Industrial Revolutions and Automation: Tracing Economic and Social Transformations of Manufacturing

Abstract

The rapid evolution of automation has become a defining phenomenon of the modern era, underscoring its great influence on industries and economies. Since the First Industrial Revolution, advancements in technology have consistently transformed manufacturing processes, progressively introducing mechanization, electrification, and digitalization. The current Fifth Industrial Revolution incorporates these changes through the integration of robotics, cobots (collaborative robots), and AI-driven (Artificial Intelligence) systems, representing unprecedented levels of human–machine collaboration. This paper examines the historical evolution of automation and industrial robotics and, based on the current state of the art, highlights the current standing of its economic and societal impacts. The adoption of automation and robotization in the supply chain and manufacturing has significantly enhanced productivity and precision, reducing errors and operational costs. However, it has also contributed to job displacement, especially in repetitive and manual tasks, necessitating the reskilling of the human workforce. Automation brings two main financial effects: it reduces long-term labor costs and boosts efficiency, but the upfront investments in advanced robotics and infrastructure are significant. Understanding these dynamics is crucial for navigating the challenges and opportunities presented by automation, which ensures a balanced approach to economic growth, workforce development, and technological adoption across all sectors influenced by successive industrial revolutions.

1. Introduction

The motivation behind this research lies in the recognition of the transformative effects that automation has on manufacturing processes. In the context of the currently observable proliferation of fully automated manufacturing processes, paired with continuous advancements in technological capabilities and dynamic social changes, the global manufacturing ecosystem can be seen to be experiencing unprecedented changes. This state of constant flux underscores the necessity for ongoing scientific investigation to better understand and adapt to these evolving trends. Furthermore, regional and national differences in industrial practices, resource availability, and socioeconomic factors contribute to a heterogeneous landscape of automation adoption and implementation. These variations require targeted studies that are sensitive to local contexts and global trends to obtain actionable insights and foster sustainable development.

Historically, the machinery manufacturing sector has undergone profound transformations, marked by a series of technological and conceptual milestones [1]. These advancements can be categorized into distinct phases, each driven by breakthroughs that progressively improved manufacturing efficiency, precision, and flexibility. From the introduction of mechanized tools during the Industrial Revolution to the adoption of assembly lines, computer numerical control (CNC) systems, and now the integration of artificial intelligence (AI) and robotics, the trajectory of manufacturing has been defined by innovation. The current era represents a pivotal moment as the industry embraces the potential of cutting-edge technologies such as Industry 4.0, the Internet of Things (IoT), and collaborative robotics, which promise to redefine the boundaries of productivity and adaptability.

However, the implications of automation extend beyond technological dimensions. The economic and social impacts of this transition are profound and multifaceted [2,3,4,5]; their regulatory aspects are major influencing factors [6]. Low-mix high-volume production (LMHV) has seen decades of successful automation due to its predictability and scalability. On the other hand, high-mix low-volume (HMLV) manufacturing facilities present unique challenges [7]. These environments, characterized by diverse product lines and smaller production runs, require more sophisticated and flexible automation systems capable of effectively addressing variability and complexity [8,9]. Such challenges amplify the need for a comprehensive understanding of the interplay between technological innovation and organizational adaptability.

In this context, innovative manufacturing emerges as a key concept, encompassing the application of advanced technologies, digital transformation, and automation for enhancing production efficiency and creating high-value products. It involves the integration of smart automation, artificial intelligence, robotics, and human–machine collaboration—elements that are fundamental to modern industrial advancements. As industries shift toward more flexible and intelligent manufacturing systems, innovative manufacturing plays a crucial role in fostering productivity, sustainability, and adaptability [10].

This paper aims to explore the economic and social impacts of automation and digital transformation in manufacturing, which are core aspects of innovative manufacturing. By analyzing industrial revolutions, smart automation, and the emergence of Industry 5.0, we provide insights into how innovative manufacturing reshapes industries and labor markets. By examining different sectors [11,12,13], the study seeks to illuminate the drivers of successful automation, the barriers to its adoption [14], and the broader consequences for the dynamics of the workforce, organizational culture [15], and economic sustainability. In doing so, the research aspires to contribute valuable insights into the ongoing discourse on the role of automation in shaping the future of manufacturing.

2. Methodology

This study employs a literature review methodology to assess the current scientific understanding of the impact of automation on labor, corporate structures, and society, with a focus on both social and economic dimensions. The literature review is structured to cover distinct thematic areas, each exploring various facets of automation’s influence, from historical development and industrial robot generations to specific impacts on labor, cost structure, and future trends.

In this summary article, the following research questions were examined, taking into account the current state of science:

• Q1: How has the historical evolution of industrial revolutions and industrial robotics shaped the trends in manufacturing automation, and what has been the trend of manufacturing automation research in the past two decades?
• Q2: What impact does automation have on the supply chain, focusing on the processing phase?
• Q3: What impact does manufacturing automation have on labor, the workforce, and society?
• Q4: What impact does automation have on the cost structure?
• Q5: What is the future direction for automation in machinery manufacturing?

The review was conducted by searching multiple scientific databases, including Google Scholar, Scopus, and IEEE Xplore, to ensure a comprehensive and diverse pool of academic sources.

2.1. Database Selection and Search Strategy

The primary sources of data for this literature review were Google Scholar, Scopus, and IEEE Xplore, selected for their wide coverage of the scientific literature across disciplines relevant to automation, economics, labor studies, and social sciences. Each database provides access to peer-reviewed journals, conference proceedings, and other scientific publications, ensuring that the review encompasses high-quality, scholarly materials. The inclusion of multiple databases minimized selection bias and ensured that a variety of perspectives on automation were considered, from technological analyses to socioeconomic impact studies.

2.2. Search Terms and Query Customization

To ensure specificity and relevance, the search terms were tailored for each thematic section of the review, allowing for a focused examination of each topic. The selection of keywords for each section was designed to capture articles that directly addressed the objectives of the study, including the effects of automation on the workforce, cost structures, and society.

Historical Overview and Evolution of industrial robotics: This section included keywords such as “history of industrial robots”, “generations of robots”, “key industrial robots” and “development of robot generations”. By emphasizing the evolution of industrial robots, the search aimed to identify seminal works that outline the major phases and innovations in robot technology, as well as studies that describe the characteristics and technological milestones of each robot generation.

The second section focuses on the impacts on the supply chain involved search terms like “automation impact on supply chain”, “supply chain automation”, “industrial robots in logistics”, and “robotic supply chain optimization”. The objective was to find studies and reports analyzing how automation, particularly robotics, has reshaped supply chain management, logistics efficiency, and delivery timelines.

For the third section, the impact of robotization on labor, workforce, and society was searched. Keywords such as “automation effect on labor”, “robotics and workforce transformation”, “social impact of industrial automation”, and “workplace automation consequences” were used. This search targeted literature discussing the socioeconomic effects of automation, including the transformation of job roles, shifts in employment demand, and the broader societal implications of reduced manual labor in industries with high automation rates. In the fourth section, research was performed for automation’s impact on cost structure; phrases such as “automation cost benefits”, “manufacturing cost reduction via automation”, “cost structure in automated industries”, and “financial impact of robotics” guided the search. This segment sought to capture studies analyzing the financial aspects of automation, including cost savings, investment returns, and the shifting cost dynamics within automated production models. Finally, for the last section on future trends, search terms as “future of manufacturing automation”, “emerging robotics technologies”, “next-generation industrial robots”, and “automation trends in manufacturing” were utilized. This search focused on forward-looking studies discussing the anticipated advancements in automation technology, such as AI-driven robotics, predictive maintenance, and smart factory systems, and their potential implications for the manufacturing sector.

2.3. Article Selection and Screening Process

The initial search yielded a total of 169 articles across the databases. Each article was first evaluated according to its title and abstract to determine its relevance to the study objectives. Articles that did not directly address the impact of automation on labor, cost structure, or social aspects were deemed irrelevant and excluded from further analysis. Additionally, duplicate entries found across the databases were removed to ensure a unique set of articles for review.

After this preliminary screening, a refined selection of 32 articles was chosen for a more detailed analysis. These articles represented a diverse cross section of the literature, including empirical studies, theoretical analyses, and review papers that together provide a comprehensive overview of the current state of knowledge regarding automation’s multidimensional impact.

2.4. Data Extraction and Analysis

For each selected article, key information was extracted, including the study’s objectives, methods, findings, and conclusions. These data were organized according to the study’s thematic sections. The extraction process ensured that each article’s contribution to understanding automation’s impacts was recorded, allowing for a structured synthesis of findings within each thematic area. Articles were also categorized based on factors such as publication year and regional focus, which helped identify trends in automation research over time and geographic variations in the studies.

