WORK DESIGN IN SMART MANUFACTURING:
HOW DOES THE PROCESS TECHNOLOGY STRATEGY AFFECT MANAGERS’ WORK DESIGN
BEHAVIOR
by
ZHICHANG CHEN S4056906
University of Groningen
Faculty of Business and Economics
Pre-MSc Supply Chain Management June 2020
Student address: Friesestraatweg 157-25, 9743AA, Groningen
Student contact details: Z.Chen.32@student.rug.nl +31 6 262 991 17
ABSTRACT
The acceleration of the Industry 4.0 technologies that consider smart manufacturing technology as the core element is not only reshaping how people do things but also how work is designed. Previous studies have found that organizational strategy can affect managers’ work design behaviour, and the findings were drawn from the human resources perspective. The topic is worth to be revisited because a strategy from an operations management perspective can also play a role in the overall organizational strategy. Also, many pieces of research have mainly taken work design as an independent variable. This research aims to fill the gap in the existing literature by taking work design as a dependent variable to explore how process technology strategy as an organizational strategy affects managers’ work design behaviour in light of smart manufacturing technology. Through four interviews in different manufacturing organizations, data is gathered and analysed by using coding. The results show that in manufacturing organizations that serve high volume markets, process technology strategy: (1) results in managers to design work that combines Socio-Technical Systems approach when they implement big data analytics; (2) results in managers to design work that combines Taylor’s Scientific Management approach when they implement ERP and MRP; (3) shapes managers’ work design behaviour through the controlled forms of motivation, and it constrains managers’ opportunity to design work. This research contributes to the current academia in the work design field by considering both psychological and operations management aspects in Industry 4.0.
Keywords: Smart Manufacturing Technology, Process Technology Strategy, Work Design Behaviour
Supervisor: Sabine Waschull
Theme: Smart manufacturing and the future of work (The role of managers in shaping work design)
INTRODUCTION
Nowadays, the acceleration of Industry 4.0 technologies is not only reshaping how people do things but also how work is designed (Cascio & Montealegre, 2016). As the core element in Industry 4.0, smart manufacturing technologies are adopted in different cases (Frank, et al., 2019). For instance, big data and analytics for predictive maintenance, artificial intelligence for automated production control. These adoptions introduce new application potentials for white-collar workers, who are reshaped in aspects such as execution, planning, control, and management. A widespread stream of research has considered how work design of employees is influenced by technologies such as lean production, enterprise resources planning systems, and computer-aided design (Parker, Van den Broeck, & Holman, 2017; Niepce & Molleman, 1998; Wall, Corbett, Clegg, Paul, & Martin, 1991). However, these studies did not consider a smart manufacturing context. The same technology can potentially bring both positive and negative effects on employees’ work design such as workload, autonomy, and skill utilization. Thus, it is rarely the influence of technology that determines employees’
work design; rather, it is the decisions made by managers that affect work design, namely managers’ work design behaviour (Parker, et al., 2017). This is the focus of this research.
Previous studies have revealed that organizational contextual influences, particularly the organizational strategy can affect work design through influencing the decisions of managers (Parker, et al., 2017). For example, cost minimization strategies stimulate managers to design work with less enrichment (Parker, et al., 2017). Companies that consider low-cost operations normally adopt large-scale, automated, and integrated process technology (Slack & Lewis, 2017). Process technology is the technology applied to operational processes (Slack & Lewis, 2017). As a type of information processing technologies in operational processes, smart manufacturing technologies such as robots, big data and analytics, and artificial intelligence belong to the concept of process technology (Slack & Lewis, 2017). Also, process technology strategy belongs to the organizational strategy context, and it can be proposed that process technology strategy potentially can affect managers’ work design behaviour. For example, a highly automated and coupled process technology may restrict managers’
opportunities to design work for employees; or managers may become less motivated to design enriching work for employees. This research focuses on the link between process technology strategy and managers’ work design behaviour.
What is missing in current academia is a study that investigates how process technology strategy affects managers to design work. Although Parker, et al., (2017) have already found that organizational strategy as a contextual influence can affect managers’ work design behaviour, the general concept of organizational strategy was drawn on the human resources perspective. The topic is worth to be revisited because the operations management perspective that links to the technology can also play a role in organizational strategy. The other study in Cagliano, R., Canterino, F., Longoni, A., &
Bartezzaghi, (2019) explored the interplay between smart manufacturing technologies
and work design in operations management aspect. However, it did not consider managers' decisions concerning work design. Another study in Parker & Grote, (2020) explained the importance of work design in Industry 4.0 and how new technologies affect work design. Rather than linking process technology strategy in an operations management perspective, Parker & Grote, (2020) presented a psychological perspective, which has limited implications for management practitioners in supply chain enterprises. Hence, the search for links between managers’ decisions on work design and strategy in process technology appears to be at a transition point in smart manufacturing, which leads to the central research question:
How does the process technology strategy affect managers' work design behaviour in light of smart manufacturing technology?
The research aims to explain the work design behaviours of different managers involved in the process of smart manufacturing technology implementation by focusing on the link between managers' work design behaviour and process technology strategy.
The research contributes to academia in work design field by considering both psychological and operations management aspects in Industry 4.0. To provide an answer to the question, a case study was conducted at four manufacturing organizations that have implemented smart manufacturing technology projects. This research paper provides insights for managers in supply chain enterprises on how to design work by considering process technology strategy in smart manufacturing, thereby to achieve desired company performances.
