Preparing for High-Mix Low-Volume Production in Thailand’s Food Industry: System Design for Linking Production Planning and Inventory Management

Target Audience: Plant managers, site managers, manufacturing department heads, and administrative leaders at Japanese-owned food manufacturing and processing facilities in Thailand and ASEAN. This article is intended for those considering a shift to high-mix low-volume production, or those already in the process but facing challenges in aligning production planning with inventory management.

Thailand’s food manufacturing industry has reached a major inflection point in recent years. The shift from the traditional high-volume, low-variety model to a high-mix, low-volume model — driven by diversifying consumer needs, premiumization, and growing health consciousness — has become a real management challenge for Japanese-owned food factories in Thailand. As the product lineup expands and lot sizes shrink, the complexity of production planning increases, and managing raw material, work-in-process, and finished goods inventory becomes increasingly difficult.

Furthermore, the Thai business environment in 2026 calls for “selective investment optimization.” The World Bank’s cautious outlook on Thailand’s 2026 economic growth, combined with persistently high logistics and energy costs and rising labor costs, means that relying solely on revenue growth is no longer a viable strategy. In this environment, the most reliable path to profitability improvement is to steadily reduce the small daily losses — food waste, temperature deviations, lot mix-ups, delayed inspection records, and inventory discrepancies — that occur every single day.

This article explains, from a frontline perspective, the concept of “system design that links production planning and inventory” that Thai food factories need in order to adapt to high-mix, low-volume production. We address how making quality, temperature, lot traceability, and yield visible connects to food loss reduction and risk mitigation, where to start, and how to explain the business case to headquarters in Japan.

1. What Is Happening in Thailand’s Food Industry: The Reality of High-Mix, Low-Volume Production

On the floor of food manufacturing plants in Thailand, many facilities have seen their SKU (stock-keeping unit) counts increase significantly in recent years. When the number of items handled by a single factory increases, line changeover frequency rises, cleaning and changeover time increases, and the variety and storage conditions of raw materials become more diverse.

The key challenges that Japanese food manufacturers face at their Thailand operations include the following:

As the number of SKUs grows, the cognitive burden on production planners increases (reliance on experience and intuition)
It becomes harder to synchronize raw material procurement lead times with production schedules
Lot management becomes more complex, with varying use-by dates and storage conditions coexisting
Production actuals and inventory figures do not match (it takes time to identify the cause of discrepancies)
Yield data is recorded only on paper or in Excel, making trend analysis impossible
Temperature control records are handwritten, making it laborious to trace and report when an anomaly occurs

These challenges indicate that shop floor management practices that “got by when there were fewer SKUs” have not kept up with the high-mix, low-volume model. The transition from person-dependent management to data-driven management will determine competitiveness going forward.

2. The Cost of Production Planning and Inventory Being “Disconnected”

When production planning and inventory management are not integrated, hidden costs accumulate. The following examples represent patterns commonly seen in Thai food factories.

Simultaneous Excess Inventory and Stockouts: When planning accuracy is low, one raw material ends up in surplus while another falls short and halts production. Excess inventory raises the risk of expiration, while stockouts generate opportunity losses and emergency procurement costs.

Invisible Disposal Losses: In food manufacturing, yield losses during the production process and disposal of work-in-process and finished goods occur on a daily basis. However, if these are not immediately reflected in cost figures, monthly financial closing arrives without knowing “which product generated how much waste.”

Reactive Response to Temperature Deviations and Quality Issues: When temperature control records are handwritten and not centrally managed, identifying “when, where, and which lot was affected” after a deviation occurs is delayed, and decisions on complaint response and voluntary recalls are made after the fact.

Delayed Aggregation of Production Actuals: When daily reports are compiled on paper or in Excel, production actuals are only available the next day or the following week, causing the factory to miss the window for plan adjustments. In high-mix, low-volume production, whether the day’s actuals can be reflected on the same day directly determines the accuracy of the next day’s plan.

3. The Four Axes of Visibility: Quality, Temperature, Lot, and Yield

“Visibility” in food manufacturing does not mean digitizing everything. What matters is focusing on the axes that directly impact profitability and risk. The four axes that TOMAS TECH prioritizes in food factory operations are as follows.