2.5. Limitations and Scope of the Review

While this methodology provides a structured approach to understanding the impacts of automation, it is worth noting several limitations. First, the selection of articles is restricted to English-language publications, which may exclude relevant research in other languages. Second, reliance on specific databases could result in the omission of relevant studies not indexed within these sources. Finally, due to the broad scope of automation’s impact, certain niche aspects of automation may not be fully captured within this review.

Despite these limitations, the methodology outlined here provides a robust framework for exploring automation’s current and future implications on various sectors. Using a multi-database approach and thematic organization, this review effectively synthesizes existing research and identifies key trends, gaps, and areas for future investigation in the realm of automation.

3. Historical Overview

The history of industrial robots dates back to the mid-20th century, when the convergence of mechanical engineering and emerging computational technologies paved the way for automation in manufacturing. Initially designed to perform repetitive and hazardous tasks, these robots were introduced to improve productivity and workplace safety, primarily in the automotive and heavy industries. The development of industrial robots can be divided into five generations, as well as industrial revolutions, and each is marked by significant technological advancements as presented in Figure 1.

3.1. First Industrial Revolution—Beginning of Mechanization

To comprehend the impact of automation from societal and economic perspectives, it is essential to examine the history of industrial revolutions. According to Groumpos, the First Industrial Revolution (1IR) was seen as a significant turning point in world history because it impacted almost every aspect of daily life across the world. Industrialization transformed the economy, transportation, health, and medicine, and it led to numerous inventions and historical milestones [1]. Industrial revolutions represent significant shifts in the technological paradigms employed during specific periods, particularly within the manufacturing and production sectors. As mentioned by Mokyr, the expansion of mechanization has initiated efforts in automating manufacturing processes involving the use of simple machines in the industry. According to Mokyr, the cotton industry is the best example of a mixed factory system where mechanization and workers were brought under one roof [16]. In connection with the textile industry during the first industrial revolution, it is important to highlight the introduction of interchangeable parts, particularly within the textile industry. The adoption and use of interchangeable parts were crucial in enabling the large-scale production of textile machinery components. To illustrate the magnitude of this development, consider the British cotton industry: by the late 1830s, factories housed 150,000 power looms, and this number had doubled to 300,000 within a decade [17]. These revolutions serve as benchmarks for technological change, illustrating the progression and transformation of industry standards over time. The beginning of the First Industrial Revolution [18] can be dated back to the second half of the 18th century. Its cradle is considered to be England, with water and steam-powered manufacturing equipment being among the most important of the numerous technical inventions. According to the 2021 publication by Kiss and Tiner, the mechanization of industrial production led to the emergence of industrial plants that produced significantly better and larger quantities of products than before [19], initially in the light industry (textile industry) and later in other sectors such as agriculture, iron industry, and metallurgy [18].

3.2. Second Industrial Revolution—Mass Production with Assembly Lines

The Second Industrial Revolution (2IR) began in the 1870s, with the most notable mechanization being the assembly line production, which, combined with the simultaneous advent of electric and internal combustion engines, enabled the introduction of actual mass production [20,21]. According to Khan’s research, many key aspects of modern society, particularly regarding the organization of technology markets, developed throughout the nineteenth century and solidified during the Second Industrial Revolution [22]. In the study by Bart and Kaplan, it is stated that younger workers were at the forefront of adapting to automation technologies, frequently transitioning into emerging occupations. In contrast, older workers tended to stay in declining roles or shift toward unskilled physical labor [23]. It correlates with the findings of Katz and Margo as before the industrial revolution, the path to middle-class status typically involved either advancing in agriculture to become a farm operator or climbing the apprenticeship ladder in craftsmanship. However, by the end of the century, these paths had narrowed, and white-collar occupations took their place, which, unlike farming or crafts, required more formal education. The demand for white-collar skills grew faster than the supply, indicating that the return on education steadily increased throughout much of the 19th century [24].

3.3. Third Industrial Revolution—Rise of Automated Production

The Third Industrial Revolution (3IR), occurring in the second half of the 20th century, was driven by the spread of digitization, advancements in information technology, electronics, and transportation, as well as the introduction of computing in industry, thereby enabling production automation. According to the research of Mohajan the Third Industrial Revolution has generated thousands of business organizations and created millions of jobs worldwide. In his study, Mohajan states that during the First Industrial Revolution, only Great Britain experienced significant advancement, while the rest of the world remained impoverished. The Second Industrial Revolution brought about revolutionary changes, primarily in the USA. However, the Third Industrial Revolution saw developments that impacted every nation, benefiting global humanity as a whole. The 3IR was marked by enormous changes, mainly in education, information and communication technologies (ICT), health, financial, and administrative sectors. This period is recognized as the shift from mechanical and analog electronic technology to digital electronics, driving revolutionary advancements across various fields [25]. In sharp contrast to the first and second industrial revolutions, which put energy at the center of industrial production, the third industrial revolution was based on machines, and especially in computing power, as a factor determining the productivity of companies [26]. An example of implementing and using computing power in the machinery industry is the first introduction of Numerical Control (NC) systems in the middle of the twentieth century. Based on Noble’s statement, the machine tool industry, a small sector focused on producing capital goods for the nation’s manufacturers, is highly sensitive to fluctuations in the business cycle, making it a boom-or-bust industry, experiencing an exaggerated impact of good times when everybody buys new equipment and bad times, when nobody buys. Moreover, there is an emphasis on the production of unique machines, essentially custom-made for users. These factors explain much of the cost of machine tools: Manufacturers devote their attention to the requirements of the larger users so that they can cash in on the demand for high-performance specialized machinery, which is very expensive due to high labor costs and the relatively inefficient low-volume production methods [27]. The concept of an NC machine was developed by John Parsons, a contractor for the United States Air Force (USAF), who was working on creating templates for helicopter rotor blades. While determining the coordinates for the hole positions needed for chain drilling the templates, he envisioned how much easier it would be if these coordinates could be directly fed into the drilling machine [28]. The development of numerical control marked a significant leap by enabling machines to perform complex tasks with high precision, thereby reducing human error. A decade later, in the 1960s, the advent of computers transformed numerical control into computer numerical control (CNC). CNC machine tools, often called integrated machining centers, consolidate functions like milling, planing, boring, and drilling into one versatile machine tool. The machine operates by using computer programming and code conversion from the controller, enabling it for more complex and precise operations, greater flexibility, and easier reprogramming [29]. These developments meant there is a straight way for robotics to appear in the industry. Science categorizes the industrial robotics era for different generations [30].

3.3.1. First Generation Robots

The birth of industrial robotics can be dated to 1956, when George Devol, who two years earlier had written a patent on a machine called a Programmed Transfer Article, and Joseph Engelberger, a space industry engineer, were very fond of science fiction and the books of Isaac Asimov. This meeting resulted in the company Unimation, the development of the Unimate robot, and the development of the industrial robot industry. The entrepreneur Engelberger and the innovator Devol complemented each other very well. First, they visited 15 car factories and around 20 other industries to better understand the need for industrial robots. Then, the prototype came out in 1961, and the very first Unimate robot was installed in the GM facility. The task of the robot was to remove parts directly from the die-casting tool. It was a fairly simple robot, compared to the one you can see nowadays, because it could only perform one task. The life span was expected to be 18 months, and since GM wanted to be able to pay off the robot in 18 months, it was sold for 18,000 USD. However, it was produced for 65,000 USD. Unimation sold it at a lower price than the production cost to establish it as a strategic reference in the industry. To increase the demand for robots even more, Unimation introduced an operating lease construction, which turned out to be a successful move. Later on, the updated versions were used for many different tasks, such as load and unload purposes, workpiece handling, and spot-welding of car bodies [31,32].

