1
Development of a framework to
characterize Smart Manufacturing technologies in light of work design change
MASTER’S THESIS By CHENG-HAO, TSAI
DD MSc Technology and Operations Management
Abstract
Background- Smart Manufacturing (SM) involving the integration of various technologies is revolutionising the manufacturing industry. Human participation is the essence of smart manufacturing, and it is vital to recognise how novel technology influences humans. The theory of work design provides a perspective to observe the impacts of smart manufacturing characteristics on operators.
Aims- This paper aims to design a framework to distinguish the specific characteristics of smart manufacturing technology to understand the associated influence on work design better.
Method(s)- This paper adopts a design science research methodology following Wieringa’s Design cycle. A literature study is adopted to investigate the problem background and to design a theoretical framework, and then case interview method is conducted to validate the theoretical framework.
Results-
This research found out there are two types of technical characteristics. "General characteristics" are those characteristics that appeared in each smart technology, while
"Unique characteristics" are the characteristics that existed in a specific technology.
Besides, these impacts of technical characteristics on work design changes differentiate between operators and managers. Apart from the technical characteristics, the research found out other indirect factors such as management decisions also affects the work design characteristics
Key word: Smart manufacturing, work design, smart technology characteristics
Table of contents
4 Chapter 1: Introduction
7 Chapter 2: Research objectives and methodology 2.1 Problem background
2.2 Research objectives 2.3 Research method
11 Chapter 3: Problem investigation - Smart Manufacturing 3.1 Development of Smart Manufacturing
3.2 The Data Life Cycle in Smart Manufacturing 16 Chapter 4: Problem investigation - Work Design
4.1 Socio-technical system theory 4.2 Job Characteristic Model
4.3 Research regarding work design characteristic
19 Chapter 5 Problem investigation Smart technology and work design 5.1 Work design in the development of manufacturing philosophy 5.2 Work design in the development of Smart manufacturing
23 Chapter 6: Framework design
6.1 Data lifecycle (Column ① in Table 6.1)
6.2 The framework of Industry 4.0 (Column ② and ③ in Table 6.1) 6.3 The characteristic of smart manufacturing (Column ④ in Table 6.1) 6.4 The impact of technology characteristics on work design
(Column ⑤ in Table 6.1)
6.5 The summary of theoretical framework 28 Chapter 7: Validation
7.1 Base smart manufacturing technology 7.2 Integration technology group
7.3 Traceability technology group 7.4 Virtualization technology group 7.5 Automation technology group 7.6 Main validation findings
46 Chapter 8. Discussion
8.1 Two type of technical characteristics
8.2 The impacts of indirect factors on work design
53 Chapter 9. Conclusion 56 References
59 Appendix
Chapter 1: Introduction
The progress of smart manufacturing (SM) has attracted growing attention. Individuals expressed their views on whether SM will subvert people's current lifestyle, especially the impact on existing manufacturing organizations and the work practice of humans. Smart manufacturing, or sometimes the term “Industry 4.0” is used to represent similar concepts, which refers to "extensively implements and integrates networked and information-based technologies in manufacturing" (Hirsch-Kreinsen, 2016; Davis, 2017).
Human’s participation is crucial in the use of smart manufacturing, which is an accord among the past literature (Rauch et al., 2020). SM technologies does not only automate the production but also augment the operators to perform tasks (Waschull et al., 2020). The traditional technological approach intended to achieve complete automated factories, yet, smart manufacturing aims to implement technology with human-centered automation, providing operators further cognitive, physical and sensorial support (Rauch et al., 2020).
With the supportive functions, operators can either receive assistance by machines (human- machine cooperation) or interact with intelligent machines (human-machine collaboration) (Ansari et al., 2018). Thus, the design of work has been changing due to the advancement of new technology.
Work design perspective, meaning "the content and organization of one's work tasks, activities, relationships, and responsibilities", is often adopted to assess the work practice (Parker, 2014). The contemporary work design studies incorporate an integrative perspective from past work design literature (Parker, Morgeson, et al., 2017). For example, the job characteristics model (JCM) explained how five core characteristics of the job affect the worker's psychological state, thereby shaping different outcomes, such as high motivation and satisfaction (Hackman & Oldham, 1975). After JCM, researchers proposed many categories of various characteristics of work design.
Previous work design studies in light of technology adopt different work design characteristics
as criteria to address the impact of smart manufacturing technology on work design (Cagliano
et al., 2019; Parker & Grote, 2020; Waschull et al., 2020). For instance, Cagliano (2019)
reviewed work design characteristics (job breath, job control, cognitive demand and social
Rauch, Linder and Dallasega (2020) identify technology from their functions and discuss how these aids shape the tasks of future operators. The result showed that the share of physical tasks of operators would decrease and cognitive tasks such as decision-making and planning will increase. Parker and Grote (2020) provided examples on how technologies positively or negatively affect work design characteristics, thereby affecting the well-being and performance of workers. However, past studies have identified the relationship between the general dimension of Smart manufacturing technology and work design, but have not addressed whether specific characteristics of smart manufacturing triggered the change in work design.
Furthermore, the determinants that influence work design are intricate. Parker and Grote (2020) stated the difficulty to predetermine the impact of technology on work design due to the multiple forces outside the technology. That is, technology itself can directly influence the change of work design, but its impacts are subject to other indirect effects. For example, organizational characteristics (Lehdonvirta, 2018; Cagliano et al., 2019; Parker & Grote, 2020;
Waschull et al., 2020); management choice (Parker, Van Den Broeck, et al., 2017; Waschull et al., 2020) or broader contextual influences like global and national factors (Parker, Van Den Broeck, et al., 2017). In this research, technology will be the primary focus, following the expanded research questions provided by Parker and Grote (2020): What are the key dimensions of the different technologies? How do these affect work characteristics?
In order to bridge the research gap, a systematic classification of the characteristics of technology enable us to better understand the direct effects of technology on work design. In many practical cases, the focus is mostly on cost improvement and technical problems when implementing SM technology, while enterprises pay less attention to the change of work design and the adaptability of the operators. It would be beneficial for organizations to identify the direct impact of acquired technology on work design which will be based on the characteristics of that technology.
