Review of seru production

Abstract

Seru production is regarded as a new production mode and derived from the production site of Japanese electronics industry. This production mode is proposed to overcome the low flexibility of the assembly line. Seru production has been successfully implemented in Japanese electronics industry, such as Canon and Sony. Benefits from Seru production include rapid response, good flexibility, and high productivity. Seru production has received extensive attention in academic research and production practice. This study reviews the background, characteristics, types, and operation of seru production. The advantages and applicable scenes of seru production are summarized from the perspective of business practice. We compare seru production and famous production modes, i.e., assembly line, cellular manufacturing, and Toyota Production System. The literature on seru production is surveyed and classified. Furthermore, future research directions are provided.

Keywords

seru production / production mode / flexibility / literature review

Introduction

With the dynamic market demands, companies must respond rapidly to the market demands and provide diversified and personalized products. The need for high efficiency and flexibility in production systems has promoted the growth of management science, operation management, and industrial engineering. Current well-known production modes are the scientific management principles developed by legendary engineer Frederick W. Taylor, mass production (assembly line) represented by Ford Production System, Toyota Production System (TPS) developed by Toyota Motor Corporation of Japan, and cellular manufacturing (CM) based on group technology (GT). However, assembly line and TPS cannot work efficiently due to high value-added products, dynamic demands, short life cycle, high variety, low volumes, and variable volumes. Japanese companies, such as Sony and Canon, developed a Japanese CM to meet the market demands. They call this mode seru production to distinguish it from the traditional CM (Yin et al., 2008; ^{Stecke et al., 2012}; ^{Yin et al., 2017}).

Seru production has been widely used in Japanese electronics industry, such as Canon, Sony, Panasonic (Matsushita), Fujitsu, NEC, and Hitachi, has achieved great actual effects (^{Sakazume, 2005}; ²⁰⁰⁶; ^{Yin et al., 2017}), and has received extensive attention (Yin et al., 2008; ^{Stecke et al., 2012}). Several Japanese monographs have introduced the characteristics and implementation processes of seru production. Singhal mentioned seru production as a popular field (^{Roth et al., 2016}). Seru production is considered more flexible than TPS and is the next generation of lean production. A paper (^{Yin et al., 2017}) in JOM entitled “Lessons from seru production on manufacturing competitively in a high cost environment” systematically introduced seru production. In the past 10 years, over 20 academic papers on seru production were published in important journals, such as JOM, EJOR, IJPE, IJPR, and CIE.

This study aims to survey and summarize the concept, characteristics, background, and research of seru production. We summarize the concept, characteristics, and types, survey the background, and analyze the advantages and application scenarios of seru production. We compare seru production and famous production modes, i.e., assembly line, CM, and TPS. We survey and classify the literature on seru production. Finally, we provide the future research directions.

Background and application scenarios of seru production

Concept and characteristics of seru production

Seru is the pronunciation of cell in Japanese, which not only distinguishes the meaning of the unit from the traditional CM but also defines the specific meaning of the Japanese cell. Seru is an assembly unit that includes simple equipment and one or several workers to operate most or all the processes of production. Seru is a human-centered manufacturing system with a high degree of Jiritsu. Jiritsu means autonomy, self-management, learning, and self-evolution (^{Yin et al., 2018}). Seru system refers to a production system that utilizes the flexible characteristics of seru and improves the productivity through rapid reconfiguration of workers, equipment, and products. Seru production is based on the seru system.

Seru comprises three types (^{Stecke et al., 2012}; ^{Yin et al., 2017}; ²⁰¹⁸), namely, divisional seru, rotating seru, and yatai. A divisional seru is a short line staffed with several partially cross-trained workers. Tasks within a divisional seru are divided into different sections. Each section is in charge of one or more workers, and one section is completed and then handed over to the next section. The workers are cross-trained but not fully skilled. As shown in Fig. 1, a divisional seru consists of two workers and five tasks, where worker 1 is responsible for section 1 with tasks 1–2 and worker 2 is responsible for section 2 with tasks 3–5. A rotating seru is composed of several workers. Each worker can assemble an entire product from start to finish without disruption, i.e., fully skilled. Figure 2 shows an example of a rotating seru. A yatai is a seru with only one worker. A yatai owner performs all operational and managerial tasks by her- or himself. In comparison with the rotating seru, a yatai contains only one worker. The balance can reach 100% without being affected by other workers. Figure 3 presents an example of a yatai.

