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Industry 4.0 with a Lean perspective

Investigating IIoT platforms’ possible influences on data

driven Lean

Master’s Thesis 30 credits

Department of Business Studies

Uppsala University

Spring Semester of 2017

Date of Submission: 2017-05-30

Alex De Vasconcelos Batalha

Andri Linard Parli

Supervisor: Desirée Holm

Abstract

Purpose: To investigate possible connections between an Industrial Internet of Things (IIoT) system,

such as Predix, and data driven Lean practises. The aim is to examine if an IIoT platform can improve

existing practises of Lean, and if so, which Lean tools are most likely influenced and how this is.

Design/Methodology: The paper follows a phenomenon-based research approach. The methodology

contains of a mix of primary and secondary data. The primary data was obtained through “almost

unstructured” interviews with experts, while the secondary data comprises of a comprehensive review

of existing literature. Moreover, a model was developed to investigate the connections between the

concepts of IIoT and Lean.

Findings: Findings derived from expert interviews at General Electric (GE) in Uppsala have led to the

conclusion that Predix fulfils the necessary requirements to be considered an IIoT platform. However,

the positive effects of the platform on the selected Lean tools could not be found. Only in one instance

improved Predix the effectiveness of a Lean tool. Overall, data analytic efforts are performed and let to

better in-process control. However, these efforts were independent from the Lean efforts carried out.

There was no increase in data collection or analytics due to the Lean initiative and Predix is not utilised

for data collection, storage, or analysis. It appears that the pharmaceutical industry is fairly slow in

adapting new technologies. Firstly, the high regulatory requirements inherent within the pharmaceutical

industry limit the application of cutting edge technology by demanding strict in-process control and

process documentation. Secondly, the sheer size of GE itself slows down the adoption of new

technology. Lastly, the pragmatic approach of the top management to align the digital strategies of the

various industries and thereof resulting allocation of resources to other more technologically demanding

businesses hinders the use of Predix at GE in Uppsala.

Keywords: Lean, Industry 4.0, Industrial Internet of Things, IIoT, Predix, Pharmaceutical Industry, Big

Data

Master Thesis | Batalha & Parli

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Table of content

1. Introduction ..................................................................................................................................... 1

1.1. Research area .......................................................................................................................... 2

1.2. Research question ................................................................................................................... 2

1.3. Contributions & Approach ...................................................................................................... 2

1.4. Structure .................................................................................................................................. 3

2. Literature review ............................................................................................................................. 4

2.1. Lean......................................................................................................................................... 4

2.1.1. Lean as a concept ............................................................................................................ 4

2.1.2. Lean in practise ............................................................................................................... 7

2.1.3. Critique on Lean............................................................................................................ 10

2.2. Industry 4.0 ........................................................................................................................... 11

2.2.1. Technological advancements ........................................................................................ 12

2.2.2. Identification, sensing and communication ................................................................... 12

2.2.3. Radio Frequency Identification (RFID) & Networks ................................................... 13

2.2.4. Middleware ................................................................................................................... 14

2.2.5. Applications .................................................................................................................. 15

3. Empirical framework .................................................................................................................... 16

3.1. Predix .................................................................................................................................... 16

3.1.2. Data capture, process and management ........................................................................ 16

3.1.3. Storage to analysis and outcomes ................................................................................. 17

4. Research Model ............................................................................................................................ 19

5. Methodology ................................................................................................................................. 21

5.1. Research strategy .................................................................................................................. 21

5.2. Research design .................................................................................................................... 21

5.2.1. Qualitative Content Analysis ........................................................................................ 21

5.2.2. Data collection .............................................................................................................. 22

5.2.3. Sample ........................................................................................................................... 23

5.3. Operationalisation ................................................................................................................. 23

5.4. Data evaluation ..................................................................................................................... 25

5.5. Scope of study ....................................................................................................................... 25

5.6. Limitations ............................................................................................................................ 26

6. Findings......................................................................................................................................... 28

6.1. Lean-Six-Sigma Certification ............................................................................................... 28

6.2. Process .................................................................................................................................. 31

6.3. Development ......................................................................................................................... 31


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6.4. GE Digital ............................................................................................................................. 33

6.4.1. The use of Predix .......................................................................................................... 34

6.4.2. The Emerald project ...................................................................................................... 35

6.4.3. What Predix can do ....................................................................................................... 35

6.5. Pharma industry .................................................................................................................... 37

7. Analysis & Discussion .................................................................................................................. 40

7.1. Predix as an IIoT system ....................................................................................................... 40

7.2. Lean tools and practises ........................................................................................................ 41

7.3. Predix and Lean .................................................................................................................... 43

8. Concluding statement & further research ..................................................................................... 46

8.1. Further research .................................................................................................................... 47

References ............................................................................................................................................. 48

Appendix A: Asset Performance Management ..................................................................................... 53


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List of Tables and Figures

Figure 1: Main concept of Lean production (Shah & Ward, 2007) ........................................................ 6

Figure 2: Input, Action & Output (Sangwan, 2013) ............................................................................... 9

Figure 3: Connected devices forecast (Ericsson Mobility Report, 2016) ............................................. 13

Figure 4: Data capture, processing, and management (General Electric, 2016d) ................................. 17

Figure 5: Research Model ..................................................................................................................... 19

Table 1: Interviews ............................................................................................................................... 23

Table 2: Operationalisation ................................................................................................................... 24

Table 3: Coding for evaluation of the interviews.................................................................................. 25


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1. Introduction

“We have seen what happens when 3 billion people get connected, next we are going to see

what happens when 20 billion machines are connected” (Comstock, 2016). These words by GEs’ Vice

Chair elegantly point out the huge potential of the fourth industrial revolution. For the first time in the

digital age, sensors have become cheap and powerful enough, clouds are able to send, receive, and

process vast amounts of data quick enough, and software is smart enough to draw meaningful

conclusions from data in real-time. These technological innovations build the foundation of Industry

4.0.

Yet, to unlock the potential of Industry 4.0, companies must understand the new technologies

and its challenges and opportunities. According to Manyika et al. (2015), to unlock the full potential

value of Industry 4.0 it is vital that various systems are fully integrated into each other, otherwise only

40-60% of the potential value will be captured. Furthermore, data analysis must be improved as the

clear majority of collected data is currently not considered in decision-making processes (Manyika et

al., 2015). Cloud-based software platforms like GEs’ Predix may be the solution to that problem. Predix

claims to deliver huge potential in streamlining operations, estimate future demand, reduce machine

downtime, improve maintenance schedules and increase efficiency of input while maximising output

(General Electrics, 2016a). All this will result in more efficient and effective operations and thereby

reduce costs while simultaneously boost revenues. The possibilities and applicable areas seem endless.

From improved efficiency in jet engines, over real-time images of a beating heart, to predicting

electricity demand and adjusting a windfarms’ output accordingly. The automated analysis of vast

quantities of information allows managers, doctors and many others to take better decisions, faster, and

with less externalities predicting market fluctuations, demand or bottlenecks more accurately. This is

achieved by creating a Digital Twin (further explained later on) that allows managers to see the

performance of the asset in real-time and take decision based on the provided information (Sanders et

al., 2016; Magruk, 2016; Miorandi et al., 2012).

This ongoing drive for higher efficiency is represented in the concept of Lean. Lean is

considered one of the most efficient manufacturing concepts and has found widespread adoption in

manufacturing companies globally. The core of the concept is the reduction of waste in production

processes, thus increasing efficiency and reducing production costs (Womack et al., 1990; De Treville

& Antonakis, 2006; Narasimhan et al., 2006; Moyano-Fuentes & Sacristan-Diaz, 2012). Such a

manufacturing concept should theoretically profit from the rise of Industry 4.0 as it uses standardised

processes and data analysis to detect waste in production and subsequently strives to remove it.


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1.1. Research area

As the first effects of the Industrial Internet of Things (IIoT) are starting to show, this trend

with wide implications needs deeper scientific investigation. Significant strides in efficiency are among

the first results to show. Efficiency has always been a major concern for manufacturing companies.

While many manufacturing philosophies focus on increasing efficiency, none embodies this approach

as rigorously as the Lean concept. How the Lean concept can be influenced by the IIoT is still unknown.

Thus, this study aims to investigate the phenomenon of IIoT through a Lean perspective. Both concepts

are large in scope and the possibly affected areas are heterogenous. To be able to capture, describe,

document and conceptualise this phenomenon, thorough literature review and in-depth interviews with

experts in the respective fields have been conducted. As a result of this research, the study examines

the most likely influenced aspects of Lean through the rise of IIoT.

1.2. Research question

Based on the above mentioned current developments in the respective fields, the following

research question is formulated:

Can established tools and practises of Lean be improved by the rise of the Industrial Internet

of Things?

1.3. Contributions & Approach

Considering recent technological advancements, especially in sensor and cloud-based

technology, the phenomenon of Industry 4.0 and smart cloud-based software platforms like Predix and

its effects on widespread manufacturing principles such as Lean needs scientific investigation. This

trend has created large investments and is highly discussion in business media. With the possible

outcomes of the technical advancements (e.g. big data analytics, machine to machine communication,

process efficiency improvements etc.), the contribution of researching this area is not only scientific,

but also managerial. The existing established literature of the phenomenon Industry 4.0 is considered

low to none existing, and the research gap diverse. As the possible implication of this trend is spread

over a number of research disciplines, areas and businesses, this research will contribute by starting to

explore both, where the trend is today and how it is developing. The contribution will not only be

regarding IIoT and Industry 4.0, but also investigate the possible influences these can have on the

established discipline of Lean. As the Lean concept is widely implemented in todays’ industries, a

possible influence from this trend will contribute to both, the development of the concept in science and

in business. As both the trend itself and the connection to Lean is not previously researched, the paper


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follows a phenomenon-based researched approach as promoted by Doh (2015). The starting point was

the interest to learn more about this occurring trend. During the initial phase of collecting information

and reading up on the phenomenon, it became clear that the phenomenon-based research approach

would yield the most interesting results. The novelty of the trend, the lack of research conducted and

the possible interesting findings also support this approach. What was not realised at that stage was how

complex and confusing the phenomenon-based research approach can be, as a number of unforeseen

obstacles and challenges arose. The most significant was the sheer endless scope of the trend. Digging

deeper into the matter, a multitude of possible alleys opened-up and appeared to be relevant and

interesting. At the time of the first interview with an expert on Predix and IIoT, the information overload

was overwhelming and the paper could have developed in various directions. There is a challenge to

make sense of the trend and decide which parts should be included or left out, and how it should be

structured, explained and presented to the reader. Moreover, finding a suitable theory through which

the trend, or at least parts of it, could be adequately explained proved to be a major challenge. A further

challenge was the fact that the trend is heavily relying on the development of information technology,

which in itself is a rapidly developing discipline. Because of the technological nature and novelty of the

trend, it proved to be difficult to find experts on the matter. Last of all, the lack of structure of the chosen

approach resulted in scattered research efforts, consequentially leading to various aspects being research

that were not included in the paper. Bringing all these different aspects together and building a logically

structured, well-formulated paper with interesting findings is considered a challenge in itself.

Nevertheless, the research did reveal interesting results and shed light on a large research gap which

should be investigated. Additionally, most of the challenges faced were due to little to none previous

research. This research will provide guidance for future researchers to further give insights, develop

concepts and provide contributions to the research and the managerial community.

1.4. Structure

The paper is divided into eight sections. In section one, the topic is introduced along with the

research area leading to the research question and a brief commentary on the approach used. Section

two comprises of the in-depth literature review of the areas Lean and Industry 4.0. The third section is

the empirical review of the platform Predix. The research model is presented in section four. Section

five is devoted to the methodological approach of the paper and reasoning for the used methods’

legitimacy. In section six, the primary data will be presented. Section seven covers the analysis and

discussion of the findings before section eight draws a conclusion and highlights key findings and future

research.


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2. Literature review

The following chapter presents previous research and established theories in the fields of Lean

and Industry 4.0. It lays the scientific foundation upon which the model is based. First off, a detailed

description of the concept of Lean is presented, including how Lean has emerged as a concept in practise

and two points of criticism towards the concept. Secondly, the main components of Industry 4.0 are

introduced and discussed, including a short empirical trend description.

2.1. Lean

After the Second World War and the large-scale devastation of the Japanese manufacturing

facilities in combination with a lack of funds to invest, Japanese manufacturing companies faced a

challenging environment. Out of this environment, Taiichi Ohno and Shigeo Shingo, two managers at

Toyota at the time, developed a new way of manufacturing goods that became world-renowned as the

Toyota Production System (TPS) (Sugimori et al., 1977). What made TPS superior to traditional models

at the time, such as the Fordism manufacturing concept, was that it enabled Toyota to manufacture cars

with lower inventory levels, human effort, investments, and defects while simultaneously offering a

greater product variety (Womack & Jones, 1996a; Moyano-Fuentes & Sacristan-Diaz, 2012; Sangwan,

2013). The origins of “Lean”, which is sometimes referred to as “Lean Manufacturing” (LM) or “Lean

Production” (LP), can be traced back to the TPS (Papadopoulou & Özbayrak, 2004; Sangwan, 2013).

