Using Data to Improve Warehouse Layout: How to Read Travel Distance, Picking Frequency, and Mis-shipment Rate

Target audience: Site managers, plant managers, and logistics supervisors at Japanese companies operating logistics and warehouse functions in Thailand and ASEAN, as well as management staff driving on-site improvement initiatives.

“Do you know exactly how far your staff walk inside the warehouse every day?” Very few logistics operations at Japanese companies in Thailand can answer that question on the spot. Inefficient picking, repeated mis-shipments, and the perpetual problem of inventory that “can’t be found” — the root causes of all these issues are hidden in two places: warehouse layout and data utilization.

In 2026, Thailand’s economic environment is becoming increasingly selective. The World Bank maintains a cautious outlook on Thailand’s growth prospects, and as logistics and energy costs rise and supply chains are restructured, Japanese logistics and manufacturing companies are under pressure to improve “operational efficiency that protects profitability.” In an environment where growth in revenue alone is not enough, eliminating the small losses that occur every day inside the warehouse — unnecessary movement, mis-picks, rework in inspection — translates directly into improvements in business metrics.

This article explains how to use data in warehouse layout improvement: how to interpret three key metrics — travel distance, picking frequency, and mis-shipment rate — and how to translate those insights into concrete on-site improvements. We address the realities of Thai operations — the reporting and communication gap between Japanese and Thai staff, work procedures that depend on individual knowledge, and the challenges of high-mix/low-volume fulfillment — and outline a practical approach to driving improvement.

Why Warehouse Layout Review Is Urgent Right Now

At many Japanese logistics facilities, warehouse layouts have been running on the “original design” ever since the facility was established. Location configurations set at launch have failed to keep pace with growth in SKU count, shorter product cycles, and changing customer shipping requirements — and before anyone notices, the operation is running on habit and experience alone.

When this situation persists, the following problems accumulate.

Only veteran staff know the locations, making new employee training costly
Travel distances during picking are long, making per-order processing time unpredictable
Without ABC analysis, low-frequency items occupy prime aisle space
Mis-shipments are treated as individual “human errors,” hiding layout-driven root causes
Inventory stagnation and expiry incidents occur on a regular basis

In Thai operations, there is a structural layer that makes these problems even harder to see. Even when local staff are aware of an issue, they may be slow to report it to Japanese managers — or both sides may simply accept the situation as “part of normal operations” without recognizing it as a problem. Data-driven visualization is the key to breaking through this structural blind spot.

Three Core Metrics for Warehouse Improvement

When tackling warehouse layout improvement, there are three metrics you should measure first. Each one reveals “where the problem is” from a different angle.

① Travel Distance

This metric shows how far a worker travels to pick a single order. Depending on the size of the warehouse and the number of SKUs, longer travel distances increase processing time and raise the risk of errors caused by fatigue. To understand current travel distances, it is common to cross-reference location data with order data. If a WMS (warehouse management system) is in place, extraction is straightforward; but even with Excel-based management, a rough estimate can be calculated through a simple simulation of shelf numbers and movement routes.

The direction for improvement is straightforward. Concentrating high-frequency items (A-rank) near the receiving and shipping areas, and moving low-frequency items (C-rank) to the back or upper shelves, can significantly reduce average travel distance. However, “which items are A-rank” cannot be determined without data.

② Picking Frequency

This metric shows how many times each SKU has been picked within a given period. It is the foundational data for ABC analysis, and visualizing it reveals “which items are truly moving fast.”

In Thai operations, locations are often managed based on staff experience, and there is sometimes a gap between the intuitive sense of “this item moves” and what the actual data shows. This gap is especially pronounced in warehouses where shelf allocation has not been updated after the addition of seasonal products or new items.

Picking frequency data can be obtained from WMS shipment history, ERP outbound data, or aggregated shipping instructions. Even three months of recent data is sufficient for an initial ABC classification.

③ Mis-shipment Rate (Error Rate)

This is the ratio of mis-shipments (wrong part number, wrong quantity, wrong destination) to total shipments. It is the most direct quality metric tied to customer complaints, and can be considered the “overall report card” for warehouse operations.

The key is not to handle mis-shipments as individual “human errors,” but to identify patterns: “at which location,” “for which item,” and “at what time of day” they occur. When concentration at a specific location or during a specific time window becomes visible, layout or procedural problems can be pinpointed.

The Reality of Data Collection: Three Barriers in Thai Operations

Even when the proposal to “improve through data” is understood, there are cases where actual data collection and analysis fail to progress. There are three common barriers seen in Thai operations.

Barrier 1: Mixed Paper and Excel Data Management

In warehouses where shipping instructions are on paper, inventory records are in Excel, and complaint records are handwritten notes — all managed separately — data collection alone requires considerable effort. At this stage, the starting point must be taking stock of “what data exists and where.”

