1. Introduction
In the 21st century, manufacturing companies face unpredictable and unforeseen changes in the market dictated by global competition. These changes include the rapid introduction of new products and the constant change in their quantity [
1,
2]. In recent years, a new trend has emerged both in industry and in other sectors, which is called the fourth industrial revolution or Industry 4.0 (I4.0) [
3,
4]. The I4.0 paradigm has introduced a new term “smart”, which is considered a key feature of the production systems of the future [
5].
Manufacturing is one of the most important industrial processes, where the initial material is transformed into the final product through the production process, which includes work equipment (machines), tools, and people (workers). One of the manufacturing processes, relevant for the study is the (manual) assembly process, which involves the composing of previously manufactured components and/or sub-assemblies into a complete product of unit of a product, primarily performed by human operators using their inherent dexterity, skill, and judgement [
6]. In light of I4.0, changes are particularly taking place in the field of (manual) assembly, focusing on reducing the workload of workers [
5] through the digital transformation of the workplace [
7,
8,
9]. Digital workplace design aims to facilitate employees’ current and future work practices through digital technologies [
9]. Proper digital workplace design is critical to sustainable business success in a new digital, consumer-centric business world. The digital workplace impacts physical workplaces, technology, and people and it is widely recognized to optimize worker productivity [
7]. The transformation of the digital workplace goes beyond the adoption or non-adoption of technology—it has far more profound implications related to workplace redesign. Future digital work not only means changing the tools used in work activities, but it often changes the nature of work activities and processes themselves [
8]. As we know, many manufacturing jobs still require manual labor involving a variety of activities, such as assembly, loading and unloading, pushing and pulling, and performing tasks that require manual material handling [
10], and these are the activities and jobs that need to be digitally transformed.
Competencies, skills, abilities, and satisfaction of workers are very important conditions for increasing the productivity of production [
11,
12,
13]. Therefore, it is necessary to develop highly skilled workers capable of performing multiple tasks [
14], or a human-centered manual assembly system with digital instructions [
15,
16] and/or a virtual worker training system [
17] that replaces extensive training and the lack of experience of the highly skilled workers.
On-the-job training and support are essential to help workers acquire the necessary skills and competencies, improve worker productivity, and ensure product quality [
18]. In addition to competencies and skills, worker performance is closely linked to their work environment. Product and workplace design should take into account the information gathered about processes, tools, machines, work items, tasks, and workers, often taking into account conflicting constraints and designing workplaces that are acceptable to all stakeholders [
19]. Redesigning existing workstations or designing new modern manual assembly workstations according to ergonomic specifications allows workers to work safely, reducing the risk of work-related musculoskeletal disorders and avoiding potentially hazardous work movements and stresses [
20]. In recent years, computer-based techniques for ergonomic assessment of work tasks have been widely used, especially virtual human simulation (DHM, Digital Human Modelling). DHM tools allow rapid virtual prototyping and evaluation of the considered workstation configuration using the technique of “what-if” scenarios without exposing the worker to risk [
21] and without investing in physical materials and resources [
22]. In this way, DHM lends strong scientific validity to proposed and already implemented improvements and solutions in manual assembly and workplace design [
6].
“Smart” assembly means that the assembly system must be adapted using smart actuators as a result of generated information and in accordance with algorithms [
23,
24]. Based on the definition of a “smart system”, Bortolini et al. [
25] propose a general framework that presents the implications of I4.0 principles on the development and design of an assembly system called “Assembly System 4.0”. The main features of the proposed systems are customization to meet the individual requirements of customers of products at a later stage of development, an assembly control system, computer-aided assembly, intelligent inventory management, traceability of products and processes, and self-configuration of a manual assembly workstation. Cohen et al. [
24] have presented a general architectural model for implementing I4.0 principles in an existing assembly system, focusing on the Operator Support System (OSS) and the Self-Adapting Smart Assembly System (SASS). OSS refers to real-time data and information that are constantly available to the operator, and SASS refers to the adaptation of the work process by actuators, as a result of the obtained data and smart algorithms. The authors emphasize that the implementation of OSS and SASS has an impact on increasing flexibility, agility, scalability, and productivity of a smart assembly system.
