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Final version: 16-‐07-‐2015 Supervisor: prof.dr.ir. H.A. Reijers Prof.dr.ir. H.A. Reijers, VU University Amsterdam. Signature: Dr. H. Leopold, VU University Amsterdam. Signature:
The added value of Internet of Things
P.J.L. Frima -‐ 10747966 Thesis Master Information Studies – Business Information Systems University of Amsterdam, Faculty of Science
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Preface
This thesis is the conclusion of my study for the master of Business Information Systems at the University of Amsterdam. This master study was preceded by the bachelor study Technology Management at the University of Groningen.
I would like to thank several people for their help and contribution to this thesis.
First of all, I would like to thank prof.dr.ir. Hajo Reijers for his support, feedback and input for my thesis. Also I would like to thank dr. Henrik Leopold for being my second supervisor.
I would like to thank Rob de Maat, for providing me with the opportunity to conduct my research at Deloitte. Rob’s enthusiasm, expertise and bright feedback were very useful. Also, I would like to thank the people who provided their insights during the interviews, especially the respondents from Canon, KPN and the Port of Amsterdam and the validation sessions.
Finally, I would like to thank my family and friends for their great support during my master study.
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Abstract
The focus of this thesis is to study in which processes value can be added using Internet of Things (IoT). Also, the current types of emerging applications and inhibitors for IoT are discussed. Many organizations working in supply chains, struggle with the question where to apply the concept in their organization. This is because of the many potential IoT applications. To be able to integrate the concept within organizations, stakeholders need to know what kind of emerging applications are suitable for their situation. Also it is important to know the inhibitors for IoT, as these prevent adoption. This thesis contributes to tackling these issues by presenting a framework that assists to the identification of the value of Internet of Things. The framework is structured using the Enterprise Value Map developed by Deloitte and the IoT value drivers. This thesis has both an academic-‐ as well as a practical contribution, by addressing the current gap in literature regarding IoT value points, practical applications and inhibiting factors seen by the industry.
Keywords: Internet of Things, value, drivers, applications, inhibitors
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Table of Contents 1. Introduction ................................................................................................................................... 1
1.1. Thesis Motivation .................................................................................................................... 1
1.2. Research Goal and Research Questions .................................................................................. 2
1.3. Research Methodology ........................................................................................................... 2
1.4. Scope ....................................................................................................................................... 4
1.5. Thesis Outline .......................................................................................................................... 4
2. Problem definition ......................................................................................................................... 4
2.1. The Internet of Things ............................................................................................................. 4
2.2. Adding Value with Internet of Things ...................................................................................... 5
2.3. Emerging Types of Internet of Things Applications ................................................................. 6
2.4. Inhibitors for Internet of Things .............................................................................................. 7
2.5. Supply Chain Management ..................................................................................................... 8
2.6. Shareholder Value ................................................................................................................... 8
3. Research Method ........................................................................................................................... 9
3.1. Framework design ................................................................................................................... 9
3.1.1. Input ................................................................................................................................. 9
3.1.2. Structure ........................................................................................................................... 9
3.1.3. Purpose .......................................................................................................................... 10
3.2. Case Study Selection ............................................................................................................. 11
3.2.1. Case Description Canon Europa N.V. .............................................................................. 11
3.2.2. Case Description Havenbedrijf Amsterdam N.V. ............................................................ 12
3.2.3. Case Description Koninklijke KPN N.V. ........................................................................... 12
3.3. Case Study Execution ............................................................................................................ 12
3.4. Analysis .................................................................................................................................. 14
3.4.1. Motivation ...................................................................................................................... 14
3.4.2. Analysis of case studies .................................................................................................. 14
4. Plan of Action ............................................................................................................................... 14
4.1. Structure ................................................................................................................................ 15
4.2. Framework ............................................................................................................................ 15
4.2.1. Short term framework, within 0 – 3 years ..................................................................... 15
4.2.2 Long term framework, within 3 – 10 years ..................................................................... 16
4.3 State-‐of-‐the-‐Art ...................................................................................................................... 16
4.4 Emerging types of Internet of Things applications ................................................................. 17
4.4.2 Short term emerging applications, within 0 – 3 years .................................................... 17
4.4.3 Long term emerging applications, within 3 – 10 years ................................................... 18
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4.5 Inhibitors for Internet of Things adoption ............................................................................. 18
4.5.1 Short term inhibitors ....................................................................................................... 18
4.5.2 Long term inhibitors ........................................................................................................ 19
4.6 Purpose .................................................................................................................................. 19
5. Validation ..................................................................................................................................... 22
5.1 Validation value framework ................................................................................................... 22
5.1.1 Short term framework validation .................................................................................... 22
5.1.2 Long term framework validation ..................................................................................... 23
5.2 Validation of inhibitors ........................................................................................................... 23
5.2.1 Short term inhibitors ....................................................................................................... 23
5.2.2 Long term inhibitors ........................................................................................................ 24
6. Discussion ..................................................................................................................................... 24
6.1 Internet of Things added value Framework ........................................................................... 24
6.2 State-‐of-‐the-‐art ...................................................................................................................... 25
6.3 Emerging types of applications .............................................................................................. 25
6.4 Inhibitors for Internet of Things ............................................................................................. 26
7. Conclusion .................................................................................................................................... 27
7.1 Research questions ................................................................................................................ 27
7.2 Research contributions .......................................................................................................... 29
7.3 Limitations .............................................................................................................................. 29
7.4 Suggestions for further research ............................................................................................ 30
References ........................................................................................................................................... 31
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1. Introduction This thesis describes the results of a research project on the topic of added value for Internet of Things (IoT), conducted at Deloitte Consulting. Organizations and scholars are trying to identify the added value of Internet of Things. Deloitte Consulting is involved in advisory projects for clients to create strategic roadmap for the future IT landscape. Many organizations struggle with a roadmap to adopt Internet of Things in which the full potential of IoT can be used. This thesis focusses on to which processes Internet of Things adds the most value, by presenting a framework to identify this value using value drivers. Furthermore, relevant types of emerging applications are covered and the current inhibiting factors of Internet of Things are discussed. In this way, this research project tries to fill the current gap in identifying IoT value.
