Information-flow waste refers to activities within organizational information flows that consume resources without creating value, yet standardized instruments for systematically measuring this construct remain lacking. This study aims to clarify the construct structure of information-flow waste and develop a corresponding scale. Candidate items were generated through literature analysis, expert interviews, and cognitive interviews. The scale was then purified and validated using two independent manufacturing samples (n = 266 and n = 287) through exploratory factor analysis, confirmatory factor analysis, measurement invariance testing, and nomological validity testing. The results support a second-order structure comprising information acquisition, information transmission, information storage, and information processing, yielding a final 23-item Information-Flow Waste Scale. The scale demonstrates good reliability, convergent validity, and discriminant validity and achieves strict measurement invariance across production and operations, professional and technical, and supervisory and managerial job groups. Information-flow waste is significantly and positively associated with information overload, providing initial support for the scale’s nomological validity. This study provides a standardized measurement instrument for subsequent empirical research on information-flow waste and, in manufacturing contexts, a measurement basis for identifying manifestations of waste across stages of information flow and conducting subsequent problem analysis.

In the digital era, enterprises face increasing pressure to align strategic objectives with operational execution under volatile and data-intensive conditions. Traditional management control systems often rely on lagging indicators and ad hoc interventions, limiting both performance visibility and sustainability outcomes. This study develops MOD-FCA, a prescriptive, multi-layered closed-loop management control framework that links value-centric outcomes to business-centric drivers through vertically aligned metrics, objective tensors, and tiered corrective routines. Using a longitudinal case study in a manufacturing enterprise, we illustrate how MOD-FCA enhances operational traceability, supports systematic deviation identification and response, and institutionalizes organizational knowledge for continuous improvement. Importantly, MOD-FCA is designed to support sustainable industrial practices by embedding sustainability-related metrics, such as resource efficiency, energy intensity, waste reduction, and process compliance, into the same metric deployment, deviation triggering, and corrective-action logic used for operational control. Qualitative feedback from managerial and operational roles indicates that MOD-FCA strengthens accountability, ensures role-aligned responses, and fosters proactive, data-driven decision-making. These findings provide both theoretical contributions to management control system design and practical guidance for enterprises seeking to achieve both operational excellence and long-term sustainability.

Purpose This paper aims to develop a systematic method to analyze and map information waste, promoting lean information management. The aim is to optimize information flow efficiency, reduce resource waste and enhance operational effectiveness within enterprises. Design/methodology/approach This paper uses the design science research process model by Peffers et al. The authors conducted a thematic literature analysis on information waste to identify its sources and forms, then developed a structured framework for information flow. By integrating tools such as value stream mapping and value-added heatmaps, the authors created a tool for evaluating and mapping value addition within information flow. The tool’s effectiveness and usability were validated through case studies in manufacturing enterprises. Findings The analytical framework for information flow comprises four elements – storage medium, information content, transmission medium and intelligent agents – each linked to specific attributes, totaling 19 and crucial for information value. Based on this framework, the authors established evaluation criteria and developed a comprehensive metric to assess value addition in information flow. Additionally, the authors created a value-added heatmap analysis tool to identify information waste across the elements – storage medium, information content, transmission medium and intelligent agents. Case studies validated the methodology, demonstrating significant improvements in information flow and operational efficiency within manufacturing workshops. Originality/value This paper presents a systematic methodology for analyzing and mapping information waste, comprising a structured framework, a value-add density metric and a heatmap tool. Unlike previous studies that used the “seven wastes” concept, this innovative framework focuses specifically on the activities within information flow. The value-add density metric provides an integrated standard for evaluating the value added in information flows, enabling the identification of enhancement opportunities and significantly improving the efficiency of information flow analysis and optimization.

The Modular Arrangement of Predetermined Time Standard (MODAPTS) is an effective method for time measurement, process planning, and enhancement of work efficiency. Its validity and practicality have been demonstrated across numerous enterprises and industries, particularly in assembly operations. However, during implementation, this method is often affected by human factors, leading to challenges such as time-consuming procedures, high operational costs, and substantial demands on operators’ expertise. To improve efficiency, this paper proposes a framework for assembly motion recognition and standard time measurement based on wearable sensors and deep learning. Within this framework, motion units and operation categories tailored to automotive assembly scenarios are defined. An improved two-stream Informer neural network model is then employed to recognize motion units and calculate standard times. Experiments show macro F1-scores of 0.9326 and 0.9395 for trunk and hand motion recognition, respectively, while the mean absolute percentage error (MAPE) for standard time measurement relative to manual assessment is below 5%, demonstrating strong recognition and measurement accuracy. In addition to enabling standard time measurement, the paper assigns value-added attributes to motion units to quantify the workstation work value ratio, thereby further enhancing the practical utility of assembly motion recognition and standard time measurement.

