Unlocking Kaizen Costing: Methods, Classifications, and AI‑Driven Strategies for Sustainable Cost Reduction
‘Kaizen’ comprises two Japanese words: Kai (change) and Zen (for the better), together meaning continuous improvement. The term Kaizen is about consistent improvement that assists with making long-term progress and doesn’t require enormous ventures. It is additionally significant for the executives and collaborators to take an interest in the progress.
In today’s rapidly evolving industrial landscape, organizations are under constant pressure to reduce costs, improve efficiency, and enhance productivity. Kaizen Costing, with its foundation in continuous incremental improvement, has long served as a strategic tool for sustainable cost reduction and operational excellence. However, with the advent of advanced technologies, particularly Artificial Intelligence (AI), there is a growing opportunity to enhance the effectiveness of traditional Kaizen practices. AI helps reduce process time, automate repetitive tasks, and streamline operations through data-driven decision-making.
When integrated with Kaizen, AI not only strengthens the pace and accuracy of continuous improvement but also unlocks new potential for real-time monitoring, predictive analysis, and smart optimization. This powerful combination offers organizations the dual advantage of human-centric innovation and intelligent automation, driving faster development, improved production quality, and significant cost savings. This article explores this evolving synergy, presenting a structured review of Kaizen Costing methods and examining how AI integration is reshaping its role in modern cost management.
Sub-Classification of Kaizen Costing
To better understand the ideas of Kaizen/Continuous Improvement, this section provides brief explanations of the following four classifications.
The Concept of Kanban
The Concept of Suggestion System
The Concept of Total Quality Control (TQC)
The Concept of Zero Defects (ZD)
The Concept of Six Sigma
The Concept of Total Quality Management (TQM)
I. The Kaizen’s Objective Category
i. The Concept of Productivity
Productivity, as defined by Tangen (2002), is the ratio of output to input in manufacturing, reflecting how efficiently resources like labour, capital, materials, and energy are used. It’s often misunderstood as mere production volume; however, true productivity requires consideration of both outputs and inputs. It is relative and is evaluated through comparisons over time or against competitors.
Productivity improves in five ways, such as by increasing output more than input or by reducing input while maintaining the same level of output.
Over time, productivity has been confused with performance, efficiency, and effectiveness. Performance includes factors like cost and quality, efficiency means “doing things right” with minimal resources, and effectiveness means “doing the right things” to create customer value. High productivity results from a combination of both efficiency and effectiveness.
The Kaizen philosophy of continuous improvement supports productivity growth by enhancing efficiency and effectiveness, especially at the shop-floor level, through incremental gains and better resource utilization. Waste reduces productivity and should be systematically eliminated to achieve sustainable improvement.
The integration of Artificial Intelligence (AI) into the Kaizen framework significantly enhances its core objective of continuous improvement. AI transforms traditional manual systems into smart, proactive processes that can detect inefficiencies, reduce waste, and drive sustainable productivity. With the use of advanced tools such as sensors, IoT devices, and AI-based analytics, organizations can now monitor operations in real time, gaining deeper visibility into performance metrics. Through predictive analytics and machine learning, AI can anticipate potential issues before they occur, enabling timely, preventive actions. Additionally, AI systems are capable of processing large volumes of data, recommending optimal solutions, and helping prioritize improvement initiatives. This not only reduces the cognitive load on human decision-makers but also ensures more consistent, accurate, and data-driven strategies to support ongoing operational excellence.
The integration of Artificial Intelligence (AI) into the Kaizen framework significantly enhances its core objective of continuous improvement. AI transforms traditional manual systems into smart, proactive processes that can detect inefficiencies, reduce waste, and drive sustainable productivity
II. The Kaizen’s Result Category
This category comprises two key concepts: Just-In-Time (JIT) and the Kanban system. These are often regarded as outcomes or by-products of Kaizen initiatives implemented during the initial stages of continuous improvement.
i. The Concept of Just-In-Time
According to Imai (1986), Just-In-Time (JIT) ensures that each stage of production receives the exact number of units required at the right time. Ohno’s system at Toyota reduced inventory by reversing the traditional supply flow. Shingo (1981) further linked JIT to minimize the time between order and delivery through small-lot production, faster tool changes, and one-piece flow. Ishikawa and Lu (1985) emphasized that quality control is vital for JIT success, as poor quality disrupts the flow of inventory. Kaizen supports JIT by helping suppliers deliver quality products on time. JIT is also closely tied to the Kanban system (Monden, 1983), which helps synchronize production.
ii. The Concept of Kanban
According to Imai (1986), Kanban is a communication tool within the JIT production and inventory control system developed by Taiichi Ohno at Toyota Motor Corporation. A Kanban, or signboard, is attached to specific parts in the production line, signifying the delivery of a given quantity. The concept of the Kanban system was inspired by the supermarket system, as noted by Shingo (1981).
