Human-centric production and logistics system design and management: transitioning from Industry 4.0 to Industry 5.0

ABSTRACT

Industry 4.0 was presented more than a decade ago as the fourth industrial revolution, aiming to significantly raise the level of sophistication of interconnected technologies and thus increase manufacturing industries’ profits. However, because the technology-driven narrow focus of Industry 4.0 on performance and profit fails to explain how to increase prosperity for all the stakeholders involved, the European Commission has introduced the concept of Industry 5.0. This vision overcomes the weaknesses of Industry 4.0 by paying explicit attention to outcomes for humans in the system and establishing an environment to create human-centric, resilient, and sustainable systems. Considering these developments, this position paper and editorial introducing the special issue of the International Journal of Production Research elaborates on the transition from Industry 4.0 to 5.0 through 10 papers focusing on the human-centric pillar of Industry 5.0 and its impacts on production and logistics system design and management. This work presents guidance for a more systemic approach needed in future research: to include empirically grounded works and data-driven multimethod approaches that consider diversity in system operators and human factors demands holistically in order to incorporate ethical implications missing from Industry 4.0 – in the pursuit of Industry 5.0 systems.

KEYWORDS:

• Industry 4.0
• Industry 5.0
• human factors
• human centricity
• ergonomics
• state of the art

1. The dark side of Industry 4.0

In 2011, the German government presented Industry 4.0 (I4.0) as a new industrial revolution, which could be seen as a natural extension of past trends in automation that would lead to cyber-physical systems, the Internet of Things, and data analytics, among other technologies (Liao et al. 2017). Its goal was to significantly raise the level of sophistication of automation and interconnected technologies and thus increase the efficiency of the manufacturing industry (Kagermann 2014), as well as demands for those I4.0 products that are part of the German manufacturing ecology. The development was followed by a pervasive trend in many countries to digitalise more aspects of life and society at large, inspired by the manufacturing industries’ investments in low-latency interconnectedness, tracking systems, collaborative robots, machine learning, and virtual or augmented reality technologies, among others. In this outright inflation of “everything 4.0” development, terms like Operator 4.0 (Romero, Stahre, and Taisch 2020), Smart Manufacturing (Kusiak 2018), and Logistics 4.0 (Winkelhaus and Grosse 2020) emerged, all based on a vision of using automation/robotisation and digital technologies, also referred to as I4.0 technologies, to enable the satisfaction of individualised demands from workers and customers. However, as observed by Neumann et al. (2021), this implicit promise of benefit to humans has been almost entirely detached from the perspectives of Human Factors (HF), even though it is undisputed that such technological developments have fundamentally changed the role of humans and the way they work in production and logistics systems, as well as their perception of the working environment. The absence of attention to HF was observed in I4.0 development (Neumann et al. 2021) and, more specifically, in production and logistics systems design and management research (e.g., Grosse, Glock, and Neumann 2017; Sgarbossa et al. 2020).

While production and logistics systems can potentially benefit from implementing I4.0 technologies in terms of performance, relatively little is known about the actual effects of these technologies on human workers, particularly from perceptual, cognitive, physical, and psychosocial HF aspects (Neumann et al. 2021). This is relevant, since advanced automation/robotisation, digitalisation, and assistive technologies are becoming more prevalent in production and logistics, although there is consensus that humans will remain an essential part of almost all operations systems (Grosse 2023; Kadir, Broberg, and da Conceição 2019; Sgarbossa et al. 2020). This raises questions about the fate of humans in I4.0 systems. Production and logistics systems are sociotechnical systems with an explicit understanding that all sub-systems involve ongoing interactions between people, technology, and organisation, and that they are rapidly transforming virtually all areas of human life, work, and interaction (Neumann et al. 2021). The impact of digitalisation is substantially changing the way human work is organised and performed. At the same time, a well-known tenet of sociotechnical systems theory states that changes targeting the performance of a single subsystem (in this case, the technical system) risk making it suboptimal because of the ripple effects between people, technology, and organisation (Hendrick and Kleiner 2001). The ongoing underrepresentation of HF in this research stream has resulted in an important research and application gap (Neumann et al. 2021). Although manifold new forms of interaction between humans and technologies exist within business transformation, it is still not fully clear to stakeholders (in both industry and research communities) what “fully digitalised” production and logistics systems might look like, how they might operate, and how they might impact human workers. For most practitioners, digital transformation and its impacts for humans and manual operations processes remain a big black box – upon which this position paper and editorial – and its associated collection of papers in this special issue of the International Journal of Production Research begin to shed light.