The objective of this paper is to design a framework in order to review the specific
characteristics of smart manufacturing technology to better understand the associated
influence on work design. This paper aims to provide a beneficial perspective to both
academia and industry. Following past literature, this paper expects to identify the critical
characteristics of the SM technology and their impact on work design to provide the
enterprise with advice regarding possible work design changes when implementing smart manufacturing.
This study chooses a design science research method to provide solutions to solve practical
problems. Following the Wieringa cycle (Wieringa, 2014), Chapter 2 comprises the research
questions and methodology of this design science study. Next, Chapter 3,4 and 5 are the
problem investigation which presents how previous studies address SM technology and work
design. Chapter 6 presents the design of a framework to characterize smart manufacturing
technologies and summarize the impacts on work design. Finally, the framework will be
validated in practice by case interviews (Chapter 7).
Chapter 2: Research objectives and methodology
This study adopted the Design Science Research (DSR) methodology. DSR is an approach to design the “artifacts” to address the problems investigated in the “context” (Hevner et al., 2004;
Wieringa, 2014). The artifacts are knowledge-involved contents including models, constructs or frameworks (Gregor, 2002). In this paper, a designed framework was presented to address problems from the investigation.
2-1 Problem background
Technological development is an influential determinant increasing the growth rate of the economy. In the rapidly developing field of technology, smart manufacturing is a keyword that has attracted considerable attention in the manufacturing industry. However, in the practice, business owners focused more on cost-saving and capacity improvement, rather than on how organizations and employees adapt to new technologies. The change in work design could be a problem encountered on the frontline. Ignoring the shift in work design may trigger a negative impact from the operator level to the organizational level. (Slack et al., 2016, 2012) Therefore, the industry started to reevaluate the adjustment of the work design of operators for better compatibility
The academic community conducted in-depth research on the connections between smart manufacturing and work design (Parker & Grote, 2020; Waschull et al., 2020; Kadir & Broberg, 2020; Cagliano et al., 2019; Romero, Bernus, et al., 2016). If the characteristics of smart manufacturing can be summarized and related to their impact on work design, it will be beneficial to the implementation of technology.
2-2 Research objectives
The main objective of this research is to: “Design a framework to understand the impact of smart manufacturing technologies characteristics on work design.”
The framework has two sub-objectives:
(1) Distinguishing the characteristics of smart manufacturing technologies and
(2) Specifying the relationships between characteristics and work design changes
2-3 Research method
This research proceeds based on the Wieringa cycle (2014) of design science research, including four phases: Context description, current problem investigation, design of a framework, and validation. The context description phase was provided in the problem background. The research questions and methodology of the three remaining phases, (1) Problem investigation, (2) Design and (3) Validation, are presented below.
Table.1 Research questions:
Research phase
(Wieringa cycle) Chapter Research questions
1. Problem investigation
3 (1) What is the development of smart manufacturing?
4 (2) What is the development of work design research?
5 (3) What is the relationship between technology and work design?
2. Design 6
(4) What are the key characteristics of Smart technology?
(5) What specific characteristics can be related to the specific change of work design?
3. Validation 7 (6) Is there a difference between the designed framework and the practice?
(7) How do the differences between the designed
framework and the practice arise?
(1) Problem investigation - Literature Study
The current problems were investigated through the literature review in Chapters 3, 4 and 5.
Chapter 3 started with the description of smart manufacturing, and Chapter 4 addressed the development of work design research. Furthermore, Chapter 5 discussed the relationship between smart technology and work design.
(2) Design the framework - Conceptual Study
Next, the initial theoretical framework is proposed in Chapter 6, mainly based on literature.
The design framework first determined the characteristics of smart manufacturing technologies (RQ4), and then tried to relate the characteristics and work design change (RQ5).
(3) Validate the framework - Case interviews
In Chapter 7, the validation was conducted to specify further the relationship between characteristics and work design change (RQ5) and examined whether the framework corresponded to the real world (RQ6) and what were the new findings or differences between the designed framework and the practice? (RQ 7)
In this study, case interview method was applied to empirically validate the technology characteristics and to validate the effects of these characteristics on work design. Case interview is suitable for investigating the proposed problems within the real context, especially when the boundary between the observed issues and the real world is not very clear to find (Yin, 1989, 1994).
(1) Case selection
The implementation of smart technologies is an emerging transformation of manufacturing
in recent years, and most companies have only adopted a few smart technologies in their
factory. Frank et al. (2019) proposed a detailed framework to categorize smart technology,
which was used to determine the technologies and select the cases in the research. People
who have employed one of the technologies mentioned in the framework (see table 6.1) were
considered adequate interviewees. Therefore, the interviewees with related experience in
smart technology operation or implementation were selected to provide their insights on
work practice change.
Six people, from managerial-level positions and the front-line operators of the manufacturing department to the personnel with experience in introducing smart technology, were interviewed to achieve multiple sources of evidence to ensure reliability.
(2) Data collection
The study planned to conduct semi-structured interviews for acquiring primary qualitative data related to the framework. The validation questions comprised structured questions and relatively opened questions to acquire detailed information. (see Appendix A Interview protocol), and follow-up emails were sent to interviewees if there were unclear or extended questions emerging after the interview.
All interviews were recorded after the interviewees granted the consent. Online or telephone interviews were adopted instead of on-site interviews because of the pandemic. All six interviews were conducted between October and November 2020.
(3) Data analysis
All of the recorded audio was fully transcribed into text to ensure the completeness of the
information. Then, the transcribed text was marked and categorized to analyze the contents
further systematically. Finally, the text was sorted by the sequence of questions and
categorized interviewees’ answers by technology group and technical characteristics.
Chapter 3: Problem investigation - Smart Manufacturing
3-1. Development of Smart Manufacturing
"Industry 4.0" initially termed in Germany represents "an integrated approach that aggregates available technologies and digitization and networking concepts into the industrial production field" (Nelles et al., 2016), achieving the potentialities of flexibility and scalability of manufacturing systems through information technologies and industrial automation (Dassisti et al., 2017). The invention of steam power and electricity triggered the first and second industrial, whereby enabling mass production. Afterwards, the composition of information and communication technologies (ICT) and computer science brought out Industry 3.0, which fulfilled the computer-controlled automated facility and developed the basis of Industry 4.0. (Saucedo-Martínez et al., 2018; Davis et al., 2012; Ahuett-Garza & Kurfess, 2018; Dassisti et al., 2017).