To improve the assembly efficiency and productivity of complex aircraft components, the optimization of an assembly line was investigated in this study. A hierarchical hybrid multi-objective optimization algorithm (HHMOA) was proposed using an improved non-dominated sorting genetic algorithm II and an enhanced longest processing time algorithm. The algorithm incorporates a two-layer framework for global–local optimization; an information entropy-based problem formulation with three objectives, including line balance rate, load balance index and assembly complexity smoothness index; and a hybrid initialization strategy for high-quality initial solutions. Based on the assembly line datasets of different scales, the algorithm performance was verified by comparing the hypervolume and the calculation efficiency using HHMOA and three benchmark algorithms, and the sensitivity analyses verified the algorithm robustness. For an actual aircraft component assembly line, the optimizations carried out with the given process time, number of workstations and precedence relationships indicate that the balance rate of the optimized line increased 72%, and the load balance index and the assembly complexity smoothing index were reduced by 80.3% and 92% respectively, which proved the reliability of the hybrid algorithm in optimizing the aircraft component assembly line. Finally, the optimization analyses with various workstation numbers and assembly process times suggest that reducing the workstations and adopting robotic automated processing can improve the aircraft component assembly line.

Automated drilling and riveting are pivotal for enhancing aircraft assembly quality and efficiency. However, burr formation during robotic drilling of aircraft panels remains a critical challenge, impacting component longevity and safety. This study systematically analyzes how clamping force, spindle speed, and feed rate influence burr generation. Finite element modeling identified an optimal clamping force threshold to suppress interlayer burrs. Further simulations quantified the effects of machining parameters on burr formation. Through response surface methodology experiments, parameter interactions affecting burr height are determined and the process is optimized: 462 N clamping force, 1239 r/min spindle speed, and 124 mm/min feed rate minimized burrs. This approach provides theoretical insights into burr dynamics and practical guidelines for rapid parameter optimization, enhancing aircraft panel reliability and sustainable manufacturing.

On large-scale product assembly lines, such as those used in aircraft manufacturing, multiple assembly positions and devices often coexist within a single workstation, leading to complex task interactions. As a result, the problem of parallel task execution within workstations must be effectively addressed. This study focuses on positional and equipment conflicts within workstations. To manage positional and equipment conflicts, a multi-objective optimization model is developed that integrates assembly sequence planning with the first type of assembly line balancing problem. This model aims to minimize the number of workstations, balance task loads, and reduce equipment procurement costs. An improved NSGA-II algorithm is proposed by incorporating artificial immune algorithm concepts and neighborhood search. A selection strategy based on dominance rate and concentration is introduced, and crossover and mutation operators are refined to enhance search efficiency under restrictive parallel constraints. Case studies reveal that a chromosome concentration weight of about 0.6 yields superior search performance. Compared with the traditional NSGA-II algorithm, the improved version achieves the same optimal number of workstations but provides a 5% better workload balance, 2% lower cost, a 76% larger hyper-volume, and a 133% increase in Pareto front solutions. The results demonstrate that the proposed algorithm effectively handles assembly line balancing with complex parallel constraints, improving Pareto front quality and maintaining diversity. It offers an efficient, practical optimization strategy for scheduling and resource allocation in large-scale assembly systems.

Intersection hole is the key connecting hole in aircraft assembly. The drilling accuracy and quality of intersection hole have great impact on the life and safety of aircraft. Traditional intersection hole takes drill plate as process criterion and uses manual processing methods, with low efficiency, low stability, and higher possibility of positional deviation. Numerical boring mill is a relatively advanced technique of intersection hole processing at present. This article focuses on the effect of processing parameters such as boring speed, feed, and cutting depth on the quality of intersection hole on aircraft vertical and horizontal tails. Surface roughness prediction model and cutting force prediction model are used to construct multi-objective optimized function. Objective constraint conditions are used, and the multi-objective optimized functions are solved by genetic algorithms. The optimization results are experimentally verified. The optimal parameter combination is obtained through the multi-objective optimized design of precision processing parameter of aircraft intersection hole, providing theoretical guidance for the selection of processing parameters in actual production.

With the growing demand for individualization, production patterns in the garment industry now require variety and small-batch production, which has led to increasingly complicated tasks and rising workloads for sewing workers. In the present study, a new measurement method for sewing operation complexity is proposed. On this basis, the relation between sewing operation complexity and sewing operation quality is analyzed in depth. The results show that action complexity and cognitive complexity are significantly correlated with the fluctuation of sewing operation time and the rate of sewing defects. Further, there was no significant correlation between posture keeping complexity and the fluctuation of sewing operation time or between posture keeping complexity and sewing defect rate. The method proposed in this paper is helpful to better understand the complexity of sewing operations and provide theoretical guidance for quality control in sewing operations.