What we see in practice more than 10 years later is that many I4.0-affected jobs have not become more challenging or interesting but, on the contrary, more monotonous – particularly for unskilled workers, there is a high risk of external determination by the specifications of computer systems (Briken and Taylor 2018; Chiabert and Aliev 2020). High specification levels, low decision-making latitude, excessive workloads, and low job control, in combination with new job and technology demands, are often found burdensome by workers. These stressors have negative consequences for both employees and operations. Many employees do not feel up to technological development and its required new skills, reject external control by computer systems, or are afraid of the technology and its associated changes. There is increasing evidence of the lack of acceptance of I4.0 technologies, such as robots (e.g., Jacob et al. 2023), or lack of human trust in artificial intelligence (AI) (Glikson and Woolley 2020) if HF aspects are not considered when implementing a new technology. High worker absenteeism and turnover can be aggravated by this negligence of HF, a phenomenon recently discussed under the term ‘the great resignation’ (Gittleman 2022). In addition, stress and mental illness are on the rise, and symptoms of burnout and anxiety disorders have increased (Waldmann et al. 2023; WHO 2022). Another intriguing example of I4.0’s detrimental effects on humans is that resistance has been found amongst employees towards such technologies, at least in terms of their personal data-gathering capabilities (Ito et al. 2021) and the feeling of being over-supervised. Furthermore, unclear added value, work overload, and feelings of inadequacy in relation to the technology increase the resistance. The irony of the technocentric design approach, in which human aspects are inadequately addressed, is that the performance of the system is often so compromised that anticipated financial gains are lost in a broad range of unanticipated costs caused, for example, by dysfunction, illness, or errors (for a review of human-system errors and quality, see Setayesh et al. 2022), a phenomenon referred to as phantom profits (Neumann et al. 2021).

The mechanism of concern is illustrated in Figure 1. As this conceptual model illustrates, the design and management of the operations system, including the implementation and use of I4.0 technologies that can be supportive or substitutive (Grosse 2023), will determine the demands placed on human operators. These demands, which relate directly to observable system design features, can be perceptual, cognitive, or physical; they also set the psychosocial environment experienced in the workplace (Neumann et al., 2006, 2021). The distinction between perceptual and cognitive factors stems from Wickens’ (1984) model for human information processing, where perceptual pertains to the human’s sensory ability to discover and perceive necessary cues and information, whereas the cognitive demands concern the processing of that information – both of which can be supported or hampered by system design features. These HF and working conditions will have some kind of effect on operators. It might be positive in terms of experience, support, knowledge, and job satisfaction, or negative in terms of fatigue, discomfort, pain, stress, and injury. These human effects will, in turn, affect operators’ performances in running operations. A fatigued operator, for example, is far likelier to underperform or to make errors, leading to quality problems and ‘scrap and rework’ in operations (Setayesh et al. 2022). Conditions that foster discomfort, stress, reduced job satisfaction, or technology resistance (Jacob et al. 2023) increase the rates of operator sickness-absence and employee turnover, with consequent disturbances and costs throughout operations. Finally, conditions such as these can also lead to the complete failure of technology implementation projects (Neumann et al. 2021).

Figure 1. Conceptual model of system design, human factors, and outcomes. Poor administration of HF has negative consequences for system operators, compromising operators’ performances and leading to poor system results.

Image of a conceptual model explaining how system performance is affected by the HF quality determined by the system designers. The system design leads to a determined state of human factors in terms of perceptual, cognitive, physical and psychosocial demands on the system operator. These human factors demands on the operator then lead to effects on operators, influencing their performance at work, in turn influencing overall system performance.