Smart manufacturing is a main concept of Industry 4.0. (Kagermann et al., 2013; Frank et al., 2019), supported by the widespread application of modern information-based technologies (Raguseo et al., 2016; Tao, Qi, et al., 2018; Cagliano et al., 2019). Smart manufacturing aims to significantly transform the data acquired during the entire production cycle into manufacturing intelligence, and then actively applied to all aspects of the manufacturing system(Davis et al., 2012; O’Donovan et al., 2015; Tao, Qi, et al., 2018). That is, the extensive application of data empowers the manufacturing system to become smart (Kusiak, 2017; Tao, Qi, et al., 2018; Qu et al., 2019).
3-2 The Data Life Cycle in Smart Manufacturing
Data drives the development of smart manufacturing. (Tao et al., 2018) At present, all aspects of daily production and operation of manufacturing enterprises are related to the use of data.
Previous research provides the concept of “data lifecycle” which describes how data is transformed in a smart manufacturing scenario (Parasuraman et al., 2000; Nelles et al., 2016;
Siddiqa et al., 2016; Tao, Qi, et al., 2018). The data lifecycle could be simplified into three
phases: Data acquisition, Data analysis and Data application in manufacturing.
3.2.1 Data acquisition
Data acquisition is “Acquire, share and integrate data from different manufacturing resources through sensor readings (Porter & Heppelmann, 2015; Siddiqa et al., 2016; Tao, Qi, et al., 2018). Big data, IIoT (Industrial Internet of Things) and communication technology bring out the Connectivity, smart manufacturing systems are able to collect a large amount of data from the production environment anytime, anywhere via sensing devices such as built-in sensors and RFID. (Siddiqa et al., 2016; Tao et al., 2018). That is, the technological advancement of data collection results in the real time operations and information transparency (Mittal et al., 2019; Qu et al., 2019).
3.2.2 Data processing and analysis
Data processing and analysis refers to “a series of operations conducted to discover the information and knowledge from a large volume of data “. In a smart manufacturing scenario, the manufacturing system has high analytic capability to process enormous amounts of data to refined and meaningful information, and then help enterprises to make an optimized decision (Russom, 2011; Tao, Qi, et al., 2018; Frank et al., 2019; Qu et al., 2019). Cloud computing and analytics technology such as data mining, visualization, statistical analysis and machine learning have been used to perform data analysis to gain insight into the relationship between data and to assist observations and predictions. (Siddiqa et al., 2016)
The technologies used in the data collection and data processing and analysis phases are
consistent with the base technologies (IoT, Cloud computing, Big data, Analytics) proposed
by Frank (2019). These technologies bring out an increase in available data, connectivity and
intelligence, thereby facilitating the transformation to smart manufacturing. The base
technologies are described as “connectivity and analytics-enabling technologies” (Cagliano et
al., 2019), and a crucial technology in the future smart manufacturing system (Saucedo-
Martínez et al., 2018; Qu et al., 2019).
3.2.3 Data Application
Data application in the paper referred to how collected and analysed data got operationalized and interacts with operators in manufacturing operations (Tao, Qi, et al., 2018; Rauch et al., 2020) For example, the data integrated and displayed in MES and ERP system, which helped the company to realize the manufacturing processes. Also, the data analyzed could be used in monitoring system to predict errors in advance.
The processed and analyzed data was widely used in the manufacturing system, triggering a transformation in manufacturing convention.
Data was widely used in smart manufacturing technologies in different ways. Previous studies described the functions that data can provide. In early research, Zuboff Shoshana, (1985) proposed the two functions of intelligent technology. "Automate" referred to adopting technology to automate production for lowering skill and labour requirements, and
“Informate” was related to how technology assisted operators to understand the work process better and consequently improved operations and encourage innovation.
Later literature on the smart manufacturing field has presented similar findings. Smart technology did not only "Replace humans by automation" but also "Empower" operators especially support the cognitive abilities of the worker to perform coordinating and dispositive tasks (Nelles et al., 2016; Rauch et al., 2020). Waschull et al. (2020) also mentioned
"Automation" and "Augmentation" as two functions of Cyber-physical systems (CPS).
Automation is to substitute human information processing functions, while the Augmentation relates to enhancing human capability. These studies corresponded to Zuboff's early finding, revealing the technology automation and augmentation function.
Fitts list revealed that humans and technology are good at different fields, but with the advancement of smart technology, technology could do more and more things, not only the cognitive tasks, but also non-routine tasks (Romero, Stahre, et al., 2016; Romero, Bernus, et al., 2016; Rauch et al., 2020; Waschull et al., 2020), which could be regarded as “Autonomous operation”(Qu et al., 2019).
The different degrees of division of labor between "technology" and "human" could be
regarded as the below functions:
1 Automation:
Webster's Dictionary defined "automation" as "the technique of making an apparatus, a process, or a system operate automatically" A fully automated system typically operated in a standard and predefined environment, limited in the width and depth of tasks it can perform (D’Addona et al., 2018; Paulin, 2018; Tao, Qi, et al., 2018).
The technology-centred automation attempted to automate all the manufacturing processes which may bring out cost-saving and uniform quality to enterprises, but could also result in human problems Like skill loss, out-of-the-loop condition and the adaptation of operating mode. (Kelley, 1990; D’Addona et al., 2018; Waschull et al., 2020)
2 Augmentation:
Augmentation emphasized how technology and humans collaborated to bring out a positive outcome. This anthropocentric automation promoted cooperation between human and technology (Sheridan, 2012; Rauch et al., 2020), augmenting workers’ capacity to solve cognitive and physical tasks by cognitive, physical and sensorial aids. (Nelles et al., 2016;
Romero et al., 2016; Rauch, Linder and Dallasega, 2020). Three types of aids and related technology examples were discussed in previous studies.
