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The Transformation of Manufacturing: Design of Work

This article is based on the findings in the report, Understanding the Impacts of Industry 4.0 on Manufacturing Organizations and Workers, prepared for the Smart Factory Institute and written by Chris Cunningham, PhD, UC Foundation Professor of Psychology, and Scott Meyers, Graduate Assistant, Psychology Department & Smart Factory Institute, from the Industrial and Organizational Psychology Department at the University of Tennessee at Chattanooga.

Industry 4.0 will create large shifts in the design and very nature of manufacturing work. This will be experienced at the level of personal work-related tasks and in terms of the extent to which manufacturing environments will need to be (re)designed to facilitate human-machine integration (Mashelkar, 2018). Each of the identified core technology pillars of Industry 4.0 summarized in Figure 1 brings intense shifts to how work is designed and conducted. These technology-related forces deserve particular note for being instrumental in the effects of Industry 4.0 on manufacturing work design: (1) advanced connectivity, (2) data-driven intelligence, (3) human-machine integration, and (4) advanced production techniques.

4 Pillars of Industry 4.0

First, advanced connectivity from the development of the industrial IoT will have particularly profound effects on various manufacturing processes. The IoT is a primary component of Industry 4.0 as it weaves together various technological developments into a connected ecosystem (Statista, 2021). This network of integrated technologies includes machine sensors, software, and cloud computing (Hernandez-de-Menendez et al., 2020), as well as the human users who interact with these technologies and whose efforts at work will increasingly come to be managed or at least guided by these technologies. These networks allow connected devices to interact with each other and the external environment, leading to a new interconnected manufacturing environment. For example, smart sensors have integrated microprocessors that can facilitate near real-time calculations, self-diagnosis, self-configuration, and interactions between other sensors and production systems (Hernandez-de-Menendez et al., 2020). These technologies together make it possible for real-time production monitoring, maintenance, product tracking, and inventory measurement (Turcu & Turcu, 2018). The IoT has the potential to blur the lines between the physical, digital, and biological worlds of manufacturing (Statista, 2021), especially when it's associated “cloud computing” and advanced data analytics possibilities are considered.

Cloud computing involves internet-facilitated networking of data storage and processing capabilities, connected to other elements of the IoT described above (Almada-Lobo, 2015). Cloud computing helps to enable many of the advanced automation technologies and data-driven processes associated with Industry 4.0. The cloud provides a single place to store and process big data from many different sources connected through IoT. Another benefit of increased utilization of cloud computing is that manufacturing organizations will benefit from a cloud-based infrastructure that is virtual (i.e., does not require extensive on-site hardware implementation and management), can be managed in real-time by dedicated support personnel, and can be scaled up or down as the organization’s computing requirements change (Wogawa, 2020).

The connectivity just described also facilitates new and potentially unique forms of human and machine collaborative production and workload management processes and practices (Statista, 2021). Although automation has been entering into manufacturing processes since the Industry 3.0 period, the current era sets the stage for a tighter form of human-machine integration than has been possible up to this point. These developments lead to the sharing and generation of vast amounts of real-time process data at all levels of production (Statista, 2021). These vast amounts of data are readily available to manufacturers and can be used to effectively inform decision-making and improve production processes. Often associated with the label of “big data” to refer to the potentially overwhelming number of data points generated by components to an IoT supported system even within just one organization, advanced computing and data analytics tools (involving AI and ML) can now provide analytics and insights that can inform business decisions. These technologies will facilitate real-time decision making by manufacturing workers and supervisors. Connected devices (e.g., augmented wearables and advanced sensor/actuator systems) coupled with data analytics will enable humans to make better informed just-in-time decisions during the production process. Similarly, virtual and augmented reality tools can allow for simulation and optimization to facilitate optimal decision-making (Statista, 2021). Job tasks within Industry 4.0 can now be tailored to respond to data-based indicators, making data management and data analytic capabilities essential for business decisions and success in the manufacturing domain. As a simple illustration of how this will impact a common work task in manufacturing, advanced data monitoring and analytics through the IoT will increasingly trigger maintenance requests and even guide core production workers to make the necessary fixes to more quickly and efficiently get back up and running. In other words, even maintenance-type jobs will have less of a human role involving experience-based skills in preference for more data analytics (Statista, 2021).

