The sociodemographic challenge in human-centred production systems – a systematic literature review

Abstract Industries are currently struggling with ageing workforce in modern production systems associated with Industry 4.0. The industrial socio-demographic problem is more and more present as the increasing of the ageing population results in the prolongation of the working life and the consecutively in the ageing of the workforce in industries. This paper aims to conduct a systematic literature review on the challenges and concerns of ageing operators, including the physical, cognitive, ergonomic, and well-being conditions of the ageing workforce in the Industry 4.0 environment. The ScienceDirect, Scopus, Web of Science and PubMed scientific databases were used to survey the studies and selected using PRISMA guidelines. This paper was structured and analysed by clusters: Ageing, Industry 4.0, Human Factors, and Ergonomics. These clusters were developed as research lines: Ageing as the socio-demographic challenge, Industry 4.0 as the technological development, Human Factors as the individual characteristics of the operator, and Ergonomics as the work environment. Thus, human-centric approaches and ideas are discussed with the insights and issues of Industry 4.0 technologies, Human Factors, and Ergonomics to achieve a sustainable system at the engineering and social level. Graphical Abstract


Introduction
With the Fourth Industrial Revolution, numerous and notable improvements in manufacturing and production systems emerged, mainly achieved through the integration of technological advances linked to information, services, and manufacturing (Salkin et al. 2018).When the Industry 4.0 concept appear, advances and improvements in services and manufacturing environments consequently arrived.Industry 4.0 has been highlighted as one of the main revolutionary industrial initiatives (Salkin et al. 2018).
With Industry 4.0, new productive systems emerged, and therefore, companies and industries were forced to adopt this concept and adjust themselves gradually, facing different paradigms.One of those challenges that continue these days is the increasingly ageing workforce in the industries, and, at the same time, more demanding production systems and also the higher quality level of the products (Calzavara et al. 2020).The workforce ageing is caused by the sociodemographic problem, namely the global ageing of populations, the increase in their average retirement age, and the average life expectancy.Therefore, the ageing workforce has an impact on production systems performance, alongside the Industry 4.0 technological advancement (Walker 2015;Calzavara et al. 2020).So, it is necessary to adopt productive strategies that accompany the Industry 4.0 development and solve the intrinsic paradigms, mainly strategies that make it possible to place human factors as the central focus of productive systems (Calzavara et al. 2020).Thus, to analyse the workforce from the point of view of the productive system, it is imperative to understand how older workers can be comprehended, supported, and involved in a productive manufacturing system.
So, in this article, a systematic review was performed to evaluate the state of the art of the main concerns and challenges about the ageing workforce in the labour context of Industry 4.0, and the physical, cognitive, ergonomic, and well-being conditions of operators in the future industrial environments.

Materials and methods
A Systematic Literature Review (SLR) was performed to systematically collect and evaluate all available data about the sociodemographic challenge in the future industries, known as smart factories, specifically the ageing workforce challenges in the new and modern production systems.This SLR aims to establish a literature review process that allows the identification, assessment, and interpretation of the available literature on human-centred approaches in productive contexts, namely those that focused on facilitating the integration of the human factor into existing production systems in smart factories.A four-phase flow diagram and the Preferred Reporting Items for Systematic Review and Meta-Analyses statement guidelines, usually known by PRISMA, were followed to perform this review article.

Focus question
Increasingly, the challenges and concerns surrounding the ageing workforce in industrial production systems have been explored and investigated.In general, the studies conducted focus only on a single direction of research line without being centered specifically on the ageing operator.Namely, as example, Sgarbossa et al. (2020) made the description of individual characteristics and human factors that affect the work capacity in the productive systems of Industry 4.0 and of the future and, on the other side, Giakoumis et al. (2019) and Mark, Rauch, and Matt (2021) made the description of technological and assistance solutions to the worker.In contrast, Calzavara et al. (2020) present management strategies in the context of the ageing workforce.
That said, it is important to study and analyse human-centred productive systems, introducing the concepts and aspects of Industry 4.0, Human Factors, and Ergonomics, to achieve a sustainable production system for the ageing operators that leaded us to our research question: Are there solutions available to assist and adapt the ageing workforce in the context of Industry 4.0 that take both human and ergonomic factors into account?