3.3.2. Second Generation Robots

The success of the Unimate encouraged others to jump into industrial robotics development. By this time, the pneumatic and hydraulic-based robots became obsolete due to the spread of sensor technologies. New microelectronic components, especially the microprocessor, came and formed the basis of the powerful and cost-effective control systems of today. Installing robots in production was also a result of the increasing oil prices and the competition from companies around the world. Industrial robots have evolved significantly since the creation of the first prototypes [32,33,34]. The integration of sensors, starting in 1968, marked the second generation of robots. These robots were able to react to the environment and offer responses that met different challenges. This section outlines some of the key advancements in their development. Some examples of pioneer robots for the industry are as follows: Shakey [35], developed by Stanford Research Institute, was the first sensorized mobile robot containing diverse sensors (for example, tactile sensors) as well as a vision camera. During this period, relevant investments were made in robotics. In the industrial environment, we have to highlight the PLC (Programmable Logic Controller) [36], an industrial digital computer, which was designed and adapted for the control of manufacturing processes, such as assembly lines, robotic devices, or any activity that requires high reliability. PLCs were considered to be easy to program. Due to these characteristics, PLCs became a commonly used device in the automation industry [33]. In 1969, Victor Scheinman, a mechanical engineering student working in the Stanford Artificial Intelligence Laboratory (SAIL), developed the Stanford Arm [37]. Due to the six DC electro motors used for the robot, the Stanford Arm has six DOF (degrees of freedom): five revolute and a prismatic joint. With this innovative solution, it became the first all-electric manipulator operated by a microprocessor. Additionally, the manipulator was equipped with sensors such as potentiometers and tachometers for measuring position and velocity, for control purposes [38]. Some years after the development of Stanford Arm in 1973, Scheinman founded a company (Vicarm Inc., Palo Alto, California) and designed Vicarm, which was dedicated to assembly tasks where it was not required to lift heavy loads. The feature of the newly developed electrical robot was the weight and size, as Vicarm was smaller and lighter than its peers, compared, for example, to Unimate. This could be taken advantage of as it could be employed for operations where it was not required to lift heavy loads [31]. Scheinman tried to shape the Vicarm to match GM’s specifications, but in 1978, he realized that he preferred research and wanted to focus on it; therefore, he sold the rights to the Vicarm to Unimation. Unimation renamed the Vicarm and marketed it under the trademarked name PUMA (Programmable Universal Machine for Assembly). According to Marsh’s publication, the most notable characteristics of the PUMA are its human-scale design. The robot arm is similar to a human arm, with six degrees of freedom that mimic human movement, comparable to the motions of the waist, shoulder, elbow, wrist, and hand. For example, the PUMA robot was able to screw in light bulbs or put together small electronic motors [39]. It operated at a pace similar to that of human workers and took up roughly the same amount of space as a worker on a small parts assembly line. The first PUMA robot was installed at the GM facility in 1979 [40]. In parallel, many industrial robots appeared, such as Famulus in 1973, developed by KUKA. The main feature was that this was the first industrial robot to use the now-standard setup of six electric motor-driven axes [41]. In the same year, Cincinnati Milacron developed the T3 robot (an acronym for The Tomorrow Tool). T3 was the first commercially available minicomputer-controlled industrial robot [30], which was installed in several automotive plants, and especially in the Volvo plants in Sweden after changing the Unimate robots [39]. Another industrial robot manufacturer that must be mentioned is ASEA. ASEA produced Unimate robots on license, but in 1971, the company decided to develop its own electric-driven robot and started the production of the deservedly famous IRB series. The first robot of this series was the IRB-6, which was largely employed in productive sites for complex tasks (machining, arc-welding), for its ability to move smoothly along continuous paths. Surprisingly, the first customer of IRB-6 was a small Swedish company owned by Leif Jönsson at Magnussons i Genarp AB. It was a small firm with 20 employees producing stainless steel pipes for the food industry. By using the ASEA robot, Magnussons was one of the first in the world to operate an unmanned factory around the clock, seven days a week [30,32,39]. In the same year, the Japanese company Hitachi developed the robot HI-T-HAND Expert, which holds significance in the history of industrial robotics due to the precision it achieved in insertion tasks (it could insert mechanical parts with a clearance of approximately 10 μm). It was also equipped with a force feedback control system and a flexible wrist mechanism [30].

3.3.3. Third Generation Robots

From the beginning of the 1980s, billions of dollars were invested into industrial robots all around the word [33]. The purpose was to make assembly and other processes as automated as possible. Here, welding, painting, packaging, and every part of manufacturing are characterized by high repetition. With the changing requirements, new types of robots appeared in industry. SCARA-type robots (acronym for Selective Compliance Assembly Robot Arm) were developed at the end of the 1970s in Japan by Hiroshi Makino, a professor at Yamanashi University. The purpose was to develop a robotic arm that can efficiently and quickly perform assembly tasks in the electronics and automotive, packaging, and pharmaceutical industries [42]. Selective compliance of SCARA robots means that the robot can only move flexibly in certain directions, remaining rigid in other directions, which provides an ideal solution for assembly and material handling tasks with high precision [43]. One of the most famous SCARA-type robots, named AdeptOne, was made by Adept Technology Inc., Livermore, California in 1984. The AdeptOne robot became particularly popular in pick-and-place operations, where it could quickly and accurately move objects from one place to another [44]. This application was widespread in electronics manufacturing, where the robot could assemble components quickly and accurately. In addition, the AdeptOne provided great benefits in assembly operations where precise positioning and fastening of small parts were required. It was widely used on the production lines of the automotive and electronics industries, as it was excellent for repetitive, precision tasks. Despite the significant progress achieved in the 1980s, the need for robots capable of performing tasks at high speed stimulated scientific research to design innovative kinematic structures. The idea of using parallel kinematic chains instead of the classical serial kinematic chains came up, and this led to a light robot capable of moving at high speeds. These types of industrial robots are named Delta robots. The Delta industrial robot is a parallel design robot consisting of three (or four) pairs of arms connected to the robot body with universal joints. Compared to serial robots, parallel robots worked with a smaller work area but at a much higher speed. The kinematic architecture of the Delta robot was replicated in a number of parallel manipulators for high-speed pick-and-place operations. A suction cup can be attached to the end of the arms, which is suitable for picking up and moving the parts. A thin and light arm can also provide sufficient rigidity, which is essential for the fast operation of the robot arm. These industrial robots are primarily mounted on a conveyor belt and are used to quickly pick up and move the parts continuously arriving through it [45]. The first delta robot was developed and presented in 1987 by Reymond Clavel, a professor at the École Polytechnique Fédérale de Lausanne (EPFL), and Demaurex was the first company to apply the technology in an industrial environment in 1992, particularly in fast-moving goods sorting and packaging processes. The company’s specialty was the creation of high-speed automation systems, with a particular focus on the food, pharmaceutical, and consumer goods industries [46]. Another considerable Delta robot, named Flex-Picker, was developed by ABB. The goal was for the robot to be able to quickly pick up and move objects on a production line while maintaining accuracy and repeatability. The robot is particularly famous for its high-speed operation and excellent applicability in pick-and-place processes, with a capacity of up to 150 picks per minute. Flex-Picker debuted in 1998 and has since become widespread in the FMCG (fast-moving consumer goods), food, pharmaceutical, and electronics industries [47]. The technological development of the 1980s and 1990s laid the foundations of modern industrial robotics. From the appearance of microprocessors and servo motors to advanced sensor technologies, network and communication systems like Ethernet standardization [33], RPLs (Robot Programming Languages)—for example, ABB’s Rapid or FANUC’s Karel [48]—safety support solutions such as light curtains and collision detection systems [49], Computer-Integrated Manufacturing (CIM), known as CAD/CAM (Computer-Aided Design, Computer-Aided Manufacturing) [50], and Flexible Manufacturing System (FMS) [51], these innovations have made it possible for robots to become faster, more accurate, and better integrated into production environments.

These technological developments, as well as the rapidly developing industry, led to the first concepts of humanoid robots and later the appearance of COBOTS (collaborative robots). The early concepts of humanoid robots appeared in the late 1980s. Honda started to develop the E (Experimental) series. Between 1986 and 1993, there were seven different model types starting with E0. E0 was able to walk in a straight line on two feet in a manner reminiscent of human locomotion. One step took around 5 s to complete; therefore, engineers realized that in order to walk up slopes, it is necessary to speed up the robot. With the end of the E-series, the E6 came out in 1993 and had the ability to balance on its own, navigate obstacles, and even ascend stairs independently. After the first successes at Honda, they continued with the P series (Prototype) from 1993 to 2000. In this phase, three different humanoid robots were developed; the latest, P3, was characterized by a high degree of mobility and had a humanoid appearance. Finally, on 31 October 2000, Honda introduced ASIMO (Advanced Step in Innovative Mobility) and revolutionized humanoid robotics. ASIMO was the culmination of decades of Honda research and was the first robot capable of continuous walking, running, climbing stairs, and balancing (two DOF on the neck, six DOF per limb). ASIMO was also equipped with advanced sensors and artificial intelligence that allowed it to handle human interactions, facial recognition, and obstacle avoidance [52,53]. This period saw the evolution of robots from basic mechanical systems to intelligent, adaptive machines capable of transforming global manufacturing and the introduction of the Fourth Industrial Revolution.