The seru system is the production system that contains one or more serus. This system can be classified into pure and hybrid seru systems. A pure seru system contains only one or more serus without an assembly line. This system can be divided into pure divisional seru system, pure rotating seru system (Figure 4), and pure yatai system. The hybrid assembly line seru system is the production system that includes one or more serus and a short assembly line. This part of the assembly line is retained because (1) the equipment is expensive and inappropriate for replication in each seru or (2) the worker can only operate one or few tasks. Figure 5 (^{Yu et al., 2017a}) presents an example of a simple hybrid seru system with five workers and five tasks. The hybrid seru system is more practical and complex than the pure seru system.

Seru production has the following characteristics (^{Yu and Tang, 2018}): (1) Kanketsu (completeness)—all required tasks of producing a product are completed form start to finish within the seru. Workers are multi- or fully skilled. Workers in a seru complete most or all tasks of a product from start to finish; (2) Autonomy—in seru production, workers are responsible for most or all tasks; therefore, workers can improve the skill by themselves; (3) Continuous improvement—this characteristic includes the continuous improvement of worker skill and seru performance; (4) Parallelization production—seru production contains one or more serus. A seru can produce one or more products; thus seru production is a parallel production. In parallelized seru production, when the equipment/worker is abnormal, only the seru with the equipment/worker is affected; (5) Reconfiguration (high flexibility)—seru production can be rapidly reconfigured in accordance with customer demands. Reconfiguration is based on simple and movable equipment and multi-skilled workers.

Background of seru production

Seru production originated from production sites in Japan in the 1990s and was related to Japanese technology accumulation, economics, corporate culture, and market environment.

Before 1970, the assembly line was popular in the assembly processes of mass production in Japan due to its high efficiency. With the customer demand change and diversity increase, companies must meet the demands of high variety and low volume. The competition among enterprises was becoming increasingly fierce. Although TPS can handle multiple varieties, its premise is that the varieties are relatively stable and the batches are inconsiderably small. For some high-tech products, such as cameras and mobile phones, the market demands are characterized by short life cycle and dynamic demands. Scientific production plans in accordance with customer demands based on assembly line and TPS are difficult to formulate due to the rapid change and uncertainty in customer demands. A new production mode is necessary to meet market demands of high variety and low volume, variable varieties and batches, and short life cycle. Thus, seru production was developed. Most electronic companies in Japan (^{Yin et al., 2013}; ^{Yu et al., 2013b}), such as Canon, Sony, Panasonic (Matsushita), Fujitsu, NEC, and Hitachi, have adopted seru production. This new production mode has also been used in companies in Dalian and Guangdong, China. In comparison with the assembly line, seru production not only saves human resources, space, inventory, and lead time but also improves productivity. European and American scholars have investigated seru production.

The reasons for the emerging seru production in Japan can be summarized as follows.

(1) Industrial and economic environment. After the 1990s, Japanese economics downturned. The sharp appreciation of Japanese yen led to labor cost increase. Many factories moved the assembly line to the countries or regions with low labor costs, such as China and Southeast Asia. Few assembly lines were left in Japan to meet the high-quality requirements of customers. Moreover, the aged tendency of employees and the reduction in youth labors were significant.

(2) Japanese unique corporate culture. As long as employees do not resign, the Japanese companies do not lay off employees. However, the abolition of the assembly line resulted in many workers remaining. Companies must produce and meet the customer demands to feed these workers. A new production mode is necessary to be profitable by meeting the dynamic demands.

(3) New market demands. The customer demands were becoming increasingly diversified (^{Tan et al., 2018}), the life cycle of products was shortened, and the varieties and volumes of products were becoming increasingly unstable. The assembly line, CM, and TPS could not meet such market environment.

Advantages and application scenarios of seru production

The case reports of implementing seru production have stated that seru production has the following advantages (^{Yu et al., 2012}; ^{Yin et al., 2017}): (1) it can adapt to the demands of high variety and low and variable volumes and can flexibly reorganize the production system in accordance with the dynamic demands; (2) it can improve productivity and save space; (3) it can reduce product defect rate; and (4) it can improve workers’ sense of responsibility. For example, after implementing seru production (1) Canon and Sony reduced their work space by 72000 and 710000 m², respectively (^{Stecke et al., 2012}; ^{Yin et al., 2017}); (2) Sony reduced 35976 workers, approximately 25% of total labors (^{Yin et al., 2017}); (3) Canon saved 55 billion Japanese yen in cost in 2003 (^{Yin et al., 2008}); and (4) Sony Kohda subsidiary reduced the makespan by 53%, which made its average productivity higher than that of Toyota.

