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Master Thesis
Technology and Operations Management
Lean 4.0:
Overcoming challenges in JIT production with the integration of an Industrial-Internet-of-Things
AngelosKakavoulas:s3873838
E-mail:[email protected]
WordCount:11.789
Supervisors:dr.ir.T.Bortolotti,dr.N.B.Szirbik
January2020
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Table of Contents Abstract 3
Preface 4
1. INTRODUCTION 5
2. THEORETICAL BACKGROUND 7
2.1 Lean and Just-In-Time 7
2.2 Drivers and challenges for JIT 8
2.3 Industry 4.0 and Internet of Things 9
2.4 Drivers and Challenges of IoT 11
2.5 Just in Time 4.0 13
2.5.1 Internet of Things and Just in Time manufacturing 14
3. METHODOLOGY 16
3.1 Research Design 16
3.2 Case Selection and Description 16
3.3 Interviewee Selection and Data Collection 17
3.4 Data Analysis 18
4. FINDINGS 20
4.1 Company A 20
4.2 Company B 21
4.3 Company C 23
4.4 Company D 24
4.5 Company E 25
4.6 Company F 26
4.7 Cross-case analysis 28
5. DISCUSSION 29
5.1 Internet of Things and Predictability/Production Readiness 29
5.2 Internet of Things and Manufacturing Flexibility 30
5.3 Internet of Things and Supplier Reliability 31
5.4 Challenges: Planning and Implementation 31
5.5 Challenges: Technical integration and operations 32
5.6 Challenges: Data Integrity and Security 32
5.7 Challenges: Employees 33
6. CONCLUSION 36
6.1 Limitations and directions for future research 36
7. REFERENCES 38
8. APPENDICES 46
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Abstract Objective: The purpose of this study is to examine the influence of an Industrial Internet of
Things on lean firms that operate under a just-in-time philosophy. Building on existing work
on interrelationships between JIT and IoT, it asks to what extent can an IoT implementation
help in overcoming JIT production challenges, as well as what challenges are anticipated after
this implementation.
Methodology: A multiple case study was conducted in the Dutch manufacturing sector. Data
was collected through semi-structured interviews, along with production-plant observations
during company visits.
Results: The results indicate that the Internet of Things withholds technological capabilities
that aid in overcoming certain main challenges of just-in-time production. Additionally, IoT is
a concept bearing substantial challenges that further intensify when combined with JIT
techniques.
Added Value: This research adds valuable insight to the limited literature on potential
opportunities and the anticipated challenges in manufacturing after a JIT-IoT synergy. Internet
of Things is a recent technological trend and its implications, especially on a just-in-time
production are still not adequately explored. This study tries to close this gap.
Keywords: Industry 4.0 (I4.0); Lean; Just in Time (JIT); Internet of Things (IoT);
Challenges; Communication; Interconnection;
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Preface I wish to express my sincere gratitude to dr. ir. T. Bortolotti and dr. N.B. Szirbik for their
guidance and their constructive feedback throughout this entire project. The completion of
this study would not have been possible without their expertise. Last but not least, a debt of
gratitude is also owed to my friends, family and especially my parents – Nikolaos and
Theofani; without your support, none of this would indeed be possible.
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1. INTRODUCTION
The rapid economic and technological developments in today’s market require manufacturing
companies that operate in a responsive, adaptable and agile manner (Kumar & Vaishya, 2018;
Schumacher et al., 2016). A widely used approach by companies seeking to operate more
efficiently is Lean production. Its target is to eliminate waste by accurately managing variability
of supply, processing time and demand (Shah & Ward, 2007). One of Lean’s main practices
that aids in delivering products according to market demand while minimizing misuses is Just-
In-Time manufacturing (Mrugalska & Wyrwicka, 2017). During the 1990s, initial efforts of
adopting automation within JIT production systems began (Hoque, 2000; Kolberg & Zühlke,
2015), though such immerse technological changes hinted unanticipated issues (Smeds, 1994).
In 2011, a new approach, which also enhances production systems surfaced: Industry 4.0, also
known as the fourth industrial revolution. I4.0 enables different forms of digitization through
cyber-physical systems in the industrial setting in order to optimize value chains (Kolberg &
Zühlke, 2015; Schumacher et al., 2016). The main reason for the emergence of I4.0 is the
Internet of Things (Kagermann et al., 2013; Thoben et al., 2017), which enables the digital
interconnection and communication between cyber-physical systems.
The potential combination of JIT with I4.0 tools has been examined by researchers. Recent
studies acknowledge several I4.0 tools that have a positive impact on Just-in-Time activities
(Wagner et al., 2017). For example, by adopting smart automation technologies, JIT techniques
can be carried out more smoothly, enabling more optimization capabilities for scheduling and
planning, avoiding overproduction and digitizing several daily tasks (Powell et al., 2018). The
combination of just-in-time activities with I4.0 tools seems to provide certain perks such as
increased transparency, shorter lead times and higher flexibility (Mayr et al., 2018). More
specifically, in-time delivery of products, travel-route optimizations and lead time reduction,
which are JIT objectives for the whole supply chain process, can be further enhanced by
utilizing several IoT technologies like Radio-Frequency Identification and Electronic Product
Code (Caballero-Gil et al., 2013).
Communication (machine-to-machine, human-to-machine), which is provided by the Internet
of Things, is considered as a very important step for further improvement and utilization of
Industry 4.0 within JIT operations (Leyh et al., 2017). However, this vision has a high influence
on manufacturing processes (Hofmann & Rüsch, 2017; Schumacher et al. , 2016), and it will
be a challenge for companies to transit into it, as its implementation hints substantial risks and
barriers (Hofmann & Rüsch, 2017; Macurova et al., 2017) that are still unsolved or not
adequately addressed (Leyh & Martin, 2017). Moreover, even though certain IoT tools can
enhance JIT activities (Mayr et al., 2018; Wagner et al., 2017), the success of JIT production
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also requires overcoming challenges associated with management and manufacturing practices
(Jadhav et al., 2014). Studies lack focus on whether the existing challenges and issues of Just-
in-Time are addressed, after implementing IoT technologies.
Literature research presents certain limitations. Most studies (Dombrowski et al., 2017; Kolberg
& Zühlke, 2015; Mrugalska & Wyrwicka, 2017; Wagner et al., 2017) address the overall impact
of I4.0 on Lean production and they only discuss and hypothesize on a conceptual level.
Empirical research of relationships between specific Lean and I4.0 tools (JIT-IoT) seems to be
scarce. Caballero-Gil et al. (2013) proposed the application of IoT technologies for JIT delivery
within the whole supply chain process. Xu & Chen (2016) developed an IoT-based production
scheduling framework for a JIT automotive manufacturing environment. However, the
aforementioned studies are theoretical, and their applicability is still uncertain. The purpose of
this research is to provide an empirical analysis of potential conflicts when implementing IoT
technologies within a JIT manufacturing environment. Furthermore, it will examine if IoT
technologies can address the challenges of JIT manufacturing. The main research questions to
be answered are:
RQ1: “To what extent can the implementation of Internet-of-Things technologies solve existing issues within JIT initiatives?”
RQ2: “What are the challenges of integrating an Industrial Internet of Things within a JIT
production environment?”
The report is structured as follows: Chapter 2 addresses the theoretical background of the study.
The third chapter explains the methodology used to conduct this research. In chapter 4, the
results are presented and in the 5th chapter, the theoretical and managerial implications of this
study are discussed. The last chapter contains conclusions, limitations and future suggestions
for this research.
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2. THEORETICAL BACKGROUND This chapter is initially divided into two sections: Lean and Industry 4.0. The historical
background, as well as definitions and descriptions of the terms and their two corresponding
main tools (Just-in-Time and Internet of Things) are described. Secondly, the purpose of
adopting these tools is addressed along with their challenges. Lastly, the focus is pointed on the
combination of JIT and Industry 4.0 tools, leading to a deeper look at studies that address the
implementation of IoT tools in order to overcome JIT challenges.
2.1 Lean and Just-In-Time Lean production is derived from the Toyota Production System (TPS). It is considered as a
“toolbox” that contains different practices, such as quality management programs, planning and
scheduling strategies, maintenance optimization, continuous improvement programs and just-
in-time production (Shah & Ward, 2003). Mrugalska & Wyrwicka (2017, p.466) define Lean
as “a production system that is oriented on learning of organization through continuous
improvements” while aiming at “reducing unnecessary variations and steps in the work process
by the elimination of waste”. Early academia highlighted the shift of Lean from just a set of
tools and practices to a system that requires the appropriate human behaviour in order to be
successful (Bicheno & Holweg, 2016; Plonka, 1997). However, recent authors address Lean as
a more complex socio-technical system and its constructs are still under investigation
(Marksberry et al., 2011; Shah & Ward, 2007; Soliman & Saurin, 2017). Researchers and
practitioners seem to agree that overall, the aim of Lean is waste reduction, value enhancement
and human involvement.
An integral part of Lean is Just-In-Time (JIT) manufacturing. JIT is a management strategy that
directly aligns suppliers' raw material orders with manufacturing schedules. This approach is
used to boost effectiveness and reduce waste by producing
the required items in the required amount at the appropriate moment (Sugimori et al., 1977),
which decreases inventory expenses (Monden, 2011). Several practices like the pull/Kanban
scheduling system, smoothing of production and preventive maintenance are required in order
to successfully implement JIT (Davies, 1989; Monden, 2011; Schonberger, 1983; Sugimori et
al., 1977). Different studies showed that JIT (Huson, 1995; Shah & Ward, 2003; Womack &
Jones, 1996) and even specific JIT manufacturing sub-tools (Fonseca & Alves, 2010; Rewers
et al., 2017) have positive impacts on firms’ performance. Some contradicting conclusions also
emerged, supporting that JIT does not have a direct effect on performance, but is a way of
improving the overall organizational infrastructure (Sakakibara et al., 1997). Further studies
revealed that within a non-repetitive context, where market demand is unstable, the effect of
JIT on performance can be neutral or even negative (Bortolotti et al., 2013).
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2.2 Drivers and challenges for JIT The achievements of Toyota encouraged several organizations to embrace different methods of
Lean (Bortolotti et al., 2015; Liker & Rother, 2011), including Just-in-Time initiatives. A wide
variety of JIT implementation drivers has been analysed in theory. Certain reviews in the field
indicate external (e.g. competition, customer satisfaction, market needs) and internal (e.g. firm
strategy) reasons (Fullerton & McWatters, 2001; Hallgren & Olhager, 2009; Nordin et al.,
2013; Singh, 2017), while a more detailed look on the operational parameters that motivate
firms into adopting JIT practices showed that proper outputs, increase of productivity, lower
manufacturing lead times and lower inventories are the main objectives that industries seek to
accomplish by incorporating JIT techniques (Ghosh, 2013; Golhar, Stamm, & Smith, 1990;
Schonberger, 1983; Staudacher & Tantardini, 2007). Sandanayake et al. (2008) used
mathematical modelling to determine three main key drivers for adopting JIT: Line balancing,
setup time reduction and multifunctional employees. According to Crute et al. (2003), one of
the key drivers for using just-in-time management (along with other lean tools) in the aerospace
industry was the reduction of lead times, following an unpredictable change in the demand of
civil aircrafts.