Information sources and data collection process
The literature search strategy used for this SLR article was based on a search of four electronic databases: Science Direct, Scopus, Web of Science, and PubMed.For the initial screening process that took place in September 2021, Boolean operators were used.The search in databases was performed using pre-determined keywords that are related to the main areas of the study such as the Ageing Workforce, Human Factors, Ergonomics, Industry 4.0, and Smart Factories, as already described above.
The keywords were chosen according to the principal keywords of the studies that served as the basis of research question formulation and with the principal words that compose the research question.Initially, a search equation was used for the databases that covered all keywords: (('ageing workforce' OR 'aging workforce') AND ('human factors' OR 'ergonomics') AND ('Industry 4.0' OR 'smart factory')).However, the research was too restricted, conditioning the study.Moreover, it would set aside potential relevant data for the study.Thus, the established sentence of keywords was (('ageing workforce' OR 'aging workforce') AND ('human factors' OR 'ergonomics')) and was first used in the advanced search of Science Direct, Web of Science, and PubMed.After that, the syntax was adapted to the Scopus database: TITLE-ABS-KEY (('ageing workforce' OR 'aging workforce') AND ('human factors' OR 'ergonomics')).The language search was English, and the period time was restricted from 2016 to 2021 to only include recent and relevant data.

Eligibility criteria
In this review, we analysed studies that present the human factors and ergonomic aspects that may affect the work capacity and well-being of operators in the future production systems, the technologies of Industry 4.0 as solutions to the worker, and strategies of engineering and management in the context of the ageing workforce.The authors first conducted the preliminary selection and exclusion based on paper titles and abstracts.The screening process was performed by two authors, which may cause a limitation for the study.The following inclusion/exclusion criteria were employed for eligibility: only studies with fulltext available, published in English, including research articles, review articles and conference papers that present and explore at least one of the main areas of the study: Ageing Workforce, Human Factors, Ergonomics, and Industry 4.0.

Principle findings
This systematic review search identified a total of 80 articles at Science Direct, 27 at Scopus, 15 at Web of Science, and 4 at PubMed, totalling 126 papers.After that, 10 articles were unavailable for full read text, and, because of that, were excluded.Of these remains 116 articles, 27 were duplicates or triplicates and were excluded.Thus, a total of 89 remained after the exclusion of repeated articles.The next step was the exclusion by the titles and by the abstract, only 63 papers were eligible for the current study purpose.The remaining 63 articles were analysed and included in this systematic review (Figure 1).
The first analysis performed gives an overview of the selected studies (Table 1).Author(s), publication date and type, country of study, study design, and type of method applied are presented.Of the selected studies 39 are journal articles and 24 are conference papers.The publication dates of the articles were verified and, once again, the growing research and investigation of the ageing workforce in productive systems was confirmed, namely growth in the last three years (Figure 2).The selected studies were conducted in several countries, with the most being in Germany and Italy.Regarding the type of study, 30 are qualitative, 9 are quantitative, and 24 are mixed.However, the method of study used in the selected studies is predominantly surveyed research (48 studies).Only 9 use interviews and the remaining 11 are observational.
The second analysis was verify the main keywords of the studies.The database information was used in software, named VOSviewer, which allows generating maps with the main   relations and interconnections of the information.In this article, was created a VOSviewer Map through the keywords' occurrence.The software analyzed the database and created a map with the keywords that have a major occurrence (Figure 3).
The keywords that had high occurrence were Ergonomics, Industry 4.0, Human Factors, and Ageing, which are interconnected and linked to another's, such as workability, ageing workforce, age management, age-productivity, social sustainability, sustainability, among others.They are organized by 4 clusters as represented in Figure 3 through different colours: Industry 4.0 as yellow, Human factors as blue, Ergonomics as red, and finally, Ageing as green.The division by clusters allowed the sectioning of the following chapters as lines of research that influence the ageing workforce.Table 2 represents the characterization and framework of the studies selected for the systematic literature review according to the clusters obtained.It should be noted that, as expected, there are several studies that correlate more than one cluster.x (Tlach et al. 2019) x (Di Valentin et al. 2016) x (Varianou-mikellidou et al. 2019) x (Varianou-mikellidou et al. 2020) x x (Villani et al. 2018) x (Weßkamp et al. 2019) x x (Wolf, herstätter, and ramsauer 2019) x

