EFFECTS OF SMART MANUFACTURING
TECHNOLOGIES ON THE TASK
CHARACTERISTICS OF WORK DESIGN
By
REMCO BERGHORST
University of Groningen
Faculty of Economics and Business
Pre-MSc Supply Chain Management
Nettelbosje 2
9747 AE Groningen
Student number: S4089952
ABSTRACT
Recently, more and more companies are using smart manufacturing (SM) technologies. This research investigates the effect of SM technologies MES and M2M communication on the task characteristics of the work design of employees who perform operational tasks. Qualitative research was performed by conducting semi-structured interviews at various companies. From these interviews, it became clear that the task characteristics are decreasing as a result of MES/M2M. Job feedback was the exception, it increased. These decreases and increases are created by the cognitive and sensitive capabilities of the technologies. These capabilities take over tasks that were first performed by the employee himself.
Supervisor:
Sabine Waschull
s.waschull@rug.nl
Theme:
Smart manufacturing and work design
Wordcount:
INTRODUCTION
At present we observe a growing trend where technologies gather and analyze real-time data, providing information to for example the manufacturing system (Lee, Bagheri, & Kao, 2014). This trend is referred to industry 4.0 by both academics and practitioners. Industry 4.0 has smart manufacturing (SM) as its central element (Frank, Dalenogare, & Ayala, 2019). Predicted to change employees’ jobs in many ways, there is still insufficient attention given to how SM alters tasks and work designs (Parker, 2019). However, high-quality work design (WD) is crucial to ensure employee well-being, positive work attitudes, and job/organizational performance (Parker, Van den Broeck, & Holman, 2017).
WD concerns the "content and organization of one’s work tasks, activities, relationships, and responsibilities” (Broeck & Parker, 2017). A WD consists of different characteristics. Several characteristics make work interesting due to increased motivation and job satisfaction (Humphrey, Nahrgang, & Morgeson, 2007). A number of these characteristics are combined by Morgeson and Humphrey (2006) under task characteristics. Task characteristics consist of job autonomy, task variety, skill variety, task identity, task significance, and feedback from job (Morgeson & Humphrey, 2006). Parker (2019) combined task variety, skill variety, task identity, and task significance under the name ‘Skill variety and use’ (Parker, 2019). Looking at the effect of SM on WD, Goos et al. (2009) expect that there will be growth for high-skilled jobs and a decline for low-skilled jobs, mainly because high-skilled jobs are more difficult to replace than low-skilled jobs (Goos, Manning, & Salomons, 2009).
It is important to investigate how SM effects WD because at the moment there is insufficient attention given on this effect, while WD is important for the employee's well-being, positive work attitudes, and job/organizational performance (Parker, Van den Broeck, & Holman, 2017). As can be read in the previous section, some attention has been given to the effect. Because the effect differs from situation to situation, it is necessary to research new situations, for example in a case of MES/M2M, that contribute to the current literature. Besides that, the implementation of SM technologies has caused whole new forms and patterns of work (and the disappearance of others) and have changed characteristics of tasks, jobs, and roles that employees perform across all industries (Cordery & Parker, 2012). Job autonomy will undergo the biggest transformation (Parker, 2019), This is because technology is becoming more and more equal to humans and has the same potential as humans themselves (Boos, Guenter, Grote, & Kinder, 2013). Because Job autonomy is one of the most import work characteristics (Karasek Jr, 1979), will undergo the biggest transformation (Parker, 2019), and make work interesting due to increased motivation and job satisfaction, it was decided in this paper to focus on the task characteristics, of which autonomy is part.
The research aims to gain more insight into the effect of the SM technologies Manufacturing Execution System (MES) and Machine-to-Machine (M2M) communication on the task characteristics of WD. This has led to the following research question:
What are the key dimensions of smart manufacturing technologies ‘MES’ and ‘M2M-communication’ and how do these affect task characteristics?
To find an answer to this question, a multiple case study has been conducted. Through interviews, information was obtained that helps to investigate the effects of SM technologies on WD. The results of this study will be relevant to companies that use or will use SM technologies. This gives the companies new insights into the importance of good WD.