This model is consistent with other conceptual frameworks in illustrating the impact of system design on the humans in the system and the ultimate determinant effects on system performance (Grosse et al. 2015; Sgarbossa et al. 2020). With human considerations routinely omitted from management and I4.0 research (Grosse et al., 2017; Grosse, 2023; Neumann et al., 2021) and companies lacking basic HF-related indicators in their design and management systems (Greig et al. 2023), the stage is set for vast numbers of I4.0 applications to underperform and suffer from phantom profits because of the failure to consider the humans in the system (Neumann et al. 2021). This situation persists even though predictive cost models exist to understand these effects in financial terms (Sobhani, Wahab, and Neumann 2015; 2016; 2017). With no way to manage human aspects in technology design, selection, and deployment processes, I4.0 promises a dystopian future for people and companies – ‘the dark side of I4.0’ (Dieste et al. 2023) – even as it preaches a utopian approach to higher profits (see also Marinescu et al. 2023).

This brings to mind a reconsideration of the why of modern production – is the purpose of ever more sophisticated and complex manufacturing to be advanced for its own sake, to purely pursue profit (and risking the phantom profits effect) or ought there to be a core ethical standpoint that I4.0 technologies in production and logistics systems technologies should serve the purpose of allowing human workers to thrive and excel? Is a transition needed? The new Industry 5.0 (I5.0) vision of the European Commission (2021) seems to imply this, with a new focus on human and societal prosperity alongside the idea that the benefits of industrialisation should accrue to the people – in particular, those who are closest to the process. This is why the clear aims set out in I5.0 are so important, and why the human-centred research presented in this special issue stands at the forefront of efforts for a more realistic and sustainable approach to production systems and logistics engineering research.

2. Industry 5.0 and the return to human-centricity

The recognised absence of human-centricity, resiliency, and sustainability in I4.0 thinking has led the European Commission to introduce the I5.0 vision, with the explicit goal of ensuring that new production and logistics systems provide a win-win for both companies and society. Specifically, in 2021, the European Commission began to promote the I5.0 vision, where ‘the wellbeing of the worker is placed at the centre of the production process and uses new technologies to provide prosperity beyond jobs and growth while respecting the production limits of the planet’ (European Commission 2021). Specifically, I5.0 aims at a more human-centred approach to the design of production and logistics systems. The I5.0 vision overcomes the weaknesses of I4.0 by focusing explicit attention on outcomes for humans in the system to create systems that are human-centric, resilient, and sustainable (Ivanov 2023).

Human-centricity, as one of the three central pillars of I5.0 (in the design sense), was defined by Norman (2013) as a practical approach where designers/developers (1) focus on people and their context, (2) ponder what the right problems or root causes to solve are, (3) view everything as a complex, interconnected system, and (4) carry out small and continuous interventions. If we consider how well this approach applies to I4.0, it becomes obvious that the technology itself is in focus rather than humans; that interoperability between technologies has emerged as a central and inevitable problem to solve; that I4.0 necessitates a complex, interconnected system; and that interventions have been large, pervasive, and sudden. In other words, the level of human-centricity in the design of operations systems has been questionable in recent decades (see also Grosse, Glock, and Neumann 2017; Neumann et al. 2021; Panagou, Neumann, and Fruggiero 2023). Boy (2017) makes the case that human-centred design during the twenty-first century has also experienced a ‘socio-technical inversion’ shift due to the modern practice of developing software before hardware, leading to issues with intangibility that may result in interactive interfaces that confuse humans.