The remainder of this paper is presented as follows: firstly, theories are explored on smart manufacturing technology, process technology strategy, work design behaviour, and influences on work design behaviour. This is followed by justifications of the used research methods, and findings and discussions. The research concludes with reflections and recommendations, providing implications for future work.
THEORY
The following section begins with reviewing the literature on smart manufacturing technology, process technology strategy, work design behaviour, and influences of work design behaviour. Subsequently, relevant sub-research questions are formulated in response to the research objectives, after which relations are explained and placed into a conceptual framework.
Literature Review
Smart manufacturing technology
The concept of smart manufacturing technology has been receiving increasing interest in recent years. Many pieces of research (i.e. Davis, Edgar, Porter, Bernaden, & Sarli, 2012; Frank, Dalenogare, & Ayala, 2019; Cagliano, et al., 2019) have provided similar definitions on smart manufacturing technologies, which are defined as networked and information-based technologies for supply chain and manufacturing organizations.
Smart manufacturing can be implemented in different variations characterized by different complexity level depending on the choice and combination of technology applications involved (Kusiak, 2018; Frank, et al., 2019). For instance, Frank, et al., (2019) categorized smart manufacturing technologies into 6 different types based on 3 stages of complexity level of implementation. However, Cagliano, et al., (2019) adapted the concept in Frank, et al., (2019), and provided 7 different main categories.
Both papers listed 5 common categories (i.e. vertical integration, energy management, traceability, automation, virtual reality). One main difference is that Frank, et al., (2019) categorized ‘artificial intelligence for predictive maintenance’ as virtual reality, while Cagliano, et al., (2019) categorized it and together with ‘artificial intelligence for production’ as artificial intelligence. The other main difference is that Frank, et al., (2019) categorized ‘cloud computing’, ‘big data’ and ‘analytics’ as the base technologies among the broad Industry 4.0 technologies, which is distinct from the smart manufacturing technologies in a narrow sense; while Cagliano, et al., (2019) categorized them as connectivity and analytics-enabling technologies, which are part of smart manufacturing technologies.
In this research, the perspective of categorizing smart manufacturing technologies from Cagliano, et al., (2019) will be taken, because the research considers smart manufacturing technology and work design aspects, which also have been considered in Cagliano, et al., (2019), while Frank, et al., (2019) mainly focus on the implementation patterns of Industry 4.0 technologies in manufacturing companies.
Process technology strategy
Technology has a profound impact on operational processes, and with the emergence of smart manufacturing technologies, this impact is becoming even more significant in the fourth industrial revolution context. Rather than leaving it only for technical experts, managers in supply chain and manufacturing enterprises are needed to understand the
strategic meaning of adopting new digital technologies in operational processes. This leads to the concept of process technology and the strategy of it.
Slack & Lewis, (2017) defined process technology as the technology that applies to the operational processes as distinct from product or service technology itself. Slack &
Lewis, (2017) classified some process technologies by their primary inputs, namely, material processing technologies, information processing technologies, and customer processing technologies. This suggests that smart manufacturing technologies are part of the information processing technologies and belong to the process technology concept. Because they are networked information-based technologies that can create a flexible and intelligent manufacturing system, which enables real-time to changing conditions (Kusiak, 2018).
Slack & Lewis, (2017) also classified direct and indirect process technology. The former is that contributing ‘directly’ to the services and manufacturing of goods; the latter is the ‘indirect’ or ‘infrastructure’ technology that supports fundamental transformation processes (Slack & Lewis, 2017). However, Slack & Lewis, (2017) argued that the distinction between direct and indirect process technology is not always clear and hence defined process technology strategy as “the set of decisions that define the strategic role that direct and indirect process technology can play in the overall operations strategy of the organization and sets out the general characteristics that help to evaluate alternative technologies” (Slack & Lewis, 2017, p. 199).
The general dimensions of process technology are coupling, scale, and automation.
Coupling can mean either the degree to which the process technology is integrated with other technologies or the degree of integration between some human-related tasks and the process technology to form a synchronised whole; scale means the capacity of the technology to process work; automation means the degree to which the process technology perform activities or make decisions for itself (Slack & Lewis, 2017).
In addition to the general dimensions, process technology should reflect market dimensions, namely, volume and variety of the market. Traditionally, companies that concentrate on high-volume, low variety markets consider low-cost operations (i.e.
mass production), and process technology need to be large, automated, and integrated;
in contrast, low-volume, high-variety operations need more flexibility with small-scale, loosely coupled technologies with substantial human-technology interaction (Slack &
Lewis, 2017). Whereas, the traditional process technology strategy is being challenged lately, because market pressures are requiring companies to be both flexible and low cost, namely, mass customization (Slack & Lewis, 2017). The increasing pressure on cost therefore pushes companies to reduce direct labour and increase automation.
As a type of process technology, smart manufacturing technologies also have three general dimensions and reflect market dimensions. Because they can be applied to operational processes just like other technologies, except smart manufacturing technologies are more complex, interconnected, and intelligent (Frank, et al., 2019).
Work design behaviour
Although work design has been studied extensively over the past decades, poorly designed works continue to exist in many contemporary organizations in Industry 4.0 (Parker, Van den Broeck, & Andrei, 2019). Industry 4.0 – also named as the fourth industrial revolution - draws on the application of digital technologies to collect data in real-time and to analyse it, providing useful information to the manufacturing system (Lee, et al., 2015; Wang, et al., 2016). In light Industry 4.0, many tasks that employees carry out today have the potential to be automated (Parker & Grote, 2020). This leads to the concern of the importance of designing work for employees. Although assumptions commonly used to simplify human behaviour in operations management models and many operations management models entirely omit the human side (Boudreau, Hopp, McClain, & Thomas, 2003), humans are part of operations systems, as both decision-makers and system operators (Kolus, Wells, & Neumann, 2018).
