The Industrial Revolution has drastically changed the world. This change increasingly involved the mechanisation and automation of work, which in turn required changes in business management. Frederick W. Taylor was the first to recognise the importance of work study. According to Taylor’s research, to effectively complete work, you have to pinpoint the associated tasks, determine the most effective manner to realise them, and give yourself the necessary time. In this regard, Taylor recognised the importance of optimising working methods and the need for time study.
Similar to Taylor, Gilbreth systematically studied work. Gilbreth and his wife, Dr. Lillian Moeller Gilbreth, published Fatigue Study in 1916 and Applied Motion Study in 1917. Therein, their approach was different from that taken by Taylor. Gilbreth and Lillian focus on the best practices and work plan design rather than on high performance. They proposed a theory: All human motion can be reduced to 17 basic elements of motion. Their theory provides the theoretical basis for MTMs. To achieve optimal working methods in terms of execution, productivity, and performance, Gilbreth and Lillian eliminated every Therblig that hinders work.
When Segur published his work in 1926 under the title Motion Time Analysis, he developed the first predetermined time system (PTS). Until the 1930s, this system was used in most U.S. industries, followed by a series of more advanced PTS systems, such as a motion time survey, established by Joseph H. Quick in 1934, and a work factor method.In 1940, H. B. Maynard studied complicated work processes for drills. Along with L. John, Schwab, and Gustave J. Tegemerten, he designed a system that became the most successful process for optimising workflow globally: method-time measurement.
H. B. Maynard, J. L. Schwab, and G. J. Stegemerten worked to develop data that supported the basic methods of MTM. Over the next few years, the data were evaluated, revised, and thoroughly tested. The findings were published in 1948 in the journal Factory Management and Maintenance. In the same year, the book, Method-Time Measurement, was published, which outlined the basics of MTM methodology. In 1966, G. C. Heyde developed Modular Arrangement of Predetermined Time Standards (MODAPTS) based on MTM, which is the most concise method to integrate time and action in PTS technology. This IE method is widely used for factory improvement.
Maynard et al. have been working on the MTM approach. Although the original MTM standard time values were refined and extended, subsequent research did not add anything new to these values except marginal modifications, and they remain unchanged thus far. However, the use of MTM provides an effective basis for productivity assessment, which considers human capabilities and provides support for identifying defects in manual processes.
With the approach of the Fourth Industrial Revolution and the improvement of informatisation and automation, changes need to be incorporated at the work areas of enterprises. At present, the academia mainly describes these changes from the following two perspectives:
First, these changes are described in terms of automation. As Becker and Stern  stated, the two major changes to future production workspaces can be summarised as follows. First, humans are absolutely necessary in the future factory. There will be fewer manufacturing jobs because of automation; however, new jobs will be created around machines. Second, the new tasks will be more complex; the increasing complexity of products and processes as well as the need to interact with computational automation equipment will make human work tasks more complex. While automation reduces the physical load on workers, it also increases the complexity of systems because automation systems are highly integrated with current products, processes, information, resources, human tasks, and organisations. Automation may make the task of an operator; however, simultaneously, it adds complexity to systems that must be managed, maintained, redesigned, etc.
Fast Berglund et al. describes the above changes using levels of automation (LoA) cognition, which he believes can improve the working environment of operators and reduce their workload[10, 11,12,13,14,15]. The definition of LoA used in this paper is proposed by Frohm[15\6] as ‘the allocation of physical and cognitive tasks between humans and technology, described as a continuum ranging from totally manual to totally automatic’. The LoA for mechanical activities is referred to as mechanical LoA, while the LoA for cognitive activities is called information LoA.
Second, the changes to be incorporated at enterprises can be describe from the perspective of operators. With the evolution of technology with time, the relationship between man and machine in production has also changed.
Gorecky, Schmitt, Loskyll, and Zühlke , Romero, Noran, Stahre, Bernus, and Fast-Berglund, Romero, Stahre, et al. , Romero, Bernus et al. describe these changes in terms of the technical assistance under which the operator completes the mission. The details are described as follows:
Operators manually operating a machine tool with the aid of a mechanical tool are defined as Operators 1.0; those working with computer support, Operators 2.0; those working cooperatively with the assistance of a robot or other equipment, Operators 3.0; those representing the “operator of the future” or an intelligent and skilled operator who, when needed, is assisted by machines to perform work, Operators 4.0. According to the type of skills augmentation, the Iveta Zolotová et al. divided Operator 4.0 into eight typologies. And they showed the feasibility of the Operator 4.0 concept in the laboratory environment by case study.