(1) Physical tasks (physical aids)
Physical tasks referred to those manual tasks related to physical power. For example, Collaborative Robot made repetitive physical actions easier. Industrial Exoskeletons increased strength and endurance and reduced the physical workload. Moreover, occupational health and safety were improved by a focus on ergonomics via wearable Tracker.
(Romero, Bernus, et al., 2016; Frank, Dalenogare and Ayala, 2019; Rauch, Linder and Dallasega, 2020)
(2) Cognitive tasks (Cognitive and sensorial aids)
As distinguished from physical tasks, cognitive tasks involved coordination, supervision,
diagnosis, planning, monitoring and decision-making (Gorecky et al., 2014; Cagliano et al.,
2019; Rauch et al., 2020). Cognitive and sensorial aids both assisted operators to conduct
cognitive tasks. Previous studies (Spitz-Oener, 2006; Mihaylov & Tijdens, 2019) categorized cognitive tasks as two aspects:
First, the analytical tasks were associated with the ability of workers to think, reason, and solve problems encountered in the workplace like analyzing, evaluating, determining and forecasting. In a smart manufacturing system, operators were aided to analyze and evaluate the information to explore the knowledge and create value. (Qu et al., 2019). For example, the monitoring support system in production lines provided more information, helping companies to yield deeper insight.
On the other hand, interaction tasks related to those tasks require collaboration and coordination skills such as coordinating, organizing and negotiating (Spitz-Oener, 2006;
Mihaylov & Tijdens, 2019). The smart technologies assisted the operator to bring out more efficient collaboration with internal and external stakeholders to create collective value. For example, the ERP (Enterprise resource planning) and MES (Manufacturing Execution System) resulted in better communication and collaboration between the departments.
It is helpful to understand how technology augmented workers because the way technologies supported or substituted workers could trigger different changes in work design (Parker and Grote, 2020),
3 Autonomous operation:
In addition to the two functions revealed by past research, smart manufacturing technology also led to “Autonomous” functions. Smart technology autonomously performed reasoning, planning and decision-making to make optimized and predictive decisions (Qu et al., 2019;
Kadir & Broberg, 2020). Slightly different from the concept of automation, autonomous function enabled manufacturing systems to react to uncertain and unforeseen situations through the Self-sensing, self-adaptive and self-decision (Park & Tran, 2014; Qu et al., 2019;
Kadir & Broberg, 2020). Automation replaced the role of humans in performing certain tasks,
while autonomous operation can replace human non-routine tasks. Sensor technology, data
dispatch, knowledge iteration, and the renewal technology for manufacturing systems drove
the autonomous function. (Qu et al., 2019).
Chapter 4: Problem investigation - Work Design
Work design refers to how one's work tasks, activities, relationships, and responsibilities are structured and experienced (Morgeson & Humphrey, 2008), and how these structures impact individual, group, and organizational outcomes (Grant & Parker, 2009). The studies of work design are beneficial to understand each worker’s jobs, the organisation they belong to, their workplace and their interface with the technology they use (Slack et al., 2016).
This section outlined the theories related to the development of work design study. First, the socio-technical system theory proposed a joint improvement by considering the technology and social factors which may affect work design. The Job characteristic model (JCM) established key characteristics to evaluate the work design, and later research has deeply discussed these elements of work design, thereby forming the contemporary work design studies.
4.1 Socio-technical system theory
Socio-technical system theory aimed to form an effective organization through determining the interdependent relationship between the technological, organizational and human elements of a production system (Trist & Bamforth, 1951). The theory proposed a “Joint optimization” approach to strike a balance between the social systems (like the needs of workers and organization) and technical systems, thereby accomplishing the quality of work organization.
The implementation of new technologies arose the new socio-technical intercommunications, thereby driving the human, technical, and organizational change. (Becker & Stern, 2016;
Cagliano et al., 2019; Parker & Grote, 2020; Kadir & Broberg, 2020). Socio-technical system theory presented another perspective upon work design changes generated by technology transformation. Three main social dimensions were widely discussed in the literature to view the digitization process towards smart manufacturing, that is external environment, organization of work, human factors (Frank et al., 2015).
The significance of Socio-technical system theory for work design was the emphasis on the
importance of considering both technology and work design as a whole to achieve better
performance. This paper employed the concept to consider the impacts of smart manufacturing on work design.
4.2 Job Characteristic Model
Besides, the past literature proposed different indicators to measure work design, and the most widely used one is the Job Characteristics Model. Job Characteristics Model (JCM), providing a reference framework of work research, has profoundly influenced the work design research (Parker, Morgeson, et al., 2017).
Proposed by Hackman and Oldham in 1975, the model reveals five core characteristics of a job and associated impacts on worker’s psychological state and organisation performance. The model aims to enrich jobs in organizational settings and assumes the job characteristics are crucial to employee motivation. Job characteristics would lead to different psychological states of the employee, thereby affecting five outcomes related to enterprises' performance (i.e. motivation, satisfaction, performance, and absenteeism and turnover).
The five core job characteristics are skill variety, task identity, task significance, autonomy, and feedback, and the first three characteristics are related to the meaningfulness of the job to the employee. Autonomy and feedback involve employee's perception of responsibility, and knowledge of results, respectively. The Job characteristic model framed the core work features and provided standards for assessing work design (Parker, Morgeson, et al., 2017).
4.3 The research regarding work design characteristic
Contemporary work design research has extended JCM's five core job characteristics in several categories. For example, Morgeson and Humphrey (2006) proposed four work design characteristics categories like task, knowledge, social and work context characteristics.
Humphrey et al., (2007) further added motivational characteristics to extend the job characteristics model, and considered broader social and work context characteristics.
Cordery and Parker’s research (2012) comprised three characteristics (task-related, relational, and contextual characteristics) and correlated these characteristics to individual effectiveness outcomes.
Attributed to previous studies, the subsequent researchers could choose different
characteristics of work design as a social variable to explore the associations with smart
manufacturing technology. For example, Parker and Grote (2020) chose the skill variety, job feedback and demands, autonomy and social aspects, and Waschull et al. (2020) selected skill variety, job autonomy and job complexity. Cagliano et al. (2019) divided the social variables into micro (i.e., job breadth, job control, cognitive demand and social interaction) and macro (organizational structure) level. This research discussed the selection of work design characteristics in Chapter 6.