3.4. Fourth Industrial Revolution—Era of Machine Learning

Over the more than 2.5 centuries since the First Industrial Revolution, humanity has undergone tremendous development. Since 2010, the world has considered manufacturing as “Smart Manufacturing”, and the paradigm “Industry 4.0” spread worldwide. The Fourth Industrial Revolution (4IR), which took hold in the 21st century, known as Industry 4.0, brought about a new fundamental paradigm shift in industrial production based on advanced digitalization within factories and the combination of the Internet and future-oriented technologies [54]. With Industry 4.0, several key technologies and tools have appeared in production. The idea of a “smart factory” adopts cyber–physical systems (CPS), which refer to advanced systems that integrate computational and physical capabilities, enabling interaction with humans in new ways. CPS manages interconnected systems by linking physical assets with computational processes, allowing communication, control, and feedback loops where physical processes influence computations and vice versa [55,56]. Additional innovations to the industry include Radio Frequency Identification (RFID). The core concept of RFID systems is to label items with tags. These tags have transponders that transmit signals that can be read by dedicated RFID readers [57], the Internet of Things (IoT) [58], and the Industrial Internet of Things (IIoT) [59], which offer significant benefits from an automation perspective due to their ability to enhance connectivity, data collection, and control over systems. Digital Twin (DT), where the main applications in various sectors include design and planning, optimization, maintenance, safety, decision making, remote access, and training, among others [60], the spread of Artificial Intelligence (AI) [61] and machine learning (ML) [62], as well as Autonomous Mobile Robots (AMRs) [63] and Cobots [64,65] from robotization point of view. With the rapid development of technology, Zamalloa et al. supplemented the industrial robots generation with two additional innovations. Generation 4, identified as intelligent robots, summarizes the evolution during the Fourth Industrial Revolution, while generation 5, named collaborative and personal robots, is a robotic technology of the Fifth Industrial Revolution [33].

3.4.1. Fourth Generation Robots

One of the most meaningful and pioneering industrial robots of the fourth generation is Frida (acronym of Friendly Robot for Industrial Dual Arm), which was developed and then introduced in 2011 by ABB. The purpose was to build a robot for the electronics industry in response to customer demand for a robotic solution in manufacturing environments where humans and robots need to collaborate. During the additional development phases, the cobot was renamed YuMi (You and Me) and officially launched in 2015. The cobot is equipped with flexible hands, part feeding systems, and camera-based part detection so that it can assemble producs with a small working area while working with human labor in synergy. Its advanced control algorithms allow the cobot to stop its movement within milliseconds upon detecting an unexpected object or even the slightest contact with an operator. The position repeatability is ±0.02 mm, with a payload capacity of 0.5 kg [66]. Baxter is Rethink Robotics’ first two-arm cobot generation to reach the marketplace and was developed in 2012. Rethink claims that Baxter is behavior-based and easily taught by shopfloor workers to perform tasks without any higher qualification requirement. The cobot was capable of performing many types of work, from packaging and kitting to line loading, machine tending, and material handling. Important to point out that Baxter was able to work safely and interactively directly next to operators without any security cage. Baxter is capable of autonomously adapting to changes in position, lighting, and various object shapes. The robot is equipped with smart sonar systems around its head that detect nearby movements, allowing Baxter to learn from its surroundings [66,67].

3.4.2. Fifth Generation Robots

Most of the fifth-generation robots are still under development. Some examples are Optimus (or so-called Tesla Bot), which was developed by Tesla and introduced in 2022 during Tesla’s AI Day. Xiaomi’s CyberOne (2022), Digit from Agility Robotics introduced in 2019, and RAISE-A1 were developed by Agibot in 2020. Apollo, developed by Apptronik, is designed for use in warehouses and manufacturing plants. Apptronik is positioning Apollo as a high-performance, user-friendly, and versatile system. It was created based on Apptronik’s extensive experience building more than 10 previous robots, including the NASA Valkyrie robot. Apollo is the first commercial humanoid robot engineered for friendly interaction, mass production, high payload capacity, and safety. Apptronik also developed a VR teleoperation approach for their upper-body humanoid robot, Astra. The modular torso of Apollo allows it to be applied in various supply chain and manufacturing tasks, such as palletizing, picking up items, and interacting with AMRs [68].

With the fourth industrial revolution, one of the most significant technological developments was artificial intelligence and its integration into the industry, and one of the prominent technologies of the fifth industrial revolution is collaborative robotics and human-robot collaboration [69].

3.5. Fifth Industrial Revolution—Focusing on Human–Machine Collaboration

Finally, in January 2021, on the tenth anniversary of the Fourth Industrial Revolution, the European Commission developed and published the principles of the Industry 5.0 strategy, aiming at the continuous further development of the economy [70,71].

According to Nahavandi’s 2019 study, Industry 5.0, or the Fifth Industrial Revolution, focuses on the cooperation of machines and people by combining human creativity and intelligent systems. In contrast to the automation of the fourth industrial revolution, Industry 5.0 emphasizes the synergy between machines and people. According to Nahavandi, Industry 5.0 will change the definition of “robot” as a concept in industry, introducing the concept of “cobot” (collaborative robot), i.e., robots capable of working together that can sense human presence and learn from human operators, efficiently performing tasks [72]. Industry 5.0 represents the first human-controlled industrial evolution and is based on the principles of industrial 6R (recognize, reconsider, realize, reduce, reuse, and recycle). This method system focuses on the planning of waste prevention and logistics efficiency in order to create value in terms of quality of life, innovative creations, and unique high-quality products. The 2020 publication by Longo et al. states that the European Economic and Social Commission calls Industry 5.0 a new industrial revolution that integrates the strengths of cyber–physical production systems (CPPS) and human intelligence to create synergistic production plants [73]. According to the approach of Maddikunta et al., Industry 5.0 envisions a human-centered solution where humans and cobots work together. Cobots are not just programmable machines; they can sense and understand human presence. In this new industrial environment, cobots are used for repetitive and labor-intensive tasks, while humans are responsible for customization and critical thinking (“Thinking out of the box”—an innovative, creative way of thinking) [74]. According to a study by Choi et al., which was published in 2022, management must strike a balance between machines and people and focus on more holistic practices and social responsibility in order to create real value for people in the coming Industry 5.0 era. Therefore, the priority area of Industry 5.0 is “human–machine cooperation” [75]. One of the research questions of the Lu et al. 2022 [76] publication was what human-centered manufacturing really means. According to their interpretation, with the help of Industry 5.0, production should become human-centered by placing the well-being of workers in the industry at the center of the production process [76]. According to Romero and Stahre’s approach, Industry 5.0 is rooted in the concept of Industry 4.0 but goes far beyond it. Its goal is to reinterpret and expand the goals of digital and intelligent technologies, while aiming to generate not only profit but also real wealth. This well-being extends to both social and environmental benefits [77]. According to Ivanov’s 2023 article, the basic principle of Industry 5.0 is that the combination of organizational principles and technologies enables flexible, sustainable, and human-centered planning and management of operations and supply chains [78]. In this publication, he presents the 3 pillars of the fifth industrial revolution: flexibility, sustainability, and people-centeredness. Based on the above definitions, it becomes clear that they are all intertwined with respect to aspects of people-centeredness, system flexibility, sustainability, and collaborative intelligence. At the same time, a further detailed analysis of the definitions can also shed light on the most important differences [79].

According to researchers in Industry 5.0, three main elements can be highlighted: people-centeredness, flexibility, and sustainability. In the publication of Xu et al., published in 2021 [80], and in relation to Industry 5.0, a human-centered approach puts basic human needs and interests at the center of the production process, moving away from technology-driven development to a fully human- and society-centered approach. As a result, workers in the industrial sector take on new roles, and in terms of value creation, employees are seen as an investment instead of a cost [80]. At the same time, technology is important to serve people and societies, which means that the technology used in manufacturing adapts to the needs and diversity of industrial workers [81]. A safe and inclusive work environment must be created for physical health and to prioritize mental health and well-being. One of the implications of high-level automation and Industry 5.0 is that workers must continuously develop and retrain themselves to achieve a better balance between work and private life [82]. In addition to the mass individualization needs of the consumer, the human role in industry cannot be replaced by machines or robots. Automation or digitization cannot be performed without human presence. The reason for this is that human presence increases the fault tolerance of the system. Industry 5.0 places human labor at the center of production systems. A prerequisite for human-centered manufacturing is that factories strive for flexibility, agility, and robustness [76,83].