THEORETICAL BACKGROUND
Smart manufacturing technologies
The industry 4.0 concept consists of different technologies. MES and M2M are two key technologies of SM technologies (Frank, Dalenogare, & Ayala, 2019). The National Institute of Standards and Technology (NIST) defines SM as "fully integrated, collaborative manufacturing system that responds in real-time to meet changing demands and conditions in the factory, in the supply network and customer needs" (Kusiak, 2018). The SM technologies investigated in the case study include MES and M2M communication. MES is an information/communication system that integrates all information regarding the manufacturing process. MES provides real-time data and supports in taking actions and decisions (Telukdarie, Buhulaiga, Bag, Gupta, & Luo, 2018). MES provides data about for example machines, personnel, inputs, and support services. A MES has 11 functionalities, namely: operations scheduling, process management, document control, data collection, labour management, quality management, dispatching production units, maintenance management, product tracking & genealogy, performance analysis, and resource allocation status (Saenz de Ugarte, Artiba, & Pellerin, 2009). The most important thing MES takes care of is tracking, monitoring, and documenting the transformation of raw materials into end products (production process) (Zayati, Biennier, Moalla, & Badr, 2012).
M2M communication is, as the name says, communication between different machines. Because of this communication, the machines are adjusted to each other and they can work automatically. Several devices can work together and make decisions without direct human intervention (Chen & Wan, 2012). Companies use M2M communication to achieve better cost efficiency and time management. In the earlier stages of M2M, it mainly served for supervisory control and data acquisition. Different sensors were connected, which then gave information about the industrial process. Today, M2M technology is also able to make its own decisions without human intervention (Kumar Verma, et al., 2016).
Smart manufacturing technologies characteristics
starting point where technologies are divided into different dimensions based on the capabilities of people that can be performed by technologies. Romero et al. (2016) indicate that SM technologies can support people in three dimensions (Romero, Bernus, Noran, Stahre, & Fast-Berglund, 2016), namely:
1. Automation aiding for enhanced physical capabilities. Physical capabilities include the ability of the employee to walk, lift and assemble and the speed, precision, and strength with which this happens. SM can assist in this through, for example, an exoskeleton
2. Automation aiding for enhance sensing capabilities. Attwood et al. (2010) describes sensing capabilities as ‘the employee’s capacity and ability to acquire data from the environment, as a first step towards creating information necessary for orientation and decision making’ (Attwood, Deeb, & Danz-Reece, 2010). SM can assist in this through, for example, using sensor devices that collect and convert data that would not be possible for an employee. As a result, more and qualitatively better data will be available to the employee.
3. Automation aiding for enhanced cognitive capabilities. Carrol describes cognitive capabilities as the employee’s capacity and ability to undertake the mental tasks needed for the job. Mental tasks are, for example, perception, memory, reasoning, decision, etc. (Carroll, 1993). SM technologies can assist in this through, for example, decision making.
Work characteristics
Work characteristics are of great importance for a company (Parker, 2019). Work characteristics affect individuals' motivation and performance, which has consequences for the success a company achieves (Hackman & Oldham, 1976). There are three categories of work characteristics, namely motivational (task & knowledge), social, and contextual work characteristics (Morgeson & Humphrey, 2006). The basic principle of motivational characteristics is that in jobs where these characteristics are present, there is more motivation and satisfaction. Social characteristics stand for the fact that work is carried out in a wider social environment and finally contextual characteristics stand for the physical and environmental context in which the work is carried out (Morgeson & Humphrey, 2006). As indicated, this paper focuses on task characteristics. One of these characteristics is job autonomy. Breaugh et al. (1985) stated that job autonomy stands for the extent to which the job allows freedom, independence, and discretion in determining how a job is fulfilled (Breaugh, 1985; Wall, Jackson, & Davids, 1992). Task variety is about the number of different tasks that are performed during work (Herzberg, 1968). Task significance is about whether a job affects people's lives inside and outside the organization (Hackman & Oldham, 1975). Task identity is about whether the work consists of a whole piece of work or is part of a piece of work (Sims, Szilagyi, & Keller, 1976). The last task characteristic concerns feedback from job. This concerns the amount of information that the job provides about the effectiveness of the task performance (Hackman & Oldham, 1976).