The generic term “Lean” was first introduced by Krafcik (1988) and later adopted by researchers from

the International Motor Vehicle Programme at the Massachusetts Institute of Technology that

researched why Japanese automotive companies outperformed Western automotive companies

(Sangwan, 2013). In the early 1990s, the LM concept was popularised through the book The Machine

That Changed the World by Womack et al. (1990), which promoted Lean as an alternative to the

traditional mass-production concept created by Ford (Moyano-Fuentes & Sacristan-Diaz, 2012;

Sangwan, 2013). In short, Lean was developed to satisfy the needs of smaller markets with an increasing

demand for customised products (Moyano-Fuentes & Sacristan-Diaz, 2012). The main idea behind

Lean is to reduce waste and stripping down production to only value adding activities, thereby saving

costs by improving productivity, quality, and delivery times while minimising inventory levels and

maximising capacity (Womack et al., 1990; De Treville & Antonakis, 2006; Narasimhan et al., 2006;

Moyano-Fuentes & Sacristan-Diaz, 2012).

2.1.1. Lean as a concept

From its inception, the concept of Lean follows these five basic principles proposed by Womack

and Jones (1996b): (1) Specify value; (2) Identify the value stream; (3) Avoid interruptions in value


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flow; (4) Let customers pull value; (5) Start pursuing perfection again. However, the concept of Lean

is under constant evolvement and thus, the five basic principles have been expanded over time. For

instance, Shah and Ward (2003), take a practical viewpoint and regard Lean as a set of 22 managerial

practises divided into four bundles, just-in-time (JIT), total quality management (TQM), total

preventive maintenance (TPM), and human resource management (HRM).

JIT aims at continually reducing waste with the goal of achieving a waste-free production

process (Sugimori et al., 1977; Narasimhan et al., 2006). According to Shah and Ward (2003), the “two

major forms of waste are work-in-process (WIP) inventory and unnecessary delays in flow time” (p. 9)

and can be reduced by a set of production flow related tools such as “lot size reduction, cycle time

reduction, quick changeover techniques to reduce WIP inventory and by implementing cellular layouts,

reengineering production processes, and bottleneck removal to reduce unnecessary delays in production

processes” (p. 9). However, for these tools to enable JIT, certain preconditions have to be established.

For instance, relying heavily on on-time deliveries, the focal company is forced to create strong

relationships with its first-tier suppliers (Sako & Helper, 1998; Womack et al., 1990). Moyano-Fuentes

& Sacristan-Diaz (2012) find that this usually means less suppliers. JIT strategies have led to stronger

relationships with lower numbers of suppliers (Sako & Helper, 1998; Womack et al., 1990; Moyano-

Fuentes & Sacristan-Diaz, 2012) consequently increasing the supply chain risk and thus the need for

higher degrees of trust and information exchange between the parties involved (Christopher & Lee,

2004).

TQM is based on the following nine key principles: “cross-functional product design, process

management, supplier quality management, customer involvement, information and feedback,

committed leadership, strategic planning, cross-functional training, and employee involvement” aiming

at continuously improving and sustaining output of quality products (Cua et al., 2001, p. 2; Shah &

Ward, 2003). A strong commitment to TQM was one of the reason the Japanese automotive sector

outperformed their Western counterparts after the Second World War (Sangwan, 2013). While

researches like Dahlgaard and Dahlgaard-Park (2006) regard TQM as a management philosophy by

itself, Shah and Ward (2003) regard TQM as a part of the Lean concept derived from the TPS and its

notion to statistically evaluate the quality of output. A crucial aspect of TQM is the standardisation of

work processes. By standardising work steps, the human influence on variation by different work

approaches is minimised and thereby also the variance in output (Cua et al., 2001; Alvarez et al., 2009).

In recent years, one can observe another trend regarding Lean and quality control initiatives called Lean

Six Sigma, where Six Sigma aspects are added to Lean initiatives (Hoerl, 2004; Assarlind et al., 2012).

Developed in the 1980s by Motorola, Six Sigma aims, like TQM and the TQM aspect of Lean, at

improving quality and can be seen as the US version of TQM (Dahlgaard & Dahlgaard-Park, 2006).


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The objective of TPM is maximising asset performance by the means of increasing equipment

efficiency and reducing equipment down-time (Cua et al., 2001). In their TPM bundle, Shah and Ward

(2003), include practises such as planned predictive and preventive maintenance, maintenance

optimisation techniques, and new process equipment or technology acquisition. Cua et al. (2001)

include the use of proprietary equipment and processes in TPM. For companies pursuing a production-

based competitive advantage, the development of firm-specific applications for their equipment or

understanding the potential of technological advancements can constitute a significant advantage (Cua

et al., 2001).

HRM accounts for the human factor in Lean. The HRM bundle is composed by the two practises

of self-directed work teams, and flexible, cross-functional work force (Shah & Ward, 2003). Shah and

Ward (2003) regard these two practises as “higher level” practises which include “lower level” practises

such as job rotation, job design, job enlargement, formal training programs, cross-training programs,

work teams, problem solving groups, employee involvement. These “lower level” practises form an

integral part of every Lean implementation and are positively connected to the workforces’ job attitude

(Groebner & Merz, 1994). Moyano-Fuentes and Sacristan-Diaz (2012) find that “for the favourable

effects of Lean to be achieved, the work does require the operative to master a wide range of skills and

to be highly identified with the task” (p. 16). Therefore, management should focus on retaining and

continuously training their workforce, especially since the Lean concept strongly depends on employee

commitment as feedback from shop-floor employees is imperative to identify and eliminate waste

(Rothstein, 2004; Suzuki, 2004; Moyano-Fuentes & Sacristan-Diaz, 2012; Sangwan, 2013). This is in

line with findings from Alves et al. (2012) promoting the viewpoint that the workers in Lean assume a

position of “thinkers, continuously looking for improvement” (p. 1).

Three years after proposing the first Lean model, Shah and Ward (2007) revised their concept.

Figure 1 shows Shah and Wards’ (2007) concept of Lean proposing three underlying constructs with

ten operational constructs that summarises 48 operational measures of Lean as a concept.

Figure 1: Main concept of Lean production (Shah & Ward, 2007)


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The ten operational constructs are:

(1) Supplier Feedback: provide regular feedback to suppliers about their performance.

(2) JIT delivery by suppliers: ensures that suppliers deliver the right quantity at the right time

in the right place.

(3) Supplier development: develop suppliers so they can be more involved in the production

process of the focal firm.

(4) Customer involvement: focus on a firm’s customers and their needs.

(5) Pull: facilitate JIT production including Kanban cards which serves as a signal to start or

stop production.

(6) Continuous flow: establish mechanism that enable and ease the continuous flow of products.

(7) Setup time reduction: reduce process downtime between product changeovers.

(8) Total productive/preventive maintenance: address equipment downtime through total

productive maintenance and thus achieve a high level of equipment availability.

(9) Statistical process control: ensure each process will supply defect free units to subsequent

process

(10) Employee involvement: employees’ role in problem solving and their cross functional

character. (Shah & Ward, 2007)

Contrary to Shah and Ward (2007), who promote a strongly practical viewpoint of the concept,

Papadopoulou and Özbayrak (2004) regard Lean as an “holistic approach that transcends the boundaries

of the shop-floor thus affecting apart from the production itself almost all the operational aspects such

as design, development, quality, maintenance, etc. as well as the entire organisation and management

of the company” (p. 4). However, the proponents of the philosophical orientation have so far failed to

establish strong linkages within this conceptual world.

Regardless of whether Lean is considered from a practical or philosophical standpoint,

researchers agree that it has transformed manufacturing companies around the globe, resulting in more

efficient and effective production (Shah & Ward, 2003; Papadopoulou & Özbayrak, 2004; De Treville

& Antonakis, 2006; Moyano-Fuentes & Sacristan-Diaz, 2012; Sangwan, 2013). However, due to the

scope of this paper, a philosophical approach to the concept of Lean poses considerable problems in

connecting the findings. Thus, the more practical approach by Shah and Ward (2003; 2007) will be used

as the Lean defining concept.

2.1.2. Lean in practise

The research is inconclusive when it comes to how to implement Lean. There is ambiguity with

regards to parallel or sequential implementation. Authors such as Hayes et al. (1988) promoting a

parallel implementation argue that Lean tools and techniques should be implemented simultaneously to


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profit from synergies. This is in line with findings from Sangwan (2013) stating that a critical success

factor for Lean is the simultaneous adoption of Lean principles in the supply chain. Opposing this view

are Ferdows and de Meyer (1990), arguing that parallel implementation allows for sustainable capability

growth. Moreover, the resources management can commit towards the implementation process may be

limited (Ferdows & de Meyer, 1990) and a sequential implementation focusing on the most important

activities is to be preferred (Womack & Jones, 1996; Zayko et al., 1997; Moyano-Fuentes & Sacristan-

Diaz, 2012). In line with these findings, Zayko et al. (1997) propose a process of creating teams, training

the teams in Lean techniques, conducting analysis of possible improvements, promoting staff

involvement, and rethinking production processes. As Lean as a concept must be tailored to the specific

environment and needs of each company individually, there is no stepwise guideline or implementation

process (Sangwan, 2013).

However, there is a set of proposed pre-implementation, implementation and post-

implementation tools for a successful employment of Lean (see figure 2). Comm and Mathaisel (2000)

argue for a strong cooperation between various partners in the supply chain, proposing a perspective

involving the entire supply chain of a given product already in the pre-implementation phase. In that

scenario companies should: (1) identify links in their supply chain to create strong relationships; (2)

target potential stakeholders to find out who is willing to participate and would gain from the initiative;

(3) create a research agenda to narrow down the focus of the Lean initiative; (4) test the research agenda

to assess strength and weaknesses of each participant; (5) use benchmarking to set quality and

performance standards; and (6) analyse the findings (Comm & Mathaisel, 2000). Sangwan (2013),

taking a single company perspective argue for a focus on raising awareness in the pre-implementation

phase. The key people involved, e.g. front-line workers, supervisors and management, should be

educated on what constitutes Lean, what shall be improved and what the drivers and barriers within the

company are (Sangwan, 2013). Additionally, Sangwan (2013) proposes that in the pre-implementation

phase, plans for the implementation and post-implementation phase should be drawn up. As for the

implementation phase, the focus should lie on identifying and eliminating waste (Sangwan, 2013) and

improving performance throughout the entire value stream (Comm & Mathaisel, 2000; Hines et al.,

2004). According to Sangwan (2013), only in the implementation phase should companies focus on

creating strong relationships with key suppliers as to ensure on-time delivery and thereof resulting low

inventory levels, and customers, as to ensure shortened delivery times. Implementing Lean is not a finite

process, therefore companies should continuously seek opportunities to eliminate waste and focus on

only value-adding activities thus outsourcing non-value-adding activities, foster knowledge sharing,

seek cost-reduction opportunities and a good organisational fit to improve the performance of the entire

system (Pettersen, 2009; Comm & Mathaisel, 2000; Sangwan, 2013). In the post-implementation phase,

the company should observe and analysing the previously taken steps and assess their respective

outcomes, focus on gradually building Lean skills and capabilities within the organisation, review


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employee performance and acknowledge excellence as well as continuously seek opportunities to

further reduce waste and increase performance (Comm & Mathaisel, 2000; Sangwan, 2013).

Figure 2: Input, Action & Output (Sangwan, 2013)

That being said, research has found various factors hindering and fostering a successful

implementation of Lean practises. For example, large manufacturing companies are more likely to

successfully implement Lean than SMEs and SMEs in general hesitate to implement Lean (Shah &

Ward, 2003; Sangwan, 2013). This can be partially explained by high implementation costs (Sangwan,

2013) and lack of skill and expertise (Achanga et al., 2006) within SMEs. Another explanation may be

that large companies can leverage their strength in creating favourable environmental conditions, hire

outside experts, and influence customers and suppliers, thus increasing the probability of successfully

implementing Lean (Sangwan, 2013). Panizzolo (1998) state that the management of external

relationships appears to be more important than internal operations to successfully implement Lean

principles. Lewis (2000), exploring three cases, agrees with those findings, stating that external factors


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such as type of market, dominant technology, and supply chain structure strongly influence the

performance of Lean.

On the other hand, common failures in implementing Lean practises are poor logistics and

supplier integration (Naylor et al., 1999; Sangwan, 2013), and wrongly predicting future demand

(Christopher et al., 2006; Sangwan, 2013). For Lean to be successful, companies need to create a level

production schedule, minimising adverse conditions such as volatility, uncertainty or variation (Naylor

et al., 1999; Christopher et al., 2006). Long lead-times and predictable demand are favourable

conditions for a Lean strategy, whereas unpredictable demand and short lead-times pose serious

challenges to a Lean strategy (Naylor et al., 1999; Christopher et al., 2006). Additionally, Naylor et al.