Ageing -the sociodemographic challenge
In recent decades, a significant demographic change across the world aroused, namely in Europe, the USA, Canada, Australia, and Japan.Population ageing is a current trend and concern as it reveals a huge impact on society and the economy, posing significant challenges in governments, companies, and industries (Varianou-Mikellidou et al. 2019).At a European level, the number of people over 65 years of age is on the rise and is expected an increase to 66 million people by 2060 (Bogataj et al. 2019a).On the other hand, a decline of 0.4% of the working-age population in the European Union is expected by 2040 (Giakoumis et al. 2019).Through these data and forecasts, there was a need to raise the retirement age and apply restrictions to early retirement to prolong the human working life (Dimovski, Grah, and Colnar 2019), and to maintain sustainable pension funds and public finances (Bogataj et al. 2019a).
The population ageing is directly reflected in the workforce, that is, the growing number of older individuals who actively remain in the labour force (Varianou-Mikellidou et al. 2019).Thus, changes in the employability rate of people aged between 55 and 64 are speculated, where workers of these ages are expected to represent more than 30% of the workforce in 2030 in several countries (Giakoumis et al. 2019).Another prediction indicates that approximately one-third of the workforce will be over 50 by 2050 (Eaves, Gyi, and Gibb 2016).
Combining the predicted shortage of workers of 13 million people by 2035 and the ageing workforce, it is important to find solutions to retain older workers in the workplace (Dimovski, Grah, and Colnar 2019).So, operators will need to recognize the requirement for change and adaptation over time, namely, they need to learn to operate Cyber-physical Systems, collaborate with robots, and operate in intelligent production cells (Dimovski, Grah, and Colnar 2019).
The human being's ageing process leads to physiological and cognitive changes that can become a challenge for the work development (Eaves, Gyi, and Gibb 2016).Increasing age is usually understood in terms of its chronological nature, however, there may be differences in the functional abilities of individuals of the same chronological age (Varianou-Mikellidou et al. 2019).Ageing can be related to the concept of age and fractionated into 4 dimensions: the functional age that is associated with health, the psychosocial age that refers to social and psychological perception, the organizational age that represents the career stage and obsolescence of skills and, finally, the age of life directly related to the stage of life or family situation (Varianou-Mikellidou et al. 2019).
In the workforce context, the concept of ageing is defined by 3 main characteristics of operators: (1) physical, mental, and social well-being; (2) high levels of commitment and performance at work; (3) professional experience and work safety (Dimovski, Grah, and Colnar 2019).Another alternative method of classifying and measuring age in terms of work performance and ability is work capacity (Varianou-Mikellidou et al. 2019).This method refers to the functional capacity to meet the requirements of work as a healthrelated resource that supports the adaptation of workers to different work environments (Panagou, Fruggiero, and Lambiase 2021), but it also focuses on the human resources characteristics, namely physical capacities, mental and social capacities, values, attitudes, and motivation (Varianou-Mikellidou et al. 2019).However, the working population has different anthropometry, abilities, and preferences that directly or indirectly affect work capacity (Hussain et al. 2016).

Industry 4.0 -the technological development
Digitization, Internet of Things, Cyber-Physical Systems, Additive Manufacturing, Cloud Computing, and multiple advances in automation and robotics in manufacturing processes are associated with the fourth industrial revolution, known as Industry 4.0 (Reiman et al. 2021).In manufacturing systems, technological solutions offer an intelligent network of interconnected value-added systems that can communicate with each other and with others.That said, Industry 4.0 technologies can be considered the crucial enablers for the development and evolution of the production systems of the future (Schlegel, Langer, and Putz 2017).
However, only companies with high technological skills can use and benefit from the technological development since manufacturing processes are increasingly complex and require new types of demands, namely in management processes and in the skills and abilities of operators (Reiman et al. 2021).
In the context of Industry 4.0, manufacturing and production must be smart and adaptive, with collaborative and flexible systems that use information technology, sensor networks, computerized controls, and production management software to be able to solve autonomously the problems that arise during the processes and, at the same time, improve their efficiency (Peruzzini and Pellicciari 2017;Reiman et al. 2021).However, the implementation and adaptation of Industry 4.0 have so far been oriented towards optimizing the efficiency of production systems in terms of time, costs, and production rates, leaving aside human factors.While intelligence, collaboration, and automation are achieved through the ideals and concepts of Industry 4.0, the flexibility of production systems is achieved through the human dexterity and cognitive abilities of the workforce (Mossa et al. 2016).This indicates that even with the technological development emerging in companies and industries, including smart factories, there are challenges related to the human being, that is, to all workers, operators, and technicians because they need to operate in new and modern ways (Reiman et al. 2021).
Therefore, it is imperative to consider and better understand the complexity of production systems, combining organizational, technological, and human perspectives (Reiman et al. 2021).During the fourth industrial revolution, the worker himself can be seen as an indispensable and crucial resource for the development of productive systems (Mark, Rauch, and Matt 2021).The role of the human being is subject to significant changes and, therefore, it is necessary to understand how technological development and Industry 4.0 will affect human operators, as they are a vital resource in future production systems.Furthermore, the production-operator interaction allows shaping competitive workplaces and centring the human operator to overcome the barriers of Industry 4.0 in a socially sustainable way (Pinzone et al. 2020).
On the other hand, smart factories or factories of the future that include Industry 4.0 technologies enhance quality and satisfaction in the workplace through improvements in working conditions (Giakoumis et al. 2019).Industry 4.0 solutions and smart technologies can be used as solutions to reduce the physical effort and training time of workers (Finco et al. 2020) as well as develop technical assistance systems to support the operator in daily tasks (Mark, Rauch, and Matt 2021).An example of this is the application of responsive user-centred production systems that employ technologies such as advanced collaboration tools, namely robots (Mateus et al. 2018).
In essence, how human beings work and operate in production systems is changing rapidly, and the technological evolution, the application of Industry 4.0, and the social changes are transforming all processes and systems of production and operations (Sgarbossa et al. 2020).Thus, Industry 4.0 presents challenges and opportunities, not only in the intrinsic issues of productive efficiency and sustainability but also in focusing on imminent demographic changes such as the ageing of workforce (Dimovski et al. 2019;Mateus et al. 2018).The new digital industrial revolution implies an intelligent manufacturing environment with interconnected and collaborative systems with human beings to expand and enhance their capacities and abilities.With this, it is possible to encourage more age-friendly work environments (Dimovski et al. 2019;Calzavara et al. 2020).