Nevertheless, seru production cannot improve productivity at any case. For example, in mass production, the efficiency of seru production is worse than that of the assembly line. ^{Johnson (2005)} studied the conversion from the assembly line to a seru system. In the production of sheet metal products, when the due date was short and the products were customized by customers, seru production should be implemented. ^{Kaku et al. (2009)} proposed a multiobjective seru production with makespan and labor hour minimization. They concluded that enterprises should implement seru production in an environment of high variety and low volume. ^{Yu et al. (2012)} stated that pure seru production should be implemented in cases of high variety and low volume through an analysis based on full-factor experiments. By using empirical study, ^{Yin et al. (2017)} highlighted that seru production is proposed in response to a dynamic market environment and high labor costs in a high-cost environment. Seru production inherits the merits of lean production, agile production, and grouping technology. ^{Yin et al. (2017)} empirically analyzed the flexibility and quality of the production system through seru production by using an example from Sony and Canon. From the perspective of management model, ^{Stecke et al. (2012)} explained that the seru system is an extension of the just-in-time (JIT) organization (relatively, the TPS model is the JIT on raw materials) and can realize a smart factory. ^{Zhang et al. (2017)} studied the key enabling technologies of seru production in terms of sustainability from a system perspective. The factors included conversion from the assembly line to seru, multi-skilled worker applications, equipment improvement, and distribution system optimization. The sustainability of seru production was evaluated through the four key enabling technologies. Seru production is appropriate for enterprises that are seeking supply and demand matching. The difficulty in matching supply and demand is how to meet the due date of orders. Seru production can reduce the lead time.

Comparison of seru production and other famous production modes

Since Frederick W. Taylor developed scientific management principles in the early 20th century, various production organization and management modes have emerged. They promoted the development of operations management and management science. The well-known production modes are scientific management principles, mass production (the assembly line or Ford Production System), TPS, and CM. Henry Ford developed the first auto assembly line, where a car was moved by a conveyor belt, while workers added components to it until the car was completed. The assembly line greatly improved productivity and reduced costs. This type of universal production mode has been widely adopted by manufacturing enterprises worldwide. However, the assembly line is mainly adapted to the market environment of mass demands and single product type. The TPS was proposed to adapt to the market environment of high variety and low volume. TPS uses a mixed assembly line to produce similar products. TPS solves the production problems of low and medium volumes with relatively stable demands. TPS includes JIT, total quality management, and team work method.

We compare seru production with the assembly line, CM, and TPS.

Comparison of seru production and the assembly line

The first auto assembly line was implemented by Henry Ford in 1913, also known as Ford Production System, mass production, or conveyor assembly line. The assembly line uses the division of labor of Adam Smith and Taylor’s scientific management principles. Assembly lines are designed for the sequential organization of workers, tools, and parts. Each worker typically performs one simple operation. Before assembly line emerged, most products were made individually by hand. Each part of a product was processed by a single craftsman. Table 1 presents the comparison of seru production and assembly line.

Comparison of seru production and CM

CM was proposed on the basis of GT by a group of European companies (^{David and Huang, 1985}; ^{Wemmerlov and Johnson, 1997}; ^{Wemmerlov and Hyer, 2002}). Cell in the CM refers to a machining center composed of machines with similar functions. Cell is centered on expensive machines, especially numerical control equipment. CM responds to market demands by changing the organizational structure and provides high flexibility for processing part families with similar tasks (^{Celikbilek and Süer, 2015}). GT combines parts with similar structures, materials, and processes into one part group (^{Yin et al., 2018}). Therefore, GT increases the volume size, reduces the varieties, and improves the productivity. When the equipment is expensive, the CM profit is significant. Cell loading is performed on the outcome of the CM system design (^{Celikbilek and Süer, 2015}). Seru production utilizes the flexibility of seru and improves the productivity through a reasonable configuration of workers, equipment, and products. Seru production is an extension of lean production, mainly for the assembly process. Table 2 presents the comparison of seru production and CM.

Comparison of seru production and TPS

TPS was proposed by Toyota Motor Corporation. The core of TPS is to reduce and eliminate all redundancy and waste in the factory. TPS includes JIT, kanban, and zero inventory. TPS produces similar products by the rapid change of the mixed line. Table 3 presents the comparison of seru production and TPS.