The introduction section of this thesis describes the motivation for this research project, resulting research goal and research questions. Furthermore, the research methodology, scope, and thesis outline is presented.
1.1. Thesis Motivation A world is emerging in which the boundaries between the digital-‐ and physical world are fading. The ‘Internet of Things’ makes use of sensors, actuators and data communications technology built into physical objects that enable those objects to be tracked, coordinated or controlled across a data network or the Internet (McKinsey Global Institute, 2013). Internet of Things is a practical and applicable technology that can generate a large return on investment and drive insights for businesses that know how to harness its strengths. Advancements in connectivity, processing power, form factors, operating systems and applications are key elements to unlock the value of Internet of Things. Linking objects to IT systems is just the first step. The real added value lies in the data that is transferred from these devices and the new business insights this data can facilitate (Microsoft, 2015).
The growth potential of IoT is enormous, with an expected 30 billion devices connected with a unique IP address in 2020 (Gartner, 2013). This means that the concept is expected to outnumber humans with a ratio of 4-‐to-‐1 by in the same year (Gartner, 2015). The prediction of Cisco (2011) is even higher, with an estimated 6.58 connected devices per person and a total of 50 billion connected devices by 2020. Such an increase is expected to add an economic value of $1.9 trillion per year in 2020 (Gartner, 2013). Cisco (2011) is backing their numbers with a bold statement, saying IoT will change everything, including ourselves. This is because IoT will help humanity take a huge leap in its ability to analyze and distribute data, which can be turned into information, knowledge and ultimately wisdom. The concept will blur the boundary between the physical and the digital world and create new relationships between people, things and business. This creates new opportunities for revenue and efficiencies for all types of enterprises (Gartner, 2015). Imagine ‘a thing’ making a purchasing decision instead of a customer, for example automatically ordering a new part for your car, when it is damaged or parts due for replacement.
The impact for business is evident, as Gartner (2015) indicates: the IoT concept can have a large impact on an organization. Initiatives to make things smart are already underway. Also, the example above from a car that orders a new part, there are initiatives from LG that has a Smart ThinQ washer that can help you to fix problems, communicating the problem with LG technicians. Also in the medical sector there are initiatives to help people take their pills1, by registering when the pill cap is opened and how much pills are left in the pill bottle (Gartner, 2015). Also, large consultancy companies as Deloitte and Accenture are looking into the industrial side of Internet of Things. Accenture (2014) sees the industrial Internet of Things as a way to improve operational efficiency and a tool to boost revenue by increasing production, fuel innovation and transform the workforce.
1 AdhereTech’s Smart Wireless Pill Bottles, see: www.adheretech.com
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Deloitte (2014) agrees and designates that IoT “has the potential to offer business value that goes beyond operational cost savings”.
Concluding, the possibilities and opportunities for Internet of Things are almost limitless and the concept will create new business opportunities for companies who know how to harness its strengths. In addition to the applications the industry suggests, in the literature there are many suggestions for applications for the Internet of Things paradigm, see: Atzori, Iera & Morabito (2010), Miorandi, Sicardi & De Pellegrini (2012), Gubbi, Buyya & Palaniswami (2013) and most recently Atzori, Iera & Morabito (2014). Hence, many applications can be conceived when thinking about IoT, the challenge is set in what IoT add the most operational value? Therefore, this thesis focusses on where Internet of Things can have the most impact, what kind of applications add that value and what the current inhibitors are for IoT integration.