The widespread adoption of advanced robotics and technology-supported augmentation of workers’ abilities will literally change the landscape within manufacturing organizations and transform production techniques. These technology developments combined with robots can create advanced semi- and fully autonomous manufacturing processes (Statista, 2021). The use of robots and increased automation in manufacturing will greatly improve the efficiency and overall quality of production (Dixon et al., 2021; Knowledge at Wharton, 2021). Digitalization (i.e., the digital transformation of manufacturing, production, and value creation) will also increase the productivity of humans (I-Scoop, 2017; Statista, 2021). Therefore, a more fast-paced work environment associated with greater production and reduced production costs (Dixon et al., 2021; Knowledge at Wharton, 2021), shorter product life cycles, and shorter time-to-market is anticipated as Industry 4.0 becomes more widely adopted (Hecklau et al., 2016).

A specific example of a prominent advanced production technique is additive manufacturing (e.g., 3D printing), which generally involves turning a digital model of an object into a physical item through a “printing” process that involves iteratively layering a construction substance to match the digital design (Statista, 2021). This technology helps create complex geometrical patterns that are not possible using traditional manufacturing techniques and processes. It helps create lighter components and allows for greater control of material properties (e.g., density). This process of construction also involves less prototype construction, less post processing, and use of fewer dies (Statista, 2021). These technologies and processes are likely to also reshape this industry in ways that are still being imagined. This general form of digital manufacturing production leads to higher quality and lower cost products (Hernandez-de-Menendez et al., 2020). Additive manufacturing, especially when combined with VR and AR systems, can allow for more rapid prototyping and faster, more efficient designs (Statista, 2021).

From these main Industry 4.0 technology pillars, several overarching themes emerge regarding general impacts to expect when it comes to the design of manufacturing work. First, stemming from the theme of increased connectivity, more collaborative work environments emerge involving human-human teams and human-machine working arrangements (Mashelkar, 2018). Furthermore, the nature of work such as the close connectedness to various parts of the manufacturing system require interdisciplinary teams to share work, manage production systems, and solve problems as opposed to one multi-faceted expert (Hernandez-de-Menendez et al., 2020). Industry 4.0 connects information systems/technology, decision science, operations research, supply chain, project management, organizational psychology, and engineering (Ivanov, 2020; Shet & Pereira, 2021).

Work environments requiring constant adaption also emerge as a function of Industry 4.0. Various task, process, and organizational changes will be continuously introduced and implemented (Hecklau et al., 2016). Furthermore, the industry will adapt as technology progresses, leading to further changes. Current examples of this include increased virtual work and work-task rotations (Hecklau et al., 2016).

From these changes in how manufacturing work and production systems are designed under Industry 4.0, various shifts will occur regarding types of work and job positions needed. Industry 4.0 brings more complex process demands and changes the role of the worker within these processes to one that is more strategic than strictly physical or operational (Hecklau et al., 2016). For example, an Industry 4.0 implementation study with a small manufacturer showed that worker tasks changed from physical labor associated with producing parts to monitoring, troubleshooting, and repairing robots that were designed to more efficiently and reliably perform that set of manual tasks (Rangraz & Pareto, 2021).