Shingo (1981) and Imai (1986) further observed that Kanban coordinates the inflow of parts and components to the assembly line, minimizes process delays and enables rapid throughput. For example, an engine block brought into the plant in the morning can be assembled into a completed automobile by evening. However, the Kanban system cannot be effectively implemented in isolation; it must operate alongside other TQC components as part of an integrated production system.
According to Gross and McInnis (2003), the benefits of Kanban can become a driver for creating a culture of continuous process improvement. They also offered the Kanban implementation method, which enables management to assess the existing state of the business, its goals, and the best way to get there.
The integration of AI allows JIT systems to leverage real-time data and advanced demand forecasting, enabling them to respond instantly to changing market conditions. In the context of Kanban, AI enhances efficiency by intelligently monitoring workflow signals and automatically regulating the number of work-in-progress (WIP) items. It adjusts task flow based on real-time capacity and demand patterns, an otherwise complex task to handle manually. As a result, organizations benefit from faster production cycles, reduced bottlenecks, and more efficient resource utilization, all of which reinforce Kaizen’s core principle of creating lean, adaptable, and continuously improving processes.
III. The Kaizen’s Main Function Category
Under this category, there are two key concepts: Quality Control Circles and the Suggestion Sheet System. The details of these concepts are discussed below.
i. The Concept of Quality Control Circles (QC Circles, QCC)
- Small Groups for Quality Improvement: QCCs are small, voluntary groups of frontline workers focused on continuously improving quality in products, services, and processes.
- Ideal Group Size: An effective QCC typically consists of around five members to allow better interaction and teamwork.
- Same Workshop Participation: Members usually belong to the same workshop or department, which enables them to address relevant, shared problems through regular communication.
- Based on PDCA Cycle: QCCs perform quality control tasks using the PDCA (Plan-Do-Check-Act) method, aiming at continuous process improvement rather than supervision.
- Voluntary Participation (Jishusei): Activities are self-initiated, internally motivated, and go beyond regular job responsibilities without formal compulsion.
- Part of Company-Wide QC: QCCs align with broader organizational quality goals and are supported by management to ensure strategic improvement.
- Personal and Social Development: Participation in QCCs helps members grow through skill development, teamwork, and increased engagement.
- Data-Driven Methods: QCCs rely on Statistical Quality Control (SQC) tools and logical analysis, avoiding decisions based on mere opinions or feelings.
- Workshop-Focused Issues Only: The topics tackled must relate directly to the circle’s work area and not to broader organizational or labour matters.
- Continuous Activity: QCCs are expected to operate continuously, regardless of staffing changes, supporting the Kaizen principle of ongoing improvement.
- Universal Staff Participation: All employees should be involved, promoting collective responsibility for quality across all organizational levels.
ii. The Concept of Suggestion System
According to Lillrank and Kano (1989), the suggestion system is the bottom-up channel through which improvement ideas and proposals are presented to management. Fundamentally, the suggestion system is unrelated to QCC activities. It is frequently used before circular activity, even in businesses without QCCs. The suggestion system can serve as a systematic tool within the QCC process to generate and refine workers’ ideas by integrating closely with QCC activities. Imai (1986) proposed that the suggestion system is an integral part of individual-oriented Kaizen. Additionally, when QCCs are viewed collectively as a group-oriented system of improvement suggestions, their role and function become more clearly understood.
The suggestion system was historically introduced to Japan by TWI (Training Within Industries) and the U.S. Air Force following the conclusion of World War II. A Japanese-style suggestion system replaced the American-style approach.