Effect smart manufacturing technologies on task characteristics
will support will be collected. In the case of a company with a low number of SM technologies, this would result in jobs with low autonomy (Cagliano & Canterino, 2019). The results of the effects are different, but it can generally be assumed that the introduction of smart technologies may increase autonomy. For example, decision-making autonomy, because SM technologies provide more and greater information to the workers, the decision-making autonomy will increase (Bayo-moriones, Billon, & Lera-Lopez, 2017).
Several studies show that skill variety and use are decreasing due to new SM technologies. For example, when introducing ERP (Venkatesh, Brown, & Bala, 2013). Several tasks that the employee previously performed are now performed by the technology. On the other hand, new tasks will also be added, but at the end of the line, the number of different tasks and thus the task variety has decreased. As with autonomy, the effects differ per situation. For example, Walsh and Strano (2018) investigated that a positive effect occurs when technology takes over "dull, dirty, and dangerous" tasks (Walsh & Strano, 2018).
Expectation effects smart manufacturing technologies characteristics on task characteristics
In this section, potential effects for task characteristics will be identified based on the SM technologies characteristics, which will be tested later in the findings chapter. These expectations are based on the literature study. Expectations will be aimed at employees who perform operational tasks (operator, expedition employee, foreman). The physical capabilities will not be discussed because it does not apply to MES or M2M.
Conceptual framework
The conceptual framework illustrates the expected relationship between the SM technologies MES and M2M, and the task characteristics of the WD. With SM technologies, these are physical, sensing, and cognitive capabilities. With task characteristics, these are job autonomy, skill variety and use, and feedback from job.
FIGURE 1 Conceptual Model
SM technologies as
‘MES’ and ‘M2M’
Task characteristics
of WD
Physical capabilities, Sensing capabilities, Cognitive
capabilities
Job autonomy, Skill variety and use (task variety, identity & significance), Feedback from
METHOD
Research design
This study conducted a qualitative research approach to collect all required data to answer the research question. Qualitative research is useful when creating a methodology for understanding, analysing, approaching, and explaining at company or social level (Delattre, Moulette, & Ocler, 2009). A quality research focuses on understanding and not necessarily seek to generalize findings (Heigham & Croker, 2009). In this study, a multiple case study has been used. A multiple case study was chosen over a single case study because multiple case studies provide a stronger base for theory building and explanation. Yin (2014) defined a case study as "an empirical inquiry that investigates a contemporary phenomenon (the 'case') in-depth and within its real-world context" (Yin, 2014). Because there is no requirement for behavioural events and there is a focus on contemporary events, a case study fits the research (Yin, 2014). Because the existing research about the effect of SM technologies on WD was limited, this research design fits the research question the best.
Case selection & research setting
The organization where the interview took place was chosen because, firstly, it uses different smart technologies, secondly because smart technologies have recently been implemented, so employees can describe the differences well, thirdly, the introduction of smart technologies has a major impact on the employees of the relevant organization. The decisive factor was the fact that smart technologies have recently been implemented, allowing the cases to be
representative.
TABLE 1
Overview of organizations
Company A B C
Sector Production Production Production
Products Caps and closures
Doors, windows, and frames for houses
and buildings
Metal products and structures
Market B2B B2B B2B
Interviewed positions Planner, Expedition employee, Foreman production Manager technical services (about operator)
CEO, Supply Chain Manager (about operator) Implemented technologies + setting MES, M2M, impact on everyone in the organization MES, M2M, impact mainly on operators, but also other jobs as
engineers
MES, Big Data, impact on everyone
in the organization
The case studies were chosen because they best match the chosen technologies MES and M2M. In this multiple case study, the unit of analysis is employees who perform operational tasks. These include operator, expedition employee, and foreman production.
Data collection
The majority of data is collected through semi-structured interviews. Such interviews are suitable because they provide a flexible but structured method of obtaining a rich set of data for analysis (Easterby-Smith, Jackson, & Thorpe, 2012). An interview guide was devised to keep the interviews semi-structured, in this way the interviews of the different researchers will be the same, so that better analysis can be performed. In the end, six interviews of three different cases are used for this research. The interviews are described word by word, after which they have been analyzed.