As the core of the Industry 4.0 era, information can improve the efficiency of production as well as bring cognitive load to operators. In this study, we aim to develop a unified information field analysis model for manufacturing systems and to estimate the cognitive load of operators through the action features of the information field. The qualitative and quantitative analytical framework model of the information field in manufacturing systems is established using information entropy and fuzzy mathematics. Furthermore, the cognitive load mechanism in manufacturing systems is clarified. The information principles that must be exercised to improve and optimise manufacturing systems under the information field framework are proposed for implementing lean production and digital transformation. Results of a case study show that the proposed information field analysis model for manufacturing systems reveal the change law of information field of manufacturing system in time and space, which has considerable guiding values for enhancing the management efficiency of shop floors and the sustainable development of enterprises.

The cognitive load of surgeons during surgery is one of the critical factors affecting the success rate and safety of surgery. In order to solve the problem that surgeons' cognitive load cannot be evaluated quantitatively, this paper proposes an evaluation method considering the complexity of surgical information processing based on the principle of information processing economy. This method is based on the information entropy theory to quantitatively evaluate the information processing process during surgery. The method is applied to dacryocystorhinostomy, which provides suggestions for the optimal design of the operation. It is of great significance for optimising the operation process, evaluating the operation and surgeon training.

Existing methods for evaluating manufacturing process chain complexity consider the number of machines, state of machines, number of parts, operation time, and processing sequence of parts. However, such evaluation methods ignore human factors. To consider human factors, human cognitive decision-making process factors are considered in the complexity evaluation of production processes. Accordingly, a new objective evaluation method of the human factor complexity is proposed. In the proposed method, sewing operations are taken as an example, and the human factor complexity is classified into perceived and cognitive complexity. Information entropy is used to measure cognitive complexity according to the type and quantity of sewing workers’ cognitive activities. The results show that various methods have significant differences in the evaluation of the complexity level of the production process chain. Specifically, the calculation results of the proposed evaluation method are much greater than those of other methods. This indicates that human cognitive and perceived complexities account for a large proportion. Therefore, human factor complexity cannot be omitted.

Workers at production sites bear two types of loads: physical loads and information loads. With the progress made in science and technology—particularly information technology—and the increased levels of automation of production equipment, the proportion of physical loads borne by workers has decreased. Traditional work study is based on the research conducted by Frederick W. Taylor. However, in this study, a method for conducting work study, called “Work Study 2.0”, is proposed, which considers the information of a production site. Work Study 2.0 incorporates information flow/field analyses based on traditional work study. Work operational complexity and cognitive complexity measurements are used to analyse and improve information elements in the production site to reduce the information processing load borne by the workers. Finally, a case study employing the proposed method is presented. The results indicate that the proposed method can further ease the labour of the workers in a lean production system. Overall, this study enriches the theoretical system of the work study and provides a new theoretical tool for lean improvement and visual management.Workers at production sites bear two types of loads: physical loads and information loads. With the progress made in science and technology—particularly information technology—and the increased levels of automation of production equipment, the proportion of physical loads borne by workers has decreased. Traditional work study is based on the research conducted by Frederick W. Taylor. However, in this study, a method for conducting work study, called “Work Study 2.0”, is proposed, which considers the information of a production site. Work Study 2.0 incorporates information flow/field analyses based on traditional work study. Work operational complexity and cognitive complexity measurements are used to analyse and improve information elements in the production site to reduce the information processing load borne by the workers. Finally, a case study employing the proposed method is presented. The results indicate that the proposed method can further ease the labour of the workers in a lean production system. Overall, this study enriches the theoretical system of the work study and provides a new theoretical tool for lean improvement and visual management.

：In the production line, the direct purpose of robot is to reduce the labor intensity and improve the efficiency of the production line.In the literature, the assignment problem of human-robot joint task is usually carried out with cost and time as the goal.A human-robot joint task assignment model considering the complexity of the task is established to obtain the optimal resource utilization scheme.In this paper, the operation complexity evaluation method of human-robot joint assembly is given from two aspects of operation process and decision process.Second, developed based on the complexity of the operation of human-robot combination task allocation model, the model to take advantage of resource equilibrium rate, average workers operation complexity and decision-making process complexity as constraint conditions, to minimize hysteresis time delay between the balance as the goal, in the task assignment, assume that the robot decision-making process complexity is less than the workers responsible for operating the complexity of the decision-making process, responsible for operation and the balance of both workers operating task complexity, optimizing man-machine combination task allocation scheme.The case study proves the feasibility of the proposed model.