The suggestion sheet is commonly used as the primary medium through which employees communicate their ideas to management within the suggestion system. When they discover difficulties with their working procedures, employees can document them on the suggestion sheet form, develop possible remedies, and propose them to their supervisors. Direct supervisors typically review these suggestions, assessing their economic value and feasibility for implementation. Following their approval, supervisors will present the employees’ ideas to management for implementation consideration. The ideas made by the employee will be carried out if the approvals are given.
To build the appropriate mindset and sustain momentum for suggestion activities, supervisors play a crucial role in sharing successful cases as best practices, encouraging wider employee participation. About half of small and medium-sized businesses and the majority of large manufacturing organisations use suggestion systems as part of their Kaizen programmes. According to Imai (1986: 112), common areas for suggestions in Japanese companies include improvements in work methods, working environments, machinery and processes, jigs and tools, office operations, product quality, and customer service.
AI improves the way data is collected and analyzed by offering real-time insights, visualizing trends, and identifying root causes of problems. This helps Quality Control Circles (QCCs) make quicker and better decisions. It also allows teams from different departments or locations to work together more easily through AI-powered dashboards, removing the barriers of time and place. In the case of suggestion systems, AI can automatically gather, sort, and analyze employee ideas using natural language processing (NLP). It not only helps prioritize the most useful suggestions but also spots patterns and recommends practical actions. This makes the entire process faster, more efficient, and more impactful.
In the case of suggestion systems, AI can automatically gather, sort, and analyze employee ideas using natural language processing (NLP). It not only helps prioritize the most useful suggestions but also spots patterns and recommends practical actions.
IV. The Kaizen’s Extension Category
Under this category, there are five key concepts: TPM, TQC, ZD, Six Sigma, and TQM. The details of these concepts are discussed below.
i. Concept of Total Productive Maintenance (TPM)
According to Imai (1986: xxv), Total Productive Maintenance aims at maximizing equipment effectiveness throughout its entire life cycle. TPM is now used at a sizable number of Japanese manufacturing organisations, and is strongly promoted by the Japan Institute of Plant Maintenance, although it is less well known outside Japan as compared to TQC.
While TPM is focused on equipment improvements, TQC’s primary goal is to raise overall management quality. TQC is more focused on software, whereas TPM is more focused on hardware. Like TQC, training is a crucial component of TPM, with emphasis placed on fundamental knowledge such as machine operations and maintenance practices at the shop-floor level.
Just as organizations excelling in TQC are recognized through awards such as the Deming Prize and the Japan Quality Control Prize, the Japan Institute of Plant Maintenance honors successful TPM implementation through the PM (Plant Maintenance) Distinguished Plant Award and other recognitions.
AI improves TPM by using predictive maintenance systems powered by IoT sensors and machine learning. These tools can forecast equipment failures before they occur, reducing downtime and improving machine reliability, perfectly aligning with TPM’s goal of maximizing equipment effectiveness.
ii. The Concept of Total Quality Control (TQC)
The concept of quality has evolved from a narrow production focus to a comprehensive management philosophy valued at all organizational levels. The most critical factor is customer satisfaction, which directly influences company profits. The idea is simple: happy customers lead to business success.
A TQC (Total Quality Control) manager, it can be argued, is more concerned with customer complaints than with stock prices or return on assets. TQC covers not just product quality, but also manufacturing processes, delivery, customer support, planning, and internal practices. In this context, quality in Japan aligns with the Western idea of “excellence.”
Despite much discussion, the Japanese quality movement has not reached a uniform definition of quality. Therefore, each company promotes TQC based on its unique conditions, competitive situation, and top management preferences. No single pattern fits all; TQC evolves through trial and error. It is guided by principles and tools, without which it would be a mere spiritual idea. Both management and shop-floor operations participate in TQC, with QCC (Quality Control Circles) and PDCA cycles being key elements.
Most firms using TQC also implement QCCs, which are often seen as a foundation for broader TQC efforts. According to Feigenbaum, quality control must be part of a system for development, maintenance, and improvement, and should be defined by the customer.
Feigenbaum stressed that if data from QC tools, like control charts and sampling, aren’t used in decision-making, they do not guarantee quality. TQC is a decision-making framework combining data processing with managerial actions. He cautioned that if quality is everyone’s responsibility, it could end up being no one’s responsibility.
