The effects of MES and M2M
communication on operators’ work design
Author: Nils Breijnaerts
(Pre-) Master Technology and Operations Management S4151364 +31625448041 n.j.breijnaerts@student.rug.nl Supervisor: Sabine Waschull s.waschull@rug.nl University of Groningen Faculty of Economics and Business
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Abstract
The purpose of this paper is to understand and elaborate on work design for production line operators in manufacturing companies in light of Industry 4.0 developments. Rapid developments cause uncertainties in development of work design. In this empirical research paper, the focus is on how work design changes for operators due to implementation of manufacturing executions system (MES) and machine-to-machine communication (M2M communication). A conceptual framework is proposed consisting of five work characteristics and the two smart manufacturing technologies MES and M2M communication. Four interviews at three case companies have been conducted for this research paper. Findings show that there are no generalizable descriptions of impacts on companies and operators. Within the three case companies, similar and different impacts occur on the operators’ job after implementation of MES and M2M communication.
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1. Introduction
The need for fast adaption of automation in industrial production requires radical advances in current manufacturing technologies and work design (Rojko, 2017). Industry 4.0, the fourth industrial revolution, will be driven by employment of digitization of manufacturing systems (Riordan et al., 2019). Industry 4.0 provides many opportunities for companies to actively shape the future of manufacturing technologies in their work design (Hermann, Pentek and Otto, 2015). Human-machine interaction pattern changes since machines are more and more autonomous. Not whole jobs of humans are affected but certain tasks will be affected (Rauch, Linder and Dallasega, 2020). To contribute to the literature on Industry 4.0 and work design, this paper gives insight on two of the existing smart technologies that influence work design characteristics. The focus of this paper is how manufacturing executions system (MES) and machine-to-machine communication (M2M communication) influences the operators’ job.
Companies experience difficulties when developing ideas or take actions on implementing Industry 4.0 in terms of work design (Hermann, Pentek and Otto, 2015). The importance for companies to properly design jobs is increasing, because the companies’ performance and quality could be negatively influenced by bad work design (Parker, Morgeson and Johns, 2017). Empirical research concerning the effects of smart manufacturing technologies and work design characteristics is limited. The research that has been done remains on a generic level. The effects of implementing smart manufacturing technologies on work design is still a subject of research today (Dalenogare et al., 2018). Understanding the key dimensions of smart manufacturing technologies will contribute to understand the underlying relationships to work design. Front-end technology enablers like MES and M2M communication are the disruptive technologies that trigger the transformation within companies (Almada-Lobo, 2016). The supposed contribution to theory and aim of this research paper is to get an understanding of how M2M and MES affect work design characteristics of the operators’ job. The research question that has been elaborated is as follows: “How do MES and M2M communication affect work design characteristics of production line operators?”
Main considerations in this research paper are how social, knowledge and task characteristics of work design are influenced by the changes caused by developments of smart manufacturing technologies M2M and MES. Work design changes for employees within a company are inherent to developments in automation. To find answers to the questions that will arise, a multiple case study will be done. Interviews will be done at several companies to get an overview of the companies’ struggles of implementing smart technologies while considering properly developed work design. Practically, the key findings of this research paper will contribute to help companies to get an understanding of how to balance development of machines and human work to create a satisfactory work environment for the operators. Also, companies will be more aware of the importance of work design since properly designed work could lead to better overall performance of a company (Morgeson and Humphrey, 2006).
3 and data collection activities are approached. Also, findings are listed and explained. At last, a discussion and conclusion sector is provided and recommendations for future research will be stated.
2. Theoretical background
In this research paper the smart manufacturing technologies machine-to-machine communication (M2M) and manufacturing execution system (MES) are chosen to investigate. It is interesting to investigate the linkage between work design and the implementation of the smart manufacturing technologies M2M and MES. The currently available literature dealing with the design of work systems in Industry 4.0 is limited (Kadir et al., 2019). According to (Cagliano et al., 2019) future research could be related to further clustering of different technologies on the basis of their different purposes and on the basis of associated tasks characteristics. Therefore, this research paper will investigate the relationships between M2M communication and MES, and work design. A conceptual model will be created to propose a relationship between the key components of M2M and MES and work design characteristics (social, knowledge and task based). The theoretical background is divided in three main topics; Industry 4.0, smart manufacturing technologies, and work design. The definitions used in this research paper are stated in the corresponding paragraphs.