However, Just-in-Time production is not an easy task. JIT implementation challenges can be
related to management or manufacturing practices. Resistance and difficulty to change, lack of
understanding, lack of top management involvement and incompetence of workforce are the
most common “soft” blockages of adopting JIT practices (Čiarnienė & Vienažindienė, 2013;
Höök, 2008; K. Wyrwicka & Mrugalska, 2015; Netland, 2016; Salonitis & Tsinopoulos, 2016).
After implementing JIT practices, it is essential that workers remain motivated and organized
towards quality and improvement (Höök, 2008). This may correspond to the fact that by
focusing mainly on processes and performance might lead to workforce-related issues like lack
of enthusiasm or lower productivity (Veech, 2004). Shah & Ward (2003) stated that plant size
and age determine the ability of a firm to implement JIT techniques. More specifically, their
results showed that older plants are less capable of implementing JIT techniques, while larger
plants are more adept at using almost all Lean practices. Jadhav et al. (2014) also state that the
layout of the plant needs to be flexible in order to avoid process related defects. JIT demands
careful production planning. For example, if supplier and manufacturer are not properly aligned
within a JIT supply philosophy, delays in deliveries can occur, partly reducing the benefits of
JIT on delivery performance (Jadhav et al., 2014).
For JIT-focused firms, manufacturing success depends on suppliers because it requires on-time
material delivery in small batches. A reliable supplier delivers small quantities more often, but
also has to maintain quality of the product (Huson, 1995). It can take a long time to develop a
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supplier that fulfils these requirements when a mature supplier fails to deliver on time (Xu &
Chen, 2016). Furthermore, proper JIT facilitation requires the plant to move towards producing
smaller batches, and reducing setup times (Matsui, 2007). Moreover, the inability to respond
quickly to changes in product design, large demand volumes or scheduling changes may lead
to a slow response to market (Jadhav et al., 2014). In addition to that, further threats of JIT
might address potential conflicts of the market (e.g. demand change) with standardized
procedures (e.g. parts commonality) (Elmoselhy, 2013). Gann (1996) addressed that flexibility
is reduced when the industrialized product does not correspond to the customer expectations.
Therefore, the challenge is to recognize how to effectively combine both the designing and
manufacturing processes (Ballard & Kim, 2007). Another crucial challenge of JIT is to possess
accurate forecasting systems, in order to identify when the demand is lower and the customer
orders are subject to profound fluctuations (Jadhav et al., 2014). Table 2.1 illustrates the
aforementioned barriers and threats of JIT implementation within a manufacturing
environment. The study of Jayaram et al. (1999) opened the doors for classifying the challenges
into the two perspectives.
Category Main Challenges Literature
Management practices
• Lack of top management involvement, commitment and support
• Workforce incompetence (lack of training, education)
• Resistance to change • Supplier reliability • Workforce motivation • Cross-functional cooperation
(Čiarnienė & Vienažindienė, 2013;
Höök, 2008; Jadhav et al., 2014; Netland, 2015;
Veech, 2004; Wyrwicka & Mrugalska, 2007; Huson,
1995)
Manufacturing performance
● Plant age and size ● Slow response to market ● Poor forecasting ● Reduce setup times ● Poor planning and facility layout ● Flexible manufacturing system
(Ballard and Kim, 2007; Elmoselhy, 2013; Gann,
1996; Jadhav et al., 2014; Shah & Ward, 2003; Y. Xu
& Chen, 2016; Matsui, 2007)
Table 2.1: JIT implementation and production challenges
2.3 Industry 4.0 and Internet of Things Research works have shown that Lean processes can be further enhanced by integrating
Industry 4.0 (Singh, 2017; Tortorella & Fettermann, 2018; Wagner et al., 2017). Industry 4.0
is a strategic action; initiated in 2011 for upgrading the manufacturing industry of Germany.
Kagermann et al. (2013, p.18) highlight that Industry 4.0 includes “vertical networking, end-
to-end engineering and horizontal integration across the entire value network of increasingly
smart products and systems”. The term Industry 4.0, which is still ambitious but realistic, relates
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to a wide spectrum of concepts (Lasi et al., 2014) which makes it even harder to identify its
main components. Researchers’ views are different about the elements that make up Industry
4.0 (Buer et al., 2018). Recent reviews tried to scan the literature in order to classify them.
Hermann et al. (2016) identified Cyber-Physical Systems, the Internet of Things, Smart Factory
and the Internet of Services, while other reviews (Alcácer & Cruz-Machado, 2019; Vaidya et
al., 2018) refer to a wider range of main key technologies (often stated as the “nine pillars” of
I4.0), which include: The Industrial Internet of things, Cloud computing, Big Data Analytics,
Simulation, Augmented Reality, Additive Manufacturing, Systems Integration, Autonomous
Robots and Cybersecurity.
The introduction of the IoT and CPS into the manufacturing environment is the main factor that
guided the emergence of this 4th Industrial Revolution (Weyer et al., 2015). The Internet of
things addresses to a recent technological concept with its central idea to be the communicative
presence of a variety of things among us. Through their unique patterns, these “things” are able
to interact and collaborate with each other in order to reach a common objective (Atzori et al.,
2010). Until now, the Internet of Things, which is widely adopted in transportation, healthcare
and utilities (Sezer et al, 2018), has been under investigation by researchers in order to
understand its wide variety of solutions and their platforms (Mineraud et al., 2016; Perera et
al., 2015). In regard to the design architecture, several authors segregated IoT into different
layers in order to identify and classify the main technologies (Alcácer & Cruz-Machado, 2019).
The most common layers noticed within multiple studies (Atzori et al., 2010; Sadiku et al.,
2017; Sezer et al., 2018; Trappey et al., 2017; L. Da Xu et al., 2014) are:
1. The “perception” or “sensing layer” to allow wireless systems control and collect
information among different devices, sensors or RFID tags.
2. The “network layer”, which processes information and allocates it to the upper layers
(e.g. USB, Bluetooth, NFC)
3. The “service” or “computation layer” depicts ways of receiving and processing
information. It consists of hardware, software, algorithmic, cloud and encrypted
platforms.
4. The “application layer” describes the Internet of Things and its applications in various
sectors.
According to Caballero-Gil et al. (2013), IoT technologies help in advancing every aspect of
supply chain management, improve demand management, customization, and automatic
replenishment of products. The authors claim that the extensive use of IoT technologies has
been possible due to components miniaturization, overcoming mobile telephone infrastructure
limitations and the creation of applications that use the information from the IoT.
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The emergence of such a fruitful trend is also expected to have an impact on the industrial field,
where industries are willing to use IoT technologies in order to develop new industrial
applications and enhance the role of existing manufacturing systems (Xu et al., 2014). In fact,
during the past decades, the trend to integrate more electronics into production systems resulted
in the merging of the Internet of Things with classical production engineering, automation and
computation systems, which is also known as the Industrial Internet of Things (Sadeghi et al.,
2015).
2.4 Drivers and Challenges of IoT Similarly to the paradigm of consumers, who were confronted with the internet in the early
1990s, the Industry 4.0 phenomenon seems to be unavoidable (Drath & Horch, 2014).
Companies, in order to deal with rapid decision-making for improved productivity and big data
issues will inevitably follow I4.0 trends (Lee et al., 2014). The implementation of an Industrial
Internet of Things has a significant impact on industrial value creation (Müller et al., 2018).
Authors have pointed out that the IoT is still a relatively new research topic and academic
literature lacks adequate drivers and challenges (Müller et al., 2018; Stentoft et al., 2019). The
main opportunities of an IoT found in literature and business reports are classified into the
notions of Strategy, Operations and People.
Strategy
From a strategic perspective, research and practitioners agree that IoT has significant
implications on competitiveness (Geissbauer et al., 2016; Kagermann et al., 2013; Kiel et al.,
2017; Türkeş et al., 2019), either by differentiation from expanding the market share and
gaining advantage based on innovative offerings (Kiel et al., 2017), or by imitating existing
competitors that practice similar techniques (Geissbauer et al., 2016).
Operations
From an operational perspective, the Industrial Internet of Things is addressed to promote
process optimization, even before its practical implementation. This is basically due to virtual
simulations of production activities (Müller et al., 2018). Short lead times and faster time-to-
market (Kiel et al., 2017; Lasi et al., 2014; Meyer et al., 2011; Moeuf et al., 2018; Wee et al.,
2015), increased efficiency (Kagermann et al., 2013; Kiel et al., 2017; Otles & Sakalli, 2019;
Renjen, 2018; Türkeş et al., 2019), lower costs (Blanchet et al., 2014; Colotla et al., 2016;
Geissbauer et al., 2016; Kiel et al., 2017; Lee et al., 2014; Moeuf et al., 2018; Wee et al., 2015),
high product quality (Kagermann et al., 2013; Kiel et al., 2017) and overall equipment
effectiveness (Kiel et al., 2017) are the main operational drivers for IIoT.
People
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From a human perspective, IoT tools provide workforce supervision systems, better human-
robot collaboration and virtual training opportunities (Park & Huh, 2018), while promising to
take care of the repetitive and monotonous tasks that decrease satisfaction and motivation of
workers (Müller et al., 2018).
The concept of Industry 4.0 entails necessary changes in the operational processes of companies
(Maresova et al., 2018). However, the actual implications of an Industrial Internet of Things
are still uncertain since scientists and professionals provide conflicting statements on its
potential challenges (Kiel et al., 2017). From the literature, customized categories with the main
IoT challenges are developed (Table 2.2). Their prime influence is on planning/implementation,
operations, security and employees.