An effective quick fix is to introduce barcode scanning for paper work records. Scan history accumulates automatically as digital data, allowing you to build foundational data without heavy manual effort. Tools like i-Reporter for going paperless allow you to replace traditional paper forms with digital forms while directly using the input data for analysis.

Barrier 2: Data Exists but “Nobody Is Looking at It”

Even facilities with a WMS or ERP may not be fully utilizing the reporting features. Japanese managers often use system output primarily for sending reports to headquarters in Japan, without diving into data utilization for on-site improvement.

In this case, what improvement requires is not additional system investment, but operational design around “which reports to check on a weekly basis.” Simply incorporating the three metrics — travel distance, picking frequency, and mis-shipment rate — into a weekly report that managers review is enough to start the improvement cycle moving.

Barrier 3: Japan-Thailand Communication Gap

Local staff are the ones who understand on-site problems, but a structural disconnect exists at many facilities where that data and those insights never reach the Japanese managers. Beyond the language barrier, a psychological reluctance — “I might get in trouble if I report this” — often acts as a functional obstacle.

Data visualization also works as a tool to change this structure. By creating a system where data entered by shop-floor staff is automatically displayed as metrics, reporting problems shifts from being “an act of blaming someone” to “sharing a situation that the data shows.” An environment where both Japanese and Thai staff can look at the same screen and have a conversation raises psychological safety on the shop floor.

Steps for Applying ABC Analysis to Warehouse Layout

Once data is in hand, the analysis moves to concrete improvement action. The most fundamental and effective technique in warehouse improvement is location optimization through ABC analysis.

In ABC analysis, items are classified into three groups based on picking frequency or shipment volume.

Rank	Definition (guideline)	Recommended Location	Shelving Approach
A-Rank	SKUs accounting for the top 20–30% of shipments	Near the shipping dock, waist-height shelves, on the main aisle	Minimize travel distance during picking. Ensure spacing between locations to prevent mis-picks
B-Rank	The next 30–40% of SKUs by frequency	Mid-zone, on secondary aisles	Avoid placing too close to A-rank to prevent mis-picks
C-Rank	Remaining low-frequency SKUs	Back of warehouse, upper shelves, off the main aisle	Prioritize storage efficiency over travel cost. Enforce strict safety management for high-shelf storage

The process begins by aggregating SKU-level picking frequency from the last 3–6 months of shipment data. Next, perform the ABC classification and identify gaps with current locations. If mismatches appear — “an A-rank item is in a back location” or “C-rank items are occupying space near the dock” — those are the targets for improvement.

An important caveat: seasonal items and newly added SKUs require periodic ABC reclassification. Rather than a once-a-year shelf reorganization, making quarterly data reviews a habit keeps the layout perpetually in sync with reality.

Reducing Travel Distance: Common Improvement Patterns

Location changes based on ABC analysis alone can dramatically reduce travel distance. Here are several improvement patterns commonly seen in Thai logistics operations.

Pattern 1: Reorganizing the Main Aisle

This is a case where the aisle design was “set at the beginning” and no longer matches the actual picking flow. A common symptom is high-volume items scattered on both sides of an aisle, forcing workers to go back and forth repeatedly during picking. By consolidating A-rank items in the zone closest to the shipping dock and simplifying worker movement to a U-shaped or L-shaped route, the same orders can be fulfilled with far fewer steps.

Pattern 2: Zoning for Set-Shipment Items

When multiple SKUs are frequently shipped together as a set, consolidating those SKUs into adjacent locations reduces travel distance per order. “Put items that always ship together next to each other” is a simple principle — but without data, it is impossible to know which combinations are most common. Aggregate “co-picking frequency” from WMS or shipping instruction data, and prioritize placing the top combinations in adjacent locations.

Pattern 3: Optimizing Flow Between Picking and Inspection/Packing

Beyond picking itself, the physical positions of the inspection, packing, and labeling steps also affect total travel distance. Reviewing the spatial relationship between these steps and arranging them so that goods flow in a single direction reduces backtracking movement.

Reducing Mis-shipment Rate: Using Data to Find “Mistake-Prone Situations”

Mis-shipments usually follow patterns. “It’s always the same location where mistakes happen,” “they concentrate during the busy period,” “there are more during a certain staff member’s shift” — confirming these patterns with data is the first step in preventing recurrence.

The Problem of Placing Similar Items in Adjacent Locations

Locations where items with similar part numbers or similar appearances are stored next to each other are danger zones prone to mis-picks. In Thai operations, there is the additional risk of local staff misreading Japanese-language labels. Countermeasures include creating “buffer locations (empty shelves)” between similar items, introducing color-coded labels, and rigorously enforcing barcode scan verification.