I4.0 principles, ergonomic design, and digital transformation of the work environment can be most easily ensured by implementing smart technologies and tools at (manual assembly) workstations. Papetti et al. [
26] propose a redesign of the manual workplace according to ergonomic recommendations in the footwear industry. They emphasize redesigning the work environment from a task-based organizational model to a human-centered model that promotes the development of personal skills and worker well-being. The paper presents the redesign of the hand sewing workstation after ergonomic evaluation by experts. The experts were asked to perform a heuristic evaluation based on direct and video-based observation of users according to standardized ergonomics assessment methods (e.g., RULA, OCRA, and NIOSH). Workers were interviewed to take into account their preferences and satisfaction with the workplace to better interpret the analysis results. Biometric and environmental parameters are automatically collected through the use of appropriate digital tools and IoT devices. As a result of the study, workplaces were effectively redesigned, mitigating the highest ergonomic risks without compromising the performance of value-added work. Weyer et al. [
3] presented the concept of a smart factory with a computerized manual assembly workstation designed to assemble small components using augmented reality (AR) and advanced sensor technology. The concept follows the paradigm of the “enriched operator” and enables flexible and modular integration into automated production lines. Using RFID (radio frequency identification) technology, the appropriate information and assembly instructions are obtained from the input product or raw material, enabling mass production of highly customized products. Workers are assisted by virtual instructions (AR) directly at the assembly workstation using tablets or smart glasses, while work progress at the manual assembly station is monitored by a static 3D camera. Thus, they enabled synchronized work of manual and automated assembly according to the concept of “man in a loop”. Bertram et al. [
14] give an overview of existing solutions and prototypes in the field of assistance systems for manual workstations in research and practice and discuss their specific focus. All systems focus on an information assistant that provides the right information for the right situation. Only a few solutions and projects are presented here in the article: Augmented Workplace from the motionEAP project (development of an augmented workstation), ProMiMo (implementation of a user-centered assistance for manual assembly at a workbench), Manual Working Station from SmartFactory KL (development of a smart workstation, equipped with an assistance system for the sequential assembly process), Operator Support System TNO (development of an operator support system for assembly that focuses on information assistance for the worker), and Active Assist Bosch Rexroth (system that serves as a configurable and open web platform. Features of this software include contextual information provision and a standardized interface for additional system components (e.g., pick-to-light, projector, touch screen, RFID reader)). Zamfirescu et al. [
27] focused on visualization and digital guidance of the manual assembly process. They claim that with increasing complexity and variability of products, the main difficulty for the operator is to follow the correct assembly procedure to correctly produce the desired product. To assist in the process, there are many possible alternatives for establishing a smart work environment (e.g., pick/put-by-light/voice/vision systems) to guide the human operator during the assembly process. There are also alternative recommendations, such as augmented reality (AR), virtual reality (VR) guidance [
15,
18], and projector-based digital guidance [
28]. The evaluation of AR visualization assistive systems in the reported papers consistently found that untrained users can assemble products faster and with a lower error rate, which is the goal in industry and digital transformation theory. In the literature, less attention has been paid to the microlevel of digital transformation, i.e., the individual workplace environment and the range of new digital tools that support or hinder the way people work [
29].
From the literature [
5,
11,
12,
13,
18,
19], it is clear that the need for human labor is great and that only with a properly designed work environment is it possible to perform tasks efficiently and ergonomically, thus increasing productivity in industry. In order to achieve an ergonomic manual assembly process and remain competitive in the market, it is necessary to develop a new algorithm that controls self-configuring and self-adaptive smart manual assembly workstations implemented with smart tools and technologies. The idea of self-configuration comes from the principles of I 4.0, which is the restructuring of today’s systems into smart factories.
The market offers us many smart solutions and tools, which in themselves do not guarantee that the manual assembly workstation will be self-configuring and adaptable. We also do not find complete solutions on the market that would provide an ergonomically designed smart manual assembly workstation for an individual worker. When designing a smart manual assembly workstation, various parameters must be taken into account, such as: type of product (dimensions, material), type of assembly (high, medium, low complexity), product structure (component assembly sequence), gender of workers, anthropometric data of workers (age, body height, limitations), type of workplace (standing, sitting, combined), etc. The solutions of individual providers in the market are partial, as they only offer solutions for specific segments described above [
30,
31]. Thus, the industry still has the problem of an inadequately designed workplace that cannot keep up with rapid market changes, “customization” of products, and the constant tendency to (re)configure the workplace in relation to the individual worker.
One of the possible solutions to overcome this problem and the main contribution of our study is the digital transformation of the workplace through the implementation of a new multi-criterial algorithm (MCA) on the manual assembly workstation. Digital transformation has been found to come in many shapes and forms. It is therefore not surprising that strategies for digital transformation take a broad perspective and emphasize the transformation of products, processes, and organizational aspects due to new technologies. In our paper, we propose the digital transformation of the manual assembly process by digitalization of the manual assembly workstation through the development and implementation of a new algorithm. An MCA configures the workstation and guides the worker according to the assembly instructions. It takes into account several influential parameters that need to be considered when (re)designing a new assembly workstation to make it smart, self-configuring, and ergonomic. Influential parameters that are considered are: the nature and skills of the individual workers, the characteristics of the products, and the complexity of the assembly. Only through the “right” combination of parameters, provided by an MCA that controls smart tools and adapts the workplace to the individual worker, we can achieve a significant increase in productivity and an ergonomically suitable workplace. At the same time, an MCA facilitates the prevention of errors during the assembly process (even for untrained workers), thus increasing the value added per worker.