The more human-centric I5.0 way of thinking harmonises well with the scientific discipline of Human Factors/Ergonomics, which has been engaged since the late 1940s in designing human-centred work systems. In fact, the terms ergonomics and HF are often used interchangeably and are defined as being ‘concerned with the understanding of interactions among humans and other elements of a system, and the profession that applies theory, principles, data, and methods to design in order to optimize human well-being and overall system performance’ (International Ergonomics Association 2000). The way to address this systematically is illustrated in Figure 1. There has also been a discussion as to whether sustainability is needed as a new approach in HF or if it is inherent (Zink and Fischer 2013). In addition, researchers emphasised the dire need to consider HF in I4.0 to avoid underperforming (sub-optimised) systems, technology rejection, and negative consequences on human workers, even before the term I5.0 was mentioned (Kadir, Broberg, and da Conceição 2019; Neumann et al. 2021; Reiman et al. 2021). Other authors have highlighted the potential of I4.0 technologies to augment individual human workers, explaining the technologies’ abilities to provide physical and cognitive support for their manual working tasks (Grosse 2023). The prominent terms that emerged from these works are ‘Operator 4.0’ (Romero, Stahre, and Taisch 2020) and ‘Human-in-the-loop’ (Turner et al. 2021). The other main pillars of the vision of I5.0 were also researched before the introduction of the term – see, for example, Beltrami et al. (2021) for I4.0 and sustainability and Spieske and Birkel (2021) for I4.0 and resilience. This means that questioning whether I5.0 is a completely new approach or just “old wine in new bottles” (see also Vereycken, Ramioul, and Hermans 2021) is not entirely unfounded. In terms of the phantom profits theory, at least, it is reasonable to argue that I4.0 with the consideration of ethics leads to I5.0, and that HF is the missing link that will make I5.0 systems outperform technocentric I.40 systems. A similar discussion is warranted for the other two pillars of I5.0: environmental sustainability and resilience.

Since 1961, the International Journal of Production Research (IJPR) has been a leading journal in the areas of manufacturing, industrial engineering, operations research, and management science. It is a flagship journal that advances knowledge and expertise for successfully managing the digital transformation of production and logistics. Most of the papers listed on the journal’s website as “most read articles” (with more than 20,000 views) are concerned with I4.0, and some are even considered seminal works on subjects such as I4.0 overview and direction (Liao et al. 2017; Xu, Xu, and Li 2018), blockchain technology (Saberi et al. 2019), risk analytics (Ivanov, Dolgui, and Sokolov 2019), artificial intelligence (Baryannis et al. 2019), circular economy (Rosa et al. 2020), and Logistics 4.0 (Winkelhaus and Grosse 2020). In addition, IJPR has published cutting-edge research frameworks that promote a paradigm change in calling for interdisciplinary, integrated research considering engineering design and management, as well as HF methods and perspectives (e.g., Grosse et al. 2015; Vijayakumar et al. 2022). In recent years, the journal has promoted research introducing and discussing modelling approaches with human-centric perspectives to develop decision support systems for the design and management of production and logistics systems. In moving human well-being from being just a constraint in the model to being one of the objectives, different HF metrics (from physical to cognitive workload) have been considered and integrated into fundamental mathematical techniques from industrial engineering, management sciences, and computer science. Moreover, qualitative approaches, based on, for example, case studies, design science, and surveys, have been promoted to gain a critical understanding and definition of the practical problems and implications of a more human-centric approach. Following the evolution of industry, particular attention has been paid to the impact of the implementation of new technologies on these HF metrics and to the overall performance of production and logistics systems.

This special issue of IJPR contributes to the further development of the transition to I5.0, which is among the important aims within the scope of the journal. The works included shed light on the human aspects of innovation in a variety of I5.0 research domains.

3. Papers in the special issue

This special issue aims to further the I5.0 agenda, with a focus on human-centred design and HF aspects in production and logistics system design and management. We draw on the technological potentials of I4.0 while adopting the human-centred goals of I5.0 to increase the sustainability of these systems for employees (Docherty, Forslin, and (Rami) Shani 2002). To this end, this special issue publishes innovative approaches for the integration of HF in production and logistics system design and management to create highly human-centric, resilient, and sustainable work systems that use sophisticated I4.0 technologies to contribute to human prosperity. The papers included in this special issue are summarised and categorised in Table 1 according to the conceptual model in Figure 1, building on established HF system design frameworks (Grosse et al. 2015; Neumann et al. 2021), and discussed below.