Human factors -the individual characteristics of the operator
Human Factors in the industry have become a major focus of attention and an emerging and relevant trend for engineering, namely the extraction of human knowledge for the integration and development of intelligent systems, the human ergonomic and cognitive analysis to develop intelligent production environments (Peruzzini and Pellicciari 2017).
An important point is the inclusion of Human Factors related to the age of operators in production systems, and when designing new productive assembly lines (Claudon et al. 2020).However, it is necessary to take a detailed approach regarding Human Factors because many aspects can be missed due to the practical limits of detail and because Human Factors are normally addressed through checklists and questionnaires during the processes (Baybutt 2016).
Human Factors can be defined as the set of human characteristics that influence complex production systems and influence the adaptation of different work environments centered on the operator (Gräßler, Roesmann, and Pottebaum 2021).Human Factors can also be described as a scientific discipline that focuses on optimizing human well-being and overall system performance through understanding the physical, cognitive, and psychosocial interactions between human beings and other elements of the system (Gräßler, Roesmann, and Pottebaum 2021).Thus, Human Factors will doubly potentiate the production systems: the human and technological point of views (Sgarbossa et al. 2020).It will focus on the cognitive, emotional, and motivational needs and skills of operators, but also on the influence of the design of work, in tasks, and in the way of work is carried out (Gräßler, Roesmann, and Pottebaum 2021;Berti et al. 2021) In addition, the application of Human Factors beneficially contributes to production systems, such as reducing errors, increasing the flexibility and performance of systems, and increasing human safety (Gräßler, Roesmann, and Pottebaum 2021).
The heterogeneity of operators in the performance of production systems, from the efficiency of the workforce to the individual skills and capabilities of operators, is crucial to be considered (Peruzzini and Pellicciari 2017;Varianou-Mikellidou et al. 2020).Physical and cognitive functional abilities show a tendency to decline with the progressive age increasing, even with interpersonal variabilities, such as the level of education, health, lifestyles, and work-related stress (Peruzzini and Pellicciari 2017).However, a natural decrease in visual, acoustic, and musculoskeletal/motor strength, reduction of memory, concentration, and learning ability, as well as balance and thermoregulation problems are described (Berti et al. 2021).
Furthermore, from an industrial perspective and particularly in production systems, physical ergonomics is analysed to avoid musculoskeletal disorders due to inadequate postures and repetitive actions.For this reason, ergonomics is often excluded from human factors and studied in isolation as the discipline of ergonomics, which includes the anthropometric, physiological, and biomechanical characteristics associated with physical activity (Peruzzini and Pellicciari 2017;Gräßler, Roesmann, and Pottebaum 2021).Therefore, the ergonomic aspects are detailed in the next section.

Ergonomics -the work environment
Currently, in the work environment, ergonomic risks cause countless losses to workers (Bogataj et al. 2019a).One of them is work-related musculoskeletal problems and disorders as they are directly related to poor ergonomics in the workplace and undesirable physical exposures in work systems (Crawford et al. 2020).One option to control and avoid these safety and ergonomic issues is to invest in ergonomics, namely in technologies to automate tasks with a higher level of physical demand or repetitive ones, such as collaborative robots (Botti, Mora, and Regattieri 2017;Bogataj et al. 2019a).
Ergonomics, like Human Factors, is a scientific discipline where principles, data, and methods are applied to understand the interactions between the human being and the other elements of a system (Reiman et al. 2021).Ergonomics aims to improve the compatibility, safety, ease of performance, and well-being of human beings, to increase their quality of life and reduce ergonomic risks while improving productivity (Botti, Mora, and Regattieri 2017;Reiman et al. 2021).Furthermore, the application of ergonomic intervention programs aims to reduce human error, reduce waste, increase productivity, and increase safety (Boysen, de Koster, and Füßler 2021).The importance of the workplace and work processes are also analysed by the discipline of Ergonomics to improve worker health and safety (Botti, Mora, and Regattieri 2017).
The implementation of ergonomic techniques and supports is one of the main challenges of occupational health and professional life, such as preventing musculoskeletal pain, high physical effort, and fatigue (Holtermann, Mathiassen, and Straker 2019).Another approach is the application of elements of participatory ergonomics where there is the involvement of key stakeholders to benefit the workplace, equipment, and healthy work behaviour (Eaves, Gyi, and Gibb 2016).
Regarding the paradigm that organizations face with the ageing of the workforce versus productivity, investments in human ergonomics and robotization can be improved to allow the elderly to work longer with increased productivity (Ore et al. 2016;Bogataj et al. 2019a;Dimovski et al. 2019).The integration of ageing workers should be supported because the operator is considered an integral part, and together with ergonomic goals, define agefriendly workplaces and processes in production systems (Battini et al. 2018).
For the assessment of physical ergonomic factors, several ergonomic risk indices are used, such as the RULA method (Rapid Upper Limb Assessment), the OCRA method (Occupational Repetitive Action), the NIOSH lifting equation, the rapid whole-body assessment method (REBA), and the Ovako working posture analysis system (OWAS).With these methodologies, it is possible to evaluate sequences of actions performed by workers and postures adopted during the execution of tasks, through modular analysis of anthropometric information, task analysis, and assessment of physical workload (Peruzzini and Pellicciari 2017;Berti et al. 2021).Another approach is the use of an integrated ergonomic KPI's (Key Performance Indicators) calculation system where the operator's performance is evaluated in terms of operation time and fatigue during and after performing different tasks (Battini et al. 2018).