① Visibility of Quality Records

Inspection records, sensory evaluations, and foreign matter contamination checks are digitized from paper and managed in association with products and lots. Creating a state in which, when a quality issue occurs, it is possible to trace back to “which lot, which process, and which operator” forms the foundation for complaint response and recurrence prevention. Paperless tools such as i-Reporter directly address the digitization of these types of on-site forms.

② Visibility of Temperature Management

Temperature management in refrigerated and frozen warehouses, temperature-controlled zones on production lines, and in-transit temperature records are automated and centrally managed. With an alert function linked to IoT sensors, temperature deviations can be detected instantly and the responsible staff notified. Deviations that are easy to miss with handwritten records can be caught early, and disposal decisions can be made more quickly.

③ Visibility of Lot Management

Lot traceability is managed consistently from raw material receiving lots through the production process to finished goods shipment. By making FIFO (first-in, first-out) compliance status, use-by and best-before date alerts, and lot-by-lot usage visible within the inventory management system, human errors and disposal losses are reduced. An inventory management system such as PEGASUS supports lot-level receiving and shipping management as well as remaining quantity tracking.

④ Visibility of Yield

Input and output quantities are recorded by product and by process, and yield rates are tracked on a daily and weekly basis. Analyzing whether yield declines correlate with specific raw material lots, operators, time slots, or equipment clarifies the priorities for improvement initiatives. By linking these figures with cost accounting, it becomes possible to see “which products are actually generating profit.”

4. Design Principles for Production Planning That Supports High-Mix, Low-Volume

In high-mix, low-volume production, the fundamental philosophy of production planning itself needs to be reconsidered. The “monthly plan → weekly adjustment” model that worked when there were fewer SKUs and larger lots cannot handle frequent line changeovers and short lead times.

Principle 1: Establish absorption layers for demand fluctuations. Analyze the sales history, ordering patterns, and seasonality for each product and set safety stock levels by SKU. Since uniform safety stock standards do not function effectively across a wide mix, differentiated management is effective: maintain a larger buffer for items with high demand variability, and set a thinner buffer for fast-moving, predictable items.

Principle 2: Link production planning and raw material inventory in real time. To prevent situations where “raw materials were available when the plan was made, but a shortage was discovered just before production,” a mechanism is needed whereby changes to the production plan are immediately reflected in inventory allocation. This is achieved through integration between the inventory management system and the production scheduler.

Principle 3: Factor changeover time into the plan as a cost. In high-mix production, the time required for changeovers (cleaning and setup) between products has a major impact on production capacity. Sequence planning (sequencing) that avoids combinations of products with long changeover times leads to a practical increase in production capacity. Digitally recorded production actuals are a prerequisite for accumulating and analyzing this data.

Principle 4: Feed production actuals back into the plan on the same day. Creating a mechanism in which the day’s yield actuals, equipment downtime, and material shortages are reflected in the next day’s plan enables continuous improvement in planning accuracy. This requires digitization of daily reports and automatic aggregation of actual performance data.

5. Food Industry-Specific Requirements for Inventory Management Systems

Inventory management in the food industry has unique requirements that differ from general manufacturing. The following points are important to verify when selecting and designing a system.

Requirement	Description	Risk if Not Met
Lot and expiration date management	Manage expiration dates and storage conditions by receiving lot, and enforce FIFO issuing	Disposal due to expiration, customer complaints from FIFO violations
Temperature zone-based inventory management	Differentiate ambient, refrigerated, and frozen warehouses and shelves, and manage inventory by temperature zone	Storage condition violations, quality issues
Yield rate recording and aggregation	Record input and output quantities by process and product, and automatically calculate yield	Inaccurate cost accounting, no basis for improvement initiatives
Traceability (forward and backward)	Consistently track raw material lot → production process → finished goods lot → shipping destination	Delayed complaint response, time-consuming identification of voluntary recall scope
Inventory allocation linked to production planning	Allocate inventory based on the production plan and automatically reconcile with actual issues	Overlooking raw material shortages at planning time
Multi-site and multi-warehouse management	Centrally manage multiple in-plant warehouses and external warehouses, and record inventory transfers	Difficulty identifying causes of inventory discrepancies

Whether a system covers these requirements and whether it can be implemented in a way that fits actual shop floor operations are critical evaluation criteria that determine the success or failure of the implementation.