This paper attempts to describe these changes from the perspective of work study. Traditional work study is based on Taylor’s research, that is, to reduce physical load and improve labour efficiency; further, the relationship between operating time and methods is studied. According to the development of automation and information technology, we believe that the physical load borne by workers is gradually decreasing, while the information load is continuously increasing. However, the current work research methods neglect the research on information problems that affect production efficiency, such as insufficient or redundant information and information load pressure.
MTM visual inspection (developed in 1990) is the basic approach for planning, designing, and evaluating the time required for visual inspection activities, which are dependent on human judgement and decision making. However, the time required for these activities is the result of highly complex psychological processes. Therefore, they cannot be reliably analysed by the usual time calculation techniques. They still require the sight and time of the operators. Further, neither did they consider the requirements of modern information and communication technologies, nor did they fully consider the characteristics or performance capabilities of the operator .
Fässberg et al.  and Fast Berglund et al.  stated that, as the main tool for lean production site improvement, traditional work study cannot solve this problem. By contrast, the increasing automation and strict quality constraints associated with the manufacturing process are making the job of the operators increasingly difficult. With the rapid development of network and information technology, the information load of staff is constantly increasing. To enable workers to complete the given field tasks more effectively and ensure the effectiveness and efficiency of operators, correct information should be provided to the right people at the right time.
With regard to information and complexity in the manufacturing process, as stated by A. Claeys et al., several manufacturing enterprises are faced with the problem of unrealistic and inefficient workshop information display. The concept of complexity involves two dimensions: uncertainty and time.
Uncertainty may be owing to the lack of information and/or the nature of the interaction among the system components and time-dependent decisions and operations.
In an assembly context, cognitive automation can support decision making to ensure error-free products are produced. The assembly task is still realised by humans and is, for the most part, entirely dependent on their own experience. Because of the product and operation complexity brought by the mixed model structure, the mental workload imposed on the operators is generally very high. Therefore, the probability of error is high, and delays may occur.
Romero and Philipp Hold believe that assembly and man-machine cooperation processes entail complexity, which is caused by insufficient information and increases cognitive load. Therefore, digital assistant systems can provide information support to reduce the corresponding complexity and cognitive load.
Several researchers are aware of the information and complexity problems faced by manufacturing field operators in the Information Age, and thus, have presented solutions. For instance, J. Abonyi proposed a modular operator support system for multi-product processes. Tan Jeffrey Too Chuan  established a framework for assembly information development from task modelling to support man-machine collaboration in cell production. Philipp Hold stated that digital information can assist humans in their work by creating working systems that remain flexible to changing products and unstable demands through human adaptation, while still utilising the potential to realise cost-effectiveness in future production scenarios. Fast Berglund [14,15] stated that the strategy of cognitive automation will become increasingly important for companies. Sonja Stork  believes that workers have various sources of information and must rapidly switch between different tasks during manual assembly tasks. The complexity of task execution can be reduced by proper information presentation and planning of work steps. First, note that bootstraps can support information processing during work, while reducing search time and accelerating assembly execution. Second, as multiple possible assembly sequences exist for a product, the optimal sequence for a single assembly step should be determined and the interference of previous task steps minimised. The solution to all the above-mentioned problems is to provide various support systems. This paper enriches the connotation of work study from the perspective of methodology. The improvement methods and basic principles for improving the output of manufacturing system are provided from the perspective of information study and complexity measurement. In the past, the content regarding work study was limited to humans, machines, and materials.
This paper states that the correct configuration of the production site information system, from the information source/accommodation channel to the optimal configuration of the information field, can solve the complexity problem and reduce the information load borne by workers (First, the information is insufficient, which requires more time to process; second, too much information exists, and more time is needed to extract effective information.). Improving labour productivity and product qualification rate is crucial. Although literature advocates the use of various digital support systems to solve the above problems, we believe that even the use of digital support systems should be based on the in-depth analysis of the information field and that flow and source/location of the production site should be effective, otherwise it will increase additional complexity and information load. Therefore, based on traditional work study, we have discussed information research and complexity measurement and defined the new work research method as “Work Study 2.0”, which expands the connotation of the original work study method to satisfy the demand of the continuous development of productivity in the Information Age.