Data analysis
follow-up code was created. Also, a summary has been made per company (consisting of several interviews).
FINDINGS
The interviews have shown that MES and M2M are often seen as one technology. That is why it was decided to look at the link between the cognitive & sensing capabilities and the SM technology characteristic instead of the technologies MES and M2M. MES mainly takes over cognitive capabilities. M2M mainly takes over sensing capabilities.
Job Autonomy
As indicated earlier, job autonomy is one of the most important work characteristics because it has a major influence on, for example, stress. From the data analysis, it becomes clear that autonomy has decreased as a result of the cognitive capabilities of the technology. Cognitive capabilities take over mental tasks, such as taking decisions. This decrease took place in all three organizations. In all three organizations it can be seen that after the implementation of the technology, the technology takes over decisions from the employee. For example, the expedition employee of company A gave the following answer whether the employee will be more driven by the technology: "Yes, that's right, it tells me what to do. If there are malfunctions or something, I get a message that I have to come. "Also, Company C's Supply Chain Manager indicated that the system is very "Compelling" with respect to the operator.
Table 2 provides more clarity regarding the decreases in autonomy.
TABLE 2
Additional supporting data on job autonomy Company Role Additional supporting data
A Foreman
“For example, the tablet can now also perform colour checks, it is not only the product that you read and how much we have to make of it, but it also keeps track of when you have to change your colour. The system calculates how many boxes still have to be made and when the colour has to be changed. The system calculates all that for you. So it is pre-chewed a lot.”
check now. So you are very much guided by what MES instructs you.”
C SCM
“In principle, the operator remains responsible for the machine but the operators' level of autonomy is lowered. He inserts a certain amount of raw material and presses start, that is basically what the operator has to do now. The operator is a link between two separately operating machines.”
“The operator has less control then the machines that run on the new technologies.”
Skill variety
It has become clear from the data that skill variety varies greatly per company. In company A, for example, the variation in skills remained the same. As a result of the implementation, certain skills have disappeared, such as collecting data. However, new skills have also been added, such as the interpretation of data that the system provides. The technology has therefore mainly caused a change in which skills are used. In company B, the variation has increased. Through technology, the operator has developed new skills that were necessary to work with the technology. The manager indicates the following regarding the operator "he has to have knowledge about the products, the production process, and how do machines work. The skills of operators from earlier days and now differ significantly." In company C, the variation in skills has decreased. The technology has changed the skills that the operator needs because technology has simplified the work. Tasks for which an operator required specific skills have been simplified and can now be performed for operators with fewer skills.
The changes in the skill variety are created by the sensing (for example collecting data) and cognitive capabilities (for example memory) of the technologies.
TABLE 3
Additional supporting data on skill variety
Skill use (task variety, identity & significance)
It can be kept brief about task significance. In all cases, it was stated that technology has not changed this. This stems from the fact that the work of the interviewed persons did not influence the lives of people outside the organization.
The cases have shown that the task variety has decreased slightly. After implementation, the technology took over various tasks that were first performed by the person himself. For example, the supply chain manager of company C said the following: "In the past, the employee still had to understand and program all that. So in that sense, digitization has ensured that we can take enormous steps in simplifying the work there." ... "You often try to automate tasks away.".
The technology has also caused a decline in task identity. As technology has now taken over tasks, as mentioned earlier, the extent to which a full task is performed has decreased. You can also see technology as supportive. If you look at this view, nothing has changed in the task identity.
The decreases in 'Skill use' are created by sensing and cognitive capabilities. These capabilities take over tasks that were first performed by humans themselves.
Company Role Additional supporting data
A Expedition employee
Interviewer: “has the variation in skills remained the same?” Interviewee: "Yes, in my opinion, it is."
B
Manager technical services
“The operator is not a craftsman anymore but has to know a lot about the machines and the process. So, the education level of an operator is higher nowadays, which in my opinion means that the job of the operator has become more demanding.”
C SCM
Feedback from job
Feedback from job has increased significantly after the implementation of the technologies. This is due to the sensing capabilities of the technologies. Because much more data is collected, there is more opportunity for feedback. In all cases, it emerged that technology has provided more possibilities. Table 4 contains text fragments that support this finding.