Furthermore, Parker, Wall and Cordery (2001) proposed a broader perspective to distinguish five variables of work design to recognise the context of work design study in-depth, i.e.
antecedents, work characteristics, outcomes, mechanisms, and contingencies. Based on
Parker, Wall and Cordery (2001)’s framework, this research regarded technology as the
primary antecedent to view the change on work design characteristics.
Chapter 5 Problem Investigation - Technology and Work Design connection
5.1 Work design in the development of manufacturing philosophy
The operation of manufacturing has changed over time because of the different technologies employed. From technology management and Lean manufacturing toward the adaptive, flexible and customized smart manufacturing system, different manufacturing philosophy also brings different impacts on the operator and work design. In this paragraph, the paper shortly discussed how work design interacts with technology and the associated change in characteristics during the early manufacturing philosophies.
(1) Scientific Management: Fredrick Taylor defined “Scientific Management” in 1911. It
referred to the redesign of the process of work, systematically investigated the relationship between labourers and tasks, and established scientific methods to maximize efficiency and production. Standardization and division of labour by functional specialism enhanced the production line but was criticized for restricting autonomy and decreasing the skill breadth of labourers (Seigel & Ollman, 1973; Slack et al., 2016), thereby generating alienation from the job. The success and critique of Scientific Management ignited curiosity on the interaction between technology and social shaping (work design). The research on work design has been discussed extensively since the emergence of Scientific Management (Parker, Morgeson, et al., 2017; Rauch et al., 2020).
(2) Lean manufacturing:
Afterwards, with the emergence of diversified market demands, Toyota's lean production
methods became a renowned discipline. Lean manufacturing system concerns human-centric
concepts (Rauch et al., 2020), for example, in Toyota Production System, the machine was
served for the operator’s purpose (Slack et al., 2016). Operators were assigned a certain level
of authority (Sugimori et al., 1977) by means of fail-identifying, line-stop authority and visual
control (Slack et al., 2016). This was described as “Jidoka” or “autonomation”. However, the
specialized and standardized process to eliminate waste was criticized to affect job autonomy
(Parker, Van Den Broeck, et al., 2017) such as the restraints on the movement area of
operators (Sugimori et al., 1977) and the limitation of skill variety (Parker, 2003; Delbridge,
2008) to ensure maximum output.
5.2 Work design in the development of Smart manufacturing
The literature in recent years discussed the relationship between "smart manufacturing" and
"work design" from different perspectives. In accord with socio-technical system theory, Romero, Bernus, et al. (2016) highlighted the technical changes on human operators, stating the companies shall not only focus on making production lines more agile and adaptable, but also consider "how to help operators adapt new technology". Follow the concepts, Romero, Stahre, et al. (2016) further proposed the term “Operator 4.0” and depicted eight types of typology to describe the development of human-automation collaboration.
Parker (2020) echoed Romero's discussion, stating the importance of "adjusting work design"
for operators to fit new technologies, thereby jointly optimizing from both social and technical side. The research discussed different example of the interaction between smart technology and the change in work design characteristics such as autonomy, control, skill use and job feedback. Cagliano et al. (2019) also adopted the Socio-technical system theory to explore the interaction organizational and human factors and technological complexity.
Besides, recent studies analyzed the difference in operator's work practice before and after the implementation of smart technology. Rauch et al. (2020) discussed the difference before and after the introduction of smart technology in terms of the production lifecycle (planning, execution, maintenance) and technical aids (physical, sensorial, cognitive), suggesting the demands of the future operator and how technologies support operators. Kadir and Broberg (2020) weighed the impact of the introduction of technology on the overall performance of organizations and the well-being of the operators during the different transitioning phases.
These studies emphasized the importance of work design when the implementation of technology. The working environment shall be designed to satisfy the needs of the organization and its workers, rather than merely pursuing new technologies. (Clegg, 2000;
Romero, Bernus, et al., 2016; Parker & Grote, 2020)
Technology, as the direct factor on work design change.Like the aforementioned, the impact of technology on work is a broader and undecided issue,
and several studies show various factors may trigger the work design transformation. Parker
were determined in sequent research. (Lehdonvirta, 2018; Cagliano et al., 2019; Rauch et al., 2020). Aligned with Parker’s research, Waschull et al. (2020) found the “management choice”
had a notable impact on tasks re-allocation resulted from the implementation of technologies, thereby affecting the work practice.
Therefore, it is not simple to understand how "technology" itself affects work design. First, there was a lack of understanding of the predetermined effect of technology on work design (Parker et al., 2017; Parker & Grote, 2020). The majority of studies has not addressed technology as an independent variable. Second, previous research (Cagliano et al., 2019;
Parker & Grote, 2020; Kadir & Broberg, 2020) has mainly demonstrated the broad features of smart manufacturing technology, but has not put a more in-depth insight into SM technology such as its characteristics.
To focus on the technology itself, this research regarded “technology” as the main direct
effects on work design changes. The study aimed to recognise what those specific
characteristics of SM technology are and how these characteristics trigger the transformation
of work design. By understanding the change in work design caused by technology, it could
be beneficial for companies to adapt to the introduction of smart technology. The following
chapter contained the framework to characterize smart technology in manufacturing and to
see how the characteristics of smart technologies impact selected work design characteristics.
① Data lifecycle
②
Technology
category
③ Example
Technology ④Characteristic
⑤
Work design
Skill Variety Job complexity Autonomy
1 Data acquisition
(1) Base technology (stage1)
● Big Data
● Analytics
● Internet of Things
● Cloud computing
• Connectivity
• Information transparency
• Real-time
• Predictivity
➕ Increased
More complex skills like Data processing and programming skills needed. (Maier & Student, 2014) .
➕ More complicated
There are more uncertain tasks appeared with more data that are driven by smart technologies. (Tao, Qi, et al., 2018; Siddiqa et al., 2016)
➕ Increased
- Connectivity, Information transparency and Context awareness cause decentralized decision making due to easier access to retrieve data. (Mittal et al., 2019) - Real-time: The operator in the front line can make
immediate decisions without asking the management.