Research review on seru production

On the basis of the used methods, the research on seru production (^{Kono, 2004}; ^{Yu et al., 2015}) can be classified into empirical study, experiments, comparative analysis, statistical analysis, model, and optimization algorithm. The research has focused on implementation scenarios, performance improvements, successful cases, influence factors, model complexity, optimal seru system formation, and optimal seru system scheduling. They can be summarized in the following aspects.

Preconditions and application scenarios of seru production

^{Liu et al. (2014)} proposed an implement architecture for seru production and indicated that multi-skilled workers are the precondition of seru production. Necessary skills must be trained in accordance with the requirements of the product process before the seru system design. After satisfying the basic needs of the seru system, they continued to conduct comprehensive skill training. ^{Kaku et al. (2008b)} investigated the effect of worker ability on the implementation of seru production. They emphasized that staff exchange should be strengthened and a good training method should be provided to improve the performance of the seru system. Aiming at the specific problem of multi-skilled worker training in seru production, ^{Liu et al. (2013)} constructed a multiobjective model with the lowest training cost and the best balance of each seru and proposed a heuristic algorithm to solve this model. The heuristic algorithm based on the relationship between product process requirements and worker ability was to find an efficient solution for worker training. ^{Ying and Tsai (2017)} studied the minimum cost problem of multi-skilled worker training and assignment in seru production, constructed a single-objective model, and proposed a two-stage algorithm from the perspective of queuing theory.

^{Yin et al. (2017)} implied that seru production is proposed in a high-cost environment in response to a dynamic market environment and high labor costs. Examples of Sony and Canon showed that seru production can improve productivity, quality, and production system flexibility. ^{Stecke et al. (2012)} demonstrated that seru production is appropriate for companies implementing JIT, which realizes a smart factory. ^{Johnson (2005)} emphasized that seru production should be used when the due date is short and products are customized by customers. ^{Kaku et al. (2009)} stated that seru production should be implemented in the market environment of high variety and low volume. ^{Yu et al. (2013b)} indicated that pure seru production should be conducted in the market environment of high variety and low volume. The efficiency of pure seru production decreases if the product needs too many operational tasks. ^{Yu et al. (2014)} stated that the hybrid seru system is appropriate for situations under which the equipment is relatively expensive and the skills of certain workers are low.

Theoretical analysis of the complexity and optimality of seru production

Different seru systems have different complexities. Seru systems with different objectives also have different complexities, and optimal solutions have different properties. The pure seru system is a special case of the hybrid seru system, i.e., no short line in the system. A comparison of Figs. 4 and 5 indicates that the complexity of the hybrid seru system is considerably higher than that of the pure seru system. Therefore, most research has focused on the pure seru system. ^{Yu et al. (2012}; ^2013b; ²⁰¹⁴) and ^{Sun et al. (2014}; ²⁰¹⁶) simplified the mathematical model of the hybrid seru system (^{Kaku et al., 2008a}; ²⁰⁰⁹; ^{Yu et al., 2017a}) and proposed the optimal operation model of the pure seru system. They investigated the optimal operation of pure seru systems with several main evaluated performances and clarified the complexity of such systems and the characteristics of optimal solutions. ^{Yu et al. (2014)} stated the key decision processes of the optimal operation of pure seru systems with makespan and total labor hour minimization. They showed that one decision variable determines seru formation (i.e., the optimization design of pure seru systems), and another decision variable determines seru scheduling (i.e., batch scheduling of pure seru systems). The complexity of the optimal operation of pure seru system was clarified from the theoretical point of view by combining seru formation and scheduling. ^{Yu et al. (2012)} identified the situations under which the pure seru system can effectively reduce makespan and labor hours by solving a large number of experiments using enumeration and intelligent algorithms based on nondominated sorting genetic algorithm II (NSGA-II). ^{Yu et al. (2013b)} established a mathematical model for the optimal operation of the pure seru system with the minimization of the number of workers and makespan. The problem was proven to be NP-hard. ^{Sun et al. (2019)} formulated a seru system operation with total tardiness minimization and analyzed the solution space. They decomposed the nonlinear model into seru formation and scheduling, which was formulated as a linear model. Thus, the small-scale instances could be solved exactly.