Category Main Challenges Literature
Planning and Implementation
• Develop specific use cases • Estimate uncertainties and expected
value • Coordination of different parties • Interdisciplinary communication
(Brettel et al., 2014; Kiel et al., 2017; Schneider, 2018)
Technical integration and
Operations
• Reliable functionality of IoT tools (i.e. RFID)
• Translate demand into effective solutions • Open hardware-software architectures • Proper implementation paths • Immature technologies
(Brettel et al., 2014; Kiel et al., 2017; Rong
et al., 2016)
Data Integrity and Security
• Internet risks (Cyber-attacks) • Data safety and system reliability
(Kiel et al., 2017; Sadeghi et al., 2015;
Wan et al., 2016)
Employees • Adequate employee training • Lack of expertise • Evaluate the impact on working life
(Kiel et al., 2017; Maresova et al., 2018;
Müller et al., 2018; Schneider, 2018)
Table 2.2: IIoT implementation and production challenges
Planning/Implementation
A major challenge of the industrial internet is to transfer its general potential into specific
projects and initiatives that aim to improve a distinct process, increase the productivity of a
specific machine (Schneider, 2018) or properly utilize product-specific knowledge (Brettel et
al., 2014). Adding to that, managers have to forecast the uncertainty and expected added value
in advance in order to decide which investments are supported and when is the right time to
invest (Schneider, 2018). Although, since such implications are linked with large investments
into IIoT technologies, skilled workers etc., a precise evaluation of profitability remains fuzzy
(Kiel et al., 2017). The authors also state that interdisciplinary communication requires a
coherent and deep understanding of the IIoT. As simultaneous production and process planning
may be required to improve product quality and reduce time to market, interdisciplinary
coordination of different parties seems like a demanding task (Brettel et al., 2014).
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Technical integration and operations
The industrial internet is viable only if machines communicate and commodities are tracked.
RFID tags, which can track products may impose technological challenges, as their reliable
functionality can deteriorate under the presence of water or big amounts of metal (Brettel et al.,
2014). Kiel et al. (2017) addressed that immature technologies could be a threat to product and
process quality. Also, there are certain obstacles to overcome when interfering with
conventional production systems (Brettel et al., 2014), as new technologies to be implemented
into the existing industrial infrastructure should not replace assets immediately or interfere with
the workflow (Schneider, 2018). Furthermore, customer demand needs to be translated into
effective solutions with the proper use of hardware and software I4.0 tools. Effective process
control requires that the implemented IoT systems have a common communication protocol
and architecture with high bandwidth and low latencies. Therefore, it is a critical task to develop
cross-platform architectures, as new technologies such as 5G are still under development (Rong
et al., 2017).
Security
The generated data by IIoT has a significant value. Thus, the secure management of the devices
set up in the IIoT and theft prevention of the data are great challenges (Wan et al., 2016). Cyber-
attacks on IoT systems are crucial as they can cause serious damage to software, hardware and
even threaten human lives (Sadeghi et al., 2015). Data exchange carries vulnerabilities and
threats such as cybercrime victimization, unauthorized access to information and industrial
spying (Kiel et al., 2017).
Employees
Employees, in order to be eligible to plan and coordinate processes, require adequate training
and development approaches (Kiel et al., 2017). Maresova et al. (2018) pointed out the
increasing need for intelligent factory operators and the education process required for their
training. Therefore, training and development of employees should be driven towards specific
IoT-related skills and capabilities (Müller et al., 2018). It is essential to understand the impact
of the industrial internet on the workplace, the role of humans in it, and forecast (in favour of
the company) how task range, task depth and task content can alter (Schneider, 2018).
2.5 Just in Time 4.0 Just-in-time manufacturing, by nature (as one of the main pillars of lean), might be independent
of any kind of Information Communication Technologies, but the evolution of advanced ICT
solutions, depicted by the introduction of Cyber-Physical Systems and the Internet of Things
ensured that the factory of the future is a vision not far from reality (Buer et al., 2018). Several
studies, that examine the combination of Lean and Industry 4.0, address the linkage between
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Just-in-Time Manufacturing and IoT tools, as well as if this integration implies a significant
impact on performance. JIT is mostly considered as the basis for a successful introduction of
IoT tools (Wang et al., 2016; Zuehlke, 2010), but it is also found (Kolberg & Zühlke, 2015;
Wagner et al., 2017) that it can be further improved with the supplementation of IoT tools. In a
different line of research, Sanders et al. (2016) indicate that IoT tools can enable Just-in-Time
techniques. More specifically, they identified multiple solutions provided by I4.0 tools that aid
in overcoming Lean implementation challenges, so companies can become Lean without
having continuous ‘strive for lean’ initiatives.
2.5.1 Internet of Things and Just in Time manufacturing
Considering that Lean is a complex and challenging process rather than a straightforward one
(Almanei et al., 2017), and as production systems continuously evolve into lean systems not
only in an organizational context but also on technologies, a common repeatable
communication architecture is needed to allow scalability and adaptability across industries and
applications (Rong et al., 2016). Caballero-Gil et al. (2013) developed a logistics scheme that
incorporates distinct IoT techniques to aid the whole supply chain process. The scheme enables
to flexibly locate and check the goods, especially in their delivery and charging phase, where
most errors occur. Geerts and O’Leary (2014) developed an IoT-based supply chain system
called the “Supply chain of Things” that promotes the visibility and interoperability of things
along a whole supply chain. Further studies developed RFID-based solutions for monitoring
parts (Ngai et al., 2009), systems (Wang et al., 2011) or the entire status of production facilities
and operations (Chen et al., 2010). In fact, the proposed system of Wang et al. (2011) has a
feature that can monitor the manufacturing progress within a wider supply chain environment.
On attempts that incorporate IoT tools to address challenges of Just-In-Time, Sanders et al.
(2016) conducted an extended literature review and identified that item tagging, wireless
tracking of goods and smart reallocation of orders can help overcome implementation
challenges of just-in-time supplier delivery. They also discovered that IoT can provide
monitoring of material replenishment, tracking of schedule and Kanban updating, which can
help overcome pull production challenges (i.e. changes in production schedule, improper track
of supplied material quantities). Xu & Chen (2016) identified the challenges of the scheduling
process in JIT manufacturing and developed an IoT-based framework, which combined
modules of real-time resource monitoring and dynamic scheduling. The proposed framework
is addressed to respond to the challenges of changes with customer orders, production progress,
and availability of resources, allowing manufacturers to maximize productivity with limited
resources. Guo et al. (2019) proposed a framework for an intelligent manufacturing system
which consisted of IoT and machine learning technologies that aided to overcome certain JIT
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challenges like: 1) lack of technologies that capture real-time information, 2) lack of manager
and employee decision making methods for daily operations and 3) lack of flexibility to respond
in unexpected issues (i.e. order changes). The framework included modules for synchronization
of customer orders, flexible control of setups and real-time synchronization of logistics with
production.
In summary, the industrial internet might withhold several technologies that could help
overcome JIT challenges. Nevertheless, I4.0 tools inhibit challenges too, and it is important to
identify if the adoption of these technologies into a JIT manufacturing environment scales down
or mushrooms existing challenges. Figure 1 provides a graphical representation, to illustrate the
focus of this research. The conceptual model (Figure 1) relates to the two research questions
that were developed earlier.
Figure 1: Conceptual model
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3. METHODOLOGY Based on the insights of the introduction and theoretical background, the methodology section
depicts the research approach, how the sources are selected and the procedure that data is
collected and analysed. This section also describes how research reliability and validity are
ensured.
3.1 Research Design The aim of this study is to identify the challenges of IoT for a JIT manufacturing environment
and if an IIoT can solve existing JIT issues. Given that this relationship is relatively unexplored,
the more appropriate method to conduct such an extensive and in-depth analysis is a qualitative
multiple case study research (Karlsson, 2016; Yin, 2011). Case studies are able to fulfil the
principles of a qualitative method which are: describing, understanding, and explaining. Adding
to that, multiple cases enhance the results by repeating the pattern-matching, thus increasing
confidence in theory robustness (Runeson et al., 2012). As the consequences of combining two
major Lean and I4.0 tools are still lacking in theory, a multiple case study was able to provide
generalized results with increased external validity and less bias (Karlsson, 2016). This method
grants a researcher with the appropriate tools for gathering insights from experienced
practitioners, in order to understand the topics of JIT and IoT (Baxter & Jack, 2008; Yin, 2011).
The unit of analysis is the firm’s top management, given that managers have the more
appropriate background and knowledge to describe this complex phenomenon that takes place
within the production environment.
3.2 Case Selection and Description A multiple case study features multiple samples, so it requires replication logic. This implies
that each case must be carefully selected in order to predict similar (literal replication) or
contrasting results (theoretical replication) (Yin, 2011). The cases were selected from the
manufacturing industry. Lean practices might be spread through several sectors (Womack &
Jones, 1996), but combined Lean and Industry 4.0 practices are mainly dominant in the
manufacturing field. The case selection criteria required each company to have already
implemented Lean and operate under a JIT culture before embedding Industry 4.0 tools in the
production. The maturity level of Industry 4.0 implementation was not a prerequisite, in favour
of theoretical replication; it was considered possible that results may vary between companies
that have been using IIoT-JIT techniques for a relatively long time and companies that just
started to integrate IoT tools within their JIT production environment. Large-sized companies
were selected, as Industry 4.0 is a capital-sensitive concept and large companies are more adept
in adopting it. In total, 5 Lean/JIT manufacturing companies that have implemented IoT
alongside other Industry 4.0 tools were selected and interviewed (Table 3.1). Plus, one service
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providing/consulting company was interviewed, in order to get an additional external view of
the phenomenon. The companies specialized in different products (electronic equipment,
automotive, plastic pipes, and metal products), which helped to obtain a complete picture of the
phenomenon and cover different viewpoints. All manufacturing companies are operating in the
Netherlands and have highly developed and implemented Lean while facing the challenge of
implementing Industry 4.0.
3.3 Interviewee Selection and Data Collection Interviews are considered as an important source of case study evidence and capable
interviewees can hand over essential observations of this evidence (Yin, 2011). Interviewees
were selected based on their years of professional experience and their involvement in
manufacturing procedures. Interviewees with at least 3 years of experience were selected in
order to provide better insight into the research topic. In regards to their role, managers involved
in the technical aspects of production (lean engineers, engineering managers) or in the more
managerial side (supply chain managers, quality managers) were selected in order to cover all
perspectives, as between companies, the titles and the structure of employees that deal with
digitization might differ. In total, 11 individuals were interviewed in order to reach a desired
conclusion. The cases and interviewees are illustrated in Table 3.1, representing six different
companies from the Netherlands.