Actually, the application of human factors knowledge in operations systems design and operation can improve both operators well-being and overall system performance (i.e.
quality, productivity, efficiency, and effectiveness), and strong evidence shows that human factors in operations systems design influences quality performance (Kolus, et al., 2018).
So far, the majority of work design research focuses on how work design should contribute to a set of positive individual and organizational outcomes (Grant, Fried, &
Juillerat, 2011; Morgeson & Humphrey, 2006; Oldham & Hackman, 2010). Thus, work design in most research is measured as the independent variable; however, the antecedents of work design are ignored (Waschull, Bokhorst, Molleman, & Wortmann, 2020).
Organizational strategy as an antecedent can affect managers’ work design behaviours.
A key task for managers is to design work that aligns best with the strategic objectives of the company (Parker, et al., 2017). There are different work design behaviours. For instance, managers are likely to design work that combines Taylor’s Scientific Management approach when they follow an operational excellence competitive strategy with standardized products or services (i.e. mass production) in the mass market (Parker, et al., 2017). Taylor’s Scientific Management approach to work design remains a dominant approach nowadays in both manufacturing and services organizations (Cordery & Parker, 2012). The core concept of this approach is job specialization and simplification. Employees perform simple, specialized, repeated manual activities; managers perform mental works such as planning, scheduling, monitoring, rewarding, and the exercise of initiative (Cordery & Parker, 2012). Thus, when managers design works that combine Taylor’s Scientific Management approach, they are likely to use low-involvement practices with low-cost, low-training, and provide low job autonomy for employees (Parker, et al., 2017).
When managers follow a differentiation competitive strategy with high-quality or innovative products or services in the niche market, they are likely to design work with high-involvement practices (Parker, et al., 2017). In this case, they are likely to design
works that provide employees with higher job autonomy, a wide span of responsibility and challenging tasks, opportunity to use specialized knowledge and skills, and extensive training (Parker, et al., 2017). This work design behaviour combines the Socio-Technical Systems theory in which both humans and technology are joined together for effective process optimization. Unlike the dominant mechanistic work design behaviour, the Socio-Technical Systems approach to work design involves the use of autonomous workgroups or so-called ‘self-managing work teams’ to control and execute tasks (Cordery & Parker, 2012). This approach enables employees to use a degree of specialization while rotating within the group and enables them to learn more related tasks within the group (Cordery & Parker, 2012). It also empowers employees with more responsibilities for coordinating within and outside the group and de- specialize some planning and decision-making activities that are traditionally done by managers (Cordery & Parker, 2012).
Influences of work design behaviour
Although organizational strategy as an antecedent can affect managers’ work design behaviours, it is mediated by managers’ knowledge, skills, abilities, motivation, and opportunity (Parker, et al., 2017). In this research paper, the focus will be only on motivation and opportunity, because they are more likely and closely affected by strategies at the company level, comparing with other influences of work design behaviour. Other paper (i.e. Ross, Koys, & Lawler, 1987) focused on motivation as well, and Parker, et al., (2017) indicated that opportunity is crucial.
Motivation is categorized into two forms, namely, autonomous forms and controlled forms (Parker, et al., 2017). Autonomous forms of motivation mean that managers are driven by themselves. For instance, managers want to retain staff or want to develop high quality works for employees (Parker, et al., 2017). Controlled forms of motivation mean that managers are pressured by external forces other than themselves. For instance, managers are required to cut staffing costs or under market pressures to imitate competitors’ technologies (Parker, et al., 2017).
Regardless of a manager’s motivation, the person can only do so if there is an opportunity in the context. Opportunity also includes power. In other words, a manager’s work design behaviour will be constrained, if the authority for the manager to organize resources to finish tasks and influence others is low. (Parker, et al., 2017).
Research Questions and Conceptual Framework
The concepts of smart manufacturing technology, process technology strategy, work design behaviour, and influences of work design behaviour have been discussed separately in the literature review. However, there are links among them. Smart manufacturing technologies are networked information-based, and they are part of the information processing technologies when categorized by primary inputs, and they belong to the process technology concept. In this case, smart manufacturing technologies also have three general technology dimensions and reflect market dimensions. Additionally, process technology strategy is part of the organizational
strategy, because it contains a set of decisions that can shape the overall operations strategy of the organization. Since organizational strategy can affect managers’
decisions on work design, it can be further proposed that process technology strategy can affect managers’ work design behaviour through the influences of motivation and opportunity.
Additionally, managers must design work that aligns best to the organizational strategy, and there are different work design behaviours to do so. For example, literatures have shown that traditionally, companies that focus on high-volume, low variety markets consider low-cost competitive strategy from the market dimensions, and process technologies are normally characterised to be automated, large scale and high integration from the technology dimensions. Thus, managers are likely to be motivated to design work that combines Taylor’s Scientific Management approach. Whereas companies that focus on low-volume, high-variety markets consider differentiation competitive strategy from the market dimensions and process technologies are normally characterised to be less automated, smaller-scale, and less integration from the technology dimensions. Thus, managers are likely to be motivated to design work that combines the Socio-Technical Systems approach.
Therefore, in light of smart manufacturing technology, it is necessary to explore:
Sub-research question 1: How do the technology dimensions and market dimensions of process technology strategy affect managers’ motivation to design work?