(Mittal et al., 2019)
- Predictivity empowers operators on problem solving and error shooting. (Romero, Stahre, et al., 2016; Mittal et al., 2019)
2 Data analysis
3. Data application
Augmentation
(2) Integration
(stage1) ● ERP
● MES
• Connectivity
• Information transparency
• Real-time
• Predictivity
• Interactivity
➕ Increased
IT-based technologies bring out more collaboration and coordination with not only intra-team and inter-team but the supply chain stakeholders. (Cagliano et al., 2019)
➕ More complicated
Operators have more interactive tasks related to different stakeholders with more complicated communication. (Romero, Stahre, et al., 2016)
➕ Increased
Operators have more interaction with internal and external stakeholders via integrating production processes, thereby experiencing more autonomy in work procedures, related to controlling and problem-solving. (Cagliano et al., 2019)
(3) Virtualization (stage1)
● Simulation
● Extend reality technology AR/VR
• Connectivity
• Information transparency
• Real-time
• Predictivity
• Context awareness
• Digital presence
➖ Decreased
The demands of hard skills and experience to determine the abnormality at the shop floor decline by technology like smart training and maintenance (Butollo et al., 2019)
➖ Less complicated
The extended reality technology provides digital aids, helping simplify the tasks in an intuitive approach, thereby reducing human errors.
(Romero, Stahre, et al., 2016)
➕ Increased
Digital presence: Extend reality technology such as AR/VR enables employees to get as much mobility and flexibility, providing operator autonomy to decide when, where and how they work. (Service Futures, 2020).
(4) Traceability (stage2)
● Supervision and monitoring support system
● Smart Products & Parts
• Connectivity
• Information transparency
• Real-time
• Predictivity
➖ Decreased
The complex supervising tasks conducted by skilled technicians might be substituted by the supervision and monitoring support system, remaining supervising and monitoring tasks are relatively routinized without special skills involved. (Hirsch-Kreinsen, 2016)
➖ Less complicated
Supervising and monitoring tasks are relatively routinized and monotonous. (Hirsch-Kreinsen, 2016)
➖ Decreased
The traceability technologies contribute to supervising tasks which are relatively routine and fixed, thereby restraining the flexibility of operators’ autonomy on work method, decision- making and work schedule. (Hirsch-Kreinsen, 2016; Parker &
Grote, 2020)
Automation
(5) Automation (stage2)
● Industrial robot (Cobot, AGV)
● M2M communication
• Connectivity
• Information transparency
• Real-time
• Automation
• Standardization
➖ Decreased
Automation leads to a reduction in active use of skills.
(Parker & Grote, 2020)
➖ Less complicated
Automation partly or completely replaces the repetitive and routine tasks. Technology increased standardisation of tasks, thereby simplifying the job. (Parker & Grote, 2020)
➖ Decreased
Automation and Autonomous operations could replace both human judgement and control. There are fewer decisions that humans can lead, thus limiting human autonomy. (Parker &
Grote, 2020).
➖ Decreased
Standardization may cause relatively monotonous work with less skill involved.
➖ Decreased
Standardized tasks may only involve simple activities. (Hirsch-Kreinsen, 2016)
➖ Decreased
Sandardization and routinization (Wall et al., 1990; Parker et al., 2001), thereby causing the loss of local employees' autonomy (Clegg, 1984; Hirsch-Kreinsen, 2016).
Autonomous operation
(6) Flexibilization (stage 3)
● Flexible and autonomous lines
• Connectivity
• Information transparency
• Real-time
• Predictivity
• Standardization
• Adaptability
➖ Decreased
Autonomous operation may also leads to a reduction in active use of skills. (Parker & Grote, 2020). The routine tasks and related skills can be replaced by automation, while the non-routine tasks may be replaced by autonomous function.
➖ Less complicated
The autonomous operations enable the
replacement of routine cognitive tasks and even the substitute the non-routine tasks under uncertainty environment (Parker & Grote, 2020).
➖ Decreased
Automation and Autonomous operations could replace both human judgement and control. There are fewer decisions that humans can lead, thus limiting human autonomy. (Parker &
Grote, 2020).
The framework comprised two parts associated with two sub-objectives, that was the characterization of the technology, and the impact of the characteristics on work design.
On the left side (column 1 to 4), the framework showed how the study sorted smart technology and related the technology group to corresponding characteristics. Figure 6.1 showed how this research categorized SM technologies. The data life cycle was matched with six technology categories proposed by Frank et al. (2019). At the theoretical framework, the study expected that technologies used in different lifecycles (categories) would rely on different inherent characteristics.
Figure 6.1 SM technology category in correspondence to Data lifecycle phase.
Adopted from (Frank et al., 2019).
On the right side (column 5), the framework tries to link the technical characteristics and work design characteristics (skill variety, job complexity and autonomy).
6.1 Data lifecycle (Column ① in Table 6.1)
As mentioned in chapter 3, the data life cycle included the data acquisition, data analysis and data application, representing a series of leap forward in the use of data and information (Tao, Qi, et al., 2018; Romero, Stahre, et al., 2016). Therefore, the
23
framework initiated from the data lifecycle to understand how smart technologies transform data into useful information.
The data application was divided into three categories: “Augmentation, Automation and Autonomous operation” to emphasize the functions of technology. Augmentation was a human-centered automation process. Automation referred to repetitive processes that are fully automated by standardized procedures, while autonomous function had an adaptive system that can autonomously learn and improve itself to solve uncertain problems.
6.2 The framework of Industry 4.0 (Column ② and ③ in Table 6.1)
Furthermore, to specify what types of technology in different phases of the life cycle.
Frank, Dalenogare and Ayala’s (2019) framework was chosen to specify the category of smart technologies. Its category of SM technology built the connection to the data lifecycle and specified the technology within each category. (see Figure 6.1)
Two aspects of the research were employed: First, the concept of base technologies corresponded to the data acquisition and data analysis phase in data lifecycle. Frank et al. (2019) defined “base technologies" that were the base technology with connectivity and intelligence like the Cloud, IoT and analytics to accomplish smart manufacturing.