(1999) stress the importance of backward integration of Lean practises and failures in doing so will

hinder the performance of a Lean production approach.

2.1.3. Critique on Lean

Lean has found wide-spread adoption as an efficient manufacturing concept. Yet, as for every

concept, there is praise and critique. Criticism on Lean is mainly focused on two specific aspects, the

universality of the concept itself and the human factor within the concept.

From a conceptual perspective, one point of criticism is the assumption of the universality of

Lean as a concept. Cooney (2002) argues that proponents of Lean ignore the “general business

conditions, the nature of buyer-supplier market relationships and the structure of social and political

institutions” (p. 5) which “all have an influence on the realisation of value” (p. 5). In Cooneys’ (2002)

opinion, the proponents of the Lean concept fail to acknowledge environmental influences and regard

the concept itself as universally applicable. Specifically, Cooney (2002) questions the universality of

long-term buyer-supplier contracts and mentions the incompatibility of the JIT concept with short-term

supplier contracts and shifting customer preferences. Cooney (2002) states: “Lean is dependent upon

production levelling throughout the whole supplier chain to achieve JIT and without this precondition

being met the utility of Lean practise is called into question” (p. 6). These assumptions are in line with

findings of Christopher et al. (2006) pointing out the limitations of Lean when demand is unpredictable

and lead-times are short. Cooney (2002) continues: “This assumed superiority of JIT is, however,

questionable on two grounds: first, because there is a range of labour and product market factors that

influence the adoption of JIT and second, because it is unclear whether the value added by JIT can

actually be realised in the marketplace in the form of profits” (p. 6). The first statement is partially

supported by Papadopoulou and Özbayrak (2004), who found that Lean is uncommon in high-variety

low-volume (HVLV) production as, the high degree of customisation and complexity of production,

among other factors, hinders a successful implementation of Lean. With regards to the latter statement

by Cooney (2002), Özbayrak et al. (2004) offer evidence to the contrary. In their research on production


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costs, the pull system as used in Lean approaches produced products at considerably lower costs than

the push-system, thereby showing that the cost-savings from JIT approaches actually translate into

profits in the marketplace (Özbayrak et al., 2004).

The other main stream of criticism on Lean is based around the factor human. Opponents of the

Lean concept argue that the claim of Lean proponents, that Lean empowers the workforce (Shah &

Ward, 2003; Taj & Morosan, 2011) is false (Landsbergis & Cahill, 1999), and that Lean actually results

in increased stress levels (Hutchinson et al., 1996; Sugimoto, 1997; Gill, 2003), increased worker

turnover, absenteeism, reduced performance and poor health (Gill, 2003). Partially agreeing while also

refuting those findings is Sangwan (2013), stating that Lean in fact increases the stress levels, but not

on the shop floor as suggested, but rather on the managerial level. However, when testing Lean against

three other work-organisation models, Taylorism model, human relations model and socio-technology

models, on the two dimensions central or decentralised orientation and human factor orientation, Lean

ranked number two overall, outperforming the Taylorism model and the human relations model

(European Foundation report, 2001).

2.2. Industry 4.0

The Industry 4.0 is the idea that the world is currently on the threshold of a fourth industrial

revolution. The phenomenon is built on the possibilities that with current technologies, such as machine

to machine communication, sensor technology and big data analysis, there is a possibility to enable a

new sort of smart end-to-end production and facilitation. (Magruk, 2016; Sanders et al., 2016) The idea

is that these technologies will support a new type of intelligent and self-optimising machines that will

synchronise themselves with every part of the value chain, from production, design, or ordering of raw

materials, to servicing and recycling (Sanders et al., 2016; Magruk, 2016; Spath et al. 2013) utilising a

higher level of predictive analysis than used today (Sanders et al., 2016). The Industry 4.0 is shaping

the development throughout the sector of information communication technology (also known as ICT)

(Miorandi et al., 2012; Atzori et al., 2010). The vision is to connect the real environment with a Digital

Twin, collecting and analysing data to better predict outcomes and support decisions, automations, and

the whole value chain (Sanders et al., 2016; Magruk, 2016; Miorandi et al., 2012). From a system

perspective, Industry 4.0 is a highly dynamic and radically distributed network environment, composed

by a huge number of nodes, producing and consuming information through a cloud-based system

(Miorandi et al., 2012). One way of viewing how this is possible, is through the recent technological

advancements and the possibilities they present.


12

2.2.1. Technological advancements

Rüssmann et al. (2015), mention nine technological advancements that are powering the digital

revolution in production; “Autonomous robots, simulation, horizontal and vertical system integration,

the internet of things (IoT), cybersecurity, the cloud, additive manufacturing, augmented reality and big

data and analytics” (p.3). Magruk (2016) argues that the driving forces behind the phenomenon are

“IoT, cloud computing, big data, cyber-physical systems…” (p. 1) among others. The industry has also

witnessed the further development of existing, indispensable technology that has reached a necessary

level of low cost and/or sophistication. GE summarised some of the cost development for driving their

major investment in the Industry 4.0 software development, i.e. Predix, as “By taking advantage of the

rapid explosion of sensors, ultra-low cost connectivity, and data storage together with powerful

analytics. These value-added services can produce business outcomes for customers and produce

incremental revenue for the company” (General Electric, 2016d, p. 4). Those recent developments have

all together enabled Industry 4.0.

Identifying the theoretical delineation of IIoT is a challenge due to the heterogeneity of the

subject and its interdisciplinary nature. Following the reasoning of Atzory et al. (2010), the IIoT can be

realised by three paradigms; Identification, sensing and communication, Middleware, and Applications.

In the following section, the most impactful technological advancements will be described following

the categorisation of the presented paradigms. The aim is to provide an understanding of the role and

function of the technology to enable a comprehensive picture of Industry 4.0, and not to provide an in-

depth, technological survey.

2.2.2. Identification, sensing and communication

The first part of the chosen categorisation is targeting the enabling technology for collecting

data, and communicating the information. Those are the wireless “things” that are pushing the vision

of the industrial revolution forward. Ericsson (a leading ITC company connecting 40% of the Global

mobile traffic through their infrastructure (Ericsson, 2014)) estimate that in 2017, IoT devices will

exceed mobile for the first time (see figure 3) (Ericsson Mobility Report, 2016). The information that

is communicated is at a device level, both from “sensing” the environment and communicating this

outward, but also digesting information sent by other devices. In this context, wireless technologies are

enabling data transfer and playing a key role as deployment is rising. (Atzori et al., 2010)


13

Figure 3: Connected devices forecast (Ericsson Mobility Report, 2016)

The rise of deployment of transmittable sensors indicates that the reduction in size, weight,

energy consumption, and cost can result in a new way of integrating “radios” in almost all objects,

which truly incorporates the basic idea of IIoT (Atzori et al. 2010). To investigate how these “things”

will work in Industry 4.0, they must be active parts of the whole business, the information processes,

and interact and communicate with each other by ingesting and transffering data of their environment,

while to some level, react autonomously to the information sensed and shared. This could be through

running processes that trigger actions and create higher value with or without direct human intervention.

(Gubbi et al., 2013) The most discussed type of “smart sensors” are called Radio Frequency

Identification Sensors (RFID). These have existed for a while and are often used as an identification

tags (highly adopted by subway systems and modern keys). The RFID system is highlighted in this

study, as other types of sensors are too diverse, depending on the targeted data they collect.

2.2.3. Radio Frequency Identification (RFID) & Networks

In the context of Industry 4.0, an important component is to enable a digital system that can

identify unique parts/machines (Finkenzeller et al., RFID Handbook, 2003; Gubbi et al., 2013; Atzori

et al., 2010; Weber, 2010). As the technology has progressed, these identification tags have been

developed to sense other RFID tags, and perform actions dependent on customised triggers. A query

for this can, for instance, be the presence of tags in the surrounding area and providing a read of the

current environment. It can therefore be used to monitor objects in real-time, without the need of being

in sight, allowing for an accurate virtual representation of the real world, creating a Digital Twin. (Atzori

et al., 2010; Gubbi et al., 2013) With this kind of virtual map, the application scenarios are multiple,

spanning from logistics, to monitoring diverse and complicated bottlenecks.

Reviewing these from a technical point of view, the RFID tag is a small chip with an antenna

that can use induction to transmit information. This means that the reader and not the sender is the


14

power supply and the RFID tags works without a battery. As these are quite simple in terms of how to

sense conditions outside, other than nearby RFID tags and possible readers, the low cost is why this is

an important piece of technology and should still be considered a breakthrough in this paradigm. (Gubbi

et al., 2013) The sensor network is crucial for the Industry 4.0. It is the cooperation of RFID systems

and other types of sensors. These can be developed to incorporate temperature and such sensor data that

is not supported by the RFID system to close the gap between the reality and the virtual world. The

combination of the different types of sensors will result in a sensor network consisting of large numbers

of intelligent nods that collect, process, and analyse valuable data gathered from a vast and diverse

environment (Gubbi et al., 2013; Presser et al., 2009). Thus, the implementation of these sorts of sensors

is crucial in a modern Industry 4.0 to enable a representation of the physical world, as a virtual Digital

Twin and enabling a self-organised ad hoc network (Weber, 2010).

2.2.4. Middleware

Middleware is the software part enabling long-distance communication and large volume data

transfers, providing more complex analysis and data storage. Experts suggest that it is possible by

centralising the computing power. It is the architecture and programs that all the sensors should be

centralised and installed on, becoming the middle step between data collection (from sensors and

machinery) to applications (programs, analysis, presentation, automation programs etc.). (Gubbi et al.,

2013; Atzori et al., 2010) One key aspect of the middleware is its feature of hiding the details of different

technologies from the diversity of the whole IoT system to enable the programmer to work on specific

issues or applications. Thus, not presenting all data collected in an unsorted manner but more specified

and sorted data storage to enable the development of a certain application tailored to a desired type of

outcome. When handling big data and complicated infrastructures, the middleware has gained

importance over the last years as the latest technology has improved in processing and computing

capacity and thereby the possibilities of developing a sophisticated middleware system (Atzori et al.,

2010). One of the main problems regarding the middleware part is to be able to uniquely identify

devices, functionalities, and environments, while providing a common set of services (Atzori et al.,

2010). To be able to integrate a large amount of different type of data streams, a multi-layered system

is suggested by several researchers as it can help in the standardisation of processing and storage of

data, referred to as Service Oriented Architecture (SOA) (Atzori et al., 2010; De Deugd et al., 2006;

Pasley, 2005).

The approach and rise in popularity of the SOA is mainly due to its principles regarding

decomposing complex and monolithic systems to more well-defined data and components. This means

having standard interfaces and protocols for the whole enterprise system. The idea is based upon that

all business data should be associated with object action, workflows and processes. (De Deugd et al.,


15

2006; Atzori et al., 2010) This should facilitate the coordination and interaction between different

sections to result in lower time needed to adapt itself in the fluctuating environment. Another popular

aspect of the SOA approach is the software and hardware reuse. As the architecture does not impose a

specific technology or program for the application implementation, it allows reusing both, the technical

parts and the hardware (Pasley, 2005). Another important aspect of the middleware is the centralised

infrastructure to support both storage and analytics in real-time. A new rising trend is the cloud-based

storage and computing solutions. One of the main advancement in cloud-computing is the newly

reached capability of handling unprecedented amounts of data but also the ability of presenting that data

on several platforms, not restricted by location. (Gubbi et al., 2013) For all this to work, smart

applications must be developed on the middleware.

2.2.5. Applications

Application is the last step of the implementation of Industry 4.0. This is the visual part of the

system on top of the architecture, exporting the whole virtual representation, data, and analysis to the

end user. (Atzori et al., 2010) As this paradigm is highly centred around the user, the possible

visualisations and applications vary as it is not as restricted by recent technological development, but is

depending on the desired visualisation of the application. This paradigm is important to separate from

the middleware, as it is used for all functionalities, exploiting the data structure and streams to enable

the top layer applications (Gubbi et al., 2013; Atzori et al., 2010). It can be described as the perfect

integration between applications and the distributed systems (Atzori et al., 2010). Gubbi et al. (2013)

argue that the visualisation is the most critical aspect of the application paradigm as the interaction

between the user and the environment is through this medium. Therefore, it must be handled and

designed with the intention of easy use, as the end users’ technical competency may vary widely. To be

able to have innovative applications, a unified framework across platforms seems to be the best solution.

Another important aspect of the application system should be the possibility to share information across

functions and finding patterns through big data analysis. This unified type of system should be

supporting the idea of cloud data storage and computing as it is one of its key abilities.

Yet, to unlock the potential of Industry 4.0, companies must understand the new technologies

and the challenges and opportunities that arise. Furthermore, data analysis must be improved as the vast

majority of collected data is currently not taken into account in decision-making processes (Manyika et

al., 2015). Companies should investigate how Industry 4.0 will change existing business models, spur

innovation and facilitate new business models (Manyika et al., 2015; Dijkman et al., 2015).