Critical Requirements for "Smart Workers"

Industry 4.0 will create job opportunities that require more than just past experience in a manufacturing production role. New roles are likely to be more intellectually challenging and involve greater strategic thinking and problem-solving (Shet & Pereira, 2021). For example, workers will face more strategic tasks, have higher process responsibility, make their own decisions, and act as entrepreneurs (Hecklau et al., 2016). Workers will also be increasingly expected to improve these processes and identify sources of error using the data and insights provided through the connected systems described earlier in this section (Hecklau et al., 2016). Furthermore, workers will be expected to use and interact a variety of advanced Industry 4.0 technologies to do their jobs. An implication is that more and different skills will be needed to operate effectively within the Industry 4.0 “smart manufacturing” ecosystem. As an illustration, “Assemblers” are being converted to “Operators” who monitor the overall process and troubleshooting machines and preprogrammed robots (Rangraz & Pareto, 2021).

Industry 4.0 also brings a host of new job opportunities to the manufacturing world, especially from within the STEM fields. Many of these positions are needed to support the increased prevalence and integration of Industry 4.0 technology into manufacturing, such as automation supported by advanced robotics, IoT, and AI systems (Shet & Pereira, 2021). New more complex critical positions are emerging within the industry. Additionally, there are new emphases and greater needs for various existing manufacturing positions. Examples of these critical new and existing positions within manufacturing include: software developers, digital specialists, data analysts, and cybersecurity testers, automation and robotics engineers, data analysts/scientist, AI/ML specialists, process automation specialists, software/applications developers, innovation professionals, service and solutions designers, product managers, industrial and production engineers, and supply chain and logistics specialists (Flores et al., 2020; Hernandez-de-Menendez et al., 2020; Strack et al., 2021; World Economic Forum, 2018).

Positions that support the IoT and its associated cloud-based technologies will also become more essential, including informatics specialists, IT solution architect, PLC programmers, robot programmers, software engineers, and cybersecurity professionals (Hecklau et al., 2016). Other examples of anticipated manufacturing-related positions under Industry 4.0 include industrial cognitive science specialists with expertise in sensor/actuator networks, robotics, perception and cognition, and specialists in automation bionics with knowledge of robotics and perception/cognition from a biological perspective (Hartmann & Bovenschulte, 2013).

It is also true that the Industry 4.0 effects described up to this point will reduce the need for other common and existing manufacturing positions. In particular, advanced automation is expected to mostly impact lower-skilled positions, as there is a diminishing need for human workers to perform routine/repetitive and physical tasks (Rangraz & Pareto, 2021; Shet & Pereira, 2021). Additionally, there will likely be notable reductions in some forms of operational and experienced-based work (Hecklau et al., 2016; Shet & Pereira, 2021), such as administrative roles that are simple and/or rule-based in nature and require limited strategic thinking or advanced cognitive processing and which can also be automated (Strack et al., 2021; Flores et al., 2020). Furthermore, Shet and Pereira (2021) note that jobs related to processing, measuring, and recognizing patterns will be more likely to be automated. There is also the possibility that within some organizations fully adopting Industry 4.0 technologies there may be less need for some forms of moderately skilled labor such as traditional line management. This is a possibility because advanced automation and the industrial IoT described earlier in this report monitors and can automatically provide feedback and guidance pertaining to many different operational metrics potentially faster and more consistently than human management often does (Dixon et al., 2021; Knowledge at Wharton, 2021). Additionally, automating moment-to-moment production performance feedback has the potential to decrease rates of human error. In other words, with Industry 4.0 technologies, close human to human managerial monitoring or oversight of constantly recurring tasks may not be required (Dixon et al., 2021; Knowledge at Wharton, 2021). The net effect of these changes is that there will be increases in the number of higher skilled workers and decreases in the number of lower skilled and in some specific cases, middle-skilled worker positions (Dixon et al., 2021; Knowledge at Wharton, 2021).


This article is based on the findings in the report, Understanding the Impacts of Industry 4.0 on Manufacturing Organizations and Workers, prepared for the Smart Factory Institute and written by Chris Cunningham, PhD and Scott Meyers from the Industrial and Organizational Psychology Department at the University of Tennessee at Chattanooga. Get access to this full report by clicking here.

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