The second pillar is flexibility. According to the approach of Breque, Xu et al., system resilience means that the system can quickly recover or maintain a stable state during and after (geo)political changes, natural disasters, or even major events caused by the COVID-19 pandemic or in the case of long-term, significant challenges [80,82]. A prominent feature of Industry 5.0 is achieving a high level of flexibility. Industry 5.0 focuses not only on the ability of businesses to handle external uncertainties such as market changes, supply chain, and customer uncertainties but also on extending flexibility to a wider range of industrial systems. This includes industrial chains and even the industrial system of an entire country or region that faces unknown risks [79]. Based on the processed literature, the third pillar of Industry 5.0 is sustainability. Sustainability rests on three pillars: economy, environment, and society. Compared to the previous industrial revolutions, the current Industry 5.0 visions tend toward people-centeredness and social needs, which results in an imbalance between the three pillars of sustainability, although they should be part of a balanced solution. The economic aspect of sustainability remains decisive in the reorganization of technologies during the implementation of the Industry 5.0 vision. Sustainable implementation of Industry 5.0 means balancing the three pillars at the right time in different stages to implement the global construction of Industry 5.0 more effectively, achieving greater volume, faster speed, better quality, and cost savings [79,84]. In order to respect the limits of the planet, the industry must be sustainable. In order to achieve environmentally sustainable solutions, you need to develop circular processes that recycle, reuse, and recycle natural resources. Through these methods, waste and its impact on the environment can be reduced, and ultimately, an economic system can be formed with a higher level of efficiency and resource effectiveness [80].

Industry 5.0 builds on the advances of Industry 4.0 by enhancing the role of intelligent communication networks and AI-driven decision making in manufacturing. Smart factories are characterized by real-time data exchange between machines, predictive maintenance, and AI-driven optimization, leading to a more adaptive and resilient production system. This transition is not merely a technological upgrade but a fundamental shift in how manufacturing is conceptualized, moving from a deterministic process-driven model to an interconnected, intelligence-driven ecosystem. The essence of Industry 5.0 lies in its enhanced utilization of AI and smart technologies to foster real-time communication and networking among machines and humans. This dynamic interplay results in predictive maintenance and optimization of operations, showcasing a move from traditional linear processes to interconnected ecosystems driven by intelligent data processing [85]. Moreover, Industry 5.0 aims to embrace sustainability while addressing its societal impacts, pushing the boundaries of how manufacturing can intersect with ecological and social objectives [86]. Yao et al. contend that Industry 5.0 extends beyond smart manufacturing frameworks by emphasizing a socio-technical revolution that integrates AI and the Internet of Things (IoT) into a comprehensive value chain, advancing economic productivity while ensuring social responsibility [87].

Table 1. Transition from traditional 5M manufacturing model to Industry 5.0 paradigm.

Traditional 5M Model	Industry 5.0 Paradigm
Man	Human–AI collaboration and human-centric production [72,80]
Machine	Intelligent, networked Cyber–Physical Systems (CPS) [55,56]
Material	Sustainable, circular resource Use [84]
Method	Adaptive, AI-driven and data-informed decision-making [85]
Money	Value creation through flexibility, well-being, and social responsibility [78]
(Not present in 5M)	Real-time communication and intelligent networking [58,70]
(Not present in 5M)	Systemic integration of product intelligence across value chains [73,87]

summarizes the conceptual shift from the traditional 5M manufacturing model to the Industry 5.0 paradigm.

As seen above, Industry 5.0 is not only a technology-driven revolution but also a value-based initiative that implements technological transformation for a specific purpose. It is important that the principle of Industry 5.0 is the ever-higher level of automation and the human–machine relationship and cooperation. Based on our literature review, it seems that Industry 5.0 responds to the social impacts of Industry 4.0 by bringing people back into manufacturing and trying to make it human-centered alongside high-level and advanced automation.

4. Automation in Supply Chain and Logistics

When discussing about the automation of the supply chain, it makes sense to take a step backward and investigate the whole process from a wider perspective. To be able to produce an item, the whole supply chain automation should be taken into consideration as all fields contain automation solutions. It shall clarify what operations are included and meant under the supply chain within a company that focuses on, for instance, an assembly manufacturing plant. Logistics can be separated into two different parts: external logistics and internal logistics/intralogistics. External logistics refers to the transportation, distribution, and warehouse processes outside the manufacturing facility, while internal logistics is the process of handling materials, parts, semi-assembled, and final products throughout the production cycle. Starting from the external logistics, including transportation and loading. Intralogistics includes handling incoming goods, material receiving, storage, warehousing, handling, and picking within an assembly area. For these tasks, different logistic robots and autonomous vehicles exist and are applied as presented in Figure 2.

In the case of external logistics, autonomous vehicles, drones, smart cargo-handling gears used for loading operations, and smart containers are used for supporting logistics and operators in their tasks [88]. Their solutions are beneficial for smart logistics operations, as they provide better quality control, integration with the suppliers, information accuracy, faster information exchange, and reduced operating expenses [89]. When discussing internal logistics, the key is the specialized robots specially developed for these purposes. Logistic robots such as AGVs (Automated Guided Vehicles) and AMRs (Autonomous Mobile Robots) have been widely used recently to solve various engineering problems in logistics and manufacturing [90]. AGV robots rely on and follow fixed paths or tracks for material transportation, typically requiring infrastructure changes like magnetic tapes, wires, painted lines, or reflectors. Many modern robots now use SLAM (Simultaneous Localization and Mapping) algorithms. These robots can autonomously generate a map of an unfamiliar environment while continuously tracking their own position within it. This mapping is usually accomplished using 2D or 3D lidar scanning, often supplemented by 3D stereo depth cameras with advanced sensors. By combining odometry with advanced filtering techniques, these robots can accurately estimate their position on the map while navigating around unknown or moving obstacles. Meanwhile, AMR robots operate autonomously and can navigate in an uncontrolled environment without the need for fixed paths or tracks [66]. These AGV robots are limited in their field of usage. Cobots are also applicated in logistics, with a wider range of usage. According to Rahman et al., cobots assist with order picking, packing, and inventory management. They can transport materials to human workers, optimizing order fulfillment processes. Cobots are used for moving heavy materials or products within warehouses and factories, reducing physical strain on human workers and improving material flow efficiency [91]. The combination of AGVs and cobots, which are determined to be mobile manipulators—the so-called “MoMas”—are automating material handling tasks in industries such as logistics. They combine the mobility of robotic platforms with the dexterity of manipulator arms. This enables them to navigate complex environments and manipulate objects; therefore, these mobile manipulators are widely applied in logistics, but their adaptability also extends to manufacturing processes where material handling and automated assembly tasks are required. Equipped with sensors and cameras, these robots perform inspections and carry out maintenance tasks on machinery and equipment. One of the significant advantages of mobile manipulators is their ability to collaborate and support human workers. The shortage of skilled labor and the lack of staff applying for factory jobs is likely to increase demand.

Table 2. Automated logistics solutions—Functions, advantages, and limitations.

Technology	Function	Advantages	Limitations
AGV [90]	Moves along predefined paths for material transport	Reliable, efficient in structured environments	Requires fixed infrastructure, not adaptable to dynamic environments
AMR [66]	Free navigation without fixed paths	Adapts dynamically, avoids obstacles	Higher initial investment cost
Cobots [91]	Works alongside humans in warehouses and manufacturing	Flexible, reduces physical strain on workers	Limited payload capacity
MoMas [89]	Combines mobility with robotic arms for handling and assembly	Handles complex tasks, enhances automation	Requires complex programming and integration

concludes the findings below:

Successful applications of logistics robots, such as the autonomous pick and place robots by, for example, Amazon and DHL in their warehouses, have greatly increased efficiency in order picking and other warehouse tasks [92]. Even in very large warehouses and their dynamic environments, the workflows can be set up and modified quickly with the help of intelligent autonomous logistics robots and robotic data cloud systems. It can be seen that robotics has become a source of competitive advantage in logistics, and the optimization of the use of logistics robots is a way to further enhance the productivity of robotic systems; these include, among others, optimization and coordinated autonomy of logistics robots [93]. Logistics robots equipped with this technology are capable of operating autonomously, even in areas they have never encountered before. The integration of robotics with artificial intelligence is resulting in more precise and autonomous robots that are highly versatile. This success in logistics robots, particularly in large e-commerce warehouses, is now being mirrored in manufacturing facilities, where logistics robots are increasingly being adopted to enhance operational efficiency among human workers. In the publication of Cimini et al. [89], they define the logistics operator 4.0 as “ a smart and skilled operator who uses enterprise wearable tech gadgets and works together with software and hardware social robot companions and helpers in order to make work easier and safer in internal and external logistics environments“, and points out the collaboration between human labor and robots and mentions both humans and collaborative robots performing the tasks and supporting each other [89]. With the appearance of Industry 5.0, the Logistics 5.0 phrase was introduced as well. According to the research of Andreas et al., supply chain 5.0 is marked by a high degree of connectivity between the physical and digital environments. Based on their research, automation and robotics support the human workforce in repetitive tasks, so only limited human interventions are needed. With the exponential advancement and interaction of digital technologies, supply chains are expected to gain a higher degree of autonomy until they can think and act for themselves, but always with human customization, critical thinking, and intelligence to enable the labor workforce and machines to work collaboratively. Technological advances must be adopted in the different supply chain processes, therefore collaboration is needed between all the involved participants and new industry technologies. Logistics 5.0 has emerged by underlining Industry 4.0 along with Industry 5.0 by developing smart logistics systems to meet consumer needs in today’s connected, digitized, and rapidly changing global logistics market environments. According to Logistics 5.0, their research highlights that the objective of Logistics 5.0 is not to replace humans in their jobs but to avoid inaccuracies and to gain faster processes in which information can be effortlessly shared in real time [94]. Implementation of new technology brings not only benefits but also potential risks. According to the research of Kodym et al., there are economic risks associated with high or false investments, which are in parallel with the advantages of automating the supply-chain and logistics processes. From a social perspective, risks in job losses are considered too. Additionally, risks can be associated with technical risks, such as technical integration, and information technology (IT)-related risks such as data security [95]. To summarize, the purpose of automating the supply chain and logistics is to improve efficiency, reduce human errors, and optimize the flow of resources, but it carries risks as well.