For the optimal operation of the hybrid seru system, ^{Kaku et al. (2009)} proposed a seru system model with makespan and total labor hour minimization. They used the model to analyze how seru production can obtain optimal performance. ^{Liu et al. (2012)} proposed a mathematical model for the optimal operation of the hybrid seru system and used a reasonable method to implement seru production on the basis of the model. ^{Gong et al. (2009)} constructed a minimal inventory model of the hybrid seru system and conducted an analysis. ^{Yu et al. (2017a)} developed several frequently used hybrid seru system operation models. They stated that the hybrid seru system operation includes four decision-making processes, namely, worker allocation, seru formation, seru scheduling, and short line scheduling. They clarified the solution space and complexity of the hybrid seru system. The hybrid seru system operation was proven to be an NP-hard problem. An exact algorithm was proposed to solve the optimal solution of small-scale instances. Several management insights on how to operate the hybrid seru system were summarized and proposed.

Seru system formation and solutions

The seru system formation is the most critical decision in the optimal operation of seru systems. The seru system formation must consider several factors, such as layout, seru type, varieties, system objective, volume, worker relationship, skill difference, and scheduling rules. The system objectives include minimal makespan, total labor hours, number of workers, and total costs and minimal total tardiness. Different combinations of factors and system objectives mean different seru system formations.

For the optimal operation of the pure seru system with makespan and total labor hour minimization, ^{Yu et al. (2014)} investigated the seru system formation of the pure seru system in detail and proved that the pure seru system formation is an NP-hard problem. They analyzed the solution space and complexity of the pure seru system. To solve the optimal operation of the pure seru system with the minimization of the number of workers and makespan, ^{Yu et al. (2013b)} formulated the problem and proved that it is also NP-hard. The solution space and complexity were analyzed, and the complexity was more complicated than the pure seru system formation without reducing the number of workers. ^{Yu et al. (2017a)} investigated the seru system formation of the hybrid seru system. The seru system formation in the hybrid seru system includes two decision-making processes: worker allocation and seru formation. They clarified the solution space complexity and proved that the problem is NP-hard. ^{Sun et al. (2019)} proposed a cooperative coevolution algorithm for the seru production with minimal makespan. They investigated the global optimal solution considering the optimization by combining seru formation and scheduling. ^{Wang and Tang (2018)} formulated a robust seru production system that can respond efficiently to the stochastic demand to minimize the cost and maximize the service level of the system. The NSGA-II was improved to solve the multiobjective optimization model.

The algorithms for solving the seru system formation can be divided into exact and intelligent algorithms. When the problem was small, the exact algorithm can be used to obtain the optimal solution. For the seru system formation of the pure seru system with makespan and total labor hour minimization, ^{Yu et al. (2014)} obtained the exact Pareto-optimal solutions by using the nondominated sorting algorithm. For the seru system formation of the pure seru system with the minimization of the number of workers and makespan, ^{Yu et al. (2013b)} proposed an improved exact algorithm to obtain the Pareto-optimal solutions. ^{Yu et al. (2017b)} proposed two improved exact algorithms to obtain the optimal solution of the seru system formation of the pure seru system with the minimization of the number of workers without decreasing productivity. ^{Yu et al. (2017a)} proposed an exact algorithm for the small-scale seru system formation of the hybrid seru system.

For large-scale problems, optimal solutions cannot be obtained in reasonable time by adopting exact algorithms. Intelligent algorithms are often used to find the suboptimal solutions. ^{Yu et al. (2014)} proposed an intelligent algorithm based on NSGA-II to solve the pure seru system with makespan and total labor hour minimization. Subsequently, ^{Yu et al. (2013a)} proposed an improved algorithm of NSGA-II combined with local search to solve such system.

The complexity of the nondominated sorting algorithm for the multiobjective seru system is O(M²N), where M is the number of objectives and N denotes the number of feasible solutions. Therefore, ^{Sun et al. (2014)} set labor hour as a constraint and formulated a single-objective model with makespan minimization to reduce the complexity. They proposed a variable neighborhood search algorithm for large-scale instances. For the multiobjective model in the reference (^{Yu et al., 2013b}), ^{Yu et al. (2017b)} proposed a single-objective model with the minimization of the number of workers without decreasing productivity. An intelligent algorithm based on variable length was proposed. ^{Liu et al. (2012)} constructed a mathematical model with makespan minimization and analyzed the characteristics of the seru number and the distribution of workers in each seru.

Seru system scheduling and solutions

After the seru system is constructed, the different scheduling rules of allocating batches to seru produce various results. Therefore, scheduling rules significantly influence the performance of the seru system. The scheduling of the seru system is a key decision process in its optimal operation. Few studies exist on the optimal scheduling of the seru system, compared with its formation. The pure seru system scheduling is an NP-hard problem.