Countermeasures for Peak-Time Concentration

When mis-shipments concentrate in the 1–2 hours before the shipping cutoff, the division of labor among order receiving, picking, and inspection needs to be reviewed. As more workers crowd the floor during this period, movement paths cross and the behavior of “skipping checks because we’re in a hurry” generates mis-shipments. Managing shipping lead times and leveling order flow are the fundamental countermeasures that precede any layout changes.

The Effect of Introducing Digital Verification

Introducing a barcode scan system to verify part numbers and quantities during picking can dramatically reduce the mis-shipment rate. However, if bypass behavior — “I don’t need to scan this” — occurs, the effect disappears. A mechanism that triggers a warning when a scan is skipped, or operational management through periodic log review, must be paired with the scanning system.

Investment Decisions: Investments to Pause vs. Investments to Continue

Under Thailand’s 2026 economic environment, warehouse improvement investment also demands more careful judgment. Below is a framework for distinguishing “investments to pause” from “investments to continue” at logistics facilities.

Category	Investment Example	Rationale
Consider pausing	Company-wide WMS overhaul (no clear path to on-site adoption)	Large investment with unclear operational burden and ROI payback period
Consider pausing	Large-scale automated robotic warehouse following a trend (thin business case)	Implementation without analysis of item mix and order patterns will result in low utilization rates
Continue	Picking data collection and ABC analysis (low cost)	Low initial investment that creates the evidence base for layout improvement
Continue	Barcode scan verification (leveraging existing systems)	Reduction in mis-shipments directly reduces customer complaints and return costs
Continue	Digitization of paper forms (form tools such as i-Reporter)	Digitizing on-site records creates the foundation for analysis. Can be started at small scale
Continue	Improving inventory accuracy (inventory management systems such as PEGASUS)	With low inventory accuracy, location management also breaks down. Foundational remediation

The fundamental principle for investment decisions is: “Can we see a 3-year payback?” To calculate the cost-effectiveness of layout improvement, start by understanding current work time (time required per pick) and labor costs, and the cost per mis-shipment (returns, re-delivery, and complaint handling costs). If these figures are not on hand, establishing a data collection system is the prerequisite for any investment.

Linking BOI Incentives with Warehouse DX

Thailand’s BOI (Board of Investment) continuously expands its incentive schemes for investments that include automation, IoT, AI data analytics, and enterprise management IT. The overhaul of warehouse management systems and the development of data utilization infrastructure may qualify for BOI support.

The key is not to “think about BOI after the investment is decided,” but to “incorporate BOI applications from the investment planning stage.” By utilizing BOI benefits (corporate tax exemptions, exemptions from machinery import duties, etc.), you can shorten the ROI payback period for the same investment amount.

When linking warehouse improvement to a BOI application, incorporating quantitative performance indicators into the investment plan — such as “increase in automation ratio,” “improvement in productivity metrics (picks per man-hour),” and “development of digital data records through digitization” — will be key points in the application review. For the latest BOI information, we recommend regularly checking the official Thailand BOI website and JETRO Bangkok’s publications.

Phased Implementation: Starting the Improvement Cycle with One Warehouse

Warehouse layout improvement does not need to be rolled out company-wide all at once. Starting with one warehouse or one area, measuring the results, and then expanding horizontally is an effective approach from the perspective of sustainable adoption in Thai operations.

The recommended phased implementation steps are as follows.

Step 1 (1–2 months): Collecting and visualizing current-state data
Aggregate SKU-level picking frequency from shipment data. Identify patterns in mis-shipment records. Estimate current travel distances. Create a state where “what the problems are” can be shared in numbers.
Step 2 (2–3 months): Conducting ABC analysis and developing a location revision plan
Perform ABC classification based on data. Clarify gaps with current locations. Confirm the balance between relocation costs (effort to move shelves) and improvement benefit. Decide on priority areas and build a plan.
Step 3 (1–2 months): Trialing layout changes in a pilot area and measuring results
Trial changes with a limited scope of impact, such as consolidating A-rank items. Compare travel distance, picking time, and mis-shipment rate before and after the change. Collect feedback from on-site staff.
Step 4 (ongoing): Rolling out changes across the facility and embedding PDCA
Extend the changes from proven areas to other areas. Systematize periodic ABC analysis updates in response to seasonal variation and SKU changes. Incorporate data reviews into monthly or quarterly operations.

A critical element in running this cycle is preparing “the language to communicate improvement results to Japan headquarters.” On-site efficiency improvements in Thailand need to be communicated to headquarters in the language of “cost reduction,” “reduction in complaint count,” and “improvement in inventory accuracy.” With data in hand, this translation is not difficult.

Failure Patterns and How to Avoid Them

There are failure patterns that companies working on warehouse improvement frequently fall into. Recognizing them in advance helps you avoid the same mistakes.

