructions and report completion with both hands free, reducing quality issues such as sorting errors and loading mistakes. This also has the effect of reducing “mis-delivery” — one of the causes of redelivery.

For all of our tools, we recommend a “start with one site, one process” implementation approach. Rather than a company-wide simultaneous rollout, the approach of measuring results in a small scope first, embedding the system in field practice, and then expanding horizontally has a higher success rate in Thailand’s operational environment. For inquiries about implementation and operations, please contact us at https://tomastc.com/contact.

Summary

The rise in logistics costs in Thailand is the result of multiple overlapping factors — fuel costs, labor costs, and growing demand for small-lot deliveries — and is not a problem that can be resolved in the short term. What is needed to protect profit margins in this environment is not only the judgment of “what to cut,” but also building a system to “accurately identify where losses are occurring.”

The three KPIs featured in this article — dwell time, load factor, and redelivery rate — are all metrics that directly impact delivery costs and are easy to link to corrective actions in the field. Taking these three as a starting point and progressively improving KPI management accuracy through a phased approach is something that can be started without large-scale system investment.

The critical step is to elevate KPIs from “recording” to “using for decision-making.” This requires standardizing metric definitions, automating data collection, standardizing action procedures, and presenting data-driven explanations to Japan headquarters. You do not need to do all of this at once. Starting with one metric, one route, and one data collection mechanism is the sustainable starting point for continuous improvement.

The business environment in Thailand is becoming increasingly demanding, but as the range of what can be managed through data expands, so too does the range of available responses. Redesigning logistics KPIs is not only a means of cost reduction — it is also an investment that elevates the competitiveness of a Thailand site from the perspectives of improved customer reliability and enhanced site management quality.