Discussion
The workforce ageing problem in the productive systems of the future has become a current focus of study by researchers and industries.However, the scientific articles that relate in detail the influence that the human factors with the technological development of Industry 4.0 have on the ageing workforce in the industrial future productive systems are limited.Many of them are articles from scientific conferences, which demonstrate the topicality of the theme and its importance.
Currently, many studies carried out are directed to technological solutions (Table 3) oriented to productive systems with the use of human flexibility and creativity and in their ability to solve problems (Schlegel, Langer, and Putz 2017), or to services such as road transport (Bedinger et al. 2016).
For example, the introduction of measures that facilitate the human-machine interface, namely the incorporation of an industrial social work network that allows the exchange of information between operators to facilitate the resolution of problems (Villani et al. 2018).Another technology that has been extensively studied is the use of collaborative robots to assist in movements and reduce physical demands (Guo et al. 2017) or to transport pieces within the manufacturing sites (Emde and Gendreau 2017).Consequently, several research focuses on the interaction of collaborative robots with the operator (Tlach et al. 2019;Weßkamp et al. 2019;Bogataj et al. 2019b) whose collaboration can be employed so that the robot performs high-risk tasks and tasks that add value are performed by humans (Marturi et al. 2016;Renteria and Alvarez-de-los-Mozos 2019).
Augmented Reality technology has been applied in more complex industrial processes to increase the efficiency of tasks and increase productivity.Furthermore, it can also be used for the training and education of workers (Jetter, Eimecke, and Rese 2018;Eder et al. 2020).
Table 3. human-centered approach for ageing workforce using tecnologies.
Technologies (industry 4.0) industrial social work network for information fluency (Villani et al. 2018).
augmented reality technology increase the efficiency of tasks and can be applied for training and educate workers (Jetter, eimecke, and rese 2018;eder et al. 2020).
simulation software to optimize the integration of human-robot collaboration (ore et al.