Industry 4.0
Industry 4.0 is the future of industrial businesses that will build global networks to connect facilities, machines and warehouses as cyber-physical systems which are connected and controlled by each other intelligently, sharing information will trigger actions (Gilchrist, 2016). The literature draws the attention to the importance of people in Industry 4.0 and emphasizes the relevance of education, training and appropriate planning tools in order to build the necessary skills (Gorecky, Khamis and Mura, 2017) (Fiasche et al., 2016). Industry 4.0 consist of front-end technologies (e.g. MES, M2M communication, AI for maintenance) and base technologies (e.g. IoT, big data, cloud) (Frank, Dalenogare and Ayala, 2019). In this paper the focus will be on the front-end technologies M2M communication and MES since these are the first implementation and integration processes.
Smart manufacturing technologies
Smart manufacturing is an aspect of Industry 4.0, in fact it is a central element of Industry 4.0 (Kagermann, Wahlster and Helbig, 2013). Smart manufacturing considers the integration of the factory with the entire product life cycle and supply chain activities (Wang et al., 2016). It relies on the adoption of digital technologies to gather data in real time and to analyze it (Lee, Bagheri and Kao, 2015).
4 implementation and integration (Li et al., 2018). MES is the systems that operates between the ERP system and the M2M communication layer (Oztemel and Gursev, 2020). MES in its traditional sense is the mechanism to ensure the required vertical integration of processes within a company (Rozs and Ando, 2020). Employees will have to interact, monitor and control more complex technical systems. Which increases the need for knowledge on e.g. automation and IT (Waschull, Bokhorst and Wortmann, 2017). The main purpose of MES is to monitor, track and document the production process (Zayati et al., 2012). The nature of the technology will change the nature of the operators’ job (Wall et al., 1990).
M2M communication is an aspect of the automation stage of smart manufacturing, which is the second stage of implementation of smart manufacturing technologies (Frank, Dalenogare and Ayala, 2019). M2M is the industrial systems that interconnects and combines a mixture of sensors, actuators, logic components and networks to function (Gilchrist, 2016). M2M communication refers to direct communication between devices using any channel, wired or wireless (Biral et al., 2015). Under Industry 4.0 environments, M2M communication is poised to reshape various aspects of manufacturing, especially on operational efficiency, quality control and decision making (Oztemel and Gursev, 2020). These three aspects are of great importance for designing the operators’ job.
Work design
Work design leads to an array of individual and organizational outcomes. Humans and machines will work alongside each other more and more. To start work design, the chosen perspective should be clear. Is the work designed human-centric or techno-centric? In other words, is the work designed in a manner where the employee is the center of importance or is the work designed in a way that the machine is the center of importance? Techno-centric work design is more likely to fail (Clegg and Shepherd, 2007) because humans will gradually be moved away from their workplace. Therefore work design is an important topic to study since there is only limited literature available on the effects of smart manufacturing technologies on work design.
In general, developments in smart manufacturing technologies affect work characteristics. The aim is to find the key dimensions of smart manufacturing technologies that affect the social, task and knowledge characteristics. Below, the characteristics and effects as described by Morgeson and Humphrey (2006), and Parker and Grote (2020) will be compared and discussed. The three main characteristics are stated and described according to (Morgeson and Humphrey, 2006).
1. Social characteristics: social interaction and feedback from others 2. Task characteristics: autonomy and feedback from the job
3. Knowledge characteristics: job complexity, information processing, problem solving and skill variety
5 1. Job autonomy and control: having job autonomy enhances meaning and motivating at work which reduces absenteeism and fosters behavior such as performance, creativity and proactivity.
2. Skill variety and use: a well-designed job involves doing varied and meaningful tasks that make good use of people’s skills.
3. Job feedback and related work characteristics: job feedback is important for ensuring effective performance because it enables and support learning. It promotes knowledge of results which in turn enhances motivation.
4. Social and relational aspects of work: the consistent recognition of human needs for social connection. Social support, social contact, interdependence and contact with beneficiaries are inherent to job satisfaction, commitment and human performance.
5. Job demands: cognitive demands are changing due to new technologies, the latter strategy can create mentally stressful jobs because of the needs for sustained vigilance which is highly fatiguing. It aims at specifying design requirements for system interfaces.
To compare the concept of Parker and Grote (2020) with the concept of Morgeson and Humphrey (2006), it can be concluded that Parker and Grote (2020) have included the work design aspects that are becoming more and more important in the digitalized world of industries. Social and relational aspects of work, and job demands have become main characteristics and the associated variables are studied more in depth nowadays. However, still limited research is done in finding the relationships between work design and smart manufacturing technologies.