Base technologies integrated other technologies into the manufacturing system, formulating the current smart manufacturing scenario (Thoben et al., 2017; Tao, Cheng, et al., 2018; Frank et al., 2019; Qu et al., 2019).
Second, their framework categorized smart manufacturing technologies into three
stages based on the complexity level of technologies implementation; namely,
Integration and Traceability, Automation and Virtualization and Flexibilization. This
classified approach was matched in the “data application” aspect of the life cycle, used
to clearly formulate the association between “technology” and “characteristics”.
25 6.3 The characteristic of smart manufacturing (Column ④ in Table 6.1)
Column 1 to 3 built the structure to determine the characteristics of smart manufacturing. The concepts of the data lifecycle and Frank’s research related specific technology in different SM technology categories.
The study summarized ten key technical characteristics in table 6.2 and tried to match the characteristics in each technology category based on the literature (see column 4 of Table 6.1).
Table 6.2 The description of technical characteristics
Characteristic Definition
1. Connectivity
Being able to connect the physical and virtual world enabling the connectivity and communication between the components within the factory such as workpiece carriers, assembly stations, and products. (Hermann et al., 2015)
2. Information transparency
Being able to acquire more information from one or more sources within the manufacturing system to assist the human. (Zuboff Shoshana, 1985; Paulin, 2018; Mittal et al., 2019)
3. Real-time
Being able to collect and analyzes data and provide the derived insights immediately, realizing real-time communication and synchronization (Hermann et al., 2015; Brougham & Haar, 2018;
Mittal et al., 2019)
4. Predictivity
Being able to predict what could happen in the future with the help of available data, thereby solving the emergent requirements (Tao, Qi, et al., 2018; Frank et al., 2019; Qu et al., 2019)5. Context
awareness
Being able to sense a phenomenon or event within the machine itself, such as its location, condition or availability within the manufacturing system. (Mittal et al., 2019)
6. Interactivity
Being able to bring out the ease of integration between different stakeholders and organisations, thereby achieving boundary-crossing collaboration and information exchange.
(Romero, Stahre, et al., 2016; Qu et al., 2019; Cagliano et al., 2019)
7. Digital
presence
Being able to create digital models to develop a simulation environment for advanced planning, decision support and validation capability, before any action is implemented physically. (Mittal et al., 2019)
8. Automation
Being able to entirely control the complete manufacturing operations in order to replace human effort. (Zuboff Shoshana, 1985; Frank et al., 2019; Qu et al., 2019; Kadir & Broberg, 2020)9. Standardization
Being able to facilitate the standardization of production processes, thereby bringing efficiency and quality improvements. (Parker 2020) Smart manufacturing is the concept of the integration of different technology, so a standardized interface and structure are essential.
10. Adaptability
The autonomous technology has self-adaptive, self-diagnosis and self-deciding functions. Being able to continuously learn and adapt to the schedule and product changes with minimal intervention. (Burke et al., 2014; Hermann et al., 2015; Tao, Qi, et al., 2018; Mittal et al., 2019)
6.4. The impact of technology characteristics on work design (Column ○ 5 in Table 6.1) This section discussed the work design change resulting from technology. Job complexity, skill variety and autonomy were selected as social variables to observe the work design change.
Skill variety, is considered as an important work design characteristic, involving how many different skills and capabilities were required to perform the tasks (Hackman &
Oldham, 1975), which related to worker’s motivations, satisfaction and related outcomes (Parker & Grote, 2020).
Furthermore, Parker (2017) suggested that the impact of technology on work design depends mainly on whether the technology complemented or substituted the task and the subsequent influence on the power of employees. The former related to job complexity, while the latter related to operator’s autonomy.
6.4.1 Skill Variety: As Cagliano (2019) mentioned, skills changed because the roles and responsibilities of operators changed as a consequence of technologies. This framework presumed the trends for the variety of skills in the future smart manufacturing system based on previous literature. The changes were represented by the symbol “+” and “-”,in other words, the respective increase and decrease of skill variety.
6.4.2 Job complexity: The job complexity considered whether worker’s tasks will become easier or more complicated due to the smart manufacturing technology. The framework indicated the increase and decrease of complexity as “+” and “-”, representing respectively the tasks becoming either more complicated or easier.
6.4.3 Autonomy: Job autonomy related to what extent the workers had freedom and independence on performing their work (Hackman & Oldham, 1975) encompassing the work schedule, decision-making and work methods (Morgeson & Humphrey, 2006).
Previous studies indicated that the use of smart technologies in manufacturing affected
27 operator autonomy. (Cagliano et al., 2019; Kadir and Broberg, 2020; Parker and Grote, 2020; Waschull et al., 2020) This framework viewed the negative or positive impacts (“+/-”) of listed technology characteristics on autonomy.
6.5 The summary of theoretical framework
The theoretical framework built the connections between social and technical aspects.
It first captured ten characteristics and allocated them in six technical categories, and then tried to connect the possible impacts of the characteristics on three selected aspects of work design change.
The theoretical framework illustrated that some overlapped characteristics among different categories, and there were also some distinct characteristics only appearing in a certain category. This finding recalled the "Base technology" concepts from Frank et al. (2019), stating certain technologies were the basic structure of the smart manufacturing system. Therefore, the research assumed the overlapped characteristics could be the characteristics of the "Base technology".
However, it was challenging to correlate each characteristic to the change of work
design due to the lack of relevant literature; therefore, the empirical interview also
employed to validate the relationship between specific technical characteristics and
work design change.
Chapter 7. Validation
The framework described the characteristics of SM technologies and the relevant impact on work design, but has not addressed the association between specific technical characteristics and work design change. The case interview connected the above undetermined relationship and validated whether the framework was aligned to the real world. (Research question 6) and how differences between them arose from? (Research question 7).
Six interviewees (see description in table 7.2) in different companies were selected to validate the framework. The interview questions were designed following the sequence of the theoretical framework (see interview protocol in Appendix).
The interview findings were discussed in two aspects below regarding five technology categories (Base technology, Integration, Traceability, Virtualization and Automation).
Flexibilization was not discussed in the validation section because it lacked the high degree of human-machine collaboration.