16

3. Empirical framework

The empirical framework will consist of a preliminary research on GEs’ platform Predix. This

was carried out as the platform had not been described and analysed in previous research. Furthermore,

this analysis was required to gain an understanding of the platform in preparation for the later expert

interviews.

3.1. Predix

GE is one of several major players which has truly invested in the development of Industry 4.0.

Due to their strong position in various industries, the access to clients and data has made GE a leader in

the development in the field of IIoT (Pelino, 2016). Their Predix platform is one of the systems designed

as an IIoT operating system. It is a cloud-based platform to enable industrial-scale analysis through

connecting employees with machines and data. The system is built as an open platform for companies

to build applications and programs on, but they have also developed applications in-house and in

cooperation with clients. Predix promises its customers to streamline operations, estimate future

demand, reduce machine downtime, improve maintenance schedules and increase efficiency of input

through maximising output (General Electrics, 2016a). This is envisioned to result in more efficient and

effective operations and thereby reducing costs while simultaneously boost revenues. There seems to

be almost limitless opportunities to apply this platform. The system has been implemented in several

different industries, and results are starting to show. The strength seems to be the automated analysis of

vast quantities of information which allows managers, doctors and many others to take better decisions

without having to travel to the asset in question. (General Electric, 2016a)

3.1.2. Data capture, process and management

The categorisation of the structure starts with collecting huge amounts of data from sensors,

gateways, enterprise databases and other data sources, which can be found on the far left side of Figure

4. The data is ingested through a cloud gateway (see figure 4) from the sources in real-time or together

with the usage of bulk upload tools. This is primary to enable identifications of the sources. The bulk

upload tools are set in place to speed up the generation of code, design and testing but also to make it

possible to create data flows that can be unstructured or structured, depending on the design. However,

as the data amount must be processed efficiently, even when ingesting vast amounts of data from

millions of assets, a data conversion takes place in the “pipeline processing” (see figure 4). Therefore,

the data format is standardised to enable the required speed for real-time predictive analytics and data

modelling. In the pipeline processing, a policy framework is set to provide governance and cataloguing


17

services, allowing data cleansing, data quality control, data tagging and data enrichment (e.g. merging

two separate data collections such as weather and location). (General Electric, 2016b)

Figure 4: Data capture, processing, and management (General Electric, 2016d)

The next and last step in the data flow is the storage, which is the purely big data management

part of the system. Predix is presented to store the data using three different structures with separate

functionalities. Time series database (TSDB), binary large objects (BLOB) and relational database

management system (RDBMS). (General Electric, 2016d) Firstly, TSDB is used when classifying data

in a sequential order. This is data that is stored chronologically, and is categorised with the aspect of

time, e.g. daily temperature, weekly sales etc. (Fu, 2011). BLOB is data which is large in terms of

memory size, typically a video or image. (Ince, 2009) RDBMS is a data management system that

supports the “relational model”. That is a way of storing data that views information as a collection of

distinctly named tables. It is widely used today as a way of storing data. (Butterfield & Nyondi, 2016)

The reason for using this is to allow for both operational and analytical purposes. It does also provide

blending capabilities, combining different aspects in an analysis (e.g. historical data over time combined

with current events) which will be further investigated below. (General Electric, 2016b)

“Storing the data is sort of different from analysing the data. Analysing the data comes

afterwards basically. What you do when you have such huge amounts of data is that you

write your algorithms to process the data independently of your algorithms that provide

the data. The analytic algorithms are actually pushed down to the storage of the data...

So rather than reading the data up to a client where the analytics are performed, the

client pushes the algorithms down to the multiple data servers.” Sloge (2017)

3.1.3. Storage to analysis and outcomes

Predix offers an industrial analytic framework that is scalable and reusable, to enable a more

data driven business model for customers. The goal is to create targeted analytics that create insights


18

and lead to better business outcomes. GE is developing analytical tools for Predix in-house, but also

encourages and supports third-party development and sharing of tools. (General Electric, 2016b)

There are two types of data analyses: operational and historical. The operational analytics are

real-time analytics that are carried out at the “edge” (i.e. aircraft engines, MRI machines). This type of

analysis is often a problem detection, and supports preventive behaviour which means making changes

to an asset to prevent damage. Operational analytics can be optimisations to enhance performance (i.e.

changing the direction of a wind turbine to maximize output). Historical analytics are big data driven

analysis that are stored in the cloud. This can be used to build largescale predictive algorithms to e.g.

optimise manufacturing plants using historical data. As both analytical types can be intertwined, Predix

enables a feedback loop between the two, with operational analytics at the edge to ensure the efficient

operation of assets, which can be improved over time based on historical analysis (General Electric,

2016d). The different types of analytics can further be categorised as descriptive, predictive and

prescriptive, intended to uncover relationships in data to drive desirable outcomes. Each type varies in

complexity and are targeting different types of outcomes, but can also be used in sequence. (General

Electric, 2016d) Below are three different types of analysis that is supported on Predix:

- Descriptive analytics are designed to assist in analysing what happened and why. It is a

summary of data to give insights from the past, to help in determining how it may influence the

future. This can, for instance, be used to assess the health of machinery over time to decide if

preventive maintenance is required. (General Electric, 2016d)

- Predictive analytics are the use of models to forecast what might happen next. This can help to

efficiently increase production, based on current events together with historical data on an entire

fleet of machines. (General Electric, 2016d)

- Prescriptive analytics is the most complex type that is supported by Predix. It is intended to

assist in the decision-making processes. This means that Predix is able to determine possible

actions that have the largest possible outcome for the bottom-line. (General Electric, 2016d)

This concludes the empirical work. From this research, a deeper understanding was achieved

and used for the development of a comprehensive research model. The insights and knowledge gained

from the literature and empirical work was also used as preparation for interviewing experts regarding

the two subjects.


19

4. Research Model

Figure 5: Research Model

Based on the literature review on Lean and IIoT and empirical research on Predix, the following

research model is proposed (see figure 5). The model is twofold. On the left side, IIoT and Predix

criterions and on the right, chosen Lean tools. From the IIoT and Predix side, the model serves as an

investigation if Predix has the potential to fulfil the previously described three paradigms of a true IIoT

platform. As the IIoT part of the research model is designed to prove if the platform is an IIoT

representative, Predix could be exchanged by another platform. If Predix fulfils these three paradigms

(Identification, Sensing, and Communication; Middleware; and Application), Predix can be regarded

as a true IIoT platform and stand as a representative of the trend. Therefore, the first part (left side) of

the model is to establish whether Predix in fact can stand as a representative of IIoT or not. This will be

done by conducting interviews with experts on Predix. Through this first-hand data collection method

together with the empirical research, sufficient evidence that Predix fulfils the required paradigms is

gathered. Consequently, it is suggested that the platform has the potential to greatly increase efficiency

in manufacturing.

The second part of the model is to investigate Lean and potential tools of the concept that can

be connected to IIoT. This was done by consulting the literature to find which Lean tools could be

influenced by data analytics. Thus, the various tools and practices within the Lean concept have been


20

investigated. Moreover, interviews with Lean experts have been conducted to investigate how the

pharmaceutical industry uses the various Lean tools. This also functions as an analysis of the

implemented Lean practises towards the conceptual framework. Therefore, if the following tools can

be connected to the IIoT part, great improvements in efficiency should be achieved, increasing the

performance of various assets involved. The selection is based on the empirical and literature review,

concluding that the following Lean practises have the highest potential to be positively impacted by

software platforms such as Predix.

If Predix is able to aid in these Lean techniques, we argue that the platform has vast potential

in the manufacturing sector allowing companies to manage their physical assets more efficiently and

thereby directly impacting the bottom-line of these companies.


21

5. Methodology

The following section will present the method used for this research. Firstly, the research

strategy will be explained, followed by the description of the research design. Next, the

operationalisation is presented continued by the data evaluation, the scope of study and finally, the

limitations of the study.

5.1. Research strategy

The purpose of the study is to explore what possible implications an IIoT system can have on

today’s businesses from a Lean perspective. As IIoT is a new phenomenon discussed in few well-

established research papers, a qualitative study is the most suitable research approach. Through a

qualitative study, the subject is explored and researched as a whole. Therefore, the investigation is

conducted to find underlaying correlations between a newly developed area (IIoT) and its possible

effects and influences on the well-researched subject of Lean (Bryman & Bell, 2011). While Lean is a

well-established discipline in both engineering and some other cross-disciplinary research areas, the

phenomenon of IIoT is not. Most scientific papers discuss how the trend might influence or disrupt in

the future, without researching an actual existing system. To be able to capture, describe, document and

conceptualise this phenomenon, the paper is structured as a phenomenon-based research paper in line

with the work of J.P. Doh’s (2015) "From the Editor: Why we need phenomenon-based research in

international business". Therefore, the research begins with investigating the phenomenon to create

sufficient knowledge to ensure appropriate theorising and build relevant research design. By using this

approach, a comprehensive knowledge base will be built to enable a well-rounded discussion of the

subject. As the phenomenon is explored, the underlying, possible connections that can be made to the

Lean approach will be brought to light through guided interviews with experts from both fields, Lean

and IIoT.

5.2. Research design

The research design reflects a combination of Qualitative Data Collection (QDA) for obtaining

secondary information and unstructured to semi-structured expert interviews for the primary data

collection.

5.2.1. Qualitative Content Analysis

To establish a theoretical framework and create a holistic view on the topic, existing literature

has been gathered and extensively reviewed. Seidels’ (1998) work on “Qualitative Data Analysis” was

used to guide the literature review. This is considered as highly necessary because of the phenomenon


22

based approach. The approach demands a certain degree of continuous changes, as the comprehension

of the phenomenon grew. QDA is a three-step process, i.e. noticing, collecting, and thinking. Noticing

is characterised as observing and gathering documents, coding things and emphasising important

statements within the texts, resulting in the production of a useable record of sources (Seidel, 1998).

The second step of the QDA is referred to as collecting and comprises of sorting the different pieces

into similar groups of information. The last step is thinking, which focuses on the relationships of the

different pieces of information to each other, ultimately making sense of the individual pieces and

combining them to a create a picture of the phenomenon. As this is a cohesive process of noticing,

collecting, and thinking, it adheres to the need of phenomenon-based research to allow new findings to

nudge the research direction of the paper as knowledge regarding the subject grew (Dubois & Gadde,

2002). Seidel (1998) refers to this process as iterative and progressive, describing it as an “infinite spiral

of thinking” (p. 2), and recursive in the sense of simultaneously collecting information while realising

which new information to collect next.

5.2.2. Data collection

The phenomenon-based approach resulted in a more unstructured research design during the

early phases (Doh, 2015). During the initial phases of the study, interviews with experts of IIoT were

conducted to gain insights in the phenomenon, and gain enough understanding to see possible

connections within the field. The first interview followed an “almost unstructured interview approach”

(Bryman & Bell, 2011) to allow the interviewee to talk freely, and permits a more open-ended

discussion (Bryman & Bell, 2011). At this stage of the phenomenon-based research, the scientific theory

against which the findings will be evaluated was still open. The information gained through the first

interview (e.g. focus on efficiency) resulted in the decision to incorporate Lean into the research scope.

As the research area involves two subjects, IIoT and Lean, experts on both topics had to be interviewed.

The interviewees were chosen based on their current work description, experience, and familiarity with

the topics. The experts had to work with various applications of IIoT and/or having sufficient knowledge

of the Lean concept to be considered to have adequate understanding of possible implications of the

researched trend. That was to not exclude experts who did not have insights in both subjects, but to

consult experts in the respective fields regarding possible connections. At this stage, interview

guidelines were composed, and the work on qualitative research by Holzhauer (n. d.) and Dubois and

Gadde (2002) was consulted. The result was a collection of questions that followed the abductive

approach by Dubois and Gadde (2002), allowing for continues changes of the questions asked based on

findings from previous interviews. After each interview, transcripts were carried out and coded

regarding the different issues discussed. A short analysis was carried out to enable continuous learning,

reflection and improvement for the next interview.


23

5.2.3. Sample

The approach for researching the two chosen disciplines was first to confirm if Predix has been

implemented at any degree at the GE LifeScience HealthCare (LSHC) Uppsala site. Secondly, an

investigation in the sites’ Lean implementations or developments was carried out. The approach resulted

in five interviews with experts in at least one of three subjects, i.e. Predix, Lean and/or data analytics.