6. The Economic Impact of Food Loss Reduction: Why Small Improvements Add Up

“Food loss reduction” is often discussed in the context of social issues, but from a factory management perspective it is purely cost reduction. Let us look at the impact that disposal losses have on manufacturing costs from a frontline perspective.

In food manufacturing, raw material costs generally account for a significant share of production cost. Even a few percentage points of improvement in yield adds up to substantial reductions in material costs. For example, if yield can be improved for a product with high monthly raw material input volume, the resulting difference flows directly into improved profit margins.

Disposal caused by temperature deviations carries not only the direct cost of the disposed goods but also secondary costs: sourcing replacements, revising the production plan, explaining the situation to customers, and loss of trust. If IoT-based temperature management can prevent this, the investment in IoT sensors can be recovered in a relatively short timeframe.

Customer complaints caused by inadequate lot management can escalate into costs for response man-hours, return logistics, remanufacturing, and in some cases compensation to trading partners. Calculating the man-hours spent responding to a single complaint makes the return on investment for a traceability system tangible.

Simulating these cumulative effects as “how much loss is eliminated over three years” and presenting the results to headquarters in Japan is a practical approach to obtaining investment approval.

7. What to Halt and What to Advance: Decision Criteria for the Food Industry

In the 2026 business environment, advancing all investments equally is difficult. Food factories, too, are required to clearly prioritize their investments.

Investments to Deprioritize

Large-scale, all-at-once ERP implementations with vague effectiveness measurement standards (scope too broad for the shop floor to keep pace)
Version upgrades only for systems with many unused features and high running costs
“Digitization for its own sake” — digitizing forms that are not directly connected to shop floor issues
Large-scale automation with ROI (return on investment) horizons exceeding three years (not suited to high-mix, low-volume with high demand variability)

Investments to Prioritize

Visibility of lot, temperature, and yield (directly linked to disposal loss reduction, with short recovery period)
Real-time linkage of production planning and inventory (improved planning accuracy, reduced material loss)
Digitization of daily reports and quality records (reduced aggregation time, foundation for data utilization)
IoT sensor-based temperature and equipment operation monitoring (preventive maintenance, temperature management compliance)
BOI-eligible data utilization, AI, and automation investments (cost reduction leveraging tax incentives)

The three decision criteria are: “Can it be recovered within three years?”, “Can the Thai shop floor staff continue to use it?”, and “Can it be explained to headquarters in Japan with numbers?”

8. Leveraging BOI for System Investment: Practical Points for the Food Industry

Thailand’s BOI (Board of Investment) offers preferential measures for investments including automation, AI, data analytics, and corporate management IT. Combining food factory system investments with BOI applications can potentially reduce the effective investment cost significantly.

The main preferential categories available to the food industry in BOI applications include those related to smart manufacturing, automation promotion, and digitization. Investments such as inventory management systems, paperless tools, IoT sensor systems, and equipment operation management systems can potentially fall under these categories.

However, BOI applications require an advance application and approval process; applying after the investment decision has already been made may be too late. It is important to factor BOI applications into the planning stage of system investment and to coordinate with specialists (BOI application consultants or legal advisors). Furthermore, receiving BOI preferential treatment requires submitting performance reports on the invested content, so maintaining proper records of system implementation is also necessary.

Failing to utilize BOI with the reasoning that “we might be able to use BOI, but the paperwork is troublesome” represents a significant opportunity loss. We recommend checking the latest BOI information on the Thailand BOI official website and consulting with local specialists in Thailand.

9. Failure Patterns and Countermeasures: The Reality of System Implementation at Thai Food Factories

Cases where system implementations failed to deliver expected results share common patterns. The following outlines representative patterns and countermeasures to avoid repeating the same mistakes.

Failure Pattern 1: Top-Down Implementation Without Involving the Shop Floor

A system was implemented at the direction of headquarters in Japan, but Thai staff could not understand how to use it, and ultimately the previous paper-based operations continued in parallel. Countermeasure: Before implementation, include key shop floor personnel (Thai team leaders) as project members, and prepare Thai-language manuals and training. Start with a small scope to create a “we can use this” experience before rolling it out more broadly.