Case Sector Turnover (Mil.) Size Number of
interviewees Title/Positions
A Metal products 14.000 150 3
1) Supply chain manager 2) Quality Manager 3) Process improvement employee
B Plastic pipes 1.158.000 5.500 1 1) Manufacturing director
C Electronics 438.700 425 2 1) Engineering manager 2) Lean manager
D Consulting/Service providing 2.100.000 19.000 2
1) Senior maintenance consultant 2) Director
E Automotive 3.653.405 5.894 2 1) Vice president I.T. 2) CI managing director
F Electronics - 200 1 1) Innovation manager
Table 3.1: Overview of selected cases and interviewees
The primary data collection consisted of 7 semi-structured interviews and a team of 3
researchers. The interviews were conducted at the companies’ sites and had a duration of
approximately 60 minutes per interviewee. The interviewees were also asked to approve the
recording of the interviews, in order to achieve more reliable results. An interview protocol
(Appendix A) and a form of consent (Appendix B) were sent before the actual interviews, in
favour of ensuring reliability and validity (Tellis, 1997; Yin, 2011). The interviewees’ identity
18
remained anonymous as confidentiality is of major importance. Afterwards, all the interviews
were transcribed in order to code the collected data. After this step, they were sent to the
interviewees to ensure their approval. This source of data, along with notes and observations of
the researchers collected during visits in the industrial areas of the companies, ensured data
triangulation and increased the exploitation of ambiguity measures, as proposed by Yin (2011)
(Table 3.2).
Tests Case Study Tactic Construct Validity ● use multiple sources of evidence
● have key informants review draft case study report Internal Validity ● pattern matching
● address rival explanations ● use logic models
External Validity ● use replication logic ● interview multiple informants, for comparison
Reliability ● interview protocol ● develop an interview database ● maintain a chain of evidence
Table 3.2: Case study measures for reliability and validity (Yin, 2011)
3.4 Data Analysis The interview transcripts were analysed with the ATLAS.ti coding software. Later on, the data
was transferred to Microsoft Excel, in order to have a more distinct visualization of the whole
coding process. The coding of data consisted of four steps that included inductive and deductive
development of codes. Data from each interview was analysed separately. First, parts of the
responses that were considered useful for addressing the research questions were distilled and
classified as example quotes. The data was initially inductively reduced into first-order codes.
Conceptually similar codes were deductively merged together into groups identified through
literature research (Saldaña, 2013). Examining the data from the different unit of analysis
perspectives, the constructs were assigned third-order codes based on the practices that the
actions were related, which were specified through the theoretical background research (Table
2.1 and 2.2). A thorough overview of the coding process can be seen in Figure 2.
Reduce data to quotes 1st order coding 2nd order descriptive groups 3rd order themes
Figure 2: Overview (with example) of the coding process steps
Each case was initially examined as a stand-alone entity (i.e., within-case analysis). Within-
case analysis helps researchers to fully immerse themselves in the evidence of a single case
“Because the whole core of the company is running with an ERP system (SAP), if it happens, then we don't know what's in our warehouses anymore or our production orders. We cannot scan all products and automatically put it in the warehouse.”
Production/schedulingdataloss
SystemReliability
Data Integrity
and Security
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(Mills et al., 2012), compare if the trends observed in cases correspond with those identified in
theory, and aids in finding parallel or contradicting theories in a case that exemplify typical or
interactive connections within the phenomena under review (Yin, 2011). Thus, each case’s
findings were used as information to be added to the whole study. Furthermore, all the findings
were also jointly examined in order to identify any similar patterns across cases. A cross-case
analysis is both a way to classify events and a step to proceed to generalization of results
(Mathison, 2011). This procedure allowed to understand the fundamental characteristics of a
case study that are similar or unique in their qualities of other studies (Mills et al., 2012).
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4. FINDINGS In this chapter, the research findings are elaborated based on the coding tree which can be found
in Appendix C. The main findings of each case are presented in accordance with each research
question and are further divided into the deductively (identified in literature) (Table 4.1) or
inductively created challenge groups.
JIT Challenges IoT Challenges
Man
agem
ent
prac
tices
Workforce competences
Planning
Coordination of parties
Workforce motivation Estimate added value
Supplier reliability Develop specific use cases
Cross-functional cooperation Interdisciplinary communication
Top management involvement T.I & Operations
Immature technologies
Man
ufac
turin
g pe
rform
ance
Slow market response Proper implementation paths Forecasting
Security Internet risks
Setup-time reduction System reliability Planning and facility layout
Employees Adequate education
Flexible manufacturing system Resistance to change (Ballard and Kim, 2007; Čiarnienė & Vienažindienė, 2013; Elmoselhy, 2013; Gann, 1996; Höök, 2008; Jadhav et al.,
2014; Matsui, 2007; Netland, 2015; Veech, 2004; Wyrwicka & Mrugalska, 2007; Huson, 1995; Xu & Chen,
2016)
(Brettel et al., 2014; Kiel et al., 2017; Maresova et al., 2018; Müller et al., 2018; Rong et al., 2016; Sadeghi et al., 2015;
Schneider, 2018; Wan et al., 2016)
Table 4.1: Overview of the (deductive) second order groups
4.1 Company A The JIT philosophy of the first case is fully reflected in what is said by the quality manager:
“just-in-time is the story of our life here.” The company had recently implemented IoT
technologies and was in the final stages of introducing an Industry 4.0 project in the form of a
cooperative robot that aids employees in production. While being at a nascent digitization stage,
I4.0 was seen as an assistant for just-in-time operations. The production environment is high-
mix/low-volume, as it is characterized by producing highly differentiated products in smaller
quantities.
Manufacturing performance - Planning
The main point of emphasis was the prioritizing of jobs in order to improve production
accuracy. Several implemented IoT technologies aid the company in dispatching the urgent
jobs instead of working on a first-come-first-serve basis, which is not accurate for a shop-floor
that has to produce differentiated products, with some of them requiring longer assembly times.
According to the supply chain manager, the ERP system can continuously send information to
the shop-floor order pallets which are assigned with lights that show which order is more urgent.
Process Improvement Employee: “…we can for example (with all the data we have) change the dispatching rule according to plan starting date for example or add to cart by starting
date, instead of first come first serve. In that case, you would improve your planning that you made beforehand. And you have a more accurate process.”
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Manufacturing performance - Flexibility
Assistive QR scanners aid workers in production by providing bills of materials with real-time
instructions for each job, making the manufacturing process more discrete. The quality manager
states that “...if the worker scans the job, on the background, he generates that program or that
happened in the past. There's a software that defines all of the tooling (which tools to use), the
positioning, which sites have been first.” Furthermore, the Internet of Things enabled to make
the machines more adaptable for production needs. For workers, it seems that now, “…the only
thing they do at laser cutting machines is just select the program that has been created over
here. And they can just start working. And actually, all the programs are available on the floor
on all laser cutting machines. So, they can even switch between machines.”
Challenges - Internet Risks/Security
The managers’ biggest concern regarding the company’s just-in-time culture is that a cyber-
attack on a fully IoT-based production environment will compromise the production
instantaneously, compared to a partial IoT-based environment, where production would
continue for a few hours.
“I think, if we get a cyber-attack right now, then we could work for maybe four or eight hours and then it would stop. But if you're compact with IoT in your entire work-floor, then it will
be too complex to continue working…”
Therefore, the top management has to raise cyber-crime awareness on employees, and
especially on the I.T department, which will bear the greatest burden of responsibility for
tackling this issue.
“...you can even hack a laser-cutting machine for example. And so, you have to create some kind of awareness on people. But basically, right now, I think the biggest challenge is for the
I.T. department to screen it off.”
4.2 Company B The interview with company B was conducted with an experienced manufacturing director of
10 plants in Northeast Europe. While not having fully implemented Industry 4.0, the company
classifies I4.0 as a culture of lean and continuous improvement. The company had already
began changing its inefficient make-to-stock production environment into make-to-order, as
according to the interviewee’s sayings:
“…the business wants to create value. So, for sure, you want to also sell more solutions. You might have combined pipe and fittings where they'd all be assembled or whatever and then
you come to a more make to order loop…”.
Manufacturing performance – Predictability and Planning (BIM)
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The director highlighted the transition of managing products with 3D model-based processes,
which enable more efficient planning, design and management of products and infrastructures.
All product information (i.e. design parameters), unlike 2D processes, remains interconnected
and stored in databases (Wong & Fan, 2013).
“... a lot of our products are already in BIM. So, we can for example get all the old products from BIM 2D format and can make a 3D card use of that, and you get an automatic article list. We have a special office in Poland with all the products scanned, which makes all the
data available ... and you see that (for example in the Netherlands but also a couple of other European countries where is already used) you can generate big bills of materials…”.
Manufacturing performance – Flexibility
The sales department generates different bills of materials according to the demand, so it can
be sent to the production department. This process may require several steps, and a lot of
information can be lost throughout the way. The director states that their target is to make the
process “more linked” by introducing IoT solutions that enable a real-time connection of the
production floor systems with the sales department, in order to avoid manual interventions and
increase automation. The purpose is that the “customers can see that we automatically produce
in certain lead time”.
Challenges – Planning and Implementation
On the challenges of IoT implementation for a JIT environment, the interviewee expressed the
complexity of enhancing a make-to-stock ERP system with make-to-order capabilities.
“...because of MTS, every article has a certain code. So, if you make a new article, because the customer wants a new article, you have to go through all your process to make it an article, it takes time. In a make-to-order environment, that's absolutely not acceptable
because it takes much, much hour spend. So, we need to do this in a completely different way. So that's one of the big challenges; that our ERP system is set up to be MTS and not so much
MTO. So, we are currently investigating how to deal with that…”.
Challenges – Coordination of Parties and Internet Security
Furthermore, the alignment of the I.T and production departments is a demanding task for top
management, as “it takes a very long time to speak the same language and to have the ability
to listen to each other”. Cyber-attacks appear as a considerable challenge for Company 2 also.
An intriguing fact is that apart from production, cyber-crime can hamper just-in-time when the
target is a supplier or even a customer.
“Because the whole core of the company is running with an ERP system SAP, if it happens, then we don't know what's in our warehouses anymore, we don't know our production orders
anymore. We cannot scan all products and automatically put it in the warehouse. It will create a big mess… Cyber-attacks is one of our biggest concerns at the moment
because we have seen big customers of us already being attacked and even suppliers.”
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4.3 Company C The company concentrates on the semiconductor and healthcare industry, producing complex
equipment for high-end customers. As a purely make-to-order/assemble-to-order manufacturer,
the main target of C, according to the engineering manager, is to keep the delivery performance
of suppliers on a high level in order to deal with production line bottlenecks (which are formed
due to untimely supplier deliveries).
Management practices – Supplier Reliability
The existence of IoT physical devices that monitor and exchange order data with suppliers
provide more time-saving capabilities than before. The engineering manager explained the
great impact of IoT on JIT compared to manual operations:
“Now, I have to pick the order of the customer and I have to transfer it to orders. And it will take days before that order is broken in different orders to supply it. If you can automate that, you win days. So, the supplier has more time to deliver that product in time. So, every point of
data transfer which is not automated (from one to the other) will take time and will hamper just in time.”
Manufacturing performance – Predictability and Production Readiness
Bottlenecks are also affected by the variability of the demand. Long throughput times are not
able to react on that variation, and the key objective is to reduce waiting times, since “...the
period of touching time on the product is way smaller than the time that it is in the factory. It's
only a small part that you are actually working on the product and more other waiting times”.