Below is a presentation of all interviewees in the order the interviews were conducted (n) (see Table

one), their current occupation and relevant work project, and finally the main topics that were discussed

during the interview.

n Name Occupation Main Interview

Topics

1 Eric Sloge Senior Software Engineer, Lead

Developer for Emerald (Predix)

Predix,

Data analytics

2 Anna Stjerndorff Production Engineer, Data Analyst,

part of Lean implementation

Lean,

Data analytics

3 Alfred Daniels Section Manager for Dextran,

Implementing a Lean Certification

Lean

4 Peter Sandblad Senior Software Engineer, Six-Sigma

Blackbelt

Predix, Lean,

Data analytics

5 Niclas Våhlin Software Engineer, Developer for

Emerald (Predix)

Predix,

Data analytics

Table 1: Interviews

5.3. Operationalisation

The research model concluded what parts of the existing literature framework will be

investigated. As the first part of the research model is influenced by the preliminary empirical research,

the IIoT and Predix part (see figure 5) was constructed by the appropriate concepts derived from several

authors. That also holds true for the second part of the research model (see figure 5), Lean. Both of

these were categorised and operationalised in order to create and present the most relevant parts of the

preliminary research. As the IIoT and Predix part of the model could be considered to have insufficient

previous operationalisation, the framework was built on theoretical specifications and arguments. With

this approach, the goal is to still be able to establish the criterions this platform should have to be

scientifically representative of the trend. For Lean, the literature was operationalised by reviewing what

Lean tools should have the best possibility of an influence regarding sophisticated data analytics. This

was carried out by reviewing the literature, but also consulting with Lean experts regarding the subjects.


24

Factor Research Reasoning Interviews II

oT

/Pre

dix

Identification,

sensing and

communication

Atzory et al. (2010);

Gubbi et al. (2013);

Weber (2010);

Finkenzeller et al. (2003)

Factor has to be fulfilled

to consider Predix an IIoT

platform

1,5

Middleware Atzory et al. (2010);

Gubbi et al. (2013); De

Deugd et al. (2006);

Pasley (2005)



platform

1,5

Applications Atzory et al. (2010);

Gubbi et al. (2013);

Manyika et al. (2015)



platform

1,5

Lea

n T

oo

ls

Planned &

Predictive

Maintenance

Shah and Ward (2003);

Cua et al. (2001); Shah

and Ward (2007)

Use of data analytics to

reduce machine down-

time

1,2,3,4,5

Maintenance

Optimisation



and Ward (2007)

Use in-machine sensor

data to locate part that has

to be replaced

1,2,3

Technology

Acquisition



and Ward (2007)

Acquire new technology

for production processes

e.g. sensors, software

2,3

Proprietary

Equipment



and Ward (2007)

Develop equipment with

producers by using

process data

2,3

Reengineering

Production Process


Narasimhan et al. (2006);

Sako & Helper (1998);

Sugimori et al. (1977);

Shah and Ward (2007)

Use data to improve

efficiency of production

process

1,2,3,4

Quick Changeover

Techniques





Automated changeover

from one product to

another

3

New Process

Equipment



and Ward (2007)

Improve efficiency with

new equipment. Decision

aided through process

data evaluation

2,3

Bottleneck

Removal




Sugimori et al. (1977);


Use data analytics to

detect and remove

bottlenecks

2,3

Table 2: Operationalisation


25

5.4. Data evaluation

To analyse and find possible connections, a similar approach to the development of the

interview guide was carried out. The continuous changes in the interview guide resulted in variations

of coding. Thus, a finalised coding is carried out after the last interview, and the presentation of data

will follow a similar coding (but not as detailed). This meant transcribing every interview after it was

conducted, code the interview, and discuss possible new topics for the upcoming interviews. After all

interviews were conducted, a case was built, presenting the empirical result through an overview

regarding the selected subjects, and using quotations from the interviews to present a true picture of the

subjects. (Bryman & Bell, 2011) The approach was preferred over a case comparative approach, as it

allowed for a holistic view where different experts contribute insights regarding various aspects within

the chosen fields. The approach is a revelatory case study, as the opportunity to observe (by interviewing

experts regarding current procedures and developments), analyse and discover the phenomenon could

all be achieved at GE LSHC Uppsala. (Bryman & Bell, 2011) Furthermore, the case is analysed through

a comparison with the presented literature. This was to investigate if the Lean tools used are aligned

with the scientific validated ones, and if Predix could be labelled as an IIoT platform. Through the

analytical approach, the researchers hope to create insights in how these topics are represented in

practice and to contribute to an under-researched area. Furthermore, analysis and conclusions regarding

possible external influences that were not part of the theoretical topics were carried out to give further

insights in the practicalities these concepts have today, and to enable a true reflection of the findings.

The finalised coding used to categorise, present, and analyse the transcripts, is presented below,

including the grouping of which interviewees discussed which topic.

Code Topic Interviewee n

A The Pharmaceutical Industry and Customers 1,2,4,5

B The current Production Processes at GE 2,3,4

C The current Data Analytics and Tools at GE 2,3,4,5

D Planned or current Data Analytical Development 2,3,4,5

E Lean and Lean developments 2,3,4

F Predix and Predix Development 1,4,5

G Interviewee Presentation of their current Work 1,2,3,4,5

Table 3: Coding for evaluation of the interviews

5.5. Scope of study

Multiple delimitations were conducted during the exploration of the phenomenon. Firstly, the

issue of security regarding cloud-based systems and data storage has been designated as not relevant,


26

as the issue of security is considered large enough to be researched as a subject by itself. Furthermore,

the willingness of sharing data is a barrier when reviewing data analytics. As the theoretical background

of these are outside the chosen research area and question, the study has limited the review of the issue.

Moreover, technical aspects are not thoroughly discussed in the paper as the technological aspects of

Predix are only relevant to ensure that it can be regarded as a IIoT system and hold no significance

when answering the research question.

As the Lean concept includes the whole focal firm, the decision was taken to only include the

aspects regarding the shop-floor. This, due to the necessary level of sophistication and complexity of a

possible developed application, including the extraction of this type of data, development and design.

One such exclusion was all of the HRM practices as these practises are related to the workforce, which

is not the primary target of advancement in IIoT. Additionally, the study excludes the entire supply

chain aspect of Lean. The decision reflects the inherent complexity of supply chain decisions an

organisation faces and would have broadened the scope if this paper to an unsustainable extend. Both

the HRM and the supply chain is described in the literature review to ensure a thorough overview of

the concept and is considered necessary to understand the concept of Lean. The study will, although

limiting itself through these exclusions, not exclude the possibility of implementation of excluded

aspects outside the chosen scope.

5.6. Limitations

Weaknesses in the research should be considered, especially as the approach inherited some

limitations. The whole sample was carried out at GE LSHC in Uppsala, which can indicate a biased

view and weak generalisation possibilities. The biased view is regarding all the participants being

situated in the same location, representing a small sample of five (Saunders et al., 2009). Thus,

weakening the overarching view on different aspects as only a small part of the organisation is

represented. This can undermine the results of the study as it cannot be strongly argued to be applicable

for other settings (Saunders et al., 2009). Although, the researched trend is not location specific, the

application of the technology is. The sample represents prominent level of knowledge and insights in

the two researched concepts to reassure validity. That also gives deeper level of context. Expert

interviews gave a more comprehensive understanding of the context and issues, compared with having

a higher sample enabling generalisation. The interview approach is also reviewed as a necessity as the

research is an under-researched phenomenon. Furthermore, the sample size is also representative of the

available knowledge as the trend is novel. The issue of reliability is considered high with the chosen

approach and in the research area. This due to the ever-changing nature of the subject and the current

development phase. Addressing these problems and presenting transparency in the methodology, the

reliability of the paper can be considered improved (Saunders et al., 2009). Moreover, the coding is

presented to help future research, resulting in higher reliability. The paper should be used as guidance


27

as it is a first attempt on researching the practical applications of the subjects together. The results may

not be similar if the research is replicated as the subject is developing fast. However, the literature

review, empirical research and the research model were designed to fit the research area and question,

thus contributing to both, research and managerial implications.


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6. Findings

The following section presents the findings from the conducted interviews. The chosen

structure follows a case-building presentation with various categorised aspects. The section is divided

into Lean-Six-Sigma Certification, Process, Development, GE Digital, and Pharmaceutical Industry.

6.1. Lean-Six-Sigma Certification

Alfred Daniels, section manager for the Dextran production at GE LSHC, and his team are

currently working towards the GE internal Lean-Six-Sigma certification. Due to GEs’ secrecy policy,

we are not allowed to show or discuss specifics of this certification. However, we are able to present an

overview of what it entails and highlight some of the achievements.

Alfred Daniels oversees two factories producing native Dextran and various Dextran products

as intermediate products, meaning that the two factories he manages are in the beginning of the

production line. On site in Uppsala, there are various other factories, however the majority of these

factories is not Lean-Six-Sigma certified and not actively working towards a Lean-Six-Sigma

certification (Stjerndorff, 2017). Thus, in the main production line of Dextran, the only factory working

towards the internal certification, which is not aimed at improving external supply chain relationships,

is the newly built first factory in the production line of Dextran (Daniels, 2017). In this factory, various

Lean tools are applied.

According to Stjerndorff, Daniels and Sandblad (2017), the biggest improvement that was

achieved during the Lean-Six-Sigma certification is the Value Stream Map (VSM). Lean in itself is a

very visual concept, and the VSM allowed the team to gain a deeper understanding of the process and

the critical aspects of it. Thanks to the VSM, the operators on the shop-floor understand the process as

a whole better, understand the implications of deviations from the norm and can actively contribute to

improving the process through another Lean tool, Kaizen, also known as the idea board (Daniels, 2017;

Stjerndorff, 2017; Sandblad, 2017). Moreover, Lean aides to create deeper process knowledge, thereby

enabling the management to work on process improvements (Daniels, 2017). Due to the Lean-Six-

Sigma certification, the engineering team around Anna Stjerndorff created a “rhythm wheel” where all

the processes for the various products have been analysed and optimised.

Another well-documented Lean tool that is regularly used in the GE internal Lean-Six-Sigma

certification is the standardisation of work. By standardising work, all the operators on the shop-floor

are following a pre-defined set of steps to ensure that all operators are working the same way and thereby

reducing variance in the process caused by different ways of working (Daniels, 2017).


29

A third Lean tool that could be observed during the interviews is the one of product pacing.

Alfred Daniels and his team have developed and tested various production scenarios to find the most

suitable with regards to production pacing and level-loading. The processes they developed aims at

spreading work equally over the entire production of a batch, as far as the production process of the

batch allows it (Daniels, 2017). However, due to the limited demand of output at the moment, the factory

does not work on optimal capacity.

Due to the work conducted on the VSM, the material management was visibly improved.

Having a better understanding of the process allowed Alfred Daniels to approach the warehouse and

negotiate standard delivery times, places and amounts. By doing so, the material needed to start a new

batch is at the required locations, in the right amounts, previous to the production start. This allows the

operators to start production on-time, preventing costly delays and reducing potentially wasteful steps

(Daniels, 2017).

Another tool applied is the performance management. Through applying this tool, the entire

factory team is consistently evaluating their own performance and the performance of the process.

Through the collection of data, the managers can see how many production break-downs, faulty batches,

or other issues occurred in a given time period, by trending the data (Daniels, 2017). Additionally,

consistently monitoring the performance in each step of the production, combined with a deeper

understanding of the production process allows the team to eliminate wasteful steps and optimise

workflow (Stjerndorff, 2017). Stjerndorff (2017) states:

“Yes, we have been aware of taking away waste for some time. So that is something we

have improved a lot in our production line. We have been more aware of what is waste,

what is adding value or what is not.”

An additional tool used for the internal GE Lean-Six-Sigma certification is preventive

maintenance and maintenance scheduling. Again, with the help of VSM and a better understanding of

the process through data collection, the managers are able to take decision of which equipment can be

run to failure without risking a production break-down or the quality of a batch, and which equipment

should be replaced prior to failure (Daniels, 2017). For critical equipment, the recommendations of the

equipment producer are usually followed or a number of batches is defined, based on past experience,

after which the equipment has to be preventively replaced (Sandblad, 2017).

The factory team also engages in bottleneck removal activities. Anna Stjerndorff (2017) is

currently working on a project to replace filtration equipment in order to add capacity for the filtration

process. Through analysing the production process, the team found that the filtration equipment

constitutes a bottleneck and slows down the production process. However, replacing the equipment to

eliminate a bottleneck is rare. The team usually focuses on smart planning of the production process

and only replaces equipment when there is no other way around the problem (Stjerndorff, 2017).


30

Although the team around Anna Stjerndorff is working closely with the producer of the equipment, the

new filtration equipment will not contain any proprietary innovations that would be GE exclusive,

thereby not utilising the Lean tool of proprietary equipment.

Four other tools that were mentioned during the interviews but not further discussed in the

context of Lean were: Shared schedule and production plans, focusing on the capacity produced; Stop

and abnormality procedures, resulting in a production halt in case of abnormalities in the process

parameters; quality, focusing on the overall quality of the output; and idea generation, or Kaizen,

allowing operators to share ideas of how to improve the production process (Daniels, 2017; Stjerndorff,

2017; Sandblad, 2017).