Failure Pattern 2: Scope Too Large, Implementation Took Too Long

An attempt was made to digitize all processes, all warehouses, and all forms at once; requirements definition took six months, development took a year, and by the time it was complete, the shop floor situation had changed. Countermeasure: Start with one process, one warehouse, and one form, and get it running within three months. Adopt a phased rollout that moves to the next scope only after confirming the effects.

Failure Pattern 3: When the Responsible Person Changed, No One Could Use It Anymore

A Japanese expatriate who was well-versed in the system returned to Japan, and the handover was insufficient, leaving Thai staff unable to operate it on their own. Countermeasure: Design a system from the outset where Thai staff can operate it independently. Include Thai-language operation manuals, regular training, and the establishment of a local support framework as mandatory requirements.

Failure Pattern 4: Data Was Collected but Never Used for Decision-Making

Data began flowing in from IoT sensors and digital daily reports, but no one analyzed it, and it ended with “just looking at the dashboard.” Countermeasure: Decide before implementation “who will look at this data, when, and to make what decisions.” Design a flow in which report recipients (plant managers, management department) actually utilize the data, and incorporate it into the monthly review.

10. A Phased Implementation Roadmap: Where to Start and How to Expand

For system implementation at Thai food factories, the “start small and ensure firm adoption” approach improves the success rate more than “all-at-once total optimization.” The following roadmap represents an example of how TOMAS TECH considers effective progression in the field.

Phase	Approximate Duration	Target Scope	Key Activities	Target Outcomes
Phase 1: Current State Assessment	1–2 months	1–2 key products, main warehouse	Begin manual recording of yield and disposal. Analyze causes of current inventory discrepancies	Visualize loss amounts. Collect data to justify investment
Phase 2: Foundation Building	2–3 months	Pilot implementation of inventory management system and daily report digitization	Digitize lot-based receiving and shipping management. Digitize quality and daily report forms (i-Reporter, etc.)	Reduce inventory discrepancies. Reduce aggregation man-hours
Phase 3: Integration and Automation	3–6 months	Link production planning and inventory. IoT temperature monitoring	Automate inventory allocation from production planning. Real-time monitoring and alerts via temperature sensors	Improved planning accuracy. Reduction of disposal due to temperature deviations
Phase 4: Analysis and Improvement	Ongoing	Rollout to all products and all processes	Routine yield analysis. Integration with equipment operation management. Routine KPI reporting	Continuous cost improvement. Automated regular reporting to headquarters in Japan

The key point of this roadmap is to confirm “measurable effects” at each phase before proceeding to the next. If the monetary value of disposal losses becomes visible in Phase 1, it provides the justification for Phase 2 investment. If inventory discrepancies decrease in Phase 2, the ROI calculations for Phase 3 carry greater conviction.

11. Explaining to Headquarters in Japan: Three Axes for Obtaining Approval

Even when the need for system implementation is clearly felt at the Thailand site, obtaining approval from headquarters in Japan is often difficult. Explanations framed as “it will be more convenient” or “we can digitize” are often not approved; it is more effective to structure the explanation around the following three axes.

Axis 1: Three-Year Investment Recovery Simulation

Calculate “how much disposal loss, aggregation man-hours, complaint response, and inventory discrepancies will be reduced by implementing this system, and how much will be saved annually.” Even with conservative estimates, demonstrating that the investment can be recovered within three years lowers the approval threshold. The current loss amount data collected in Phase 1 serves as the basis for this calculation.

Axis 2: Quantification of Risk Reduction

Calculate the cost of a temperature deviation, lot mix-up, or customer complaint occurrence (disposal, response man-hours, loss of trust), and compare “the cost assuming this risk occurs once per year” against “the investment amount for a system that prevents it.” The risk reduction perspective is a compelling angle for headquarters that are demanding quality management improvements.

Axis 3: Reduction of Management Time and Improvement of Information Quality

Calculate the time that Japanese expatriates and the management department spend on monthly and weekly aggregation and report preparation. If digitization reduces aggregation man-hours by “3 hours per week × 12 months,” that translates to a specific monetary amount in labor cost terms. The perspective that real-time data reaching headquarters in Japan improves the quality of reporting and the speed of decision-making is also effective.