The global manufacturing model is changing, and the manufacture of precision box parts is developing in the direction of automation and flexibility. Through the application of the information integration management module, integrated device information is collected to develop the system operation plan, which is transferred to the logistics management module and converted into control information executable by the processing module. Finally, the flexible machining of precision box parts is automatically implemented. Through the dynamic scheduling optimization strategy based on filtered directed search, the dynamic scheduling of system processing is completed. The machining target segmentation method based on symmetric differential algorithm is used to accurately extract the contour information of the precision machining target of the box. This process is the working principle of the precision boxlike flexible manufacturing system based on the symmetric differential algorithm designed in this paper. The results show that the system can effectively reduce the number of marks and the maximum completion time of the processing of precision box parts, and its balance ability and processing efficiency are fast.

Purpose FOV splicing optical remote sensing instruments have a strict requirement for the focal length consistency of the lens. In conventional optical-mechanical structure design, each optical element is equally distributed with high accuracy and everyone must have a high machining and assembly accuracy. For optical remote sensors with a large number of optical elements, this design brings great difficulties to lens manufacture and alignment. Design/methodology/approach Taking the relay lens in an optical remote sensing instrument with the field of view splicing as an example, errors of the system are redistributed to optical elements. Two optical elements, which have the greatest influence on modulation transfer function (MTF) of the system are mounted with high accuracy centering and the other elements are fixed by gland ring with common machining accuracy. The reduction ratio consistency difference among lenses is compensated by adjusting the optical spacing between the two elements. Findings Based on optical system simulation analysis, the optimized structure can compensate for the difference of reduction ratio among lens by grinding the washer thickness in the range of ±0.37 mm. The test data for the image quality of the lens show that the MTF value declined 0.043 within ±0.4 mm of space change between two barrels. The results indicate that the reduction ratio can be corrected by adjusting the washer thickness and the image quality will not obviously decline. Originality/value This paper confirms that this work is original and has not been published elsewhere nor is it currently under consideration for publication elsewhere. In this paper, the optimum structural design of the reduction relay lens for the field of view stitching applications is reported. The method of adjusting washer thickness is applied to compensate for the reduction ratio consistency difference of lenses. The optimized structure also greatly reduces the difficulty of lenses manufacture, alignment and improves the efficiency of assembly.

结合当前信息技术的发展,从信息场的角度重新审视了教学过程,按照人、机、料、法、环、测的工程思维方法构建了教学模式设计结构矩阵,给出了以信息场为中心教学模式设计的质量功能展开,并据此定义了教师的教学特性与学生的学习特性及其可测度的概念.给出了以信息场为中心的教学模式设计流程,提出了教学信息场中信息传递与知识形成的信息加工机理,并以学习发生的信息加工机理对教师在教学过程中的价值和作用进行了重新定义.本文认为教师的价值和作用是学习信息场的构筑者、信息的加工者以及学生知识形成的催化者.

The one-person-multi-machine assignment is a typical feature of lean production systems. The major disadvantage of this type of assignment is that it could cause system delay due to human failure. Therefore, it is important to analyze the degree of efficiency loss among machines caused by interference between operators and machines. In this paper, a methodology is developed based on the decomposition technique. The whole U-shape production line is modeled as several subsystems where the efficiency loss mentioned above can be treated as machine failure. Hence, each subsystem can be simplified as an unreliable workstation with a certain failure rate. With finite buffers between consecutive subsystems, the influence of human failure can be analyzed and verified with an industry-based case study. Data was collected from an automotive electronics plant as well as corresponding computational and simulation test results. Statistics show that the method developed in this paper will make contributions to solving industrial problems.

：The research of manufacturing system reliability has a history of several decades, but the research scope defined by related research is fuzzy, lack of a unified analysis framework, and easy to be confused with the traditional equipment system reliability research.On the basis of literature research, the definition and connotation of manufacturing system are analyze d, and the differences and relations between the performance and reliability of manufacturing system are re explained.The concept of manufacturing system is redefined from the perspective of equipment process twinning, and it is pointed out that the descript ion of manufacturing system neglecting the role of human in the system is incomplete, especially in the study of manufacturing system reliability.Based on the functional perspective, the failure mode of manufacturing system is redefined, the framework of manufacturing system reliability analysis is established, and the research method of the mechanism of manufacturing system reliability changing with time is discussed.It is pointed out that the big data method will be one of the important methods for reliability research of manufacturing system in the future.