In general, the Lean-Six-Sigma certification did not result in an increase of data collection for

this native Dextran producing factory. According to Stjerndorff (2017), the collection of data constitutes

merely on aspect to better understand the process and data has been collected previous to the

certification based on the need of having extensive production knowledge. However, the Lean-Six-

Sigma certification demanded a stronger visualisation and presentation of the data at hand (Daniels,

2017; Stjerndorff, 2017). Sandblad (2017) regarding increased data collection:

“That is not something that is required for the Lean certification, to collect more and

more data. But what is required, is to do a full process map of the whole manufacturing

process. We need to identify what are the really critical steps, we need to identify at those

critical steps, do we have enough control, Quality Control (QC) testing or monitoring of

the processes; and are those measuring techniques good enough to measure what we need

to measure. For the Lean certified lines, it is really just a process to make sure that what

we do is good enough. There is not a huge effort to increase the amount of data for each

point, for instance. It's just, do we have good enough control of the critical parts.”

Yet, other factories on the Uppsala site, which do not collect production data at the level of Stjerndorff

(2017) mentions an increase in data collection of critical and non-critical production parameters due to

the Lean-Six-Sigma certification. The native Dextran factory also experienced improvements due to the

Lean-Six-Sigma certification in trouble-shooting. Stjerndorff (2017) confirmed that the Lean-Six-

Sigma certification has made it easier to troubleshoot and better understand processes by working in

cross-functional teams. Although the majority of the factories in Uppsala is not Lean certified nor

actively working towards a certification, some aspects of Lean appear to have found widespread

application with the GE LSHC Uppsala site. According to Sandblad (2017), all other factories strive for

better visualisation to assist the operators on the shop-floor better understand the process. The

development is striving to have real-time data, as opposed to the reviewing of previous batches

(Sandblad, 2017).


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6.2. Process

With the Lean initiative, standardisation of both processes and critical parameters were carried

out in cooperation with key personnel. Today, data is measured all over the production line with around

2000 different data points (Sandblad, 2017). Out of this raw data, data specialists carry out constant

analysis, monitoring 50 parameters. Six of these have been considered critical for the production

process. These critical parameters help the operators to follow the most important parts of the

production process. Operators write down by hand different parameters on the batch record, e.g. how

many kilos of raw materials, which is afterwards entered manually into a database. The database is an

excel file containing the 50 parameters, creating graphs with process limits, mean and two standard

deviation boundaries. (Sandblad, 2017; Stjerndorff, 2017; Daniels, 2017)

“We have automatic graph presentation that uses the data from each batch. The operators

are putting in the data and Anna (Stjerndorff) is analysing the graphs to see how we are

doing. In the graphs, we have a normal limit (two standard deviations) and we have the

specification or the control limits that we need to be inside. Whenever we go out of two

standard deviations we have a red flag. Is there somethings we need to do, has something

changed, what is happening, is there something with the raw material, is there a sensor

or pump that has been bad or miscalibrated and so on.” (Daniels, 2017)

Furthermore, weekly trending of historical and new data is carried out to follow any outliers or

trends going outside the mean. Anna Stjerndorff (2017), lead data analyst for the first two factories,

follows the trends from the collected data and starts investigations if a parameter trends towards the

outer limits. This is the proactive initiative to ensure long term production process control. The

procedure for this type of investigation is done through meetings where trends are discussed and

investigative actions are carried out for the Dextran factories. This is done by contacting the responsible

designers, scientists and experts of the affected parts, discussing possible causes behind the trend and

work through all of them until the source of the tendency is discovered. After the cause is found, a

report is compiled and can be used to help in future investigations. (Stjerndorff, 2017)

6.3. Development

One of the upcoming developments for data analytical processes is going from manual to

electronic batch records. The manual batch records were put in place to comply with industry

regulations. These regulations created barriers in terms of possible developments of the process. One

of these barriers is to have a paper trail of all the process steps containing signatures of the responsible

operator. This has now been confirmed to be open for change, using an electronic signature which

enables the implementation of an electronic replacement (Sandblad, 2017). The electronic batch record


32

development is for all factories in the Uppsala site. That will help to further improve the data analytics,

increasing the amount of data available for analysis and also save hours of manpower by removing the

process-step of writing everything down manually and transfer the batch records to excel. Furthermore,

this opens up possibilities for different types of developments and applications that are highly desired.

(Daniels, 2017; Stjerndorff, 2017)

“We are in the process of entering a more modern way of looking at this. In the history,

a lot of data was only paper batch records. It still is to some degree, but we are going

forward to electronical batch records, which means we will have more data to look at.

That is a trend, we are getting more and more data electronically and we are getting

better data and easier access to use the data. We still have those issues that we do not

have all the data, so when we enter some trouble shooting or some improvement there, it

is really a lot of time just cleaning up data, entering data from paper batch records, these

hours and hours of entering data from paper batch records into Excel spreadsheets...”

(Sandblad, 2017)

As the communication and sharing of data between the factories can be considered close to

none, the electronic batch records enables some interesting developments. Today, factories can only

decide what parameters are critical for their factory, and monitor these parameters closely. With the

implementation of electronic batch records, knowledge and data regarding the entire process from raw

material to end products will be available. One possible development of this is multivariate analytics.

This type of analysis requires high computing power to calculate millions of possible connections

between various parameters. For instance, how temperature, pressure, moist and chemicals in the early

process steps can influence the product in later stages. These systems can create deeper insights and

highlight connections within the data, allowing for potential optimisations of production processes.

(Daniels, 2017; Sandblad, 2017) Currently, these types of multivariate analysis are not used as the

process cannot be changed without knowing how it may influence the product in later stages of

production.

“... We have specialist for each product, so I maybe ask them how do you think that these

parameters can be affected, to see that you follow that right kind of parameters. It is not

so easy, something in process one can affect something in process four. It can jump to the

next step, but it can also jump four steps ahead. It is just so many possible ways it can

affect each other. It is really big data.” (Daniels, 2017)

Currently, the multivariate analytical development is not yet applied as the electronic batch record is

not yet implemented, but the possibilities have been discussed by higher decision-makers (Daniels,

2017; Sandblad, 2017). Niclas Våhlin (2017), lead software developer, states that currently two larger

trends of development can be observed.


33

The first one is aggregating data and making it usable and visible. As data points are spread

out, aggregation and visualisation of these is understood to have the largest impact. It is viewed as a

significant improvement without acquiring new physical assets. An important aspect of this, that has

gotten a lot of attention at the GE LSHC site in Uppsala, is to make these applications user-friendly.

Before, software engineers made smaller applications that catered to their specific needs. These

applications then spread from site to site and the scope was enlarged. By doing this, the actual data and

software became a threat as the knowledge of who developed what, and what procedures were

undertaken etc. was not defined and the continuous maintenance of these was non-existing. These

“home-made” issue-specific applications have been replaced by official applications, that have a

specific owner and lead developer. (Våhlin, 2017)

The second trend is feeding data into the applications which has improved as GE is moving

more towards real-time data. The earlier described trending of data in the factories is moving more

towards real-time. The goal is to have better visualisation, helping the operators on the shop-floor to

see what is happening, as soon as it happens, instead of reviewing previous batches. There is a current

development project looking into new in-process techniques that should be able to measure aspects in

hours, instead of several days. (Våhlin, 2017; Sandblad, 2017) When the time intervals of the data-loads

decrease, meaning that the systems are fed more often with new data, the aggregation can be performed

more frequently, resulting in better visualisation closer to real-time. This offers possibilities to replace

manual processes with automated systems, hopefully resulting in less variations in the production

processes. (Sandblad, 2017)

It will not change the actually chemistry, but what it will change is [...] e.g. control over

adding chemicals, stirring speed or aeration of gases and addition of raw materials. When

it is on paper and it is done manually, it could be more operator dependant. It is still

people, people drink coffee, they have lunch, sometimes the process time can change from

62 min to 64 min. It is within the specifications of the method, but the more things that are

automated, the less variation we will see and that is something that probably can increase

the quality. Just by adding automation, we reduce variations in time and variations in

operators, how they will perform tasks, if they open this vent before this one, or this one

before that one. (Sandblad, 2017)

6.4. GE Digital

GE is currently experiencing a large shift. A couple years back, the company realised, that an

extensive portion of their engineers were working with software (Sloge, 2017). Due to that discovery

and the emerging trend of increased digitalisation in the industrial sector, the firm decided to undergo


34

a significant change towards a software and analytics company (Sloge, 2017; Våhlin, 2017). One major

step towards that goal was the formation of GE Digital and the formulation of a long-term strategy for

the digital transformation. GE Digital is and will be impacting all the different divisions within the firm

(Sloge, 2017). Although the various division follow their own digital strategy, the goal is to

continuously nudge towards aligning the divisions digital strategy with the over-arching strategy of GE

Digital. Due to that objective, there is a push towards developing all new software within GE on their

Predix platform. (Sloge, 2017)

6.4.1. The use of Predix

Because of the digital transformation, GE is heavily pushing the use of Predix within the

company. However, the LSHC department here in Uppsala has so far been utilising Predix in very few

instances when working on new software solutions (Våhlin, 2017; Sloge, 2017). According to Sloge

(2017), the bio-processes part of the LSHC division has not leveraged Predix that much yet while other

departments with GE LSHC have developed products for the platform. Våhlin (2017) mentions that

although his team has been asked to develop products on the platform, they have hardly used it to

develop software on. The team around Våhlin has been focusing on the previous platforms such as

Unicorn when developing new software. According to Våhlin (2017) his team only uses certain web-

based functionalities or interfaces from Predix compatible with their system architecture. Another

barrier to a wider use of Predix for the Uppsala software engineers is the sensitive nature of the data

their systems store and analyse (Våhlin, 2017; Sloge, 2017). As Predix is a cloud-based service

platform, the sensitive data would be stored in the cloud which raises security concerns from the

customers’ side (Våhlin, 2017; Sloge, 2017). According to Våhlin (2017) the security measurements to

secure confidential and sensitive data in a cloud ramp up exponentially. Moreover, Predix is a platform

that has certain tools and possibilities, but all the problem-specific applications concerning a certain

process have to be developed by software engineers understanding the issue at hand. According to

Sandblad (2017), it is costly and time-consuming. When asked about if an upcoming Statistical Process

Control (SPC) software will be developed on Predix, Sandblad (2017) answered:

“If you ask me today what I think, without any decisions, I will say we go with something

else. There are multiple companies that are working with this full-time. They have

commercial packages ready that don't need all the programming and customisation. So,

today I would say we would go for a more commercial SPC tool than developing it

ourselves on Predix, but it may not be that all the aspects that we want are possible to get

from a commercial tool and this could possibly be an alternative for some of the in-houses

(development).”


35

Sandblads’ statement reflects the overall pragmatic approach than can be observed within GE. Although

the long-term strategy of the firm is to gradually align the digital strategy of the various divisions with

the over-arching strategy from GE Digital, the managements understands the difficulties involved in

such a process (Sloge, 2017). While some divisions of GE are further in that process than the LSHC

division in Uppsala in their pursuit to align strategies and utilise Predix as the primary software platform

to develop applications on, the roadmap indicates that Predix is something the whole GE will be dealing

with sooner or later (Våhlin, 2017; Sloge, 2017).

6.4.2. The Emerald project

In the case of the LSHC division in Uppsala, the first notable use of Predix is a project called

Emerald. Although the project could have been developed on other software platforms, the management

decided to use Predix as a first step to align with the corporate strategy and account for the global trend

towards putting software and services in the cloud (Sloge, 2017). The aim of the Emerald project is to

structure raw material data, so that it can be used for analytics and thereby achieve higher levels of

control over the production process. The software is designed to pool data from GEs’ production and

customers in order to create a data pool that can be mined to find patterns and production optimisations

(Sloge, 2017). According to Våhlin (2017), the big challenge with Emerald is that the data is usually on

various historians in a lot of different areas onsite, which makes it difficult to aggregate all the necessary

data. However, the volume and nature of the data analysed with Emerald does not classify the project

as a big data project (Sloge, 2017).

6.4.3. What Predix can do

The Emerald project is a first glimpse of what can be achieved with Predix. As mentioned

previously, the LSHC division in Uppsala has only touched upon the possibilities of Predix, while other

divisions, such as Aviation, are utilising Predix to a higher degree (Sloge, 2017). According to Sloge

(2017), GE has started to introduce more and more sensors into their machinery and this generates more

and more data that can be analysed with Predix in order to find hidden connections or patterns. Predix

is able to do that and becomes therefore a really interesting tool for GE and its customers. Through the

data created by the machines and analysed by Predix, the customers of GE machinery have greater

control over their assets. They can see how each machine is performing and if it is in need for

maintenance, and if so, which parts of the machine are to be replaced. Predix is designed to collect the

data, to handle large data volumes and to make sense of that data and analyse it. The ambition is to

perform real big data analytics. The software platform gathers data from various sources in a data lake

where algorithms can evaluate the data and then present it in a way that allows managers or engineers

to take decisions. The Aviation division is utilising these aspects of Predix. Their turbines have


36

thousands of sensors monitoring the engine during flight that can be downloaded and evaluated

afterwards. But also the Oil and Gas division is heavily utilising Predix. One big advantage of a cloud-

based platform like Predix is, that if the data is available in the cloud, software engineers from GE and

their customers can write analytics applications, access and mine the data. What makes this even more

interesting is that Predix will deliver the information in real-time. (Sloge, 2017; Sandblad, 2017; Våhlin,

2017)

“There is big drive in GE Digital to move into the big data analytics because they think

that there is a lot of money to get there. There is a lot of interest in relationships, in

interesting correlations in data that we don’t know of yet. But if we just have all the data

available, we can write the cool algorithms’ that find this knowledge.” (Sloge, 2017)

But also at the LSHC division of GE there seems to be interesting applications for Predix. Based on the

previously described production processes of Dextran products and the implementation of the electronic

batch records, Sandblad (2017) sees a possible application for Predix monitoring the various parameters

in the Dextran production:

“This could be done, as we did here, looking at one variable at a time. The next step then

of course is looking at this more multivariate, that we can produce the principal

component analysis or the partial least squares analysis so we can combine all these

different signals that we get and build a model for what is normal. Then we can transform

all this into one signal that could be trended, that you could see if this is normal and if it's

not normal, we can see which parameter is really pulling the strings to make this

abnormal and what'd be the cause for this.”