2016).
application of wearable sensors, intelligent devices, and trackers for the analysis of work time, effort, and physical fatigue (Berti et al. 2021).
learning factories can also be used for solving the problems of the ageing workforce adaptation (Wolf, herstätter, and ramsauer 2019).
Regarding Human Factors, some studies analyse the diversity of the workforce in terms of age, gender, individual capacities, and abilities in productive systems (Katiraee et al. 2019;Mokarami, Kalteh, and Marioryad 2020;Gräßler, Roesmann, and Pottebaum 2021;Table 4).Therefore, the importance of Human Factors and Ergonomics in the context of work performance and rapid technological development is described (Reiman et al. 2021), to centralize people in the processes and operations of intelligent and sustainable systems (Sgarbossa et al. 2020).An example of this is the use of time planning systems that allow shift choices that facilitate the balance between work and family, and also the balance between genders (Lefrançois and Probst 2020).
Other studies have been dedicated to the design of human-centred workstations (Mateus et al. 2018), considering ergonomic aspects (Diefenbach and Glock 2019), the reduction of physical activities with the help and collaboration of robots (Diefenbach, Emde, and Glock 2020), and human factors such as age, gender, and health conditions (Hussain et al. 2016).An example of technology application for the design of workstations is the use of simulation software to optimize the integration of human-robot collaboration (Ore et al. 2016).More simply, the use of padded insoles became effective for reducing musculoskeletal discomfort in workers who had to stand for prolonged periods during work (Speed, Harris, and Keegel 2018).On the other hand, the participation of workers in industry change has been increasingly introduced through awareness of physical demands and the introduction of numerous ideas from operators to improve health and wellbeing in industries (Eaves, Gyi, and Gibb 2016).
From an ergonomic perspective (Table 5), studies emphasize the development of sustainable work systems that avoid the development of musculoskeletal disorders, such as a simple replacement of bags/boxes with wheeled garbage containers in the work of garbage collection (Thomas, Hare, and Evangelinos 2021).On the other hand, psychosocial risks, behaviours, and working conditions that influence musculoskeletal disorders and workers' health are  elderly workers can educate and train younger or inexperienced operators (Finco et al. 2020).
older operators prioritize the main task (arnau, Wascher, and Küper 2019).schedule job rotation depending on age, physical capacity, and experience level to maximize productivity (Finco et al. 2020).
Table 5. human-centered system with ergonomic approaches for ageing workforce.
ergonomics control the psychosocial risks, behaviours, and working conditions that influence musculoskeletal disorders (sorensen et al. 2016;oakman, macdonald, and Kinsman 2019).
utilization of body sensors to capture information of ergonomically unfavourable positions (Di Valentin et al. 2016).
use of exoskeletons to improve the health and quality of life (omoniyi et al. 2020) reduce musculoskeletal discomfort with better equipment, such as padded insoles (speed, harris, and Keegel 2018) Participation and integration of workers (eaves, gyi, and gibb 2016).
also studied (Sorensen et al. 2016;Oakman, Macdonald, and Kinsman 2019).Other cases use body sensors as assistance systems that capture information about workers to assess ergonomic data and report when ergonomically unfavourable positions occur during the execution of the workflow (Di Valentin et al. 2016).Also at the ergonomic level, one can resort to the use of exoskeletons to improve the health and quality of life of users, however, the exoskeleton design must be executed for greater practicality and feasibility of use (Omoniyi et al. 2020).
In addition to technological solutions, another major theme of the study is the existing demographic change and the respective ageing workforce.Initially, the industry focused on improving production systems and almost neglected human factors, namely the tendency for workers to age (Peruzzini and Pellicciari 2017).However, for older operators, physical demands limit the performance of their own jobs (Skovlund et al. 2020).Keeping workers active is a great challenge, mainly because older workers have accumulated experience and knowledge that may present limitations for future work, due to the decline of their functional abilities (Grah et al. 2020).Several studies show that the inclusion of age management practices is crucial, namely the integration of human-machine support, intelligent work environments, production cells, and exoskeletons (Bogataj et al. 2019b;Dimovski et al. 2019;Grah et al. 2020).Other studies report that the application of digitization and automation as operational tools, using, for example, wearable sensors, intelligent devices, or trackers allow the analysis of work time, effort, and physical fatigue (Berti et al. 2021).
An effective decline in cognitive control functions related to mental fatigue and age has been described (S Arnau et al. 2017;Stynen, Jansen, and Kant 2017).Studies have revealed that older operators prioritize the main task, are more vulnerable to outages (Stefan Arnau, Wascher, and Küper 2019), and have a longer response time while performing tasks (Norheim, Samani, and Madeleine 2021).On the other hand, older operators showed a positive attitude towards industrial innovations, contrary to expectations, due to stereotyped opinions (Barska and Śnihur 2017).Furthermore, as older workers have higher levels of experience (Berti et al. 2021), they may be able to educate and train younger or inexperienced operators (Finco et al. 2020).Learning factories can also be a point of solving the problems of the ageing workforce (Wolf, Herstätter, and Ramsauer 2019).
A current challenge is to include measures in the organization of the operators' work process according to their profile and physical and cognitive restrictions.Numerous studies of mathematical modelling and programming have arisen to plan work schedules by age (Berti et al. 2021) or to schedule job rotation depending on age, physical capacity, and experience level to maximize productivity (Finco et al. 2020).Other mathematical models have been described to schedule tasks and activities for older workers when exposed to the risk of repetitive work to reduce ergonomic risk and select workers according to their abilities and skills for the job (Mossa et al. 2016;Botti, Mora, and Calzavara 2017;Hochdörffer, Hedler, and Lanza 2018).
Other studies focus on occupational health and safety to reduce risk factors mainly related to age and ageing, through the awareness of a safe and healthy working population (Varianou-Mikellidou et al. 2019) and others with the promotion of participation and involvement of more old workers at work (Steenstra et al. 2017).
Regarding practical cases, there are already several projects under study.A humanadapted manufacturing system, which adapts to the needs of ageing workers due to physical and cognitive decline, to improve the human-machine interaction and consequently the well-being of the worker in a carpentry industry.In this project, simulation and virtual reality were used to design the new manufacturing system and also the application of intelligent devices to assist the operator during tasks (Peruzzini and Pellicciari 2017).Another aircraft parts manufacturing industry conceptualized a centralized and individualized work system at the operator through technological information assistants and individual adjustments to support each operator (Mayrhofer, Rupprecht, and Schlund 2019).For operator monitoring, the use of a motion capture system integrated into immersive reality combined with heart rate monitoring was studied so that the operator can move and integrate a virtual work environment for planning the pre-production and workplaces to avoid undesirable ergonomic situations and have real-time feedback of operator fatigue (Battini et al. 2018).
Finally, one of the most highly regarded studies with evident practical results is the case study by BMW which designed a pilot production line specifically for older workers.70 changes were made including ergonomics and management aspects and production levels increased and absenteeism decreased.This indicates that workforce involvement in industry change can help retain older workers and, at the same time, increase productivity (Eaves, Gyi, and Gibb 2016).
That said, it is clear to say that the human being is an important key for the modern and future production systems.However, particular attention must be paid to the ageing workforce that is heavily present in today's industries.Older workers can be actively integrated into smart factories and the human factors become a key point in Industry 4.0.The heterogeneity of human resources has to be stimulated and integrated into smart industries.The individual capabilities and skills of elderly workers can be used and deployed to positively improve production systems.Namely, the experiences, knowledge, and decisionmaking capacity of operators can increase productivity.On the other hand, social and interpersonal skills allow the dynamization of the work environment and build socially sustainable workplaces with the well-being of operators.On the other hand, new productive models enable the utilization of technologies, that are linked to Industry 4.0, such as Virtual Reality, Augmented Reality, Simulation, Exoskeletons, Collaborative Robots, that help and assist the ageing operator during work tasks.
As the existing socio-demographic problems and, consequently, the ageing of the workforce is a growing concern for the industries and society, measures and strategies need to be taken to help the adjustment of the ageing operator to the modern industrial environment.Training and continuous learning, the application of Industry 4.0 technologies, and the introduction of heterogeneous and rotating work teams are some of the strategies that can boost and improve the engagement of ageing workers.The current existing solutions focus especially on increasing the productivity of industries, not considering human and ergonomic factors.Unfortunately, the thinking of companies is to increase their productivity and incomes with the lowest costs.Therefore, the operators' health, safety, security, and well-being are frequently neglected.The introduction and application of automation, robotization and other technologies are prioritized in industries, without concerning the operators.Responses and alternatives are needed that consider the well-being, health, and safety of operators in digitized production systems.
Thus, human-centred productive systems for the ageing workforce with the main concepts and influences of Industry 4.0, Human Factors, and Ergonomics are critical to achieve a sustainable system both at the engineering (economic and ecological sustainability in the production system) and social level (social sustainability in the humans interactions and involvement) (Figure 4).
Furthermore, with the recent focus on human-centric manufacturing discussion, the concept of Industry 5.0 has emerged with the intent of placing human workers and their well-being at the center of the manufacturing process (Aceta, Fernández, and Soroa 2022).This ideology follows the Industry 4.0 which is mostly driven by efficiency and quality improvement and cost reduction, being a system-centric strategy (Lu et al. 2022).In contrast, Industry 5.0 will be based on 3 key pillars: human well-being/centricity, sustainability, and resilience addressing human needs from basic occupational safety and health to the highest level of self-esteem, leading to the digital transformation of industry and society (Thorvald, Berglund, and Romero 2021;Aceta, Fernández, and Soroa 2022).Furthermore, human-centric systems must need to have proactive communication, two-way empathy, and collaborative intelligence to establish trusted human-machine co-evolution relationships with high performance (Aceta, Fernández, and Soroa 2022).
Therefore, with the numerous challenges that companies face in adapting their workforce to new work methods and intelligent production systems, it is particularly important to engage and adapt the ageing workforce, as the human capital of industries is critical to their success.