Each production line within the plant has an interconnected board that shows warehouse
information on whether the line is running low on products. Thus, the operators can get new
orders to that specific part of the shop-floor, so they do not run empty.
“You will see in the automated line that there is an order and you have three columns to the right. One is from engineering; one is from the warehouse and one is from the preparation on
the shop floor. And this is noted in green red and orange. If the warehouse is ready for that order, if preparation is ready and if engineering is ready. It’s red when the engineering is not
ready, it’s orange when the engineering is ready, but it has to be present when the order starts at the line. So, we try to combine as much as possible information which makes it easy
for the operators to understand what’s going on.”
Challenges – Internet Security and Adequate Employee Education
The most challenging task of implementing an IoT within a JIT production environment is
internet failure. Similar to the perceptions of the other interviewees, the managers addressed
that since the production is connected to the ERP system, crashes, cloud failures or cyber-
attacks could compromise the entire production.
“… if you have a crash in the system, the production is done. So, the biggest risk is Internet risk, internet failure, cloud failure. Everything is in the cloud and it's vulnerable for cyber-
crimes and spying.”
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Adding to that, older employee training seems burdensome, compared to the relatively
effortless flow of knowledge to young employees.
“You will see that the engineering department is relatively young. They adapt quite easily, and you see that the changes for the older people are more difficult. Tooling is becoming too
complex”
4.4 Company D The profile of the fourth company differentiated from the rest, as its primary role was to advise
and provide maintenance services to other manufacturing companies. Two expert consultants
with thorough I4.0 knowledge were interviewed in favour of obtaining an external viewpoint
of the issue, which could also add to the reliability of the findings.
Manufacturing performance – Predictability and Production Readiness
The maintenance consultant highlighted that the proper use of data can provide information on
the “influence factors” of the processes.
“I think now, JIT mainly relies on prediction. Using data. When you know what the influence factors are on your process, you're able to produce just in time. The better you know the
higher reliability your model is. The higher, the more reliable you are, more able to deliver just in time. So, there is a huge need of having information of what are the influence factors
on the delivery. You have to address all the influence factors. If it's the weather, if it's the different kind of crew you have at night or the day. And how they use the machines... So, all
those influence factors have influence on your just in time. Positive and negative. So, the more you know... the more influence factors there are, the less complex it will be. The human brain cannot analyse it anymore. You have to use data. To see the correlation and to act on it,
to ensure just in time”
Manufacturing performance – Preventive Maintenance
Furthermore, with an IoT-based live connection, the service provider can have access to several
machine data of customers and use them in favour of preventive maintenance.
“...information is needed to combine the process data as the registration of all the process information like flow, temperature, pressure or maybe also some other process, logistic information. Mostly, it's stored in as history in the server so you can use it for years, this
historical data. To combine with the maintenance data.”
Challenges – Resistance to Change
On the contrary, the director addressed that in order to successfully integrate IoT and
connectivity, you have to deal with the occurrence of resistance to change from the old and
trusted subsystems into something new.
“Biggest challenge I think is that it's now working in subsystems. Sub-systems have developed in the past 20 years, they are mature, very well-known and trusted. And now they go to
something that is new and they don't trust it. So then, you have to convince that something new also works and that integration makes it simple and not more difficult. People think that
when you integrated it, the risk gets higher.”
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Challenges – Internet Risks (Security)
The senior maintenance consultant mentioned information security as the most important
element of their clients that needs to be kept intact.
“...the main thing is they are aware about security. I think that's the main challenge. Security, to ensure that and nothing is done with the data. When we start a conversation, just within five minutes it pops up: Discussion about data security. Because they have a reputation to
keep high. And if there is an accident or something, all the data is at our server, at our cloud.”
4.5 Company E The fifth case was about an experienced Dutch manufacturing company that has been building
cars for over 50 years. During the past 5 years, the number of employees mushroomed from
roughly 1500 to 7200. This rapid rate of progress included the development and implementation
of several Industry 4.0 technologies. Yet, the CI managing director stated that the company is
still struggling with it because the lines still use both old and new systems, which need to be
combined.
Management Practices – Supplier Reliability
As reported by the vice president, Just-in-Time is an integral part of the automotive philosophy,
which “will never work without it”. The company is dealing with thousands of suppliers, as
there are over 12000 parts that need to be assembled. A change at the OEM can have an
immediate effect on the demand for the supply chain. Hence, no stock levels and a fast supply
chain are considered as prerequisites for being JIT. In order to address the complexity of this
environment, IoT tracking technologies were implemented across the whole supply chain
network.
“… what you see nowadays (on getting the order to the supplier and see that he has delivered) with IoT implementations is that you also want to track the whole supply chain
exactly by kilometre at G.P.S. tracking, where’s the transport companies and so on. That you can really track if he manages the ETA (estimated time of arrival) date, if he’ll be in time at
the factory. So, this whole chain will be more transparent.”
Manufacturing performance – Traceability/Predictability
The company was in the initial stages of using geo-fencing devices across the production. The
deployment of geo-location-aware devices can offer traceability of assets. Additionally, the
devices can be programmed in order to reduce or even prevent production failures.
“We are now starting up a pilot with ultra-wideband network in our final assembly so that we can tag all the cars in the production line, tag our equipment and that you can use geo
fencing to say: okay, this tooling gets now instruction to do this screw connection in this car that you can combine the tool and of course the AGV pilot... The ultra-wideband network, it is used for asset tracking and also for our forklift trucks and our rolling material. It can prevent
failure. It can reduce the downtime or costs. The sooner you know that you will have a problem, the less costs will be to prevent the problem or to prevent the downtime at all.”
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Management Practices – Workforce Competences
The utility of employees can be significantly increased with the use of smart, wearable
interconnected devices. Top management can track and deploy workers on jobs based on their
warehouse location, profile, etc.
“… the tracking system exactly knows that this guy from the maintenance department can be the quickest to this robot. He also knows he’s not busy with any important thing. So, this guy
will go on and the message will be pushed to his own watch and then you have a good combination between what do we like to do in the system, what’s the information flow and
how can it work together.”
Challenges – Data Integrity and System Reliability
The managing director addressed manufacturing liability as a major challenge for the
combination of Just-in-Time and Internet of Things. In other words, the biggest issue is to
maintain a fast JIT, even when there are network reliability issues.
“… manufacturing liability is one of the biggest issues. If you know by phone, then nice. If you have a 5G but if the banking app is not working, everybody in the Netherlands has a
problem. It's the same in production. So, you got to have a very fast Just in Time and reliability. If there is a break, how they organized that you can still use the phone and we can
still give a lot of orders to them.
Challenges – Counteractive actions
The continuous monitoring of employees’ movement has an impact on their behaviour. The
management might be able to get all the data and notice possible production stops or delays of
employees. Nevertheless, most of the time, the employees have a negative reaction to that.
“… all the data is available and you can exactly see what people are doing, how they are working, if they stick to what they have learned or not and that is the biggest resistance, that
people sometimes say I avoided it because you are controlling.”
Challenges – Higher Dependency on New Technologies
The more IoT-capable is a firm, the higher is the dependency on new technologies, and a quite
difficult task is to combine new with old technology. An example was stated by the CI
managing director: “How do you combine that the simple barcode scanner is integrated in a
hardware tool which is just made for screwing?”.
4.6 Company F Company F was developed to provide electronic and mechatronic products to high-end
customers. Most of the time, the customers are the ones to design and hand the product over to
the company for assembling and shipping. The high-mix/low-volume assemble-to-order
production environment is characterized by “high complexity”, as the list of components for a
product can be quite long and in order “to start production, you need all the components there”.
In its continuous strive for innovation, the company had developed and implemented several
Industry 4.0 projects (e.g. assistive co-bot). However, I4.0 is still considered as a trend with
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infinite elements, and it is a crucial challenge to “decide which elements are of importance for
the company, and then implement them.”
Manufacturing Performance – Predictability and Production Readiness
Multiple devices and systems within the plant are connected with each other and to a central
hub, producing and exchanging large amounts of data. The innovation manager explained that
due to the easy access and high availability of data, the company is now able to analyse a
production problem quicker and also make predictive models and spread information to the
entire supply chain, which supports JIT operations. Furthermore, the integration of an IIoT
provides an easier collection of data.
“So if a customer designs, for example, that resistor and we know that there have been problems before, we can already in the design phase of our customer, we can say hey, do not use that resistor because we know based on the data that that problem may become a supply
risk. So maybe an alternative resistor is a better plan. So that's the goal which we are registering and collecting all the data: to translate that data into predictive models”
Challenges – High dependency on new technologies
However, integrating an IoT within a JIT production environment demands a solid I.T
infrastructure. According to the interviewee, a connection stop, even for a negligible amount of
time can stop the production.
“... all the technology, the new technologies, new machines are very much depending on a high network and a good infrastructure. A good I.T. infrastructure. So, it's really demanding. In the new lines we have, for the automatic placement, if there is a, well, one millisecond of
no connection then the machine stops right away.”
Challenges – Counteractive actions
The aspect that went through a big change is the monitoring of employees. The big amount of
registered data made easier for managers to thoroughly analyse the mobility of workers and
conjunct it with machine information in order to discover if the workers are working according
to plan. However, this policy is usually backlashing, since workers do not like to be controlled
all the time.
“if they are not correctly checking out, then they didn't have a break for example. Or they have one for half an hour instead of 15 minutes for example. And now based on all the data
we can see hey you've been away for half an hour. You didn't check out correctly because the machine was half an hour idle. So, based on that kind of data we can say to that person hey
you are lying to me... well they don't like that of course. That's for sure. Because this is much more controlled”
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4.7 Cross-case analysis Tables 4.2 and 4.3 summarize the cross-case analysis and comparison of the findings. The
widely differentiated profiles (size, production process, aim) of the companies made the
objectives, ways, and challenges of incorporating JIT-IoT techniques to vary. However, certain
challenge groups are addressed in multiple cases. The main findings are further discussed in
chapter 5, taking into consideration the cross-case analysis.
RQ1: “To what extent can the implementation of Internet-of-Things technologies solve existing issues within JIT initiatives?” Company A B C D E F
Preventive maintenance X X Planning and facility layout X Predictability and readiness X X X X X
Flexible manufacturing system X X X X Workforce motivation X X
Supplier reliability X X X Table 4.2: Cross Case Analysis (a)
RQ2: “What are the challenges of integrating an Industrial Internet of Things within a just-in-time production environment?”