Table 1. Classification of papers included in the special issue according to topic area and the type of HF addressed.

					Human Factors
Authors	Application Areas	Technology	Humancentricity Perspective	Study Type	Perceptual	Cognitive	Physical	Psychosocial
Ulmer et al. (2023)	Manufacturing	Augmented Reality (AR)/Personalised Assistant Systems	Worker Diversity and Skills	Empirical, Technology Implementation Trial	X	X
De Lombaert et al. (2023)	Logistics/Order Picking	Automation and Robotisation	Worker Diversity and Skills	Literature Review and Semi-structured Interviews		X		X
Pasparakis, De Vries, and De Koster (2023)	Logistics/Order Picking	Mobile Robot	Job Satisfaction	Empirical and Experimental study		X		X
Vijayakumar and Sobhani (2023)	Logistics/Order Picking	Pick and Transport Robot	Worker Wellbeing	Multi-objective Optimisation and Case Study		X	X	X
Thylén, Wänström, and Hanson (2023)	Manufacturing	Automated Guided Vehicles (AGVs)	Human-Technology – Organisation Interaction	Empirical and Case Study				X
Abdous et al. (2022)	Assembly	Collaborative Robot, Mobile Robot, Exoskeletons	Worker Diversity	Multi-objective Optimisation			X
van Oudenhoven et al. (2022)	Maintenance	Data-driven Decision-making Aids	Worker Diversity and Skills	Empirical	X	X
Enang, Bashiri, and Jarvis (2023)	Manufacturing/Logistics	Digital Technologies	Human-Technology-Organisation Interaction	Literature Review	X	X	X	X
Peltokorpi et al. (2023)	Assembly	Assistive Technologies	Worker Diversity and Skills	Empirical, Technology Implementation Trial	X	X
De Assis Dornelles, Ayala, and Frank (2023)	Manufacturing	Collaborative Robot	Worker Diversity and Skills	Empirical and Case study		X		X

Ulmer et al. (2023) investigate how technology used to assist operators (also called assistance systems) in manufacturing sectors can be personalised for the individual user experience and adapted to the operators’ capabilities. Because most assistance systems have a fixed hardware layout, they contribute to the design of a new human-centred workplace in line with the I5.0 concept by individualising hardware components, information provision, and feedback generation of assistance systems. The authors report that they developed and tested a modular system architecture in an AR-enhanced manual workstation whose supporting elements, such as technical equipment, processes, and supporting tools, are individually arranged to suit the product and individual worker skills. To address workforce diversity, the enhanced assistance system uses gamification to adapt the information to company and individual goals.

De Lombaert et al. (2023) build on the seminal work of Grosse et al. (2015) and review the literature on how HF can be integrated to attain order picking models that improve operational performance, worker well-being, or both simultaneously. Automation and robotisation are advancing in warehouse operations, but human operators still play an important role because they have specific skills, behaviours, and perceptions, which are only partly accounted for in order picking planning models. The authors take a multimethod approach to assess the relevance and adequacy of HF modelling in the academic literature, with practice-based insights gathered via semi-structured interviews. In line with the transition from I4.0 to I5.0, they consciously account for workers’ opinions. Five major HF integration constructs are introduced, each of which converts relevant human-centred phenomena into modelling functionalities: (1) varying work rates, (2) quantitative physical state indicators, (3) stochastic worker behaviour and work execution, (4) subjective worker experience and judgment, and (5) socio-demographic worker differentiations. Dedicated recommendations on how to refine and integrate them with leading research methodologies in the context of I5.0 are included. Key topics for future research include attention to psychosocial phenomena and their impact on operational performance. Please note, De Lombaert et al. (2023) published in an earlier issue of IJPR and can be found in Volume 61, Issue 10.