1. Validation of the technical characteristics
First, this research asked interviewees "What are the characteristics that suit the technology you used?" With respect to different technology category to validate each technology characteristics. After the interviewees answered the above open-questions, this study discussed each feature with the interviewees and asked them "Do they agree that a single feature is related to the technology they use?" They could also add features.
2. Validation of the impact of technical characteristics on work design change:
Furthermore, the validation moved from the data life-cycle phases to the specific
characteristics and their impact on work design. To understand the relationship
between specific technical characteristics and work design change. The interviewees
were questioned “What are the impacts of specific characteristics on skill variety, job
complexity and autonomy?"
29
Table 7.1 Validation questionsTopic Framework Question
Validate the technical characteristics
Table 6.1 Column ④
• What are the characteristics that suit the technology you used (each interviewee replied based on one or two technologies, see table 7.2)
Validate the impact of technical characteristics on work design change
Table 6.1 Column ⑤
• What are the impacts on work practice caused by the technical characteristics you selected for the specific technology?
The following sections presented the interview findings based on two groups: Base technology with "General characteristics" and other technology groups with "Unique characteristics".
①
Data lifecycle
②
Technology
category
③
Example Technology
④
Characteristic
⑤ Work design
Skill Variety Job complexity Autonomy
1 Electronic manufacturer
Production Line Manager
• Supervision and monitoring support system
• Industrial Robots
Traceability Automation
China
Production Line Manager working in the factory in China manages 13 production line operator. His main task is to ensure high quality and performance of production and the technology used in the production line including sensors, controllers and supervision and control systems.
The main products areelectronic devices and PCB.
2
Consumer goods
manufacturer
Front line operator
• Supervision and monitoring support system
Traceability Hong Kong
A production line operator works in a kitchen appliance OEM which is a supplier of one Dutch enterprise. Working in a semi-automation production line, he has to operate the robotic arm and monitor the production status showing on the screen.
3 Enterprise
software Consultant • MES and ERP
system Integration Taiwan
A consultant working in a global software provider sells the software like MES and ERP system to solve an industrial solution. His job is to understand customers (manufacturer) needs of digital transformation and guide the employees to acknowledge the software before the introduction.
4 Consultancy Consultant • MES system Integration Taiwan
The consultant responsible for the introduction of automation and intelligent technology, whose main job is to assist small and medium manufacturers in upgrading and improving factory operations.
5 Electronics Production engineer
• Simulation Automation technology
Virtualization Taiwan
This interviewee worked as the head of the engineering department in a Taiwanese company that provided production line automation equipment, and used simulation software to design production lines based on customer needs. This interviewee also had experience in the manufacturing side of the factory.
6
Consumer goods
manufacturer
Production manager
• Industrial Robots (robot arm)
Automation China
A production line supervisor working in the factory belonged to a European enterprise, which mainly produces small electrical appliances such as electric cookers, razors and electric toothbrushes. The factory has introduced robotic arms in recent years to help assembly operations such as lock nuts. In addition to managing production lines and employees, he also needs to know how to adjust parameters, find problems from existing operational data and optimize them.
31 7.1 Base smart manufacturing technology
7.1.1 The characteristics of Base smart manufacturing technology - General characteristics
The theoretical framework assumed base technologies and their associate characteristics existed in all smart manufacturing technologies.
All interviewees were asked whether they accept the assumption of the base technology.
Most answered that they agreed on the concept of the base technology. Meaning, some technologies were regarded as the foundation of smart manufacturing technology.
All interviewees were asked, “What do you think is the base technology about smart manufacturing systems?” Five out of six interviewees expressed that the connectivity brought by the Internet of Things technology was the base of a smart factory, and four out of six interviewees mentioned Data analytics technology. Furthermore, all interviewees agreed that Cloud technologies are the basis of smart manufacturing systems. For example, interviewee 3 (the consultant) revealed cloud technology is an element to fulfil digital transformation. Upon accepting the assumption, the interviewees were further asked “What are the characteristics of base technologies?”
Similarity: Same as the theoretical
framework, all six interviewees selected Connectivity and Information transparency. The connectivity was regarded as the starting point of all smart factory, and the "Information transparency" represented the necessary digitalization for a factory to become smart.
Also, "Real-time" and "Predictivity" were selected as the characteristics of base SM technology by respectively five and four interviewees. These four characteristics were aligned with the original framework.
Characteristic Agreed
Connectivity 6/6
Information
transparency 6/6
Real-time 5/6
Predictivity 4/6
Standardization 2/6
Automation 1/6
Selected in theoretical framework
“An intelligent production must have the technology to record each data in real time, and then there must be a sufficient construction to support its instant analysis, and the system must have enough capacity to keep up with the huge amounts of parts they can produce every hour... Of course, the sensor-related hardware is a very basic requirement.” - Interviewee 2, Front line operator
Difference: Two interviewees (Interviewee 4, Consultant, MES and Interviewee 5,
Production engineer) expressed that Standardization was also a prerequisite characteristic of all smart technologies, and Interviewee 1 believed that a high level of automation is a trend of smart technology.
“Standardization and digitization are the first stages that many small and medium-sized factories need to achieve. Real time and predictivity are not realistic for them “ - Interviewee 5, Production engineer
“Obtaining more data from production line is a starting point to be intelligent. The establishment of connectivity like the installation of the sensor helps provision real-time information.”-Interviewee 3, Consultant
According to the interview result, this research confirmed four characteristics of base technology: Connectivity, Information transparency, Real-time and Predictivity.
Technical characteristics appeared in the different technology group and had similar effects on work design; Consequently, the main emerging pattern was identified as
“General characteristic”.
7.1.2 The impact of General characteristics on work design change
This paper discussed the general characteristics’ impacts on work design with interviewees framing the table presented below based upon the finding.
➕Increase the WD characteristic ➖Decrease the WD characteristic
？Indeterminable ☐ Empty cells mean no impact on WD characteristic
④ ⑤ Manager ⑤ Operator
Characteristic Skill Variety
Job
complexity Autonomy Skill Variety Job
complexity Autonomy
Connectivity ➕ ➕
Information
transparency ➕ ➕ ➕ ？ ➕ ？
Real-time ➕ ➖ ➕ ➕
Predictivity ➕ ？ ➕ ➖ ➖