5. What Impact Does Manufacturing Automation Have Either on the Labor, Workforce and the Society?

The impact of automation and robotization on the human workforce, particularly in manufacturing plants, has become a pivotal area of research in recent years. The integration of robots into production processes has been shown to significantly alter labor dynamics, leading to both displacement and transformation of job roles, alongside production efficiency and quality improvement. Numerous studies have shown that automation and robotization have a substitution effect on labor and the workforce. The literature highlights the multifaceted effects of automation on employment, the challenges it presents, and the future implications for the workforce.

According to Manyika, automation, in general, has huge benefits but also has challenges. According to the research, the main advantages for the economy and society are boosted productivity growth in parallel with GDP growth and prosperity. The findings of the study in terms of challenges are jobs and wages, skills and trainings, dislocation and transitions, and acceptance [96].

The findings of Koch et al. [97] highlight that in case companies implement robots, they show no negative employment effects, independently of specific skills or groups of workers. They pointed out that robot adoption generates substantial output gains in the vicinity, reduces labor costs, and leads to net job creation. Interestingly, negative employment effects are found where they were least expected: in companies that do not automate their production processes. They also reveal substantial job losses in firms that do not adopt robots, and thus a productivity-enhancing reallocation of labor across firms, away from non-adopters, and toward adopters [97].

In the publication of Welfare et al. [98], the juxtaposition considered between the benefit of the employer and job loss when discussing robotics: from a management point of view, it means money saving and efficiency increase, but on the other hand, it means job losses. It was noted that while robotics would improve quality and speed while reducing error and risk, those benefits would primarily be to the employer, and the potential loss of jobs was a negative for the workers [98].

The research of Zande et al. [15] explored the potential of technology, including AI, machine learning, and robotics, to replace human labor. It found that while automation can handle a growing range of tasks, non-routine tasks involving mobility, creativity, problem-solving, and complex communication are harder to automate. Most jobs will see automation of some tasks, but few can be entirely replaced, with routine jobs being the most at risk. Automation could lead to short-term unemployment and require retraining. Industries with high automation potential include food services, transportation, retail, and manufacturing. Five key factors affect the adoption of automation: availability, implementation costs, economic benefits, labor market, and social/legal acceptance. Despite challenges, technology will have an increasing impact on workplaces, requiring a greater focus on education and training to handle the transition [15].

In the publication of Jiang et al. [99] on the impact of automation in manufacturing firms, the government should support automation in manufacturing to improve productivity, research and development, and labor quality through incentives such as tax breaks and low-interest loans. Automation not only increases long-term labor productivity and output but also disrupts jobs, especially for those in simple, repetitive roles. To address this, the government should invest in the upskilling of low-skilled workers and foster a highly skilled workforce through subsidies, training, and education while also promoting new automation-related careers. Attracting top talent and increasing investment in automation skills is crucial. Furthermore, the positive impacts of automation on productivity and output should be maximized with financial incentives to improve high-quality employment in manufacturing [99].

Carro Fernandez et al. [100] highlight the fear of workers as there is significant fear among workers that robots will replace their jobs, leading to unemployment and job insecurity. Additionally, they mention the role of workers in automated manufacturing: robots require human supervision, maintenance, and programming. This creates new roles and responsibilities for workers, often leading to higher qualifications and potentially better pay. With this, robots can create new job opportunities, such as supervisory and maintenance roles. Properly integrated robots can enhance worker safety and efficiency, leading to a more productive and secure work environment. While fears of job loss are valid, the article argues that with proper integration, training, and communication, robots can co-exist with human workers, enhancing productivity and safety without leading to significant job displacement [100].

The effects of digitalization on the social aspect of sustainability are expected to be significant. According to Beier et al., the results align with more critical perspectives that predict significant job losses due to increased automation not only in industrialized nations but even more so in developing countries where automation levels are currently lower. This could lead to substantial social challenges as competition intensifies for the remaining jobs [101].

The study of Arntz et al. [4] suggests that current assessments of job automatability may overestimate risks, as they focus on technological capabilities rather than actual usage. Even with increased adoption, employment effects depend on how workplaces adapt, as workers might take on roles that complement new technologies. New technologies are also likely to create jobs and boost labor demand if they enhance competitiveness and income. However, low-educated workers are more vulnerable to technological changes, facing significant challenges in upskilling and retraining due to the rapid pace of current technological advancements. The study highlights the importance of addressing potential inequalities and training needs rather than focusing solely on the risk of unemployment [4].

According to the study by Acemoglu and Restrepo [14], automation and artificial intelligence (AI) are fundamentally reshaping the labor landscape. Their research emphasizes that automation tends to displace human labor by replacing tasks traditionally performed by workers, leading to a reduction in labor demand and wages. This displacement effect is particularly pronounced in sectors such as manufacturing, where routine tasks are increasingly automated. The authors argue that while automation can enhance productivity, it simultaneously raises concerns about job security and the need for a workforce capable of adapting to new technologies [14].

For instance, Dauth et al. [102] emphasize that the introduction of robots has led to observable negative effects on employment and wages, particularly for low-skilled workers, while simultaneously enhancing productivity. The findings illustrate the dual impact of automation on the labor market, particularly in the machinery manufacturing sector. Although the introduction of industrial robots has led to displacement effects, especially among young workers entering the labor force, these losses are counterbalanced by the creation of higher-quality jobs in the service sector and within existing firms, fostering a more stable employment landscape for incumbent workers. This research underscores the necessity for adaptive educational strategies and highlights the potential of automation to improve job quality for those engaged in complementary roles, thus suggesting a nuanced societal effect of manufacturing automation that transcends mere job loss [102].

The findings presented by Vermeulen et al. [103] elucidate the nuanced relationship between automation and employment, suggesting that while automation may lead to labor displacement in traditional sectors, it simultaneously fosters the emergence of new labor-intensive industries that can absorb displaced workers. This dual effect highlights the importance of adaptive workforce strategies and policy interventions to mitigate job loss while capitalizing on the opportunities presented by automation in machinery manufacturing and beyond. Ultimately, the research underscores the necessity for a proactive approach to workforce development in the face of technological advancements [103].

The findings presented in Autor and Salomons explain the complex relationship between automation and labor dynamics within the machinery manufacturing sector. Although automation indeed displaces jobs and diminishes labor’s share of value-added, the authors highlight that this displacement is counterbalanced by various economic responses that can sustain or even enhance overall labor demand. This nuanced understanding underscores the necessity for strategic policy interventions to mitigate job loss while harnessing the productivity gains of automation, ultimately shaping a more resilient labor market in the face of technological advancement [104].

The findings presented by Badet highlight the dual nature of automation’s impact on the workforce. While the research indicates that automation may lead to the displacement of repetitive jobs, it simultaneously suggests that it fosters the creation of “smart jobs” that leverage human comparative advantages, thus offering a pathway to mitigate job loss and enhance workforce adaptability. This nuanced perspective underscores the importance of strategic workforce development in addressing the social challenges posed by increasing automation [105].

The findings presented by Azman and Ahmad highlight the profound implications of automation and robotization on the manufacturing workforce, emphasizing that while these technologies can enhance productivity and economic growth, they also pose significant challenges such as job displacement [106].

With the continuous development of technology, new phases appear. According to Ziardinov et al., Society 5.0, underpinned by technological innovation and a focus on societal well-being, is set to profoundly reshape the work landscape and the workforce. As automation and artificial intelligence take center stage, routine tasks will be significantly transformed, requiring the acquisition of new skills and a shift in job roles. Society 5.0 also promotes human–machine collaboration, where individuals do not need to compete and adapt to work harmoniously with AI and robots. In addition, personalized learning, augmented reality, and ethical considerations are expected to play an important role in the future workforce [69].