Failure 1: Shelves Were Reorganized but Nobody Knows the New Layout

A case where a layout change is implemented but local staff are not adequately informed, leading to a situation where workers wander searching for items in their old locations. Countermeasures include posting the updated location map on the shop floor, simultaneously updating location master data in the WMS, and conducting follow-up training for a period after the change.

Failure 2: Post-Improvement Data Is Not Followed Up

A case where, after a layout change, no measurement of results is conducted and the change is considered “complete.” The effect of a location change needs to be confirmed with data from 1–3 months after the change. Without a mechanism for regular monitoring, improvement cannot be sustained.

Failure 3: Leaving Personalized “Unofficial Rules” in Place

A case where, separate from the official location system, on-site staff have created their own “temporary staging areas” or “personal storage zones.” These unofficial rules are not reflected in the data, reducing the accuracy of ABC analysis. It is important to conduct physical verification during inventory counts and regularly reconcile gaps between the system and physical reality. Implementing an inventory management system and improving stocktake accuracy lead to the fundamental resolution of this problem.

Failure 4: Top-Down Changes That Ignore the Shop Floor

A case where Japanese managers implement large-scale layout changes without explanation to on-site staff, causing confusion and resistance on the floor. Particularly in Thai operations, it is important to carefully explain “why” changes are being made, and to run the process in a way that gives local staff a sense of participating in the improvement. If data can be used to explain “the numbers show the current situation is like this, so we’re making this change,” it becomes much easier to gain staff buy-in.

TOMAS TECH’s Perspective

TOMAS TECH supports the utilization of on-site data at Japanese companies’ logistics, manufacturing, and sales operations in Thailand and ASEAN through a range of solutions. In the context of warehouse layout improvement, TOMAS TECH contributes to on-site challenges in the following ways.

Inventory Management System PEGASUS
Centrally manages inventory receiving, shipping, location management, and stocktaking in a single system. Supports real-time visibility of “which items are where,” and builds the data infrastructure for ABC analysis from outbound history. By improving warehouse inventory accuracy, the impact of location changes based on ABC analysis is maximized. Migration from on-site Excel or paper management is also possible through a phased implementation approach.

i-Reporter (Paperless Operations)
Replaces paper forms in the warehouse (receiving inspection records, picking lists, shipping confirmation sheets, etc.) with digital forms. Since the entered data can be used directly for analysis, it eliminates the “double entry burden” that comes with digitizing paper-based records. Forms can also be created in both Thai and Japanese, which has the added effect of bridging the Japan-Thailand communication gap.

Operations Monitoring System
Tracks the operational status of warehouse equipment (forklifts, conveyance equipment, automated lines) in real time. By identifying the time periods and areas with low equipment utilization, it provides information for pinpointing layout change priorities and supporting equipment investment decisions.

Smartwatch System
Collects worker movement paths and work time via wearable devices. Can be applied to obtain actual measured values of travel distance, and can also be used to visualize imbalances in workload, supporting optimization of shift design and location allocation.

The hallmark of TOMAS TECH’s approach is a small improvement cycle that starts with “one metric, one process, one warehouse.” Rather than assuming a large-scale system overhaul, the process begins with visualizing challenges from current-state data and implementing in a form where results can be measured with minimal investment. Support is also available for creating explanation materials for Japan headquarters and for aligning implementation with BOI applications, tailored to the realities of the local operation.

If you have questions about the first steps in warehouse improvement, please feel free to contact TOMAS TECH.
https://tomastc.com/contact

Summary

Warehouse layout improvement is an entry point for operational improvement that can be started without large-scale investment. By using three metrics — travel distance, picking frequency, and mis-shipment rate — as the starting point and advancing location optimization through ABC analysis, both operational efficiency and shipping quality can be improved simultaneously.

In Thailand’s 2026 economic environment, rising costs, difficulty securing talent, and rising customer quality demands are progressing simultaneously. Maintaining competitiveness in this environment requires the operational capability to “find and reduce the small daily losses through data.”

Here is a summary of the key points.

Start by creating a mechanism to collect data (WMS, barcodes, digital forms)
Use ABC analysis to consolidate A-rank items in locations near the shipping dock
Identify patterns in mis-shipments to determine whether the cause is “layout-driven” or “procedure-driven”
Start with one area or one warehouse, measure results, then expand horizontally
Communicate improvement results to Japan headquarters in the language of “cost reduction, complaint reduction, and payback period”
Incorporate BOI applications from the early stages of investment planning to maximize incentives

The unique challenges of Thai warehouse operations — the Japan-Thailand reporting and communication gap, work procedures that depend on individual knowledge, and the complexity of high-mix/low-volume fulfillment — can also be overcome through data visualization and a phased improvement cycle. The transition from “an operation that runs on experience and instinct” to “an operation that data speaks for” cannot be achieved overnight, but it can be advanced steadily, one small step at a time.