Multivariate analysis enables several possible process implementations. The analysis can be used to

have total control over the parameters from raw material to end-product. This means that the analysis

can indicate how minor changes in the early production process, e.g. stirring speed, temperature, can

affect the product later on. Multivariate analysis also enables process improvements by reducing

variance in the final product. (Sandblad, 2017)

Another possible development is having data presented in real-time. This trend has been noticed

by all interviewees and when asked if this type of analysis can be performed in real-time, Sandblad

(2017) responded:

“All the techniques and all the algorithms are in any standard statistical package you

have, this can be done. And we also do it for special cases when we want to follow a

process that maybe has a problem, or if it's a process that we are making changes to, or

if it's a new product that is to be introduced we use our multivariate tools to trend and

keep parameters, we can't do it real-time on every process because that's too much manual


37

labour. When the data is there we will, I’m not sure about the timeframe, 3, 5, 7 years

before we have it on everything. I think we start a pilot right after we have the electronic

batch record, the next step is really sorting out the data to know what kind of data we

have, sort them with identifiers to see where in the process they come up and then just

throw them into a statistical package.”

After presenting the technical possibilities, the following paragraphs will highlight the external

environment, which was mentioned to have a significant influence on the adoption of Predix at the GE

Uppsala site.

6.5. Pharma industry

The various drug enforcement agencies such as the U.S. Food and Drug Administration (FDA)

or the European Medicines Agency (MDA) put very high requirements on the production lines of the

various pharmaceutical companies. Companies manufacturing drugs must have extensive knowledge

and high levels of control over their processes (Sloge, 2017) and they must file the entire process,

including all the raw materials or intermediate products, such as the various Dextran products from GE,

with the respective authorities (Sandblad, 2017). Therefore, if GE changes its production process, GEs’

customers will have to extensively test the new process in order to prove that it is safe and then refile

the process with the respective authorities. Or in the words of Sandblad (2017) regarding the

administrative effort of re-filing the process:

“If we would make major changes to our product, it may be a change to the actual

product, it may be a change to the regulatory support filing, it may be a change to just the

QC (Quality Control) method to verify its (the products) quality. Depending on how our

customer has filed their process with the FDA, the European, or the Japanese

pharmaceutical agency, they may do a refiling of their process.”

This is a costly and time-consuming endeavour and thus only undertaken if absolutely necessary

(Sandblad, 2017). Therefore, all the producers in the pharmaceutical industry are more or less locked

in with certain suppliers, as they have designed their process with that companys' specific product, and

the switching costs are unreasonably high. This is also the reason why companies like GE still produce

products developed 50 years ago, as there are still pharmaceutical companies that rely on intermediate

products produced in that specific way (Sandblad, 2017). Moreover, various aspects of the

pharmaceutical industry make real-time analysis difficult. For instance, certain bio-chemical processes

measuring the density of proteins cannot be performed in real-time, as they are analysing extracted

samples and that process can take up to two days (Sandblad, 2017).


38

Apart from the industry specific barriers to apply cutting edge technologies, there appears to be

a reluctance in sharing data with suppliers or customers. On one hand, the sensitive nature of the data

seems to be one of the factors hindering data sharing, an essential part of systems such as Predix.

According to Sloge (2017), one of the major concern of the customers is the security of cloud-based

services. This is confirmed by Våhlin (2017) who states that even secure cloud technology like in the

GE Health Cloud is hardly convincing the IT departments of GEs’ customers. Another limiting factor

for data sharing can be traced back to the industry specific regulations of the pharmaceutical industry.

The more data is shared, the more the customer knows about the product, the more GE is locked in into

the process of its customer, without the ability to even make minor changes to its product.

“The customer would like us to (share more data), but we prefer not to. It's a give and

take from both sides. The more data we give them, the more we will lock our product, in

that we cannot make any changes because this is something that we share with our

customers. So that is why we normally tend to keep it pretty much as a company secret.”

(Sandblad, 2017)

Additionally to the lock-in scenario, GE is facing the prospect of being copied if they provide the

customers with too many specifications or exact values of their products (Sandblad, 2017). The firm is

currently discussing the possibility of offering additional specifications for their products as there seems

to be a high demand from the customers to get more data in order to feed their data analytics systems

and be able to better trend (Sandblad, 2017). This additional information would be sold to the customers

willing to pay a higher price and would be coded. That would allow the customer to trend with an exact

value, perform multivariate analysis for all incoming raw materials but without the possibility of

knowing the exact specifications of the product (Sandblad, 2017). Sandblads’ (2017) statements are

confirmed by Våhlin (2017), who can see a trend towards a better overview of the production data.

Apart from improvement in the data infrastructure, another trend in the market is the reduction

of variance in the raw material or intermediate products. Since the pharmaceutical producing companies

need to have good knowledge and control over the processes, the main point the firms struggle with is

the variance of the raw materials (Sloge, 2017). As the variance of raw materials can greatly impact the

yield or quality of the output, the companies are demanding more specifications from the producers of

intermediate products, such as GE, in order to adjust their production processes accordingly. Designing

robust production processes that can handle variances in 50 or 100 different raw materials is a

challenging task, but a necessity for pharmaceutical companies. To mitigate the risk of these variances,

multivariate analysis tools have found widespread adoption in the later stages of the drug production

(Sandblad, 2017). But also companies like GE, that produce intermediates early on in the supply chain,

have started to look into such tools to decrease variance in their output and thereby increasing the

quality. As Sandblad (2017) explains:


39

“If we talk quality from a customers’ perspective, I would say the absolutely most critical

is the variation of the process. To reduce the batch to batch variation for our customers

would be the key quality aspect. Of course, that would also give economic benefits. We

could have higher yield if we reduce variation risk.” (Sandblad, 2017)

Yet, all these advancements and improvements have to be regarded within the context of not changing

the actual product. According to Stjerndorff (2017), GE aims at improving the process without changing

the actual product. That can be seen by the statement from Sandblad (2017) when asked about the new

factory that was built 2016 on the Uppsala site.

“We haven't done very much modernising. It is of course something we need to do, but for

this factory, it is more or less a copy of the old one. Just a capacity increase, we haven't

fundamentally changed the chemistry or the technique to produce. It is more or less a

copy-paste from before with new and a little bit more equipment, with the same control

system.” (Sandblad, 2017)


40

7. Analysis & Discussion

The following section compares and contrasts the findings from the interviews and tests them

against the proposed model. First off, the question of “can Predix be regarded as an IIoT application”

is answered. Secondly, the proposed Lean tools from the model incorporated within GEs’ Lean-Six-

Sigma certification are presented. Lastly, the second part of the model, the Lean tools are tested against

Predix to determine if they are positively influenced by Predix.

7.1. Predix as an IIoT system

Following the model proposed (see figure 5), Predix should support three separate technical

paradigms to be conceptually recognised as a potential Industry 4.0 tool. The technological paradigms

that have to be confirmed as supported by the IIoT system are; Identification, sensing and

communication; Middleware; and Applications.

The first paradigm Identification, sensing and communication is not a technical specification

or part of the system per se, it is instead that the system needs to be able to incorporate this kind of

technology. RFID tags are by far the most discussed technical parts of the first paradigm (Finkenzeller

et al., Gubbi et al., 2013; Atzori et al., 2010; Weber et al., 2010). The technology primarily functions

as the identification and sensing of the IIoT system. This is needed to enable the system to create a

digital representation of the chosen reality by sensing its environment. Sloge (2017) confirms that

Predix supports the incoming data from sensors, allowing it to be identified and analysed as a

representation of the physical machine. Furthermore, it can be developed to the use of communication

between the sensors and machines. Sloge (2017) regarding the digital representation of GE machines:

“…The capability to monitor and have a complete control over all their assets and also

make GE able to easily access the customer machinery for services etc. So that you have

an easy way of ... or the customers can easily see what machinery they have, and what

condition they're currently in, where they are installed, what are the maintenance parts

that you need, or that... when do we need to send out service engineer for these particular

equipment based on measurements that are being made on particular pieces of equipment.

So it is increasing the level of control that customers have of their inventory.“ (Sloge,

2017)

It can therefore be confirmed that Predix supports the first paradigm: Identification, sensing and

communication.

The second paradigm, Middleware is where the data is stored, analysed and also contains the

structure that the IIoT applications are built upon. It is also the part where the analytical computing


41

takes place, and it should support big data analytics. Some researchers suggest (Gubbe et al., 2013;

Atzori et al., 2010), that this is to be centralised in order to support both, storage and analytics, in real-

time. According to Gubbi et al. (2013), it can be solved by having a cloud-based storage and computing

solution. This supports the idea of enabling data solutions on several platforms that are not restricted

by location (Gubbi et al., 2013). The Predix platform has been confirmed to utilise a cloud-based

solution for the middleware (and application) paradigm (Sloge, 2017; Sandblad, 2017; Våhlin, 2017).

The middleware is therefore the connection between the data digestion (sensors, machinery etc.) and

the applications (visualisations, programs, automation etc.). Sloge (2017) states regarding the

connection of the Predix service:

“Part of that is also to transfer all the analytical data that is taken from the different

machines... so transfer of the service data is also part of the Predix platform… they

download the sensor traffic from the aircraft engine and it is gigabytes of data for each

flight, basically... Big data analytics mean that you collect huge amounts of data, and it

can be unorganized… Storing the data is sort of different from analysing the data.

Analysing the data comes afterwards basically. What you do when you have such huge

amounts of data is that you actually write your algorithms’ to process the data

independently of your algorithms that provide the data and the analytic algorithms are

actually pushed down to the storage of the data. So, it is distributed calculations. Rather

than reading the data up to a client where the analytics are performed, the client pushes

the algorithms down to the data servers, the multiple data servers. You can process data

in different nods in the data lake, if you will. You extract data at different nods where data

is available, then you mine the meta data and push it out to the client again.”

Thus, it can be confirmed that the second paradigm, the Middleware part can be reviewed as a fully

functional IIoT system paradigm.

Application is the last paradigm, and also the visualisation of the IIoT platform. This paradigm

is not technically specified but centred around the user-experience, and is the part of the platform where

data is presented (Gubbi et al., 2013). The Predix platform can be confirmed to support visualisation of

data. (Sloge, 2017; Sandblad, 2017; Våhlin, 2017) Therefore, our conclusion is that Predix can be

confirmed to support the conceptual description of an IIoT platform.

7.2. Lean tools and practises

As the evaluation of the interviews has shown, GEs’ internal Lean-Six-Sigma certification

makes use of a variety of established Lean tools. The most notable practises within the certification, all

of which are well-established and often mentioned tools of Lean, are the practises of VSM (Womack et


42

al., 1990; Sugimori et al., 1977; Narasimhan et al., 2006; Womack and Jones, 1996b), standardisation

of work (Cua et al., 2001; Alvarez et al., 2009), JIT / delivery management (Sako & Helper, 1998;

Womack et al., 1990; Shah & Ward, 2003; Moyano-Fuentes & Sacristan-Diaz, 2012), performance

management (Cua et al., 2001, Shah & Ward, 2003), preventive maintenance (Shah & Ward, 2003;

Shah & Ward, 2007; Cua et al., 2001), and bottleneck removal (Shah & Ward, 2003; Shah & Ward,

2007). However, when comparing these tools to the pre-defined Lean tools from our model, a

discrepancy becomes evident. From the eight tools defined in our model, only two, planned and

predictive maintenance and bottleneck removal, were commonly utilised as part of the GE certification.