Conclusion, limitations and future agenda
The ageing workforce is an acquired fact for industries today.The centralization of human beings in the productive systems of the future is increasingly imperative, together with the adaptation of these systems in accordance with human factors and ergonomics, that is, with the capacities and abilities of each operator.Therefore, adaptation and engagement of the ageing workforce through Industry 4.0 technologies and age management strategies must be accomplished.With the emergence of the introduction of the centralization of human well-being, sustainability, and resilience with the future ideology of Industry 5.0 in industries and companies, a symbiosis of the combined implementation of the concepts and ideas of Industry 4.0 and 5.0 becomes crucial.Thus, it would be possible to obtain new productive systems with high performances and healthy and safe workers.
However, the application and realization in real industrial cases with described results is reduced, being considered a major limitation.Since the topic in question is urgent and topical, another limitation may also be associated with the restriction of scientific data of the research conducted in English.There are various other large language areas such as Germany, France, Spain, Japan, and Nordic countries that have the same problem with rapid technologization and ageing population in their industries.Hence, as future perspectives of research we could use publications not only in English to provide valuable new insights in this study.The screening process can be a limitation due to the small number of people involved in this step, which becomes more subjective.Therefore, in the future, it is intended to improve this point by adding an extra verification step in the screening process with the use of a third person.Studies in a laboratory environment may represent another limitation, as they may lead to results that are out of touch with reality, since the laboratory manufacturing environment may not consider all the social and environmental factors that influence operators' skills and competencies.In addition, the case studies should include groups of workers with various age groups and different work experiences.Another major limitation in the case studies is the difficulty of the openness and acceptance of the ageing operators to new ideas and technologies.
In addition, from the low implementation of Industry 4.0 in industries to the neglect of the human factor, it is necessary to take measures and create real and achievable strategies and methodologies to place the human factor at the centre of production, especially the ageing workforce.
In the future, it is intended to conduct studies in multiple industrial contexts, with special attention to the ageing workforce.At the level of ergonomic strategies, we intend to evaluate and improve the physical conditions and health of operators.Particularly for repetitive or ergonomically unfavourable tasks.In the work context, we want to help older workers to adapt to modern industrial technologies, through involvement with younger workers, multigenerational teams, or training sessions and appropriate coaching.If possible through management measures we want to improve the industrial environment and thus the well-being, health, and safety of older operators.