Sub-research question 2: How do the technology dimensions and market dimensions of process technology strategy affect managers’ opportunity to design work?
The conceptual framework below illustrates the concept relationships.
FIGURE 1 Conceptual Framework
RESEARCH METHOD
Research Design
The purpose of the research design is to use the exploratory study as a means to ask open questions that start with ‘What’ or “How” to gain insights about the research topic (i.e. the impact of process technology strategy on managers' work design behaviour in light of smart manufacturing technology). Thus, qualitative research is suitable, as it often associated with an interpretive philosophy (Denzin & Lincoln, 2011). The research is designed to use a single data collection technique as the methodological choice. Because it only includes semi-structured interviews and its corresponding qualitative analytical procedure (Saunders, Lewis, Thornhill, 2016). Semi-structured interviews are used in this research because they provide the reasons for interviewees’
attitudes and opinions, and there are a large number of complex and open-ended questions to be answered (Saunders, et al., 2016).
The multiple case study is s selected as the research strategy. A case study is a detailed investigation into a topic or phenomenon within a real-life setting (Yin, 2014). The multiple case study is particularly suitable for this research because it enables the researcher to gather data from different representative cases for either literal or theoretical replication purpose (Saunders, et al., 2016).
Case Selection & Research Setting
The unit of analysis is manufacturing organisations that have implemented smart manufacturing technologies. Table 1 shows the overview information of the selected cases. Appendix 2 provides more descriptions of the selected organizations for this research.
The manufacturing organizations were selected based on the following criteria, namely: (1) nearly all of them target at both B2B and B2C markets, which can make the research findings generalizable regardless of their targeted market; (2) they are either large companies or SMEs, which can make the research findings generalizable regardless of their size; (3) they follow either differentiation or cost as competitive strategy; (4) they have implemented information processing technology and indirect process technology as the type of process technology; (5) they have implemented either connectivity and analytics-enabling technology, or vertical integration and horizontal integration as the types of smart manufacturing technology.
Organization A is a large beverage manufacturer, and it implemented a big data and analytics software as the smart manufacturing technology. The Data Quality manager was interviewed. Organization B is a large dairy manufacturer, and it implemented MES and ERP as the smart manufacturing technologies. The Planning and Logistics manager was interviewed. Organization C is a medium-size water producer, and it implemented a big data and analytics software as the smart manufacturing technology.
The Sector manager was interviewed. Organization D is a small start-up manufacturer,
and it implemented MES and ERP as the smart manufacturing technologies. The Operations and Engineering manager was interviewed.
TABLE 1
Overview of Selected Organizations
Data Collection
To collect data, four semi-structured interviews were conducted by 2 research teams from the University of Groningen. 1 interview was conducted in this research team, and 3 interviews were conducted by the other research team. All the interviewees stayed anonymously and signed consent forms for agreeing for the participation. In this way, ethics and privacy standard have been met. The interviews were voice recorded and were written down in a script form so that all the interviews could be used by students without missing essential information.
Most of the interviews were conducted through internet voice calls or video calls and were taken for approximately 1 hour. Organization A was approached by the researcher through internet voice calls to the Data Quality manager. The conversion was 1.5 hours, also another 30 minutes follow up session was added in another time. Organization B was selected from the other research team. The interview was able to be done with the Planning and Logistics manager at the manufacturing location. Organization C and D were selected from the other research team as well. The interviews were able to be done by voice call and video call with the Sector manager and Operations Engineering manager respectively.
Operationalisation of concepts
To answer the research questions, a selection of interview questions was generated into an interview guide. The interview guide has 6 sections, from which most of the sections have been made identical for all the research teams, except for section 5. The complete interview guide can be found in appendix 1.
The main theoretical concepts of this research have been operationalised through a set of specific questions. This can be found in appendix 3.
Data Analysis
After transcribing the interviews, a code tree was created by considering the 3 main concepts derived from the research question. A list of main codes was subsequently derived from the theoretical concepts by linking the relevant information from each interview. The code tree is shown in Table 1 below, and a more comprehensive code tree with the most important concepts and definitions can be found in appendix 4.
The processes of making and narrowing down codes were iterative since the interviews were read in repetitive ways for multiple time. After that, those text fragments that indicated a linkage with the assigned theoretical concepts and main codes were moved to Excel for making the coding scheme and each interview has been made with a coding scheme.
After that, similar codes were categorized into pattern codes, and this was done per interview. The pattern codes were those that indicated the most important insights needed to answer the research question. Following that, a comprehensive coding scheme is made by gathering all the coding schemes. The comprehensive coding scheme can be found in appendix 5. Likewise, the process was also iterative.
Last but not least, an analysis was done based on each interview result. The results were compared to find similar of different patterns in cross-case analysis. The insights that generated from the results were used to answer the research question, which is discussed in detail in the discussion section.
TABLE 2 Code Tree
Theoretical background Main code A Smart manufacturing
technology
(Cagliano, et al., 2019;
Frank, et al., 2019)
A1 Connectivity and analytics-enabling technologies
A2 Vertical integration and horizontal integration B Process technology
strategy
(Slack & Lewis, 2017)
B1 Information processing technology
& indirect process technology B2 Coupling
B3 Scale B4 Automation B5 Volume/variety B6 Competitive strategy C Work design behaviour
(Parker, et al., 2017;
Cordery & Parker, 2012;
Morgeson & Humphrey, 2006)
C1 Job specialization C2 Job simplification C3 Job autonomy C4 Skill variety C5 Mental activities C6 Training
C7 Coordination
C8 Feedback from others
D
Influences of work design behaviour (Parker, et al., 2017)
D1 Motivation D2 Opportunity
RESULTS
This section shows the findings gathered from the interview cases across different manufacturing organizations. The findings are structured according to the two sub- research questions that formulated in the theory section. Last but not least, the central research question is answered by showing the key findings.