Pasparakis et al. (2023) deal with robotisation in order picking, where the environment is increasingly becoming a place of collaboration between humans and robots rather than one in which robots will merely substitute humans. In this context, they first conduct an experimental study to investigate how collaboration with robots influences order pickers’ job satisfaction, proposing job satisfaction and core self-evaluations as metrics for assessing the long-term success of human-robot collaborative systems. They then establish that the introduction of human-robot collaboration positively affects job satisfaction for the contrasting collaboration dynamics of (1) gaining control (the human leading the robot) and (2) ceding control (the human following the robot). This positive effect is larger when the human follows the robot. It is also interesting that following the robot positively affects pickers’ self-esteem, and that self-efficacy related to interactions between humans and robots benefits from the introduction of collaborative robotics, regardless of the setup dynamics.

Vijayakumar and Sobhani (2023) focus on a specific order picking solution in which a pick and transport robot (PTR) called Grab^TM is implemented in a traditional warehouse. In this solution, a robot arm is mounted on an automated guided vehicle (AGV) and puts items onto a pallet carried by the AGV. Their research aim is to improve the performance outcomes of picker-to-part systems using a human-centric approach while taking both PTRs and order pickers into consideration. A multi-objective optimisation model is developed to tackle the challenges relating to a human-centric approach with PTRs and order pickers, hence improving the productivity, well-being, and quality of the system. The model has been tested and validated in a real industrial case in which the solution is implemented. An extensive analysis allows better knowledge of how to set up such PTR solutions, based on the number of robots and the size of the pick zones, while also considering work-related HF consequences.

Thylén et al. (2023) explore the interactions between humans, technology, and organisation when AGVs are introduced into a production facility. Through two cases, they investigate the challenges companies face in the introduction of such technology and propose some actions that companies can take to address them. They consider the different phases of an AGV introduction project, acknowledging that different actions may need to be taken in each individual case. By highlighting organisational aspects, their work has gone beyond the technical aspects and the individuals involved, contributing to theory by identifying aspects at the organisational level relating to the management of roles and responsibilities. In line with I5.0, the findings highlight the importance in operational design of considering humans and technical equipment together, both individually and at the organisational level.

Abdous et al. (2022) propose a multi-objective approach for solving the design problem of collaborative assembly lines where operators and technologies, such as collaborative robots (cobots), exoskeletons, and mobile robots, interact in shared workspaces. The objectives are the optimisation of investment costs and ergonomics with a fatigue and recovery criterion. A new MILP formulation for the multi-objective problem is developed. It is solved with an ε-constraint algorithm on different instances from the literature. This contribution sets the stage for future advancements in the field, such as developing multi-objective metaheuristics based on the use of the algorithm proposed here to provide information about the domain of solutions to help practitioners in decision-making process. This research also opens the door to the consideration of workers’ diversity, providing more adaptable and individualised solutions, in line with I5.0.

Van Oudenhoven et al. (2022) deal with a different context, that of predictive maintenance (PdM), where operators and decision-makers are called to base their decisions on data-driven and system-generated advice. They address the related acceptance issues by studying how PdM implementation changes the nature of decision-makers’ work and how these changes affect their acceptance of PdM systems. Having investigated the human-related, task-related, and organisational characteristics of PdM implementation, they distil 10 propositions regarding decision-making behaviour in PdM settings whose relevance has been verified through in-depth interviews with experts from both academia and industry. They identify control, trust, matching demands to resources, and organisational allocation of decision-making as the overarching factors that enhance PdM adoption.

Enang et al. (2023) synthesise the literature on I4.0 while using the principles of the multiple-level perspective to explore the forces driving the transition from technocentric I4.0 to value-centric I5.0. They achieve this by investigating the exogenous factors and technological developments that have contributed to changes in policy, stakeholder expectations, and stakeholder perceptions. They also provide an in-depth overview of the key contextual-, regime-, and niche-level factors that influence the transition to I5.0.