Company A B C D E F Coordination of parties X X X
Internet risks X X X X X X Data integrity & System reliability X X X Adequate employee education X X X X
Resistance to change X X Proper implementation paths X X X
Counteractive actions X X X Rules and regulations X
Table 4.3: Cross Case Analysis (b)
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5. DISCUSSION This research set out to uncover the influence of an Industrial Internet of Things on a Just-in-
Time production environment, by focusing on two norms: the associated opportunities for
overcoming JIT problems and the challenges of combining JIT and IoT. The findings suggest
that there is an indirect influence of IoT on the success of JIT, via the leveraging of certain JIT
challenges. However, combining JIT and IoT might initiate the intensification of existing or the
occurrence of new production challenges that require a prudent approach.
Figure 3: Proposed conceptual framework based on results.
5.1 Internet of Things and Predictability/Production Readiness Five out of six cases addressed the challenges of timely forecasts and prediction in order to
have a production which is always capable and ready to respond to any demand changes or
production defects. The Internet of Things has a wide spectrum of applications and several
capabilities were unveiled that aid the companies to overcome these challenges. The ability to
“collect” and work with data more easily than before was highlighted in three cases.
Interviewee C2 stressed: “we also try to make data visibly understandable. So, for example, we
have specific boards which show the warehouse”. Furthermore, according to A and C, the IoT
can relieve the operators from time-consuming preparation tasks, thus providing more
throughput time reduction capabilities. Companies D and F agree that JIT is highly dependent
on prediction and after IoT implementation, the ability to analyse data is increased. This
capability is essential when the goal is to “convert data into prediction models and ensure just-
30
in-time”. Several studies exist suggesting that JIT 4.0 establishes higher transparency by
applying data analytics techniques (Ding & Jiang, 2018) and provides shorter lead times (Mayr
et al., 2018) but limited knowledge exists on which JIT challenges are addressed by IoT-driven
capabilities. As such, the existing knowledge can be extended by making this following
proposition:
P1: The Internet of Things can tackle the challenges of poor forecasting and slow market response by providing Data Transparency, Through-put Time Reduction capabilities and by enabling Advanced Use of Analytics.
Findings appear to be in the same line of research with the study of Xu & Chen (2016), who
argue that IoT improves JIT, by providing real-time monitoring and dynamic scheduling which
address the challenges of demand change, production progress, and resource availability. In this
context, the particular study comes to add more insight with additional IoT perks and which
JIT challenges are addressed by them.
5.2 Internet of Things and Manufacturing Flexibility The JIT trend resulted in a growing pressure to reduce inventories of work-in-progress (WIP)
while at the same time increasing quality obliged companies to develop more flexible
manufacturing systems. JIT organizations, in order to succeed in today’s globally competitive
industry, an important prerequisite is to develop creative advancements in manufacturing
technologies, otherwise, systems will decrease in flexibility (Singh & Singh, 2013). According
to five interviewees from four companies, IoT implementations advanced the manufacturing
environment with more versatile and adaptable systems that are now capable to provide abrupt
real-time information of each product’s bill of materials. Interviewee A2 argued: “the biggest
benefits as well for the vending machines is that you have a step file connected to your bill of
material and it has a connection with an order (or a job order)”. This resulted in less material
handling and reduced setup times, which are necessary conditions for preserving shop-floor
flexibility. Rüttimann & Stöckli (2016) suggested that the IoT makes JIT systems more flexible.
This study supports the existing literature but also extends it by positing which IoT capabilities
are compelling a JIT system flexible. Accordingly, it is proposed:
P2: The Internet of Things can tackle the challenge of decreased manufacturing flexibility by providing Direct Access to Bills of Materials and more Versatile/Adaptable machinery.
Findings support what has been already mentioned in the literature; that IoT enables more
production control and optimization by providing real-time data collection and communication
within systems, thereupon aiding in developing a smarter and more flexible manufacturing
system (Zhang & Tao, 2017). Moreover, no other study considered how the IoT can aid the
manufacturing inflexibility that is often seen after JIT implementation.
31
5.3 Internet of Things and Supplier Reliability Lack of real-time exchange of information between a firm and its supplier can have disastrous
impacts on production (Jadhav et al., 2014). Previous studies addressed that IoT technologies
can be utilized to precisely track and localize assets across the value chain (Caballero-Gil et al.,
2013; Mayr et al., 2018). Similarly, the findings support the previous statements and further
point out that with IoT implementations, companies are able to track their whole supply chain
exactly by kilometre, check if the estimated time of arrival is achieved and “if he (supplier) will
be in time at the factory. So, this whole chain will be more transparent...”. However, no other
study has considered that supplier reliability issues can be leveraged by the fact that IoT-based
manufacturing systems are able to automatically transfer order data. According to A, the
supplier is able to receive a signal when a customer is out of stock on specific equipment.
Automatic ordering was also mentioned as an IoT capability by company B, in the effort of
linking the production with wholesalers. The engineering manager of the third case stressed the
importance of having an IoT-based ordering system, as the company is now able to “win days”,
and the supplier has “more time to deliver a product in-time”. Thus:
P3: The Internet of Things can compensate Supplier Reliability issues by providing Supply Chain Tracking and Automated Ordering systems.
5.4 Challenges: Planning and Implementation Three cases expressed the need for a further change after the implementation of smart systems.
Interviewees E1 and E2 highlighted: “…in the old-fashioned way I just had to move the worker
to another workstation and tell them now you have to do the same job over there. Now I have
to also do a technical change (i.e. I have to move this flat screen to another workstation) ...
that’s why we work together, to be part of that department… We are reliable for the process
and the information and that’s a complete mindset because that means also that you have new
tasks, new ways of working. You need cross-sectional teams to make this a success”. It is also
mentioned in the literature that just-in-time requires a cooperative organizational atmosphere
that promotes togetherness (Singh & Singh, 2013). Similarly, organizational transformation is
considered a critical task for IoT implementation, as interdisciplinary departments with flexible
hierarchical levels need to be formed (Kiel et al., 2017). However, it is not fully addressed
whether or not these requirements are intensified after conjunction of JIT and IoT principles.
The empirical evidence of this research show that coupling the two concepts necessitates
companies to focus on change management and re-establish the direction of every department
under common thinking. IT departments’ responsibilities increase along with their involvement
in production, so the challenge is to develop the ability to communicate “in the same
language”. Interviewee B1 stated: “It takes a very long time to speak the same language and
to have the ability to listen to each other”.
32
P4: The implementation of an Industrial Internet of Things on a just-in-time production environment necessitates greater change management efforts.
5.5 Challenges: Technical integration and operations According to the companies’ representatives, one of the biggest challenges is the successful
combination of old and new technologies. The managing director of company E expressed his
concern for combining not only in-line systems but also simple hardware tools. Likewise,
Schneider (2018) has already mentioned the importance of planning how the new I4.0
technologies should be implemented without affecting the current movement of work. While it
is already addressed that new technologies could threaten process quality and production
robustness (Kiel et al., 2017), this study comes to add that new IoT technologies urge JIT
companies’ productions to become more and more dependent on the I.T infrastructure.
Therefore, a high-speed network with no disruptions is required to secure the essential JIT
requirement of high manufacturing velocity without defects. The innovation manager of the
sixth case mentioned that their company hasn’t resolved this issue yet: “we also have to
upgrade the whole infrastructure of course”. This study contributes to the literature exploring
the technical integration of IoT, by providing an unexplored implication for just-in-time
production environments:
P5: For proper implementation of IoT on a JIT environment, the main challenge when interfering with conventional productions systems is the successful Combination of Old with New technologies under a robust operating environment.
5.6 Challenges: Data Integrity and Security As the IoT expands, new security concerns arise with it. Data integrity and Security, derived
from every participant within the context of JIT-IoT, prevails as the most common challenge
for companies. Cyber-attacks and System reliability issues are the main threats of a robust and
secured JIT production system. Interviewee B1 stated: “Cyber-attacks are one of our biggest
concerns at the moment because we have seen big customers of us already being attacked and
even suppliers”. Cyber-attacks, according to the interviewees are twofold: focusing on
damaging the systems (and as a result the production), and on stealing information. Theory
mentions that the Industrial IoT has strict real-time requirements and computational memory
constraints, which make a system unable to be restored and continue working after an attack
(unlike classic IT systems) (Sadeghi et al., 2015). Findings confirm this thinking for a JIT
production also, as it is stated by interviewees that when a JIT manufacturing environment is
IoT-compact across its entire infrastructure, a cyber-attack could compromise the whole
production. Findings also revealed that privacy is another big topic that has escalated in the past
few years, mostly on behalf of information security issues that have been encountered. The
intense connectivity of IoT production systems requires new methods that offer protection of
33
confidential information (Sadeghi et al., 2015). Additionally, the cases further contribute to this
matter by revealing that JIT companies nowadays also receive external pressure (regulation,
stakeholders) for security, “to ensure (customers and suppliers) that (security), and that
nothing is done with the data”.
Just-in-time aims to smooth the downstream material flow by speeding up the manufacturing
process (Yeh, 2012). JIT companies are concerned that a system breakdown on an IoT-based
production will completely erase any kind of information about warehouses, orders, and
products. Interviewee B1 stressed that: “if it happens, then we don't know what's in our
warehouses anymore, we don't know our production orders anymore. We cannot scan all
products and automatically put it in the warehouse”. Therefore, one major challenge is to be
able to upkeep a fast JIT in case of system reliability issues (E2). Theory mentions that
companies have to constantly operate IoT technologies under a standardized interface, where
components will not be owned by different vendors in order to assure interoperability of devices
among different business units (Kiel et al., 2017; Wan et al., 2016). Findings from the cases
though showed that literature lacks an approach to mention what a system breakdown could
mean for standardized communication activities, especially for a fast-paced JIT production
environment. The aforementioned facts led to the development of the next propositions:
P6a: Pressure for Privacy of Information and Cyber-attacks pose as the main Security challenges for JIT-IoT firms.
P6b: Production/Scheduling Data Loss and Upkeeping High Just-in-Time Reliability in case of System Breakdowns are the main System Reliability challenges of an IoT-capable, JIT production environment.
5.7 Challenges: Employees The appropriate training and development approach for employee qualification in planning and
coordination tasks is addressed in literature as a main human resource challenge of IoT (Kiel et
al., 2017). Four cases of this study replicate these findings by mentioning that
coaching/education of employees (especially for relatively older ones), presents itself as a
critical task. For example, according to the manufacturing director of B, “if you talk with all
the companies, they say the same, implementing takes one month, getting people to use it
regularly takes six months to a year.”