As for the bottleneck removal, the team around Anna Stjerndorff analysed the process to find

bottlenecks and used the collected data to either optimise the production schedule, or support an

equipment replacement decision (Stjerndorff, 2017). For the planned and predictive maintenance, data

analytics played no significant role on the decision-making process of when to engage in maintenance

activities. Maintenance was either scheduled based on past experience or equipment producer

recommendation. Moreover, maintenance optimisation as a part of the Lean-Six-Sigma certification

was not conducted and there was no effort made to use production data to optimise maintenance

schedules (Sandblad, 2017; Stjerndorff, 2017). Possible reason could be that the factory applying for

the certification currently runs on around 30% of its capacity (Daniels, 2017). This is largely due to the

estimated future demand of the product and therefore built-in overcapacity. With regards to new process

equipment the findings are inconclusive. Although new equipment has been purchased in one instances,

to remove a bottleneck, the aim of the purchase was merely an increase in capacity without improving

the process or product (Stjerndorff, 2017). Thus, the equipment purchased did not constitute a

technology acquisition or propriety equipment. This is due to the industry specific conditions previously

discussed, where even minor changes to the product could result in considerable cost for the customers

(Sandblad, 2017). Lastly, the tools of quick changeover techniques and reengineering production

processes could not be found during the investigation of GEs Lean-Six-Sigma certification. Whereas

the first mentioned tool was simply not part of the certification and thus not performed, the latter is,

again due to the industry specific conditions, economically not feasible to perform (Daniels, 2017).

Another interesting finding was the GEs Lean-Six-Sigma certification is purely internal.

Research suggest that such efforts are carried out in combination with suppliers to achieve greater

improvements, as long-term buyer-supplier relationships are proven to positively affect Lean efforts

(Cooney, 2002). Possible reason for this may be the fact that GEs Dextran production is very early in

the supply chain and the raw materials purchased by GE are readily available. Thus, a strong upstream

supplier-buyer relationship is not paramount for GE. On the other hand, the previously discussed lock-

in scenario plays heavily into the hands of GE and results in stable long-term supplier-buyer

relationships downstream. This can be regarded as an external factor fostering the adoption of Lean


43

practises and is in line with findings from Cooney (2002) arguing that the business environment plays

a significant role in the success of Lean adoptions.

7.3. Predix and Lean

After having established that Predix constitutes an IIoT platform and GEs’ Lean-Six-Sigma

certification uses various well-established practises found in the Lean theory, the question that has to

be answered is: does Predix increase the efficiency of the selected Lean tools from our model?

A reason why Lean has been successful for decades and found wide-spread adoption across the

globe in various industries is the fact that the underlying philosophy of the concept drives efficiency by

eliminating wasteful steps in the production process. This is achieved by following a data driven

approach with a strong focus on standardisation of work steps, production processes and a continuous

drive for improvement. On the other hand, the current development of IIoT platforms is regarded by

some as the catalyst of the fourth industrial revolution. The corresponding trend of increased data

collection, bandwidth, computing power, and smart sensors at lower costs facilitate the rise of these

platforms. The key to this development is big data and statistical analytics humans are incapable of

processing. These algorithms are capable of finding patterns and connections in unstructured data,

producing outcomes that cannot logically be foreseen. Furthermore, multivariate statistics are a well-

known tool among the interviewed experts, but have not yet found large-scale implementation at GE

LSHC. Although we have found that data-driven Lean is used, and that Predix can be labelled as an

IIoT platform, we could not find support for a large-scale development connecting these two concepts

at the GE Uppsala site. An IIoT platform such as Predix could be useful for both; supporting the current

process evaluation and control - automating the current manual 50 parameters analysis, and also more

effortlessly scaling and incorporating more of similar analytical efforts. The current data development

(e.g. electronic batch records, continues production analytics, nearing real-time data) does indicate that

a possible implementation of an IIoT platform could deliver Lean based applications and drive

efficiency. These developments do have a clear goal of higher control over the production through data

analytics, but the scope of redesigning the whole process-line to incorporate Predix seemed to be too

large a challenge to be considered.

From the eight selected Lean tools that could potentially be experiencing a large impact through

the use of Predix, only one, i.e. planned and predictive maintenance, can be considered to be directly

impacted by the implementation of Predix, and even that was not true for the GE LSHC site in Uppsala

but only other divisions within GE. For three other Lean tools, maintenance optimisation, bottleneck

removal and reengineering production process, we have expert stating that there is evidence of Predix

improving the efficiency of these tools, but there is no implementation at the Uppsala site. Moreover,

we could not find any support for the use of new process equipment, technology acquisition, propriety


44

equipment, and quick changeover techniques on Predix. We argue this is due to the following three

reasons:

Pharmaceutical industry: As mentioned in the findings section, the industry specific regulations

of the pharmaceutical industry pose a large barrier to process optimisation. Whereas in other industries,

companies are free to optimise their production processes as they wish, for instance in the IT industry,

where software is continuously updated (Sandblad, 2017), the pharmaceutical industry faces strict

regulations and documentary requirements for their production processes. It appears that these

regulatory demands hinder the implementation of cutting edge technology in the production processes

as customers are dependent on the intermediate and/or end-products to stay within defined limits and

each improvement in the production process may willingly or unwillingly result in a changing product.

In such a scenario, the customers must refile their production process with the responsible authorities

in order to prove the safety of the product, a lengthy and costly endeavour. Moreover, as the customers

are locked-in with the intermediates they design their production processes with, the refiling of the

production process would break that lock and allow the customer to reconsider their supplier. Therefore,

this is a risky and undesired outcome for GE. Moreover, the pharmaceutical industry does not face as

high pressure from competitive pricing as, for instance the paper or steel industry, and therefore the

profit margins are considerably higher (Sandblad, 2017). It is this combination of regulatory

requirements on processes and higher margins that limit the implementation of the latest technological

advancements. The early adoption of new technology, such as big data projects to improve production

processes, is simply not feasible for companies operating in this market, as the costs heavily outweigh

the benefits. For these above-mentioned reasons, the pharmaceutical industry is not on the technological

edge, such as the paper industry or steel industry, both of which have considerably higher levels of real-

time in-process data available and thus also very high levels of in-process control (Sandblad, 2017).

Industrial giant: As discussed in the GE Digital section, the company is currently refocusing

the strategy from an industrial giant to a software and analytics company. This is no fast and easy

endeavour. As Predix is not only an application software, but a fully integrated platform, the redesign

will have to be a complete overhaul of existing systems for it to be fully implemented. This means that

the whole current data structure needs to be redesigned from scratch using Predix. Furthermore, even

“smaller” applications developments that are desired and greenlit today, are not being prioritised due to

the backlog (Sandblad, 2017). The software engineers primarily work on fixing bugs and making sure

that the current software and applications are functioning and thereby facilitating day-to-day work.

Thus, redesigning the software architecture of a production site, such as GE LSHC Uppsala, is not only

about developing functioning software, but also making sure that the thousands of other applications

used today are still fully functional.


45

Resources: Reviewing the various industries GE is currently operating in, GE LSHC is not

among the industries that are in need to be on the technological forefront with regards to modernising

production processes. As previously stated, the industry faces multiple barriers regarding changing

processes while simultaneously having high margins. Therefore, resources and development efforts are

better allocated to an industry where the competitive environment demands constant technological

improvements. This is not to say that GE LSHC is not subjected to competition and the need for

technological development, but as the transition is vast, prioritisations must be made.


46

8. Concluding statement & further research

Based on extensive research of Lean and IIoT, eight Lean practises were selected that have high

probability to be influenced by IIoT. The selection was based on the tools’ capability to drive efficiency

through data analytics. The selection criterion was in line with the fundamental philosophy of IIoT

systems. To embody the IIoT platform, Predix was chosen to be investigated if it can be a valid

representative. Our research confirmed that Predix fulfils the necessary paradigms; identification,

sensing and communication; middleware; and applications. It was further confirmed that GE internal

Lean-Six-Sigma certification makes use of various established Lean practises. According to our

literature research on the chosen Lean tools used in the model, an increase in data analytics should result

in greater efficiency. Therefore, an implementation of an IIoT platform, such as Predix, should have

positive influences on the Lean tools, utilising its data analytical capabilities. However, when evaluating

the results, only one of the eight tools, i.e. planned and predictive maintenance, was confirmed to be

directly influenced by Predix, while three tools, maintenance optimisation, bottleneck removal and

reengineering production process, were partially influenced, thus questioning the current development

and implementation phase of IIoT.

During the evaluation of the conducted interviews, it became apparent that the pharmaceutical

industry itself seems to hinder the implementation of cutting edge technological advancements.

Platforms such as Predix, which operate at the edge, are scarcely applied in the pharmaceutical industry.

This may be due to various factors that hinder widespread implementation. For instance, the external

environment in this particular industry appears to limit the early adoption of new technology by

imposing strict regulatory requirements regarding process safety and control. As GE produces

intermediates used in drug development and manufacturing, even small alterations in the production

process may result in unforeseen changes of the final product. Thus, the risk of a changing product may

force the customers to refile their processes with two negative consequences. Firstly, this is a costly and

time-consuming task and secondly, but more important for GE, it allows the customers to redesign their

production process and therefore reconsider their supplier. Moreover, the GE LSHC Uppsala site is

highly profitable and estimate an increase in demand for the foreseeable future, driving investments in

production capacity rather than process development and improvement. It can also be seen in the overall

approach GE takes with regards to implementing Predix at the Uppsala site. The company follows a

gradual implementation through new projects when the use of Predix is feasible. This steady alignment

of digital strategies may be faster in more tech savvy industries within GE. The statement can be

supported by the successful implementation of Predix in various other business areas, such as the

predictive maintenance and optimisation of windfarms (General Electric, 2016a). This may be reflected

by allocating resources to other parts of GEs’ diverse business portfolio. We conclude that this could

be a reason for the slow adoption of Predix at the GE LSHC site in Uppsala. The tendency can also be

seen through the neglection of Predix that is occurring at the same time as there are efforts to gain more


47

control over the whole production-process through the use of data analytics. The general conclusion is

therefore that even though the company is investing heavily in GE Digital and its flagship Predix, the

firm seems to shy away from sacrificing business income in the pursuit of pushing forward Predix.

8.1. Further research

We recommend researchers to further investigate our research area and the question of Lean

and Predix, using our research to guide the process. The first suggestion is to carry this out in an industry

without the external barriers that are inherent in the pharmaceutical industry. We suggest a further

investigation of this trend in the aircraft engine industry, which has shown highly sophisticated levels

of data analytics through the use of Predix. The industry faces high volumes of data and could provide

interesting insights in the development of big data analytics. Moreover, the energy industry has been in

the frontline of the development of predictive maintenance applications on Predix. Through the develop

of more complex machine to machine communication applications, the industry has achieved automated

process optimisations. Both of these industries could provide further insights on how the trend of

Industry 4.0 is developing.

IIoT can be considered a new development regarding knowledge and information transfer.

Therefore, it is also suggested to further develop the established research regarding this area (e.g.

Szulanski). The trend might disrupt and change how researchers regard information and knowledge

transfer, as it is often reviewed as rooted in individuals and organisations. Machine to machine

communication and automated process improvements could result in a gap in the current literature.

Furthermore, as both Lean and Industry 4.0 are efficiency driven, the concepts fit. But as the

development is still in the early phases, predictive maintenance was the main Lean aspect developed on

the platform. Another possible perspective that was often suggested is the Assets Performance

Management (APM). A short review of this subject can be found in Appendix A. The concept also

incorporates predictive maintenance, but could give further insights in how the business is reviewing

their assets, and how the trend is developing towards more real-time data. One has to recognise that

APM is rather a practical concept than a scientific discipline. Nevertheless, it seems that this area is

highly developing from a business view, and the scientific research regarding the subject is lacking.


48

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Appendix A: Asset Performance Management

“Asset Performance Management” (APM) can be described as a set of tools and techniques,

coordinated over various levels and departments within an organisation to optimally manage its assets

as to mitigate risks and expenditures while increase asset performance (Brown & Humphrey, 2005;

Brint et al., 2009). It is therefore a management strategy rather than a scientific discipline (Brint et al.,

2009). Alegre (2007) regard APM as “systemic and coordinated activities and practices through which

an organisation optimally manages its assets, and their associated performance, risks and expenditures

over their lifecycle” (p. 2). In that sense, APM managers deal with a large set of questions regarding

acquiring, disposing, maintaining, outsourcing, financing, developing, and training assets (Hastings,

2010).

Figure X, Alegre, 2007

Although APM includes a variety of assets, such as physical assets, human assets, financial

assets, the scope of this paper limits itself to APM with regards to physical assets.

According to Alegre (2007), APM is synonymous with corporate strategy, meaning that all

decisions regarding a firms’ assets must be in line with the overall strategy of the firm. Thus, managers

deciding whether to invest in new assets, liquidate old assets, decrease or increase maintenance budgets

for existing assets and so forth, should do so with the overall strategy in mind. To take these decisions,

the managers must have information about the specific assets and its performance at hand. Information

include such parameters as age, utilisation, condition, performance, risk, lifecycle etc. (Hastings, 2010).

Through the rise of Industry 4.0, the assets are collecting vast amounts of data that firms are currently

unable to benefit from. At this intersection between physical assets and information technology,

software platforms such as Predix may be able to tap into APM and fill the void between the physical

and the digital world.

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