Table 3 presents the overview findings from the interviews. The findings were grouped by the descriptive codes, pattern codes, and main codes that were discussed in the research method section. Relevant quotes from the coding scheme can be found in appendix 5.
Table 3
Overview Findings from Interviews
How Do the Technology Dimensions and Market Dimensions of Process Technology Strategy Affect Managers’ Motivation to Design Work?
The findings from all selected cases indicate that managers implemented smart manufacturing technologies in their operational processes for different strategic reasons.
In general, they aimed to increase process efficiency and resolve process obstacles.
What is similar to the findings in all selected cases is that managers’ decision about work design is shaped by controlled forms of motivation. However, their motivation levels are different, and this is found through the comparison between companies that implemented big data analytics software (organization A and C) and MES & ERP (organization B and D).
Although organization A and organization C were different in size, competitive strategy, and implemented smart manufacturing technology with different coupling and scale, both of them implemented moderate automatic big data analytics and served high volume markets. In this case, managers were mainly driven by technology, and employees were placed in second.
For example, the Data Quality manager in organization A said:
"We started in the Netherlands, and we are…what they call ‘tech finder’. […] They are also now looking at to use data analytics in whole Europe."
"So, the impact of our solution has to do with a lot of account managers, so that’s what we need to take an account, right? We need to inform them that the strategy is going to change them etc. etc."
Although organization B and organization D were different in size and competitive strategy, both of them implemented closely coupled, large scale, and highly automatic MES and ERP systems in high volume markets. In this case, managers were even more tech-driven and employees were not really been considered.
For example, the Planning and Logistics manager from organization B indicated that they did not consider employees’ feelings regarding their tasks. Although the Operations and Engineering manager from organization D believed that human factor was important, yet, employees’ feelings were not really taken care of as he expected.
Process technology strategy shapes managers’ work design behaviour through controlled forms of motivation. All the interviewed managers indicated that they increased employees’ job specialization and job simplification, except for the manager in organization D, who admitted that they released a 200-page document to implement the integration of MES to ERP system, and novice production employee needed to operate more control steps and experienced production employees were unhappy to work through those steps all the time. Additionally, the manager in organization B who implemented MES and ERP technologies increased employees’ job specialization and job simplification through the reduction of manual tasks (i.e. less machine control steps).
Whereas, managers in organization A and organization C who adopted big data and
analytics increased employees’ job specialization and job simplification through the reduction of manual tasks (i.e. labour-intensive data collection) to the rise of more mental tasks (i.e. analysis and decision making).
How Do the Technology Dimensions and Market Dimensions of Process Technology Strategy Affect Managers’ Opportunity to Design Work?
Although managers in all the selected cases claim that they have the opportunity that includes the power to mobilize resources to get things done and influence others, their work design-related actions are constrained to some extent. Because they cannot effectively mobilize resources especially the human resources in reality.
Both organization A and organization C implemented moderate automatic big data analytics and served high volume markets. The two interviewed managers in these organisations expressed their words that the organisational accountability was not clear during the project implementation and caused chaos in sometimes.
Both organization B and organization D implemented closely coupled, large scale, and highly automatic MES and ERP systems in high volume markets. The two interviewed managers in these organisations expressed their words that it was hard to involve employees and change the way that employees used to work.
For example, the Planning and Logistics manager from organisation B indicated that it was hard to get people involved to stick to a predetermined and standardized framework.
Although the Operations and Engineering manager from organization D wanted to match employees’ personalities and skills with their tasks, he admitted that employees still had to work like the same screws in the same place.
Although process technology strategy constrains managers’ work design behaviour to some extent, their work design behaviours differ from dissimilar types of smart manufacturing technologies. Managers in organization A and organization C who adopted big data and analytics designed work that improved employees’ job autonomy and skill variety through the increase of decision making and data analytic skills.
Managers in organization B and organization D who adopted MES and ERP designed work that reduced employees’ job autonomy and skill variety. Additionally, managers in organization A and organization C designed work that increased employees’ social contact interaction specifically in coordination within and across teams.
For example, the Data Quality manager in organization A said that:
"We use the Scrum. So, we used different sprints secessions. And during the time, we set goals every two weeks, and then we separated our team. We did it like we were work together two days a week. And then, we had up two weeks we get see like how we are doing, we keep check of the performance."
Answering the Central Research Question
The objective of this paper is to answer the central research question: How does the process technology strategy affect managers' work design behaviour in light of smart manufacturing technology? This is done by answering the two related sub-questions.
Three key findings emerged through the comparison of results between companies that implemented big data analytics software (organization A and C) and MES & ERP (organization B and D).
Key finding 1: when manufacturing organizations implementing moderate automatic big data analytics in high volume markets, managers are mainly driven by technology, and employees are placed in second. Although their opportunity to design work is constrained to some extent, they tend to design work for employees with increased job specialization and job simplification, improved job autonomy and skill variety, and more social contact interaction.
Key finding 2: when manufacturing organizations implementing closely coupled, large scale, and highly automatic MES and ERP systems in high volume markets, managers are driven by technology and hardly consider employees. Their opportunity to design work is constrained. They tend to design works for employees with less job autonomy and skill variety, and they do not create more mental activities for employees.