Literature suggests that both JIT and IoT enable companies to use employees more efficiently
(Cheng & Podolsky, 1996; Valencia et al., 2019). Findings also indicate that the employees’
competencies and cross-functional cooperation are enhanced. On the other hand, employee
motivation is the task that poses as one of the most demanding and critical for the success of
just-in-time (Cheng & Podolsky, 1996). Several tasks, previously done by top management
34
now become shop-worker obligations. It is rather possible to have more resistance than
motivation as a response to JIT, due to the increased employee responsibilities and continuous
reassignments to different areas (Yeh, 2012). Internal resistance can be also observed when
implementing IoT, due to fear of new technologies and job loss (Kiel et al., 2017). This study
supports the aforementioned statements but also offers more insight in the growing yet
insufficient JIT-IoT body of literature by addressing that combining the two concepts could
intensify employee motivation problems. Three cases addressed that now, with everything
interconnected, employees “feel more observed” and this resulted to further counteractive
actions like task avoidance (E2: “people sometimes say I avoided it because you are
controlling”). Additionally, findings extended the aspect of employee resistance (Kiel et al.,
2017), by revealing an additional reason for it in the form of “trust in the old systems”: “Top
management sees it as a risk because you throw away a lot of proven technologies. Middle
management is used to working with sub-systems so you will take away their tools. (D2)”.
Leading to the conclusion that:
P7: For JIT firms, IoT integration implies that Employee Motivation will be further threatened from Resistance to Change due to Trust-in-the-Old/Fear-of-the-New technologies and by Counteractive Actions due to Continuous Monitoring and Control.
Just in time and Internet of Things
Opportunities
“To what extent can the implementation of Internet-of-Things technologies solve existing issues within JIT initiatives?”
Challenges “What are the challenges of integrating an Industrial Internet of Things within a just-in-time production
environment?”
Cases
IoT implementations enable more Data
Transparency, Through-put Time Reduction and
Advanced Use of Analytics capabilities,
which can increase production systems’
predictability and readiness for the volatile
market demand.
IoT provides more Versatile/Adaptable
systems and devices with direct bom connection capabilities, which can
decrease setup times and material handling,
resulting in maintaining manufacturing flexibility.
With IoT implementations,
companies are able to track their SC exactly by km and check if the ETA goal is achieved,
while also enabling automated ordering
capabilities.
The integration of IoT technologies on JIT a production requires
changing the organization (i.e. align departments),
with a focus on communication between
the production and IT departments.
One of the biggest challenges is to
combine old with new technologies. Plus, the company’s production
becomes highly dependent on the I.T
infrastructure.
Cyber-attacks, loss of information and System
reliability issues are considered as the main IoT
threats of a robust and secured JIT production
system.
Implementing IoT technologies within JIT-
oriented productions might lead to employees: a) feeling more observed
and reactive b) unwilling to embrace
new types of technologies.
Literature
IoT-based monitoring and scheduling can aid to
respond to the challenges of changes in customer orders.
I4.0, which adds IoT possibilities, can make Lean
(JIT) Production more flexible; whether it is faster, smoother, more reliable or
accurate.
IoT technologies can be utilized to precisely track and localize assets across
the value chain.
JIT requires a cooperative organizational atmosphere
and IoT requires interdisciplinary departments
with flexible hierarchical levels.
I4.0 technologies require careful integration that
will not affect the current movement of work.
JIT aims to speed up the manufacturing process. IoT has
demanding requirements that can make a production system unable to be restored after an attack or
breakdown.
Resistance rather than motivation is the most
frequent response to JIT. Internal resistance also poses
as a threat, after implementing IoT.
Propositions
P1: The Internet of Things can tackle the
challenges of poor forecasting and slow market response by
providing Data Transparency, Through-
put Time Reduction capabilities and by
enabling Advanced Use of Analytics.
P2: The Internet of Things can tackle the challenge
of decreased manufacturing flexibility
by providing Direct Access to Bills of
Materials and more Versatile/Adaptable
machinery.
P3: The Internet of Things can compensate
Supplier Reliability issues by providing
Supply Chain Tracking and Automated
Ordering systems.
P4: The implementation of an Industrial Internet of Things on a just-in-
time production environment necessitates
greater change management efforts.
P5: For a proper implementation of IoT on a JIT environment,
the main challenge when interfering with
conventional productions systems is
the successful Combination of Old
with New technologies under a
robust operating environment.
P6a: Pressure for Privacy of Information and Cyber-attacks pose as the main
Security challenges for JIT-IoT firms.
P6b: Production/Scheduling Data Loss and Upkeeping
High Just-in-Time Reliability in case of System Breakdowns
are the main System Reliability challenges of an IoT-capable, JIT production
environment.
P7: For JIT firms, IoT integration implies that
Employee Motivation will be further threatened
from Resistance to Change due to Trust-in-the-Old/Fear-of-the-New
technologies and by Counteractive Actions due to Continuous Monitoring
and Control.
Table 5.1: Summary of Discussion
6. CONCLUSION In this study, a thorough narrative on the outcomes of applying an Industrial Internet of Things
to various just-in-time production areas is provided. As such, after exploring the IoT in a JIT
context, the main technological enablers of IoT that address specific JIT challenges are
provided. A bibliographic analysis with a literature body of up until mid-2019 was analysed
and literature gaps with respect to the potential combination of JIT and IoT were identified. The
research set out to provide an informative overview of which JIT challenges can be aided with
the recently developed IIoT technologies. The aim was also to classify existing or reveal new
challenges within the JIT-IoT context.
The findings contribute to theory by addressing the gap that existed, as no other study (to the
best of the author’s knowledge) had examined if the challenges associated with just-in-time
production can be overcome with the aid of technological IoT capabilities. The theoretical
contributions consist of establishing that IoT, due to the influence its technologies have on
people, processes, and suppliers, can have a vital role in the efforts to overcome associated JIT
challenges. Furthermore, studies focus on the co-existence of the two broader concepts (Lean-
Industry 4.0) and have often neglected interrelations between different dimensions of Lean and
Industry 4.0. The research presented in this thesis contributes to the closure of this extensive
gap.
Aside from theory contributions, this study can offer insights for practitioners, too. At the
present state, just-in-time production is already considered as a phenomenon that is highly
dependent on the overall strength of an organization and its manufacturing performance.
Considering the recent shift of focus into more smart manufacturing solutions, managers often
do not recognize the added value to existing production infrastructures. Companies can benefit
from the presented findings since the majority of them is precarious about how effective is the
unfamiliar IoT technology. The findings also help them to identify several examples of IoT
models (i.e. production flow monitoring – company C) and prepare for them properly.
6.1 Limitations and directions for future research This research has certain limitations. First, using the words “Internet-of-things” and “Industry
4.0” during the research process might have neglected references addressing innovative
production technologies that do not present this exact phrasing and have been recently
published. A further limitation concerns the used method. The informants had not the same
understanding of what constitutes a production “just-in-time”, probably due to the different
nature, goals, and stage in development of each company. Moreover, the retrieved data are only
37
based on subjective perceptions of the interviewees. Due to the small amount of data generated
through the interviews, generalizations from this study are limited to the organizations under
investigation.
Several avenues for future research can be pointed out. One main goal of this study was to
identify to what extent can the internet of things help in overcoming just-in-time challenges.
However, an interesting pattern is also formed in the opposite direction; if IoT challenges can
be addressed by implementing just-in-time. IoT challenges such as employee qualifications and
organizational transformation (having an adaptable and flexible culture, more top management
involvement) (Kiel et al., 2017) could be able to be addressed after implementing JIT, due to
the ability (of JIT) to facilitate the empowerment and involvement of employees (Yeh, 2012).
Furthermore, there is an absence of attention given to empirically identify how specific I4.0
tools can influence (positively or negatively) certain Lean practices. It is strongly believed that
these areas are worthy of particular attention in the future and shall be plentiful in rewards.
38
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8. APPENDICES Appendix A – Interview Protocol The current interview contributes to answering the research questions: “Can the implementation of IoT technologies solve existing issues within JIT initiatives?”
“What are the challenges of integrating an Industrial Internet of Things within a JIT
production environment?”
Questionnaire General questions 1. What is your function within this company? 2. How long have you been working at this company? 3. To what extent is a product customized? 4. How would you characterize the production process? 5. Did you find the change necessary? 6. Was there a need for change? Or why not? What were the reasons?
• Intrinsic motivation 7. To what extent do you think the change was ‘smooth’? Did you experience any
obstacles? 8. If yes, how were these obstacles solved? 9. Was this the best way to solve the problems? Why/why not? JIT 4.0 Challenges 1. What kind of networked/interconnected devices (Internet of things) does your
company use in a manufacturing context? 2. Which are the desired outcomes, when using these devices? 3. Is the role of existing manufacturing systems enhanced, by using these devices? 4. What kind of JIT initiatives does your company use in a manufacturing context? 5. Which are, in your opinion, the main objectives of using JIT initiatives? 6. Did these initiatives augment after implementing an Internet of Things? How? 7. Can you think of challenges within a JIT manufacturing environment (before
implementing IoT)? • From a management perspective • From a manufacturing perspective
8. Do you think that some of these challenges were addressed after implementing IoT? If yes, which and how?
9. Can you think of new challenges for a JIT manufacturing environment (after implementing IoT)?
• Does it make the process more complex than it already is? 10. Are there any conflicts created that might affect manufacturing procedures? 11. Can you think of challenges/risks of integrating an Industrial Internet of Things
within a JIT production environment in the following categories? • Planning/Implementation • Operations • Security • Employees
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Appendix B – Consent form
Form for informed consent concerning human subject research
Informed consent
I (name participant)
hereby consent to be a participant in the current research performed by
I have agreed to take part in the study entitled
Lean and Industry 4.0 and I understand that my participation is entirely voluntary. I understand that my responses will be kept strictly confidential and anonymous. I have the option to withdraw from this study at any time, without penalty, and I also have the right to request that my responses will not be used. The researcher is responsible for a safe storage of the data. For questions about privacy protection: mr. A.R. Deenen ([email protected], data protection officer of University of Groningen). The following points have been explained to me: 1. The goal of this study is
Participation in this study should help advance our understanding of
2. The current study will last approximately 60 minutes. At the end of the study, the researcher will explain to me in more detail what the research was about. 3. My responses will be treated confidentially, and my anonymity will be ensured. Hence, my responses cannot be identifiable and linked back to me as an individual. 4. The researchers will answer any questions I might have regarding this research, now or later in the course of the study. Date: Signature researcher(s):
Date: Signature participant:
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Appendix C – Coding tree (a) (The extensive coding tree can be provided upon request)
Illustrative quote 1st Order code 2nd Order category 3rd Order theme Maintenance Consultant Sr. (4): information is needed to combine the process data as the registration of all the process information like: flow, temperature, pressure or maybe also some other process, logistic information. Mostly, it's stored in as history in the server so you can use it for years, this historical data. To combine with the maintenance data. We get that information quite easily. It is no problem. Getting the process information, all the alarms and trips and also a lot of data collection
Quick access to process data
Preventive Maintenance
Manufacturing Performance
Maintenance Consultant Sr. (4): If you don't have the capacity (human or budget capacity for purchasing the software) we can take care of their asset that's a big advantage for them and for us. Or when they have I.T or O.T restrictions. So, we take care of that and we have our own environment and we can raise that. And sometimes the customer says: okay, everything is okay but assure me an availability of 95 percent. We have a lot of trouble in getting the availability on 90 percent. They want to let us organize their assets in that way and get rid of all the problems with the asset they have. And they're wondering if we are probably able to do it in a good way.