TABLE 4
Additional supporting data on feedback from job Company Role Additional supporting data
A Expedition employee
“The amount of feedback will increase in the future. More feedback can be obtained from MES in the future. MES does not provide the feedback itself, but MES does provide the information from which feedback can be obtained.”
B
Manager technical services
“Operators get feedback from the machines in terms of the output per day. The quality checks that operators do could also give feedback, because when an operator nearly doesn’t make any mistakes with handling machines the do their jobs correctly.”
C SCM
DISCUSSION AND CONCLUSION
The following section will include the interpretation of the results, the implications for theory and practice, and a critical reflection of the study.
Interpretation of the results
The results show that technologies cause work characteristics to decrease. This stems from the sensing and cognitive capabilities of the technologies. Feedback from job is the exception to this story. This has increased as a result of sensing capabilities. The autonomy, skill variety and use decrease because various tasks are taken over by the technology, which was first performed by the employee. This is also confirmed by Romero et al. (2016). Where in industry 2.0 and 3.0 the machines help people and collaborate with people, people are in industry 4.0 only needed if the machine needs a man (Romero, Bernus, Noran, Stahre, & Fast-Berglund, 2016). Industry 4.0 is focused on automating various processes without employee input. The functions examined are mainly low-skilled positions (operator, forwarding employee). Taylor et al. (2020) have concluded that SM technologies take over many simple tasks because the technologies can perform them faster and more accurately. Man is not made superfluous, but man is increasingly supporting the machine instead of the other way round (industry 3.0) (Taylor, et al., 2020). This conclusion also applies to the cases studied. Tasks are taken over, but the employee is not superfluous.
The results of the different cases show a pattern in which the WD of the employees has decreased (feedback from job is an exception). This stems from the fact that the people surveyed perform tasks that are easy to replace with technologies. The sensing and cognitive capabilities of the employees are taken over by the technologies. Also, sensing capabilities improve the employee's cognitive capabilities. Because an employee has more and qualitatively better data available, the employee can make better decisions.
Technologies take over tasks, but also improve work. There is, for example. more data available (sensing) that leads to employees being able to make better decisions. Technologies, therefore, provide automation, but also enrichment.
Implications for theory
This study contributes by expanding the existing literature. The study focused on the effects of MES/M2M, and their characteristics, on task characteristics. It confirms the described consequences of the conceptual models of various researchers. The cases examined showed results consistent with what the literature says. As indicated earlier, the relationship between SM technology and work characteristics differs from situation to situation. In the area of job autonomy, it can be concluded that the case results are in favour of what (Bayo-moriones, Billon, & Lera-Lopez, 2017) claims. This stems from the fact that the roles under investigation are 'low skilled jobs', resulting in a decrease in autonomy (Parker, 2019). The skill variety and use have decreased in the case studies as a result of the implementation of the technologies. This confirms what has been said by (Venkatesh, Brown, & Bala, 2013). Because the sensing and cognitive capabilities of the technologies have taken over various tasks that were first performed by the employee, the skill variety and use have decreased. Finally, the cases also confirm the change in feedback from job that the literature prescribes. Due to the sensing capabilities of the technologies, much more data is available, which can be used for feedback.
Implications for practice
Critical reflection
In this study, research was conducted at three companies. Because this research has been conducted on a small scale, it is more difficult to draw general conclusions (Flick, 2009). Also, interviews were conducted at two of the three companies about operators with employees who performed other, higher positions. The operator himself did not contribute to these interviews. These interviews can give biased results because the person interviewed sometimes says what he thinks and what is sometimes not the truth. To draw a better conclusion, interviews will only have to be conducted with employees who perform operational tasks and not, for example, a CEO speaking for the operator.
The semi-structured interviews have ensured a rich data set, this is because the interviews were conducted in a flexible, but structured way. However, the interviews mainly relate to low-skilled positions. Because there is mainly input from interviews about low-low-skilled positions, it is difficult to draw general conclusions. To draw a general conclusion, different functions with different levels of skill will have to be investigated on a larger scale.
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