Lambrechts et al. have shown that different human factors influence the cobot implementation processes and lead to recommendations for the successful implementation of cobots in order picking processes. Resistance to change appeared to be a crucial human factor and can be divided into three items. Prejudice: pointing to the importance of overcoming initial prejudices and deferring judgment until sufficient information is available. Skepticism: the majority of people are skeptical by nature and often go into resistance when a change is applied. Unfamiliarity: because most operators are working with a cobot for the first time. These factors show that it is crucial to involve employees in changes in advance to transform resistance to change into more trust and willingness to participate [107].

Filippi et al. highlight that the research on how automation technologies impact employment is highly complex, uncertain, and still in its early stages. The complexity arises from the wide range of analytical levels, various assessment methods, and the variety of automation technologies studied, leading to very detailed findings. In addition, the inconsistency of the results contributes to uncertainty in the field. Even studies that use similar methodologies, analysis levels, and technologies often yield conflicting outcomes, with few providing definitive and conclusive results [12].

Table 3. SWOT analysis of the impact of automation on labor in a highly robotized manufacturing environment.

Strengths	Weaknesses
Productivity growth [96] Efficiency improvements [97] Improved precision [98] Worker safety [100] Supervisory roles [100] Smart jobs [105]	Job displacement [14,101] Non-adopters face losses [97] Resistance to change [107] High costs [107] Skills gap [4,15] Income inequality [12,101]
Opportunities	Threats
Retraining incentives [99] Workforce strategies [69] Growth in robotics [97] Labor-intensive sectors [103] Reduced waste [98]	Job insecurity fears [100] Competition for jobs [101] Labor market polarization [102] Ethical concerns [104] Global inequality [101]

summarizes the findings in the form of a SWOT analysis based on the literature.

In conclusion, the impact of automation and robotization on the human workforce is profound and multifaceted. Although the potential for increased productivity and efficiency is significant, the accompanying challenges, such as job displacement, wage inequality, and psychological impacts, cannot be overlooked. Policymakers, educators, and industry leaders must collaborate to develop strategies that support workers in navigating this transition, ensuring that the benefits of automation are equitably distributed across society. The future workforce will likely require a blend of technical skills, adaptability, and resilience to thrive in an increasingly automated world.

6. What Impact Does Automation Have on the Cost Structure?

According to Fernandez et al., the economic impact of introducing robots in manufacturing involves high initial costs and does not guarantee immediate cost savings or productivity gains. Long-term benefits depend on proper integration and training [100].

The findings presented by Müller et al. in their study highlight that the implementation of Industry 4.0 technologies, including automation and robotization, significantly alters the cost structure within firms by optimizing operational efficiencies and reducing labor costs while simultaneously introducing new capital expenditures for technology acquisition and maintenance. This dual impact requires a strategic approach to balance initial investments with long-term savings, ultimately shaping the financial landscape of modern manufacturing enterprises [108].

Acemoglu and Restrepo, in their study, highlight the significant influence of automation and robotization on the cost structure within machinery manufacturing firms, particularly through their impact on labor dynamics. The research indicates that increased usage of industrial robots correlates with reduced employment and wages in local labor markets, suggesting that while automation may lower operational costs, it simultaneously poses challenges to the stability of the workforce and compensation structures [3].

The study of Graetz and Michaels indicates that the integration of modern industrial robots significantly enhances labor productivity and total factor productivity within machinery manufacturing firms. Specifically, the research highlights that increased robot utilization contributes to a reduction in output prices, suggesting a transformative impact on the cost structure of these companies by optimizing operational efficiency and resource allocation. Also, the evidence suggests that marginal returns on increased robot densification seem to diminish fairly rapidly. We also caution that the rise of robots is not a blessing for all: We find that low-skilled workers, in particular, may lose out [109].

The findings presented by Campilho underscore the transformative impact of automation and robotization on the cost structure of machinery manufacturing firms. The research mentions that the high costs of investment in setting up automation and robotics systems can be considerable. This can make it difficult for small and medium enterprises (SMEs) to afford, limiting the broader adoption of this technology. By enabling flexible production lines and reducing the dependence on dedicated automation or manual labor, companies can achieve significant cost savings, particularly in small- and medium-scale operations. Furthermore, the integration of advanced robotics not only enhances operational efficiency but also fosters a collaborative environment that leverages both robotic precision and human cognitive skills, ultimately optimizing the overall manufacturing process [110].

Kromann et al., in their study, highlight a significant correlation between automation, particularly through the use of industrial robots, and total factor productivity (TFP) within machinery manufacturing firms. Specifically, the research indicates that an increase in robot intensity correlates with over a 6 percent enhancement in TFP, suggesting that automation not only streamlines operations but also positively influences the cost structure by improving efficiency and productivity across various industries and countries [13].

Acemoglu and Restrepo explain the nuanced effects of automation on the cost structure within machinery manufacturing firms, particularly highlighting the dichotomy between low-skill and high-skill automation. The authors argue that while automation can lead to significant reductions in labor costs, it simultaneously requires investments in advanced technologies, which may alter the overall cost dynamics and productivity levels in the sector. This interplay suggests that firms must strategically navigate the balance between capital investment and labor utilization to efficiently optimize their cost structures [2].

Silva et al. discuss the economic impact of automation and collaborative robots (cobots) in machinery manufacturing by highlighting the critical decision criteria that companies must evaluate before adoption. Silva et al. emphasize that a thorough cost-benefit analysis is essential to understand the financial implications of the integration of cobots, guiding manufacturers in enhancing productivity and competitiveness while managing market volatility. This study serves as a foundational resource for decision makers, providing a structured approach to assess the economic viability of cobot implementation in manufacturing processes [111].

The research of Simoes et al. provides a detailed examination of the factors influencing the adoption of collaborative robots (cobots) in manufacturing, emphasizing their potential to enhance operational efficiency and align with strategic objectives such as productivity and flexibility. By analyzing empirical data from various companies, the study illustrates how the integration of cobots can lead to significant economic impacts, particularly in terms of improved productivity and adaptability in response to market demands. This understanding is crucial for companies looking to leverage automation effectively to optimize their economic performance in a competitive landscape [112].

Table 4. Economical impact by source.

Reference	Key Findings
Fernandez et al. (2012) [100]	High initial costs; long-term benefits depend on proper integration and training.
Müller et al. (2018) [108]	Industry 4.0 alters cost structure by reducing labor costs but increasing capital expenditures.
Acemoglu & Restrepo (2020) [3]	Automation reduces employment and wages, lowering operational costs but impacting workforce stability.
Graetz & Michaels (2018) [109]	Industrial robots enhance labor productivity and reduce output prices; diminishing marginal returns.
Campilho (2023) [110]	High investment costs limit SME adoption; automation improves efficiency and enables flexible production.
Kromann et al. (2020) [13]	Robot intensity increases total factor productivity by over 6%.
Acemoglu & Restrepo (2018) [2]	Low-skill automation reduces labor costs but requires high capital investment.
Silva et al. (2022) [111]	Cobots require thorough cost-benefit analysis to enhance productivity and competitiveness.
Simoes et al. (2019) [112]	Cobots improve productivity and adaptability, aligning with strategic objectives.

below summarizes the economic impacts of automation extracted from the reviewed literature.

In conclusion, the body of research indicates that automation and robotization significantly impact the cost structure in machinery manufacturing. While initial investments may be substantial, the long-term benefits-ranging from reduced labor costs and increased productivity to enhanced flexibility and responsiveness underscore the necessity of embracing automation in modern manufacturing practices. As industries continue to evolve, the strategic implementation of automation technologies will be crucial to maintaining competitiveness and achieving sustainable growth.

7. What Is the Future Direction for Automation in Machinery Manufacturing?

In their research, Arents and Greitans highlight the transformative potential of smart industrial robots in the context of manufacturing automation. The authors emphasize that advances in artificial intelligence are paving the way for more flexible and cognitively capable robotic systems, which are essential to improve competitiveness in the evolving landscape of Industry 4.0. By identifying current trends and challenges in robot control strategies, this research underscores the critical role of AI-driven automation in shaping the future of machinery manufacturing [113].

According to Perez et al., the pivotal role of intelligent automation and collaborative robotics in the evolution of machinery manufacturing within the framework of Industry 4.0. The authors propose a comprehensive methodology that leverages digital twin technology and virtual reality to optimize multi-robot systems, facilitating enhanced flexibility and efficiency in production processes. This innovative approach not only addresses the integration challenges posed by various robotic systems but also emphasizes the importance of real-time monitoring and operator training, thus paving the way for future advancements in automated manufacturing environments [114].