Outsource maintenance and process
optimization analysis
Vice President supply & IT (5): It can prevent failure. It can reduce the downtime or costs. The sooner you know that you will have a problem the less costs will be to prevent the problem or to prevent the downtime at all.
Prevent failure
Process Improvement Employee (1): Well, we have the laser cutting process. The combined orders can be from the past and from the future. But it creates problems. On our workstations that come off, and we also saw it in the lanes, the FIFO lanes. It could be that the last carts could be more urgent than the first carts. And that's a problem you create at the first workstation and therefore orders have to wait a long time, for assembly for example. And I think that's where we can make the biggest improvements as well… I think at this moment we work according to a pretty simple dispatch first come first serve. It's very easy to manage. It's just what comes first goes first. But it's not accurate. And Internet things can help us by determining the right priority.
Sort jobs by priority Planning and facility layout
Supply chain manager (1): Referring to what you said about being accurate in the priorities and the way you manufacture things. Sorting order on your job orders. You can have some kinds of lights (red and green) on the pallets. And it's continuously receiving messages from our ERP system, whether it's in a hurry or not. So that might be one solution of IoT where it can help us producing more accurate. I think it can help us in determining the right priority and which order to work on next. Manufacturing Director (2): ... a lot of our products are already in BIM. So we can for example get all the old products in BIM 2D format and can make a 3D card use of that, and you get an automatic article list. We have a special office in Poland with all the products scanned, which makes all the data available so we continuously give this to all customers and you see that (for example in the Netherlands but also a
Data transparency
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couple of other European countries where is already used) you can generate big bills of materials by doing big projects.
Predictability and Production Readiness
Engineering Manager (3): ...you see that the flexibility of the demand and flexibility of the customers is increasing. And our throughput time is too long to react on that flexibility, and we need to decrease that throughput time. So what we are trying to do now is to decrease the throughput time that the product is under construction because the period of touching time on the product is way smaller than the time that it is in the factory. It's only a small part that you are actually working on the product and more other waiting times. And it is how can we remove those waiting times and achieve a good flow.
Throughput-time reduction
Maintenance Consultant Sr. (4): I think now, just in time mainly relies on prediction. Using data. When you know what the influence factors are on your process, you're able to produce just in time. The better you know the higher reliability your model has. The higher, the more reliable you are, more able to deliver just in time. So, there is a huge need of having information of what are the influence factors on the delivery. You have to address all the influence factors. If it's the weather, if it's the different kind of crew you have at night or the day. And how they use the machines, which buttons they press. So, all those influence factors have influence on your just in time. Positive and negative. So, the more you know... the more the influence factor there are, the less complex it will be. The human brain cannot analyze it anymore. You have to use data. To see the correlation and to act on it, to ensure just in time
Advanced use of data analytics
Engineering Manager (3): So as you define a process handling to a component and you change, you can bring automatically a b-o-m to the work, to the shop-floor and there, automatically would be generated a work instruction based on the components of that product.
Direct real-time access to bills-of-materials
Flexible manufacturing
system Managing Director CI (5): a good example from Kaizen. We have to do screwing and we have to also scan the part. For example, now with 4.0 we can have two devices but combined. So now I have my screw tool, but I can also automatically scan it. So, I have less handling and that’s what I’m searching for.
Versatile/Adaptable systems and tools
Vice President supply & IT (5): what you see nowadays (on getting the order to the supplier and see that he has delivered) with IoT implementations is that you also want to track the whole supply chain exactly by kilometer at G.P.S. tracking, where’s the transport companies and so on. That you can really track if he manages the ETA (estimated time of arrival) date, if he’ll be in time at the factory. So, this whole chain will be more transparent.
Supply chain tracking
Supplier reliability and transparency
Engineering Manager (3): So why do I need to put an order manually through to a supplier? I want to do it automatically. And now I have to pick the order of the customer and I have to transfer it to orders. And it will take days before that order is broken in different orders to supply it. And if you can automate that, you win days. So the supplier has more time to deliver that product in time. So every point of data transfer which is
Automated ordering
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Appendix C – Coding tree (b)
not automated (from one to the other) will take time and will hamper just in time.
Management practices
Vice President supply & IT (5): I think training is very interesting in the context of the industry 4.0. You want to ramp up to maximum volume as soon as possible because the peak in market demand of a new car is always in the beginning, in the market introduction. So, your ramp up should be as soon and therefore you need a huge training phase of people. And if you digitally train your people now, then you can make it more compact. You can train more people parallel because it’s still not physical. So I don’t use training room, I don’t need training cars, I don’t need training parts so.
Flexible employee training
Enhanced workforce
competences Managing Director CI (5): if they have an iPhone or they have an iwatch, you can also say some data can be displayed on the iwatch. You can also say industry 4.0 is automated because the tracking system exactly knows that this guy from the maintenance department can be the quickest to this robot. He also knows he’s not busy with any important thing. So this guy will go on and the message will be pushed to his own watch and then you have a good combination between what do we like to do in the system, what’s the information flow and how can it work together.
Track and employ workers
Managing Director CI (5): And that’s why we work together to be part of the department. Should the level in production be skilled enough that the workers can do it all themselves? That they support the system. We are reliable for the process and the information and that’s a complete mindset because that means also that you have new tasks, new ways of working. You need cross sectional teams to make this a success.
Interdisciplinary alignment
Change Management Planning Manufacturing Director (2): ... the IT people sometimes
ask me a question and I go back like: I don't understand anything about your question because from the beginning all you had was Buzzwords, which I absolutely don't know. So that means that we don't speak the same language. It takes a very long time to speak the same language and to have the ability to listen to each other.
Communication between departments
Manufacturing Director (2): Because the whole core of the company is running with an ERP system SAP, if it happens, then we don't know what's in our warehouses anymore, we don't know our production orders anymore. We cannot scan all products and automatically put it in the warehouse. It will create a big mess.
Cyber-attacks
Internet Risks
Maintenance Consultant Sr. (4): I think the main thing is they are aware about security. I think that's the main challenge. Security, to ensure that and nothing is done with the data. When we start a conversation, just within five minutes it pops up: Discussion about data security. Because they have a reputation to keep high. And if there is an accident or something, all the data is at our server, at our cloud.
Information Security
51
Quality Manager (1): We have a backup for three months back and are able to go back to that. But that brings you to deal with new problems, with new orders and new customers etc.
Production/Scheduling data-loss
System reliability
Data integrity and Security
Managing Director CI (5): for example in the airplane industry and in the shipping industry you have fancy just in time where there are still a lot of paperwork in the background and cd burnings that if the system breaks down they exactly know where they are because then from legal-wise in an airplane, that all the documents that cannot be related to airplanes has to stay on the ground and then it goes like this. So liability always manufacturing liability is one of the biggest issues. If you know by phone nice if you have a 5G but for example if the banking app is not working, everybody in the Netherlands has a problem. It's the same in production. So you got to have a very fast Just in Time and reliability. If there is a break, how they organized that you can still use the phone and we can still give a lot of orders to them.
Upkeep a fast JIT in case of network
reliability issues Data Loss
Manufacturing Director (2): the most difficult path it's making people use it and change the organization. And if you talk with all the companies they say the same, implementing takes one month, getting people to use it regularly takes six months to a year.
Coaching of employees Adequate employee education
Employees
Engineering Manager (3): You will see that the engineering department is relatively young. They adapt quite easily, and you see that the changes for the older people are more difficult. Tooling is becoming too complex
Coaching of older employees
Director (4): when you may be more flexible then you have to abandon those standard ways of working because you get a new one. So, I think that this is a risk that they will keep doing on the way they did it. People following the old procedure and not the new one. So that is a threat because people will stay working in the old controlled way because then they can prove that they did that right. And won't listen to the new wave. There you have a risk. They could have the perception that it is a risk.
Trust in the old
Resistance to change
Managing Director CI (5): Then you’ve got the link between old and new systems. But also, for generations. You have the guys from the beginning they’re trustful and with the new generation they say: what is this?
Fear of the new
Managing Director CI (5): It’s also that people say: “you are controlling me” because you get all the data so you can see he’s stopping always or is too slow on Mondays…. And if you have a break down of 15 minutes, you always start after 18 minutes and that you are rushing up because all the data is available and you can exactly see what people are doing how they are working if they stick to what they have learned or not and that is the biggest resistance that people sometimes say I avoided because you are controlling.
Continuous employee monitoring
Counteractive actions
Manufacturing Director (2): Things get more transparent and people in general don't like that because they feel that they feel more observed and less freedom.
Employees feel "more observed"
Managing Director CI (5): you have old and new systems in one line to combine. So, it means that the need for skills, we are still struggling with it because you have new and old technology and you have to combine them. That’s sometimes very difficult.
Combine "old" with "new" technologies
Proper
Implementation Paths
Operations
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Innovation Manager (6): .. all the technology, the new technologies, new machines are very much depending on a high network and a good infrastructure. A good I.T. infrastructure. So it's really demanding. In the new lines we have, for the automatic placement, if there is a, well, one millisecond of no connection then the machine stops right away
Demanding I.T infrastructure
Vice President supply & IT (5): Nice example somebody told me of the Belgium post. They have the guy who empties the postal boxes while you gather all the letters in every village they have a barcode reader and every postbox has a 3D barcode and he has to scan this barcode so that they have a KPI that the postal boxes emptied in time and in the service level agreement before 7:00 or after 7:00. Whatever. The postal guy makes a copy of this QR code goes to the pub drink beer and scans the barcode at the exact right moment when he's drinking beer in the pub he scans his barcode to satisfy his KPI. He didn't realize that this barcode reader also has a G.P.S. tracker so that the management can see that the barcode was not scanned at the postal box but in the pub. Now the question is can you use the data to blame that guy. Is that privacy data. Yes or no. It's a nice example.
Data usage restrictions
Rules and Regulations Compliance
Managing Director CI (5): So if you show people in the line with instructions and people are on it with their face. With the new law, If someone leaves he can say please remove all my pictures and all instructions so then you have to know digitally which guy is on which picture so it is no more even. And you see also on YouTube now you see guys but then the face is white because you are not allowed to do it. How do you support the digital advantage with this kind of stuff.
Prohibition on the use of personal data in
retrospect