This paper is organized as follows:
Section 2 focuses on the step-by-step presentation of the MCA and the description of the case study.
Section 3 presents the results and discussions on the error analysis and ergonomic evaluation of the workstations.
Section 4 summarizes the final results of the study.
4. Conclusions
Nowadays, there is a need to restructure companies according to the principles of Industry 4.0 in order to ensure higher productivity and manufacturing efficiency, while introducing ergonomic working conditions. Despite the modernization and automation of production, the involvement of workers in the industrial environment is still high. In order to ensure workers’ well-being and a worker-friendly environment in manual assembly, it is necessary to modernize and adapt workstations to workers’ needs and implement virtual modelling approaches in order to prevent work-related injuries and diseases.
In this paper, as a solution to this problem, we propose a newly developed multi-criterial algorithm (MCA) that monitors and controls smart tools implemented on a smart manual assembly workstation. The main controlled smart tools related to the I4.0 that have an impact on increasing efficiency are a height-adjustable worktable, adjustable lighting according to direction and intensity, rotational assembly nest, and self-adjusting grab containers that adjust according to distance and tilt. The smart manual assembly workstation is also equipped with digital instructions that show the structure of the product (assembly steps) and pick-by-light technology that uses a laser beam to guide the worker to the grab containers where the required part is stored. The MCA configures the manual assembly workstation according to three groups of influential parameters: the individual worker (body height, gender), the complexity of the assembly process (precise, normal, heavy), and the type of product (dimensions, product structure).
The MCA was tested with experimental analysis in a laboratory environment, where we compared the times of manual assembly in a smart and a classic assembly workstation to show the increase in efficiency, productivity, and time savings. In the virtual environment, we performed several ergonomic analyses to show the suitability of the working conditions during the manual assembly process for the individual worker. The performed analyses were related to the reach envelope, the distribution of the parts in the grab containers and the forward reaches, as well as the load on the joints during the assembly process without (basic version) and with smart tools (improved version).
In the time analysis, we compared the times obtained during the assembly process on a smart manual assembly workstation controlled by an MCA with the times obtained during the assembly process on a classic manual assembly workstation. For all eight products for which we compared times, shorter times resulted when workers performed assembly at a smart manual assembly workstation controlled by a new algorithm. On average, times were 13.6% shorter. Despite the fact that we achieved shorter times for manual assembly at a smart manual assembly workstation, it must be emphasized that this is the result of the idea and purposeful development of a smart manual assembly workstation controlled by a new algorithm with the individual worker in mind, and not an intention to restrict or burden workers with new, even stricter time standards. By achieving shorter times without “official” time standards for manual assembly, we have shown that each worker performs the assembly process faster at a manual assembly workstation implemented with an MCA and smart tools, increasing the efficiency and productivity of the overall work process.
For ergonomic analyses, we used Siemens Jack simulation tool, which allows us to evaluate the suitability of the workstation and use “what-if” scenarios to identify health risk factors without actually exposing the workers.
The ergonomic reach envelope analysis showed that the manual assembly workstation is ergonomically suitable for each worker in terms of smart tool arrangement (distribution), according to the calculation of the MCA. The results of the reach envelope analysis can be interpreted as the maximum range in which the worker can reach for parts or tools without health risk. From the results of our case study, the arrangement of the grab containers calculated by the new algorithm is within the reach envelope, which means that the distribution of the smart tools is ergonomically suitable for the long-term work of an individual worker.
The ergonomic analysis of “reaching forward” has shown that the arrangement of self-adjusting grab containers according to the calculations of the new algorithm is appropriate on a smart manual assembly workstation, as the worker never enters the area C, which poses a health risk and the possibility of work-related diseases and injuries in the long term.
When analyzing the stress on the joints, we ran two “what-if” scenarios. The first represents the basic version of the manual assembly process at the workstation without implemented smart tools, and the second an improved version focusing on the assembly at the workstation with implemented smart tools and algorithms. The results show that with the improved version, the time of overloading individual joints is reduced or eliminated, which means that the assembly process is more ergonomically appropriate.
The results of all ergonomic analyses performed on an MCA, which controlled the manual assembly workstation show that any configuration of the workstation is ergonomically suitable for individual workers to perform manual assembly without the risk of work-related diseases and injuries.
All the results of the analyses have shown that the introduction of I4.0 principles, the digital transformation of existing workplaces, and the implementation of a multi-criterial algorithm at manual assembly workstations is recommended, both to preserve the health (ergonomic analysis) and to increase the efficiency (time analysis) of workers, leading to an increase in productivity and competitiveness of the entire company.