12. Japan–Thailand Communication Challenges and the Role of Systems

One of the challenges felt by Japanese expatriates working at Thai food factories is the inability to grasp the current state of the shop floor in real time. Due to linguistic and cultural differences with Thai staff, it is not uncommon for problems to be reported late — or for issues to be reported as “no problem” when there actually was a problem.

If a mechanism exists whereby the situation is automatically made visible through data, the management style shifts from “waiting for reports” to “checking the data.” When a temperature alert sounds, anyone can verify it; when an inventory quantity discrepancy is automatically detected, hidden problems become easier to surface.

Additionally, when monthly reports to headquarters in Japan shift from manual aggregation to automated output, the burden on expatriates preparing reports decreases and content accuracy improves. Being freed from “management for the sake of management” allows expatriates to focus on their core duties: shop floor improvement, talent development, and customer relationship management.

13. TOMAS TECH’s Perspective: How We Approach Shop Floor Challenges in Food Factories

TOMAS TECH supports Japanese manufacturers and food factories in Thailand and the ASEAN region with IT system implementations that fit the realities of their operations. For the challenges of high-mix low-volume production, food loss reduction, and quality management improvement, the following solutions have contributed in actual field deployments.

PEGASUS (Inventory Management System): Supports inventory management by lot, temperature zone, and warehouse, and realizes allocation linkage with production planning, FIFO management, and best-before date alerts. It covers the unique inventory management requirements of the food industry and has an implementation track record in food factories in Thailand. As high-mix, low-volume production advances, it directly contributes to improving lot tracking accuracy and reducing inventory discrepancies.

i-Reporter (Paperless Application): Digitizes the various forms that arise on the shop floor — quality inspection records, daily reports, temperature logs, inspection forms — using tablets and smartphones. Because the layout of existing paper forms can be digitized with minimal changes, the learning burden on shop floor staff is low and adoption rates are high. Recorded data can be automatically aggregated and exported, and utilized for yield analysis and quality trend monitoring.

Equipment Operation Management System: Records and visualizes the operating and stopped states of production lines in real time. By classifying and aggregating the causes of downtime, the system enables identification of yield degradation trends attributable to equipment issues and unplanned stoppages, and provides data-based justification for changeover time improvements.

Smartwatch System: Enables alert notifications and task instruction reception without requiring shop floor staff to free their hands, and can be utilized for immediate response to temperature deviations and equipment anomalies. Effective for information transmission in environments where operating a smartphone is difficult, such as refrigerated and frozen areas.

TOMAS TECH’s recommended approach is to first listen to the shop floor challenges, start with small units — “one process, one warehouse, one form” — and expand laterally while confirming results. Rather than selling aggressively, we engage as a partner that verifies together “whether the investment can be recovered within three years” and “whether Thai staff can operate it independently.”

If you would like to consult about system design for your food factory, please contact us through the TOMAS TECH contact page.

Summary

Adapting to high-mix, low-volume production in Thailand’s food industry is becoming difficult to sustain competitively without system design centered on the integration of production planning and inventory management. The key points covered in this article are summarized below.

The four axes of visibility (quality, temperature, lot, yield) form the starting point for food loss reduction and risk mitigation.
Real-time linkage of production planning and inventory is the key to improving planning accuracy and reducing material loss in the high-mix, low-volume model.
Food industry-specific inventory management requirements (lot, temperature, yield, traceability) are an important selection criterion — verify whether the system covers them.
Phased implementation (start with one process and one warehouse, get it running within three months, and expand after confirming results) improves success rates.
Explanations to headquarters in Japan should be structured around three axes: “three-year recovery simulation,” “quantification of risk reduction,” and “reduction of management time.”
BOI preferential treatment factored in from the planning stage can potentially reduce the effective investment cost.
Failure patterns (not involving the shop floor, scope too large, person-dependent, not using data) must be designed against in advance.

Not DX as a buzzword, but “DX that changes the numbers on the shop floor” — this is what leads to profitability improvement and sustainable growth for Thai food factories. Start with a small first step: use data to understand the current state and build the basis for investment.