The findings presented in Evjemo et al. highlight the evolving interplay between human labor and robotic automation within the context of smart manufacturing and Industry 4.0. The authors conclude that while significant advancements in enabling technologies such as IoT and cyber–physical systems have emerged, the challenge remains to achieve an optimal balance between human involvement and automation to fully realize the potential of smart factories. This underscores the need for ongoing research and development to refine these interactions, paving the way for a more integrated and efficient manufacturing landscape [115].

8. Findings and Conclusions

The evolution of automation and robotics has significantly shaped the economic and social dimensions of manufacturing. From the First Industrial Revolution to the emergence of Industry 5.0, technological advancements have continuously redefined production landscapes. Key innovations, such as assembly lines, intelligent robots, and AI-driven automation, have driven productivity while introducing new challenges related to labor dynamics, cost structures, and workforce adaptability.

In the realm of supply chain operations, automation has led to transformative efficiencies. Technologies such as Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs) have optimized logistics, improving material handling and reducing physical strain on human workers. The introduction of Supply Chain 5.0 further emphasizes a shift toward collaborative systems where humans and machines work together, increasing adaptability in response to market fluctuations.

However, manufacturing is not solely about robotics, supply chains, or respective automation processes. There is a need to highlight progress in areas such as design, planning, control, and the broader impact on tool development and engineering methodologies. Scientific advancements in these domains are critical to the holistic evolution of manufacturing, yet they remain underrepresented in many discussions, where the focus is predominantly placed on automation and robotics. Future research should address how innovations in these areas contribute to the manufacturing landscape and how they integrate with emerging technologies.

In addition, the conceptual framework of manufacturing has undergone a paradigm shift due to recent advancements. The traditional focus on man, machine, material, method, and money (5M) has evolved toward a more modern vision that emphasizes product, intelligence, communication, and communication networks. This transition reflects the increasing role of digitalization, smart manufacturing, and interconnected systems in shaping industrial processes. While this shift has profound implications for manufacturing strategies and industrial policies, it is often overlooked in discussions on automation. A clearer articulation of this transformation is essential to understanding how Industry 5.0 integrates intelligent systems and human-centric approaches into manufacturing. However, due to the scope of this study, we were unable to explore this shift in detail, and thus, we recognize it as a research gap. Future studies should examine how this evolving conceptual framework influences automation strategies, workforce dynamics, and industrial competitiveness, ensuring a more holistic understanding of modern manufacturing transformations. Future research should also examine the implications of profit-driven versus human-centric automation strategies, assessing their impact on workforce well-being, productivity, and long-term sustainability.

The dynamics of the labor market have also been deeply affected by automation. While routine tasks are increasingly performed by robots, new employment opportunities have emerged in areas such as robot maintenance, programming, and system supervision. Collaborative robots (cobots) allow human workers to shift toward more strategic and decision-making roles, mitigating some of the displacement effects. However, these transitions require comprehensive reskilling efforts to ensure a workforce that can thrive in automated environments.

Economically, automation has altered the cost structures in manufacturing. Although the initial investment in robotics and AI systems is substantial, the long-term benefits include reduced labor costs, increased operational efficiency, and improved product quality. However, small and medium enterprises (SMEs) face distinct challenges in adopting these technologies due to financial constraints, though scalable AI-driven solutions offer pathways for gradual integration.

Looking toward the future, Industry 5.0 marks a paradigm shift toward human–machine collaboration, sustainability, and adaptability. Emerging trends, including AI-driven digital twins, predictive analytics, and autonomous robotics, are expected to further reshape manufacturing by enhancing precision and efficiency while addressing environmental and societal concerns.

In conclusion, the progression of automation and robotics presents both opportunities and challenges for the manufacturing sector. Although these technologies improve productivity and innovation, they also introduce risks such as job displacement, labor polarization, and economic restructuring. As automation advances, it is critical to implement strategic policies that support workforce adaptation and ensure equitable benefits. Moreover, addressing the overlooked aspects of design, planning, control, and tool development, as well as acknowledging the shift from traditional manufacturing paradigms to intelligence-driven models, will enhance the scientific and practical contributions of manufacturing research.

To navigate this transformation successfully, a balanced approach is required, one that aligns technological progress with workforce development and regulatory frameworks. Investment in upskilling programs will be essential to equip workers with the necessary competencies for automated environments. In addition, policymakers and industry leaders must ensure that economic policies support SMEs in integrating automation without exacerbating inequalities.

Industry 5.0 signals a transition to human-centric automation, where collaboration, sustainability, and flexibility are prioritized. By addressing the economic, social, and environmental implications of automation, the manufacturing sector can foster a more inclusive and sustainable future, ensuring that technological advancements benefit both businesses and society as a whole.

9. Future Research Directions

The rapid advancement of automation technologies presents a wealth of opportunities for further investigation, particularly as the manufacturing sector continues its transition to increasingly intelligent and human-centric systems. Although existing research has established a solid foundation, there are several critical areas that warrant further exploration to ensure that automation is implemented in a way that balances technological progress with societal well-being.

As manufacturing systems evolve, it becomes increasingly important to outline a structured research road map to guide future studies in automation, robotics, and smart manufacturing. This research road map should consider not only technological advancements but also the economic, social, and ethical implications of automation. Addressing the intersection of technology and human adaptation, along with redefining manufacturing paradigms, will be essential to shape the sustainable future of production systems.

A key avenue for future research lies in the study of human-centric automation and workforce adaptation. Collaborative robots (cobots) are reshaping the dynamics of human–machine interaction, but questions remain about their optimal integration into diverse manufacturing environments. Exploring the psychological and social factors that influence the acceptance of automation by workers, alongside the long-term impacts on job satisfaction and career progression, will be critical. In addition, the effectiveness of reskilling and upskilling programs deserves further scrutiny to ensure that workers are adequately prepared for increasingly automated workflows. As the workforce undergoes structural changes, research should aim to develop new competency models that support the transition from manual labor to knowledge-driven roles, ensuring that human expertise remains relevant in the era of advanced manufacturing.

The impact of automation on labor dynamics is another pressing area of investigation. Workers displaced by automation often face significant challenges in adapting to the new labor market. Future research should examine the long-term trajectories of displaced workers, focusing on the effectiveness of transition programs, access to new employment opportunities, and the psychological effects of job loss. Furthermore, while many studies have concentrated on the impact of automation on blue-collar workers, the implications for white-collar roles are increasingly relevant. As automation and artificial intelligence advance, tasks traditionally performed by white-collar professionals, such as data analysis, financial forecasting, and even aspects of legal work, are becoming automated. Research should explore how these changes will shape the nature of white-collar employment, including potential shifts toward more strategic, creative, or supervisory roles. Understanding which job sectors will emerge, evolve, or decline due to automation can provide actionable insights for workforce planning and policy making.

The ethical and policy dimensions of automation adoption also require significant attention. Rapid integration of intelligent systems raises questions about equity, transparency, and fairness. Research should focus on developing regulatory frameworks that address income inequality and job displacement while fostering inclusive growth. In addition, exploring the ethical implications of designing and deploying intelligent automation systems will help ensure that these technologies align with societal values and priorities. A critical research priority will be the establishment of global standardized policies that balance automation with employment stability, ensuring that industrial progress does not come at the expense of workforce security.

As the technological frontier continues to expand, advancements in areas such as artificial intelligence, digital twins, and the Internet of Things (IoT) are poised to redefine manufacturing. Investigating the integration of these technologies into hybrid systems that combine human intuition with machine precision could unlock new levels of productivity and innovation. Similarly, the development of cost-effective autonomous robotics tailored for small and medium enterprises (SMEs) will broaden the accessibility of advanced manufacturing technologies, driving economic growth and competitiveness across diverse markets. However, for a fully adaptive and intelligent manufacturing ecosystem, research must address real-time decision making in autonomous production environments, sensor-driven manufacturing intelligence, and the role of quantum computing in next-generation industrial systems.

Automation’s socioeconomic impacts also vary significantly across regions and sectors, making comparative studies particularly valuable. Understanding how automation affects emerging economies versus industrialized nations, or high-mix low-volume manufacturing (HMLV) versus low-mix high-volume (LMHV) facilities, could provide nuanced insights into how different contexts shape automation outcomes. The cultural and demographic factors that influence the acceptance and adaptation to automation represent another important area of study, particularly in regions where industrialization is still in its early stages. Future research should also explore how digital and automation-based economies create economic disparities between technologically advanced nations and those with limited access to automation infrastructure.

Finally, although this review highlights the role of automation and digital transformation, an important area requiring further exploration is the fundamental shift in the conceptual framework of manufacturing. As industries move from a 5M-driven approach to one centered on intelligence, communication, and interconnected networks, understanding its implications for production efficiency, workforce adaptation, and economic sustainability remains a key avenue for future research.