By linking all the results together:
Key finding 3: In manufacturing organizations, process technology strategy shapes managers’ work design behaviour through the controlled forms of motivation. Also, it constrains managers’ opportunity to design work, because managers cannot effectively mobilize resources especially human resources. This can result in some process inefficiencies while implementing smart manufacturing technology.
Discussion and Conclusion
The central research question has been answered previously. It can be found that process technology strategy can result in relatively two opposite work design behaviours, when managers implement either big data analytics software, or ERP and MES systems. The main commonality is that process technology strategy shapes managers’ work design behaviour through the controlled forms of motivation, and it constrains managers’ opportunity to design work.
This section includes the discussion and conclusion of this paper. It begins with the interpretation of results followed by the implications for theory and practice. It concludes with the critical reflection of this research and suggestions for future research.
Interpretation of Results
In terms of key finding 1, on the one hand, it partially confirms the results in the existing literatures from Cordery & Parker, (2012) and Parker, et al., (2017). Although not
necessarily happens in the context of low-volume, high-variety niche market with differentiation competitive strategy, it can happen in the high-volume markets that managers in manufacturing organizations are still likely to design works that combine the Socio-Technical Systems approach, which can provide employees with high job autonomy, a wide span of responsibility, opportunity to use specialized knowledge and skills, regardless what competitive strategy they follow.
On the other hand, even though the Socio-Technical Systems approach to work design that involves the use of autonomous workgroups can increase employees’ social contact and interaction through the increasing coordination, this approach can also cause unclear organisational accountability, which can bring inefficiency to organizational performance. This is evident from the Scrum team and project team that used in organization A and organization C. One possible explanation may be the fact both manufacturing organizations adopted big data and analytics. Although big data tools can help organizations to improve existing processes and increase employee interactions, they can also cause poor data quality can unclear organisational accountability regarding roles involved with keeping, processing, and analysing the data, particularly because employees are normally active contributors to big data in organisations (Jeske & Calvard, 2020).
In terms of key finding 2, it partially confirms the results in the existing literatures from Cordery & Parker, (2012) and Parker, et al., (2017). Although not necessarily happens in the context of traditional high-volume, low-variety mass market with cost minimization competitive strategy, it can happen in the high-volume markets that managers in manufacturing organizations are still likely to design works that combine the Taylor’s Scientific Management approach, namely, low job autonomy, narrow span of responsibility, repeated manual activities, regardless what competitive strategy they follow.
One possible explanation may be the fact that both manufacturing organizations adopted ERP system. It is suggested that the implementation of a contemporary ERP system can perform as a catalyst for the application of lean production practices (Powell, Alfnes, Strandhagen, Dreyer, & Heidi, 2012). Another study also implied that the use of the ERP system can contribute to applying lean principles (Iris & Cebeci, 2014).
Whereas, many studies have found that lean production or the application of ERP systems in lean production can negatively impact employees through the increased workload, increased stress, and reduced job autonomy (Niepce & Molleman, 1998;
Delbridge, 2005; Bala & Venkatesh, 2013). These studies imply that the implementation of an ERP system can negatively impact managers’ work design behaviours.
In terms of key finding 3, it confirms the results in the existing literatures from Kolus, et al., (2018), where it argued that the application of human factors knowledge in operations systems design and operation can improve both operators well-being and overall system performance, and strong evidence shows that human factors in
operations systems design influences quality performance. Besides, this finding also confirms the controlled forms of motivation that explained by Parker, et al., (2017).
Additionally, presented in the theory section, Slack & Lewis, (2017) argued that the traditional process technology strategy is being challenged lately, because market pressures are requiring companies to be both flexible and low cost, namely, mass customization. This is evident from the findings in organisation A and organization D.
However, in contrast to what Slack & Lewis, (2017) suggested, this research found that the increasing pressure on cost did not push companies to replace direct labour by increasing automation, rather they implemented either moderate or large automatic new technology to be used by employees.
One possible explanation may be the fact that this research paper only studied information processing technologies. Whereas, material processing technologies and customer processing technologies may lead to different results.
Implications for Theory
The acceleration of the Industry 4.0 technologies that consider smart manufacturing technology as the core element is not only reshaping how people do things but also how work is designed.
Previous studies have found that organizational strategy as a contextual influence can affect managers’ work design behaviour, and the general concept of it was drawn on the human resources perspective. The topic is worth to be revisited because process technology strategy from an operations management perspective can also play a role in the overall organizational strategy. Besides, many pieces of research have mainly taken work design as an independent variable
Thus, to fill the gap in the existing literatures, this research contributes to the current academia in work design field by considering both psychological and operations management aspects in Industry 4.0, and took work design as a dependent variable and discussed how process technology strategy affect managers' work design behaviour in the context of smart manufacturing technology.
Although this research provides several key findings, it should be addressed that some findings mainly partially confirm the results in the existing literatures. In other words, the level of association between process technology strategy as the organization strategy and managers’ practices is relatively low, which indicates that there might be a misalignment between process technology strategy and work design in many manufacturing organizations. This might due to managers’ inability to align them, or misinterpretation of the strategy, or other reasons. Future research should make efforts to explore the answers.
Additionally, it should be aware that this research only studied two types of smart manufacturing technology, namely connectivity and analytics-enabling technologies, and vertical integration and horizontal integration, and used big data analytics and MES
and ERP as examples. Thus, other types may lead to different results. Also, other process technologies such as material processing technologies and customer processing technologies may bring different insights. Future research should continue to find how process technology strategy affects managers' work design behaviour in a more comprehensive approach that includes more smart manufacturing technologies, or in another context rather than smart manufacturing technology.