Conceptual model
6 Figure 1: Conceptual model task characteristics to work design
Relationships smart manufacturing technologies and work design
There is only limited empirical evidence on the possible effects of Industry 4.0 on work design. Especially, in the case of MES and M2M on work design. For this research, empirical evidence will be collected on the basis of case study interviews conducted at manufacturing companies to uncover the effects of MES and M2M on work design for operators.
The expected relationships in this research will be that M2M and MES have their own specific characteristics that will affect the operators’ job. However, in general it is expected that implementation of MES and M2M will fulfill or take over tasks of operators. This results in a changed task pattern for operators that could lead to changing job requirements.
On the basis of the five potential effects derived from Parker and Grote (2020), five expected effects of MES and M2M on work design are stated below.
1. Operators’ job autonomy and control will not change much since the operator gets a more and more process controlling task and therefore the operator gets more responsibilities. 2. The operator will need to be trained to keep up the skills needed to execute the job. Tasks
7 3. Feedback for the operator will come more and more from computer data than from colleague operators. The data from the machines and systems are factual and straight to the point, which makes it easier for the operator to see what should be improved.
4. Social aspects of the operators will decrease because the operator will get individualized tasks and therefore has to communicate less with colleague operators. Also, tasks given by managers will come through the computer and not face-to-face.
5. The operators’ job will become more mentally demanding than physically demanding. Stressful jobs will arise when operators take more than they can handle.
3. Methodology
This section of the research paper will verify the way this case study is done and how data has been collected. Also, the companies that have been assessed are briefly introduced and how these companies have been suitable for providing input to get answers for the research question. This chapter is divided in four paragraphs; research design, research setting, data collection and data analysis.
Research design
To investigate the relationships between smart manufacturing technologies and work design characteristics, a multiple case study is conducted (Eisenhardt and Graebner, 2007). This qualitative research setting is suitable for this research paper because a multiple case study research is particularly suitable for exploring a real-life phenomenon in-depth (Yin, 2009). The unit of analysis in this study is the production line operator within manufacturing companies. The focus on operators is chosen to specifically discover how they are affected by the implementation of smart manufacturing technologies. The research relies on the facts and figures provided by the case companies and literature of renowned recent journals. Limitation of this study is that only a small number of interviews and case companies have been addressed, which could affect the generalizability of this study.
Research setting
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Case A Case B Case C
Industry Manufacturing Manufacturing Manufacturing
B2B/B2C B2B B2B B2B
Type of products or services
Production of doors, windows and frames for houses and buildings Production and development of closures Production of non-standard window frames of plastics Number of employees ~400 ~100 ~100 Manufacturing technologies MES and M2M communication MES and M2M communication MES and M2M communication Position of interviewees Manager technical services, previously operator
Planner and logistics employee
Operator
Duration of
interview (minutes)
75 60 and 60 60
Table 1: Overview of selected cases
Data collection
The needed data for this research has been collected by conducting interviews at manufacturing companies. A general interview script has been made and this gives a clear semi-structured overview of key areas to assess during the interviews. The general interview script can be found in Appendix A. The main subject of the interviews is how do specific smart manufacturing technologies in a certain department of a company affect work design changes of operators. Validation of information gathered will be stronger when several persons are interviewed. Therefore, for this research paper three case companies have been addressed to conduct a total of four interviews. Interviews are done with operators, a technical services manager, a planner and a logistics employee. Every interview took about an hour to complete. Gathered data was analyzed by using only qualitative data. If quantitative data is provided by interviewees as extra information, this has not be used in this research paper. The interviews are transcribed in a word-by-word form. The main findings have been stated in chapter 4 of this paper.
Data analysis
9 quotes. The last column contain the link to collaborative activity which are the theme codes. Coding the interviews has been an iterative process. Only quotes from the interviews that are relevant for this research have been used in the coding scheme and the findings section. The analysis of the quotes gave insight on differences and similarities between cases.
4. Findings
This section is dedicated to present and elaborate on the findings and results from the interviews that were conducted at the case companies. For each of the five work design characteristics, it will be discussed how smart manufacturing technologies MES and M2M communication possibly affect the job of the operator.