Peltokorpi et al. (2023) study how cognitive assistance systems can help people with learning disabilities to increase their skills in manual assembly. Through an industrial experiment conducted at a sheltered workplace, their work investigates the effects of repetition or work cycle alongside the form of instruction and type of disability. Four forms of instruction (paper-based, animations, projection, and adaptive projection) are tested to assist operators with three types of disability (illiterate, psychosocial, and cognitive) with a manual assembly task. The authors’ main finding was that the form of instruction should be personalised because everyone adapts to cognitive assistance systems in a unique way. According to the findings, the use of projection improves the initial assembly cycle, while the moving images in video-based instructions seem to create problems for many of the participants. Experienced operators, when presented with filtered instruction content, are challenged to be more independent and gain a better understanding of their tasks. Nevertheless, adaptive instructions may create an obstacle for operators who rely heavily on mentorship.

Finally, De Assis Dornelles et al. (2023) focus on the use of cobots in manufacturing environments and their effect on shaping workers’ skills. Using a multi-method qualitative research approach to analyse the implementation of cobots from a leading global provider, the authors investigate the effects of cobots on workers’ skills in four types of interaction between humans and cobots: coexistence, synchronism, cooperation, and collaboration. Using the labour process theory, they explain the relationship between technology, skills, and organisation. The findings show that most companies are currently in the initial phases of implementation, primarily prioritising worker substitution. The results also show that deskilling and reskilling are commonly observed as unintended consequences of this substitution approach. However, a significant upskilling effect has been observed in both large and small companies, particularly those using advanced forms of interaction between humans and cobots. Finally, the authors explore how companies can shift towards a smart working environment by enhancing workers’ skills through interaction with cobots.

4. Conclusions and directions for future research

This special issue, in one of the flagship journals of production and logistics research, stands as a milestone in the research agenda of transitioning to I5.0. We are confident that this position paper and editorial – and its associated collection of papers will help advance the development of human-centric I5.0 systems.

In considering the body of work presented here, we emphasize the following directions for future research. First, we see a strong need for more empirically grounded works and data-driven multimethod approaches. They are needed to validate conceptual and literature-based research results related to interactions between humans and technology, especially the consequences of using I4.0 technologies that assist operators in their manual work, such as augmented reality, adaptable workstations, and cobots, related to human performance, errors, work motivation, job satisfaction, and technology acceptance.

Second, future research should consider worker diversity factors, such as gender/sex, age, and a broader range of individual capability levels. Men and women may not respond similarly in terms of psychosocial and stress responses at work and may experience different interactions because of size and strength variability (physical design) that affect their work differently. In addition, more research is warranted considering demographic changes needed to support older workers in an increasingly digital I5.0 work environment. By creating more inclusive systems in which people with a broad range of capability levels can contribute meaningfully, I5.0 designers can help overcome long standing social inequities that are a drain on our social systems.

Third, we still see the fallacy that human-centricity and HF considerations are solely related to injuries or physical aspects. Design for humans in I5.0 needs to go beyond physical HF to consider the psychosocial effects of technology usage and interactions between humans and technology. This is the only way to avoid increases in stress and psychosocially related disorders and save technology implementation projects from certain failure. This can help control the rising costs of burnout and other mental health problems that are currently causing problems at organisational and social levels (WHO 2022).

Fourth, we see a strong need to study the ethical implications of I4.0-supported systems, including, for example, performance monitoring. To what extent do employers have the right to access and use employees’ biophysical signals and data in their efforts to simply squeeze more production out of them? How do such systems undermine job satisfaction and commitment of employees? Here especially, interdisciplinary research teams, such as management, information systems, human resources, law, and psychology, can achieve significant progress in understanding the long run implications of these surveillance and monitoring approaches. Here, as with the other issues noted in this discussion, it becomes crucial to understand the long run (not just short term immediate) impacts of the technologies on the humans in the system and the performance consequences of these technologies. Systems designed without attention to these human affects may calculate savings and benefits of their expensive innovations, only to have these erode over time leaving merely “phantom profits” (c.f. Neumann et al. 2021).

Finally, this work needs to be extended to include environmental sustainability and resilience for a holistic view of I5.0. Research in this area needs to examine how the design considerations for these goals interact and seek possible synergies. In particular resilience in design has potential to support both human and environmental goals and warrants closer examination as a linking concept between these two performance domains.