Notes on contributors
Joel Alves is a Ph.D. Student in Industrial Engineering and Management at the University of Beira Interior.Master's degree in Industrial Engineering and Management at the University of Beira Interior (2020).
Tânia M. Lima obtained her PhD in Industrial Engineering and Management at the University of Beira Interior (UBI), in 2013.She worked from 2001 to 2010 in a civil construction and public works company in which she performed technical functions in the Department of Studies and Projects.She was an Auditor of the company's quality management system and was responsible for the safety and health at work in public and private works contracts.She is currently an invited Assistant Professor in the Department of Electromechanical Engineering of UBI and integrated member of Center for Mechanical and Aerospace Science and Technologies (C-MAST) Research Group.She is involved in several research projects and in the supervision of master dissertations and doctoral thesis.Also, she is an author or co-author of articles published in several international journals and congresses proceedings.
Pedro Dinis Gaspar has a Ph.D. degree in Mechanical Engineering.He is a Professor in the Department of Electromechanical Engineering of the University of Beira Interior (UBI), Portugal.He is an integrated researcher at the R&D Centre for Mechanics and Aerospace Science and Technologies (CMAST), researcher at the collaborative laboratoty Food4Sustainability and a collaborator researcher at the ALLab -Assisted Living Computing and Telecommunications Laboratory, Instituto de Telecomunicações (IT).He is the Coordinator of the course of M.Sc. in Industrial Engineering and Management.He has coordinate the courses of M.Sc. in Mechanical Engineering and M.Sc. in Bioengineering.He is the professor in charge or assisted than more 15 curricular units of M.Sc.and undergraduate courses.Currently he teaches curricular units such as Air Conditioning and Industrial Refrigeration, Industrial Robotics, Industrial Automation, Decision-Support Methods.He has participated/coordinated several national and international research projects as well as R&D&I contracts with industry, mostly, related to energy efficiency and energy conservation systems, and automatic systems (robotics and automated systems) for agricultural activities.He has supervised several Ph.D. Thesis (8) and M.Sc.Dissertations (53).He has authored or co-authored more than 180 papers in refereed book chapters, journals and conferences proceedings and he is co-author of 6 patents.He was the editor of the two handbooks concerning refrigeration systems and technologies.He has been also involved in the organization of several national and international scientific events.His main research interests are related to Thermodynamics and Heat Transfer (Refrigeration and Air conditioning); Energy systems: Renewable energies, production, Rationalization and Sustainability; Computational Fluid Dynamics; Automation, Robotics, Control Systems and Embedded Systems applied to agricultural activities.

Figure 1 .
Figure 1.Prisma flow diagram displaying the results of the systematic research.

Figure 2 .
Figure 2. graphic representation of the number of articles published by year.
human factors explore the human flexibility and creativity(schlegel, langer, and Putz 2017).consider the ability to solve problems(schlegel, langer, and Putz 2017).Diversity of age, gender, and individual capacities influence productive systems(hussain et al. 2016).application of shifts and balances between work and 'out-work' (lefrançois and Probst 2020).Promotion of participation and involvement of old workers at work(steenstra et al. 2017).Keeping workers active with accumulated experience and knowledge(grah et al. 2020).

Figure 4 .
Figure 4. schematic representation of the concept of a human-centred production system with industry 4.0, human Factors, and ergonomics influences.

Table 1 .
characteristics and information of the reviewed studies.

Table 2 .
The framing and categorization of the reviewed studies with the study clusters.

Table 4 .
human-centered approach for ageing workforce considering the human factors.