Job autonomy and control
From analyzing the data, it becomes clear that the operators’ job autonomy has decreased as a result of the implementation of MES and M2M communication. These two smart manufacturing technologies take over physical tasks and the decision making process. This decrease of job autonomy took place at all three case companies. At case company A and C this decrease is stronger because they have implemented the smart manufacturing technologies earlier than case company B. Case company B has just implemented MES and M2M communication. However, the control that the operator has increases, as can be seen at case company A. The operator becomes more of a process controller that needs to possess a broader variety of skills and knowledge. Human interference within the production department is still of great importance for companies that implemented MES and M2M communication.
Table 2 below contains supportive quotes that provide clarity regarding the decrease in the operators’ job autonomy and control.
Case Role Supportive quotes from interviews
A Manager
technical services
"The operators perform way less manual activities and became more process controllers."
B Logisctics employee
"The factory is ready to relieve everyone of their work, so that we can focus more on the quality of the product. Now it is a lot of physical activities, such as sealing and processing the boxes, which will all be taken care of later by MES and the robot-crate-system (M2M)." C Operator "From engineering, the files are put in the system. This way we can see
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Skill variety and use
From analyzing the data, it becomes clear that the operators’ skill variety and use has changed significantly. The most important facet is that an operator has to possess a wider variety of skills after the implementation of MES and M2M. Also, the wider variety of skills asks for a better understanding and knowledge. Therefore, the expected level of thinking has increased. The operator needs more and more training and education to keep the job. The skills of operators from earlier days differ a lot form the skills of operators today, especially after implementing smart manufacturing technologies like MES and M2M communication. However, this argument is only valid for case company A. At case company C, the interviewed operator indicates that his job is more standardized and that task are being rotated through the week so that operator do not have to do the same task every single day.
For case company B a planner has been interviewed, and he states that he needs lesser skills because the system takes over the more difficult tasks and gives a better insight on how much time orders are going to take. It could be concluded that the planners’ job becomes easier and the operators’ job becomes more difficult and demanding.
Table 3 below contains supportive quotes that provide clarity regarding skill variety and use in the operators’ job.
Case Role Supportive quotes from interviews
A Manager
technical services
"The tasks of operators are still as important as they were 30 years ago. Without them we would just not make any products so they are also a vital group of employees for our company."
B Planner "I think you need less skills. The system now indicates well how long an order will take, if we use the system longer, the system will also predict better based on previous orders."
C Operator "Actually, I do more of the same work now and that is rotated throughout the week. One day I am preparing, the next day I am in the middle area and so on."
Table 3: Supportive data skill variety and use
Job feedback
11 Table 4 below contains supportive quotes that provide clarity regarding job feedback in the operators’ job.
Case Role Supportive quotes from interviews
A Manager
technical services
"The MES and M2M system have been implemented in 2006 with the main reason of quick and efficient control of machinery. And to obtain data to, for example, give operators feedback."
"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 they do their jobs correctly."
B Logistics employee
"In the future, more feedback can be obtained from MES. MES does not provide the feedback itself, but MES does provide the
information from which feedback can be obtained."
C Operator "So without those challenges I wouldn't have been able to keep myself motivated here for long."
Table 4: Supportive data job feedback
Social and relational aspects
Social and relational aspects are changing slightly when smart manufacturing technologies are implemented. At case company A, the operators have low interaction. The more experienced operators help and train their new colleagues to learn the job, thus there still is social interaction. Due to M2M communication, the operators do not have to speak to each other anymore to spread information about machinery or products. They can now find information they need in a digital system. Also, foremen and planners can stay in their departments when they need data. Data is provided through MES and is accessible to anyone who needs certain data. Above mentioned statements also are valid for case company B and C, interaction with colleagues has decreased so the social and relational aspect of the job has decreased.
Table 5 below contains supportive quotes that provide clarity regarding social and relational aspects of the operators’ job.
Case Role Supportive quotes from interviews
A Manager
technical services
"Interaction amongst operators is relatively low in production."
"More experienced operators of course help their inexperienced colleagues to learn about machinery or products."
12 C Operator “You look for interaction in your work somewhere else, informal
talking with colleagues and so on." Table 5: Supportive data social and relational aspects
Job demands
Due to the smart manufacturing technologies cognitive demands are changing. Mentally stressful jobs can be created, the need for sustained vigilance is highly fatiguing for operators. At case company A, the education and training of operators has a higher priority so that they can analyze more and more complex processes. At case company B, system interfaces that indicate for example errors are designed to take this stressful and fatiguing factors away from operators. At case company C, MES and M2M communication are designed to make the job clear and as simple as possible for the operator. Smart manufacturing technologies MES and M2M communication at case company B and C are being implemented with the thought of making the operators’ job easier. At case company A this is not happening, in this case MES and M2M make the job for operators more difficult because the systems are aimed at producing as efficient as possible without being concerned about work design of operators.