Pure seru system scheduling is an NP-hard problem, that is, when seru system formation and scheduling are considered, the optimal operation of seru production is a complex problem including two NP-hard problems. Therefore, in existing studies, seru system formations are often investigated with a given scheduling rule, such as first come first served (FCFS) and shortest processing time (SPT). ^{Yu et al. (2015)} clarified the complexities of solution space with FCFS and SPT rules in the pure seru system. For the pure seru system with makespan and total labor hour minimization, ^{Yu et al. (2012)} investigated a reasonable scheduling method by conducting 64 full-factor experiments with the given FCFS rule. On the basis of the analysis of the experimental results, they concluded that a batch should be assigned to the seru with the SPT to minimize the total labor hours, and the batch allocation should consider the balance among serus to minimize the makespan. ^{Yu et al. (2016)} investigated the influences of 10 scheduling rules on the complexity and performance of the pure seru system. The 10 scheduling rules were divided into two categories: scheduling rules unrelated and related to the seru sequence. The complexities of the solution space with the two scheduling rules were clarified.

Future research in seru production

The review on seru production and the requirement from industrial and academic fields indicate many problems in seru production that require further research.

Analyses of specific seru production

The hybrid and divisional seru systems are not fully analyzed yet. What are the characteristics of human resources, worker skills, market environment, production environment, and prospects when implementing the hybrid seru system or the divisional seru system?

Other analyses include determining when the divisional seru system, hybrid type seru system, and seru production with fully skilled workers should be used. These analyses are important and can provide managerial insights on how to select the appropriate seru production in accordance with the characteristics of companies. These analyses require such methods as mathematical modeling, case analysis, comparative analysis, and statistics analysis.

Evaluated performances and formulation

The formulated evaluation performances of seru production include makespan, total labor hour, number of workers, and balancing (^{Yu et al., 2018}). Other performances, such as due date, idle time, space, total makespan, WIP, finished-product inventory, and carbon emissions, are still unformulated. Therefore, quantitative analyses of these advantages of seru production must be performed. After the evaluation performances are formulated, constructing mathematical models with these performances, analyzing the features of these models, developing exact and intelligent algorithms, and providing managerial insights for the optimal operation of seru production are necessary.

Demands for multi-skilled workers

Multi-skilled workers are necessary prerequisites for seru production. Existing research assumes that all workers are fully skilled; hence, the mathematical model is simple. However, all workers might not be fully skilled. When workers are not fully skilled, only the divisional and hybrid seru productions can be implemented. Therefore, we must investigate the demands for multi-skilled workers of divisional and hybrid seru productions under optimal performances. For the divisional and hybrid seru productions, the relationship of minimal demands for multi-skilled workers and optimal performances must be investigated.

Multi-skilled worker training

Seru production must train multi-skilled workers. The training methods include apprenticeship, team, and interactive training. Appropriate training methods for multi-skilled workers are necessary in a special seru production. What are the appropriate methods of multi-skilled worker training for implementing the divisional seru production, seru production with fully skilled workers, and hybrid seru production? Multi-skilled worker training must consider the skill differences among workers.

Joint optimization of seru production

The optimal operation of seru production includes seru formation (seru system optimal design) and seru scheduling (seru system optimal scheduling), which are NP-hard problems. For simplicity, existing studies focus on the optimal seru system formation with a given scheduling rule. However, this result is not the global optimal solution of seru production operation. Therefore, seru formation and scheduling should be considered to obtain the global optimal operation of seru production.

Theories and methods for seru production

Although seru production has various benefits, all existing studies are based on numerical analyses of specific examples. The theoretical analyses of seru production are lacking, thereby causing the reasons for low carbon emissions, high leanness, and other good performances provided by seru production to remain unclear. For example, theoretical analyses can be performed as follows: to identify the situation under which seru production should be used, to develop the bound of performance improvement provided by seru production, and to clarify the reasons for the benefits of seru production. In comparison with other production modes, seru production is a decentralized management. Workers’ game exists in seru production. The performance of seru production under game equilibrium must be investigated.

Conclusions

This study reviewed the background, characteristics, types, and operation of seru production. The main contributions are presented as follows. Initially, we summarized the concept, characteristics, and types, surveyed the background, and analyzed the advantages and application scenarios of seru production. Subsequently, we compared seru production and famous production modes, i.e., assembly line, CM, and TPS. We surveyed and classified the literature on seru production. Finally, we proposed the future research directions.

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