Implications for Practice
This research paper provides insights for managers in manufacturing and supply chain enterprises on how to design employees’ works by considering process technology strategy in the context of smart manufacturing technology. The insights can help managers to improve current organizational performance and to achieve desired company outcomes.
Our findings show that managers in contemporary manufacturing organizations are still mainly driven by technology and are likely to implement off-the-shelf solutions to adapt existing business processes to the new technology. However, human factors are often overlooked in this case, which can negatively impact organizational efficiency and quality to change. Therefore, managers must include human factors during project implementation. This can be done for example, by formulating clear data policy and project governance and accountability, providing training and feedback sessions, constantly informing employees the impacts and results of change and taking care of their feelings, encouraging and rewarding employees for the positive progress that have been made, and creating an organizational-wide culture that embraces change.
Additionally, managers who lead the implementation of smart manufacturing technology projects may not necessarily be the same people who design the project. It is advised for those designers to consider human factors at the design phase. This can be done for example, by designing customized solutions to adapt the new technology to match the underlying business processes.
Critical Reflections and Suggestions for Future Research
Although this research provides important implications for both theory and practice, several limitations needed to be underlined, and future research should minimize these limitations and improve research methodology.
Firstly, although this research used a qualitative study that successfully explored and provided insights about the research topic, the use of semi-structured and in-depth interviews also rose some data quality issues in terms of forms of bias and reliability.
Because interviews were almost conducted through voice calls and video calls, participation bias, interviewer bias, and interviewee bias existed to some extent and can affect the quality of the results. Also, the quality of interview answers was largely depended on interviewees’ knowledge on this topic and skills and abilities for participating interviews. Future research should try to overcome these limitations to increase reliability.
Secondly, although four extensive in-depth interviews were used and led important results to answer the research question, the sample size considerably limits the generalizability of the research findings and the external validity can be criticized.
Besides, although the cross-case analysis was used to generate more results and to generalize the findings across different manufacturing organisations regardless their size, markets, industries, this approach is also questionable in terms of the data credibility. This is due to the reason that other managers inside the same organisations can provide different answers which may significantly affect the findings of this research. Thus, future research is advised to be done by collecting interview data extensively through different managers within and among manufacturing organizations across various industries, markets, and with different sizes and products and services.
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APPENDIX 1
Interview Guide for Sub-Group 3 Section 1: Background information
1.1. What is your role in the company, and your responsibilities?
1.2. What is your previous working experience?
1.3. Could you briefly describe your company in terms of:
• Industry
• Products and services
• Types of market served (B2B/B2C)
• Number of employees
• Type of production processes
1.4. What study have you followed/completed?
1.5. How do you characterize the overall organizational culture and manager- employee interaction inside the company?
Section 2: Information on the smart manufacturing project
2.1.Could you describe the smart manufacturing project/program, and the implemented technologies?
2.2.What were the main project activities?
2.3.*What is/was your role, and your daily tasks in the project?
2.4.Who else is/was involved in the design or implementation, what are/were their roles? Who has/had what responsibility?
2.5.Have you had any previous training and experience regarding the implementation of such technologies?
2.6.What was the main reason for the choice of the technologies adopted/implemented?
2.4. Which were/are the main objectives and motivations for the project?
2.7. Do/did you have all the resources necessary to make the project a success?
2.8. Where there any constraints/hurdles that you faced during the project, if so, could you elaborate?
Section 3: Unwrapping mindset and work design behavior
3.1. How do you judge a good design and/or implementation of the (smart manufacturing) technology?
3.2. What are the ultimate criteria for success (of the implementation)? How do you meet these? (you could take each criterion in turn and probe how its achieved to see if work design is mentioned)
3.3.To ensure the technology helps enhance productivity, what else needs to be in place, beyond technology that works?
3.4. What is your general ‘vision’ or perspective on the role of humans in the factory?
3.5. What ‘human’ considerations have you made in the project?
3.6. What have you done to take into account the perspective of employees, can you name examples?
3.7. Who was responsible for the consideration of human factors (their work, tasks, skills) during the project?
3.8. Have you involved the user of the new technology in the development/implementation project of the new technology, how?
3.9.a Questions to ask in case of designers of a technology: What considerations have you made about how the technology will be used in the work? What processes do you have for thinking about how the system will actually work when implemented?
3.9.b Question to ask in case of implementors of a technology: What role/staffing issues does the new technology give rise to? What skill requirements? What
specialized skills and knowledges are required and how deep are them? Will people work in the same way as now? If not, what will be different?
3.10. What functions do you/did you allocate to a person rather than a machine/software? How do you make these decisions?
3.11. What human considerations have you made/ are you making during this project to ensure engaged and motivated workers? What are you doing to ensure maximum productivity?
3.12. (If human factors are not considered): Why are human factors not considered?
Section 4: Work design changes
4.1. What employee group was/is most affected by the implementation?
When the discussed technology is not yet implemented then ask:
4.2. When the technology is finally in place, how might their work design change?
4.3. Will people work in the same teams in the same roles/ different?
4.4. If more detail is needed, ask more specific questions:
Will levels of autonomy change? What control will the person have relative to the machine?
Will the person get feedback from the machine/system?
Will job demands change?
Will the person need to develop new skills?
What about social interaction, will people interact more or less with others?
….
Only ask when the discussed technology is already implemented:
4.5. How did the work design change of X due to the implementation of the technology?
4.6. Do people work in the same teams, and in the same roles or different ones?
4.7. Were any of these changes unexpected?
4.8. If more detail is needed, ask more specific questions:
Will levels of autonomy change? What control will the person have relative to the machine?