Table 6 below contains supportive quotes that provide clarity regarding job demands in the operators’ job.
Case Role Supportive quotes from interviews
A Manager
technical services
"Now that the machines are very difficult and highly automated, a lot of unknown errors could occur."
"The education level of an operator needs to be higher and higher. The operator needs to analyze more and more complex processes." B Logisctics
employee
"Soon my tablet will indicate that the sensor is broken, for example. I immediately know where the problem is. There is no longer any doubt about it, now we do not know where the error is. Soon the technical service will also know exactly where the problems are."
C Operator "Although they are reasonably designed to make it as clear and simple as possible for people. However, maintenance is something you have to learn. You must of course know what to look for in the machine and be able to recognize where, for example, a malfunction comes from." Table 6: Supportive data job demands
5. Discussion and conclusion
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Interpretation of results
As can be seen in the findings, it depends on the company how the smart manufacturing technologies MES and M2M communication affect the operators’ job. Predicted in the conceptual model was that the smart manufacturing technologies MES and M2M communication would have generalizable effects on work design characteristics of the operators’ job. After insights were gained of the effects, it turns out that there is no clear path to follow for creating a satisfying work design for the operators. For each of the five work characteristics addressed in the research there are similarities and differences in how the operators’ job has been affected. Thus, there are no generalizable effects of smart manufacturing technologies on work design. This is also emphasized by (Parker and Grote, 2020), they stated that there is no predetermined effect of Industry 4.0 on work design.
As can be seen in recent literature, the use of the term “operator 4.0” is arising (Romero et al., 2016; Zolotová et al., 2020). It can be seen as a typology for exploring a set of key technologies that can support the development of human-automation symbiosis work systems in the future Industry 4.0 framework (Romero et al., 2016). Due to implementation of M2M communication, the social and relational aspects of work have decreased for all three case companies. At company C the operators’ job, in terms of skill variety and use, has become easier. At company A the operators’ job has become more complex after the implementation of MES and M2M communication.
With MES, there is a reduction of non-conformity products in the manufacturing process. Also, machine setup times are reduced by automatically setup equipment (Neves et al., 2015). This is confirmed in this research paper for company A. MES also support in the operators’ activities through the provision of electronic work and documentation that guide them throughout the production flow (Perico et al., 2019). MES also improved the data collection for providing feedback to the operator. This extraction and analysis of information is crucial for the social learning process (Martinsen, Downey and Baturynska, 2016).
Concluding, this research paper was dedicated to answer the following question: “How do MES and M2M communication affect work design characteristics of production line operators?” Based on the three case studies that have been elaborated, there is no straight forward answer available that applies to all three case companies. The findings show that there are similarities and differences between the addressed companies in how the operators’ job has been affected.
Implications for theory
14 previous research. However, in the research there are also contrasting findings between case companies. The desired satisfying work design for the operator can be achieved by keep acquiring knowledge on how to develop work design. For future research it is therefore advised to keep doing multiple case study researches with a decent amount of interviews and case companies. When more research is done the generalizability of findings and results will be better founded.
Implications for practice
This research paper can be used by companies to become aware of the fact that work design for operators is important when smart manufacturing technologies are being implemented. The changes caused by the smart manufacturing technologies influence the tasks and responsibilities for the operators. Also, motivation and performance of the operators could be negatively influenced when not enough attention is given to the human aspect of implementing smart manufacturing technologies. As described by (Clegg and Shepherd, 2007), the job should be designed in a manner where the employee is the center of importance. If the focus is too much on the technological perspective, implementation of smart manufacturing technologies is more likely to fail.
Critical reflection of study
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Appendix A
Sectie 1: Achtergrond informatie
1.1 Wat is jouw rol in het bedrijf, en wat zijn jouw taken en verantwoordelijkheden? 1.2 Kun je het bedrijf kort beschrijven wat betreft:
• Industrie
• Producten en diensten • Type markten (B2B/B2C) • Aantal medewerkers • Type productie proces
Sectie 2: Informatie over de technologieën, MES en M2M
2.1 Kun je de fabricagetechnologie MES en M2M beschrijven en nader ingaan op:
- de setting / context waar ze werden geïmplementeerd (vervanging of uitbreiding van welke processen, activiteiten)?