From a Western perspective, TQC has shifted quality from an operational to a strategic concern, gaining importance as top management becomes involved. If quality is seen only as an engineering issue, it will be ignored by top leaders. However, in competitive markets, defining product features has become essential. Related concepts like productivity, turnaround time, responsiveness, and operational effectiveness are now central to strategic thinking. Innovations like JIT (Just-in-Time) have redefined competition by boosting efficiency and quality.
iii. The Concept of Zero Defects (ZD)
According to Calvin (1983), Zero Defects (ZD) means producing products that perform flawlessly in the field, with zero operational failures, not necessarily zero flaws. Flaws may exist, but must not cause failures. Achieving zero faults requires “designing it right the first time” and ensuring specifications support performance, reliability, and manufacturability. Statistical methods like process capacity studies and design of experiments help, but traditional tools like control charts and sampling need re-evaluation for near-zero failure levels. Both Kaizen and Zero Defects aim to reduce defects, emphasizing continuous improvement and process excellence to minimize product failures.
AI enhances TQC and ZD by enabling real-time quality monitoring using computer vision and deep learning. Defects can be detected automatically during production, and root causes can be identified quickly, helping maintain high-quality standards and achieve zero-defect goals.
iv. The Concept of Six Sigma
Imai (1986) did not discuss the relationship between Kaizen and Six Sigma, as Six Sigma emerged as a management concept at a later stage. According to Klefsjö et al. (2001), sigma is a statistical measure of process variation, commonly referred to as the standard deviation. The term Six Sigma generally implies the occurrence of defects at a rate of 3.4 defects per million opportunities (DPMO).
The sigma value indicates how often defects are likely to occur; however, according to Hahn et al (1999) and Linderman et al (2003), Six Sigma has not been carefully defined in either the practitioner or academic literature. Six Sigma uses unique metrics, including Process Sigma measurements, critical-to-quality metrics, defect measures and 10× improvement measures (Hahn et al., 1999; Harry, 1998; Hoerl, 1998). Whatever method is chosen, however, it is essential that the technique is carefully followed, and a solution should not be offered until the problem is clearly defined. Common features of Six Sigma programmes include a top-down implementation approach, a highly disciplined methodology, and a data-driven framework that makes extensive use of statistical decision-making tools. These programmes typically follow the DMAIC cycle—Measure, Analyse, Improve, and Control—to achieve sustainable process improvement.
Six Sigma is supported by AI techniques like data mining and statistical learning algorithms, which enhance the Define-Measure-Analyse-Improve-Control (DMAIC) procedure. AI makes Six Sigma projects quicker and more accurate by accelerating data collection, identifying hidden patterns, and suggesting process improvements.
v. The Concept of Total Quality Management (TQM)
Even Total Quality Management was not mentioned by Imai (1986); however, when analysing the components and definition of this concept, we found the similarity between the concept of Kaizen and the idea of TQM. Therefore, this research provided some basic idea of the concept of Total Quality Management (TQM). Powell (1995, 16) further noted that the TQM must be capable of having a mentality of zero defects. Instead of having to check and redo the task, it needs to be able to detect defects as they happen. When comparing the TQM and Kaizen philosophies, TQM’s core idea might be considered as Kaizen. When businesses prioritise the fundamentals of Kaizen from the start, TQM implementation may produce more significant results.
AI facilitates organization-wide quality improvement within the larger context of TQM by means of continuous feedback loops, real-time dashboards, and automated insights. It supports long-term quality excellence by enabling teams and management to make data-driven decisions and rapidly monitor performance metrics.
Statistical methods like process capacity studies and design of experiments help, but traditional tools like control charts and sampling need re-evaluation for near-zero failure levels. Both Kaizen and Zero Defects aim to reduce defects, emphasizing continuous improvement and process excellence to minimize product failures.
Conclusion
To sum up, the continuous improvement concept of Kaizen is still an essential tactic for attaining long-term cost effectiveness and operational excellence. The integration of AI into Kaizen Costing amplifies its impact by enabling real-time monitoring, predictive analytics, and process automation. Without making major expenditures, this synergy enables firms to realize considerable cost reductions, improved quality, and speedier advancements. As industries continue to evolve, leveraging both human-driven innovation and AI-driven intelligence will be key to maintaining competitiveness. In the end, the combination of AI with Kaizen is a revolutionary strategy for contemporary cost control and sustained company performance.
Reference
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