Will the person get feedback from the machine/system?
Will job demands change?
Will the person need to develop new skills?
What about social interaction, will people interact more or less with others?
Section 5: Process technology strategy (in the context of smart manufacturing technology)
5.1. How do you describe your own degree of openness and how does your company treat the value of openness?
5.2. What is the degree of product (or service) variety (high/low) and volume (high/low) that your company serve for the market?
5.3. Which competitive strategy does your company mainly follow? (i.e. mainly cost, or differentiation on quality and innovation)
5.4. To what extent (small, some, moderate, great, very great) is the technology automated? (i.e. the degree to which the technology perform activities or make decisions for itself) Could you elaborate on this?
5.5. To what extent (small, some, moderate, great, very great) is the technology exist in or impact your company operational processes? (i.e. is that a large-scale technology or small-scale technology) Could you elaborate on this?
5.6. How do other technologies in your company integrate with this technology?
5.7. How do you think the amount of capital investment in this technology? (i.e. high or low) Is it worth to invest this amount? Please elaborate on this.
5.8. What are the intended benefits to be achieved by adopting this technology? And what were the actual results?
5.9. Did you have alternative technologies that could be selected instead of using the current one? If so, could you elaborate on this?
5.10. What are the risks if this technology goes wrong? How will (did) you deal with it
5.11. What factors that motived you to adopt this technology? (e.g. costs, power, opportunity, market pressure from customers or competitors, to increase employee performance, etc.) Could you elaborate it?
Section 6: Wrapping up
6.1. Do you consider the implementation a success and why?
6.2. What were negative outcomes of the project?
6.3. Would you change the approach if you could do it again, if so what?
APPENDIX 2
Organization Information
Firstly, organization A was chosen. The company is a large beverage manufacturer in the Fast-Moving Consumer Goods sector that serve both business-to-business and business-to-consumer markets. It manufactures and distribute in 13 different countries with more than 23,000 of employees. It has around 400 employees in the headquarters of the business unit of the Netherlands. It is chosen because the Dutch business unit has carried out a project for implementing a connectivity and analytics-enabling technology (big data and analytics software). The objective of the project was to analyse and forecast the potential growth of their indirect business customers, thereby to allocate resources more efficiently and to achieve more volume and revenue. The project team consisted of around 10 people. The Data Quality manager was approached because the manager was part of the implementation team and was the middleman between the external party and the company and was responsible for connecting all the resources needed.
Secondly, organization B was chosen. The company is a large dairy manufacturer in the Fast-Moving Consumer Goods sector that serve both business-to-business and business-to-consumer markets. It operates over 100 countries with more than 24,000 employees. The location in the Netherlands where the interview was conducted has around 230 employees. It is chosen because the location was one of the seven locations that has implemented vertical integration technologies (Manufacturing Execution System, and Enterprise Resource Planning) to streamline processes. The project team consisted of 34 people. The Planning and Logistics manager was approached because the manager was part of the implementation team.
Thirdly, organization C was chosen. The company is a medium size water producer in the utility sector that serve both industrial water to business-to-business market and home-use water to business-to-consumer markets in the Netherlands. It has 5 production plants and with approximately 200 employees. It is chosen because the company has implemented a connectivity and analytics-enabling technology (big data and analytics software), which measures the water quality continuously for example, the values, and pressures in the pumps. Before implementing the project, the data collection was done manually, and some data did not even exist. After implementation, more data and actual information were collected, and important decisions can be made by process technicians. Instead of collecting data manually, the work is shifted to check if the data generated by the technology is valid. The Sector manager was approached because the manager was the supervisor of the implementation team.
Last but not least, organization D was chosen. The company is a start-up electric motorcycle manufacturer that includes around 50 people. It shares (leases) parts of the production hall to other start-ups who might not have the required capital to start the production of their own product. The company is chosen because it has implemented a project to integrate the Product Lifecycle Management software and Manufacturing
Execution System into their existing Enterprise Resource Planning system. The Operations and Engineering manager was approached because the manager was responsible for the implementation of the project.
APPENDIX 3
Operationalization of The Most Important Concepts
Most important concepts
Related interview questions
Process technology strategy
5.2. What is the degree of product variety and volume that your company serve for the market?
5.3. Which competitive strategy does your company mainly follow?
5.4. To what extent is the technology automated? Could you elaborate on this?
5.5. To what extent is the technology exist in or impact your company operational processes? Could you elaborate on this?
5.6. How do other technologies in your company integrate with this technology?
5.8. What are the intended benefits to be achieved by adopting this technology? And what were the actual results?
5.10. What are the risks if this technology goes wrong? How did you deal with it?
5.11. What factors that motived you to adopt this technology?
Could you elaborate it?
Smart
manufacturing technology
2.1. Could you describe the smart manufacturing project/program, and the implemented technologies?
2.7. Did you have all the resources necessary to make the project a success?
2.8. Were there any constraints/hurdles that you faced during the project, if so, could you elaborate?
Work design behaviour
1.5. How do you characterize the overall organizational culture and manager-employee interaction inside the company?
3.5. What ‘human’ considerations have you made in the project?
3.8. Have you involved the user of the new technology in the implementation project of the new technology, how?
3.9.b Question to ask in case of implementors of a technology:
What skill requirements? What specialized skills and
knowledges are required and how deep are them? Will people work in the same way as now? If not, what will be different?
4.5. How did the work design change of X due to the implementation of the technology?