2.2 Op welke afdelingen en functies hebben deze technologieën betrekking?
2.3 Wat waren / zijn de belangrijkste doelstellingen en motivaties voor het adopteren van MES en het adopteren van M2M?
2.4 Hoe beoordeel je een goed ontwerp en / of implementatie van de technologie?
2.5 Wat zijn de uiteindelijke succescriteria (van de implementatie)? Hoe worden deze behaald?
Sectie 3: Wijzigingen in functieontwerp beoordelen
Algemene vragen over de verwachtte veranderingen in functieontwerp van operators
3.1 Hoe kan het werk van operators veranderen wanneer de technologie van MES en M2M aanwezig zijn? Zal de operator op dezelfde manier werken als nu?
3.2 Per verwachte verandering van 3.1: Wat zijn de onderliggende mechanismen voor de
verandering: hoe denk je dat de technologie MES en M2M precies tot de verandering zal leiden? 3.3 Wat zijn de nieuwe activiteiten die de operator zal moeten uitvoeren vanwege de
implementatie van MES en M2M?
19 3.5 Zal de taak van de operator cognitief veeleisender of eenvoudiger zijn, waarom?
3.6 Zou je zeggen dat de taak van de operators wordt vereenvoudigd of verrijkt als gevolg van MES en M2M, zo ja waarom?
3.7 Moeten operators nieuwe vaardigheden ontwikkelen als gevolg van MES en M2M? Welke type vaardigheden?
3.8 Zullen mensen in dezelfde teams werken, in dezelfde rollen of in verschillende rollen? 3.9 Zullen er nieuwe medewerkers worden aangenomen vanwege een wijziging in vereiste vaardigheden? Welk type werknemers? Zo ja, kunt je dit nader toelichten?
Inzoomen op de veranderingen van de belangrijkste dimensies van werk
Autonomie (timing, methode en besluitvorming):
In hoeverre verandert het niveau van autonomie van de operators?
Welke controle heeft de operator met betrekking tot de nieuwe technologie MES en M2M? Zullen de technologie MES en M2M de keuzevrijheid van operators veranderen bij het beslissen over hoe ze hun werk plannen? Hoe verandert dit?
Zullen operators nog steeds de controle hebben en veel beslissingen nemen?
Hoe zullen de nieuwe technologie MES en M2M de vrijheid veranderen die operators hebben om te beslissen hoe ze hun werk doen (bijvoorbeeld welke methode wordt toegepast)?
Vaardigheden variëteit:
Zal de verscheidenheid aan vaardigheden van operators die nodig zijn om hun werk te doen veranderen? Waarom?
Taak variëteit:
Hoe zullen de taken van operators veranderen door de implementatie van MES en M2M? Waarom?
Welk type taken worden vervangen / gecreëerd / uitgebreid?
Interactie met andere werknemers:
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Afhankelijkheid:
Hoe zullen MES en M2M de manier veranderen waarop een operator afhankelijk is van het werk van anderen voor de voltooiing van hun werk? Waarom?
Hoe zullen MES en M2M de manier veranderen waarop andere taken rechtstreeks van de operator afhangen? Waarom?
Feedback van de functie:
Zal de operator meer en betere feedback krijgen van de machine / het systeem over zijn prestaties? Waarom?
Feedback van anderen:
Verandert MES en M2M de hoeveelheid feedback die de operator van zijn collega's / managers krijgt? Waarom?
Functie eisen:
In hoeverre veranderden de functie-eisen van operators door MES en M2M? Waarom?
Probleemoplossing
In hoeverre heeft MES en M2M de manier veranderd waarop operators creatief moeten zijn en problemen oplossen die nog niet eerder zijn opgetreden? Waarom?
Taak significantie:
In hoeverre is de taakbetekenis veranderd? (d.w.z. hebben de resultaten van het werk invloed op het leven van anderen?) Waarom?
Sectie 4: Afronden interview
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Appendix B
Coding scheme (partially as example): Link of smart
technology to work design
Data reduction (first-order codes) Descriptive code (second-order categories) Link to collaborative activity (third-order themes) Job autonomy and control
"Als een order wordt ingevoerd maakt het systeem de afweging of het te behalen is." (Case B, planner)
Performance Autonomy
"Bij de weekstart krijgen we een inzage over de week prestaties, die komen voort uit de data die het systeem bijhoudt." (Case C, operator)
"Het is niet zo dat mijn creativiteit is afgenomen of toegenomen." (Case B, planner)
Creativity
"The operator needs to be creative in sense of solving issues with machines and issues with products that fall outside specifications." (Case A, manager technical services)