The primary contribution of this position paper and editorial – and its associated collection of papers – is to demonstrate that a more evolved, systemic view of HF (than simply equating it to “physical loading”) is necessary to transition meaningfully from I4.0 to I5.0. These studies also show that more wholistic approaches are entirely feasible. The sought-after prosperity and sustainability that I5.0 promises will not be achieved unless the understanding of modern-day systemic HF problems (such as mental exhaustion, reduced job satisfaction, stress, demographic developments etc.) is reflected in how HF is taken account of in the design, implementation and research of digitalised technologies. As evidenced by several of the papers, humans in future production and logistics systems will not always have their active role reduced, but rather will collaborate with and make qualified decisions based on the information made available by these new technologies. The quality of this decision making, and subsequent system performance will (per Figure 1), therefore, hinge on the ability of system designers to manage the HF demands of the technologies they design and deploy.

We would like to thank all the authors who submitted their valuable work to this special issue and encourage future work in this direction. We are also grateful to the reviewers from various disciplines who, despite their high workload, supported the special issue and contributed their interdisciplinary expertise to the selection and improvement of submissions. Finally, we would like to thank the editorial assistants for their support – in particular, the Editor-in-Chief, Professor Alexandre Dolgui – for giving us the opportunity to organise the special issue of the International Journal of Production Research, which we hope will set the stage for future research on human-centric production and logistics system design and management, thus helping practitioners to successfully manage the challenges of digital transformation by including explicit attention to outcomes for humans of human-technology interaction and related system effects in working environments.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Eric H. Grosse

Eric H. Grosse is a Junior Professor and the Head of the Chair of Business Management and Digital Transformation in Operations Management at the Faculty of Human and Business Sciences at Saarland University, Germany. His research interests include sustainability in logistics and operations management with a focus on human-centric digital transformation. He is an area editor of Operations Management Research and co-edited several special issues in international leading journals. He is co-director of the Center for Digital Transformation (CeDiT) at Saarland University and serves as a co-chair of IFAC TC 5.2 ‘Management and Control in Manufacturing and Logistics’.

Fabio Sgarbossa

Fabio Sgarbossa is Full Professor of Industrial Logistics at the Department of Mechanical and Industrial Engineering (MTP) at NTNU (Norway) from October 2018. He was Associate Professor at University of Padova (Italy) where he also received his PhD in Industrial Engineering in 2010. He is leader of the Production Management Group and responsible of the Logistics 4.0 Lab at NTNU. He has been and he is involved in several European and National Projects. He is author and co-author of more than 170 publications in relevant international journals, about industrial logistics, material handling, materials management, supply chain. He is member of Organizing and Scientific Committees of several International Conferences, and he is member of editorial boards in relevant International Journals. He is associate editor of International Journal of Production Research for the thematic area: Human Factors in Production Research. He is chair of IFACT TC 5.2 ‘Management and Control in Manufacturing and Logistics’.

Cecilia Berlin

Cecilia Berlin is an Associate Professor of Production Ergonomics at the Division of Design & Human Factors, Department of Industrial Materials Science, at Chalmers University of Technology in Gothenburg, Sweden. Her own research focuses on physical, cognitive and systemic approaches to human factors and ergonomics in various workplaces, primarily in manufacturing contexts. Her research collaborations include methods development for product and production development; socially sustainable workplace design; and conceptualizations of Smart Maintenance. She is a frequent public speaker on the topic of cognitive ergonomics in the workplace and is a certified Human Factors Professional (Eur.Erg.) through CREE.

W. Patrick Neumann

W. Patrick Neumann is a Full Professor in the Department of Mechanical and Industrial Engineering of Toronto Metropolitan University, Canada where he is Director of the Human Factors Engineering Lab. His research focuses on the design of work systems that are both effective and sustainable from human and technical perspectives. He is a Design Scientist and certified Human Factors Professional (Eur. Erg.).