1.2. Research Goal and Research Questions This thesis is an exploratory research. To structure this research, the following research goal was defined.
To determine where the most value can be added using Internet of Things, using which applications and what kind of inhibitors exist.
This goal leads to the following research questions:
1. What is the current state-‐of-‐the-‐art of Internet of Things? 2. What are the possible applications using Internet of Things? 3. In which processes can Internet of Things generate the most value? 4. What are the current inhibiting factors for Internet of Things adoption?
1.3. Research Methodology For this research project, a Business-‐Problem Solving (BPS) focus has been chosen, in combination with a case study focus. Business Problem Solving projects are started to improve the performance of a business system, department or a company on one or more criteria (Van Aken, 2007). A BPS project uses the logic of a problem-‐solving cycle, here in the form of the regulative cycle. For this study, two research methodologies will be combined. Namely, the BPS focus from Van Aken (2007) and the case study research focus of Eisenhardt (1989). The case study research focus will be applied during the analysis and diagnosis phase. The BPS approach from Van Aken (2007) consists of the following phases:
1. Problem definition: the problem statement and scoping of the project is defined. Also, the project plan and approach to the subsequent analysis, diagnosis and design is defined.
2. Analysis and diagnosis: for this phase methods of business research are used, resulting in specific knowledge on the context and nature of the problem.
3. Plan of action: in this phase, the solution for the problem is designed and the associated change plan.
4. Evaluation: during this phase, most of the learning has been achieved and one looks what still has to be done to unlock the full potential of the new system (Van Aken, 2007).
As Van Aken (2007) describes in the original regulative cycle, after the plan of action phase there is an intervention phase. In this phase, the roles and work processes are changed on the basis of the solution design and change plan (Van Aken, 2007). Van Aken (2007) explains that, ‘usually the student has left the company by then’, implying that in a master thesis there is not enough time to perform the intervention phase. Therefore, this study will not include this phase in the research methodology.
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The case study approach from Eisenhardt (1989) is applied during the qualitative inductive case study research. The method of Eisenhardt (1989) distinguishes the following steps, which will be elaborated in each corresponding paragraph.
1. Selecting cases 2. Crafting instruments and protocols 3. Entering the field 4. Analyzing within-‐case data 5. Enfolding literature 6. Researching closure
The complete methodology approach can be seen below, in figure 1. The cycle that is displayed is the regulative cycle from Van Aken (2007). The resulting steps, originating from the analysis and diagnosis phase, are adapted from the case study approach (Eisenhardt, 1989). The corresponding chapter numbers are stated in the figure.
Figure 1: Regulative cycle, adapted from Van Aken (2007), combined with the case study approach (Eisenhardt, 1989)
The field of adding value by using Internet of Things is still largely unexplored. Previously not much work has been done to build upon. This means that no grounded hypotheses can be constructed using previous scholars. “Qualitative research based on an interpretive paradigm is exploratory in nature, thus enabling researchers to gain information about an area in which little is known” (Dickson-‐Swift, James, Kippen and Liampottong, 2007). Due to the pluralization of life-‐worlds, qualitative research is relevant to study social relations. Also, rapid social change create new contexts and perspectives. This change is so rapid, that traditional deductive methods are failing because of the diversification of objects. Therefore, inductive study research is more and more common (Flick, 2009). Furthermore, a case study investigates a contemporary phenomenon within its real-‐life context when the boundaries between phenomenon and context are not clearly evident (Myers & Avison, 1997). Case study research is well suited for information studies research, because the object of our discipline is the study of information systems in organizations and interest has shifted towards organizational issues (Benbasat, Goldstein & Mead, 1987). Consequently, this research uses a qualitative inductive case study approach. The definitions of the different aspects of this project –‘qualitative research’, ‘inductive research’ and ‘case study research’-‐ are given below.
-‐ Qualitative research: A nonmathematical process of interpretation to discover concepts and relationships in raw data and organizing these into a theoretical explanatory scheme (Corbin & Strauss, 1994).
-‐ Inductive research: A process which begins with an area of study and allows the theory to emerge from the data (Corbin & Strauss, 1994).
-‐ Case study research: An empirical analysis that investigates a contemporary phenomenon within its real life context (Yin, 2013).
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1.4. Scope This master thesis project is executed at Deloitte Consulting B.V. in Amsterdam, The Netherlands. Due to the broadness of the subject in which ‘Internet of Things’ can be applied, it was required to scope this thesis because of time constraints. This was done using the following guidelines; First of all, only companies who deal with supply chain management or provide services for the supply chain management sector where taken into consideration. This is because, the supply chain management sector, together with the agriculture sector, is most mentioned in papers which describe IoT applications (Sprenkels, 2014). A complete list of the number of IoT papers per sector can be found in Appendix B.5. The choice for supply chain management was made because Deloitte has the most clients in this sector. This increases the chance of finding suitable cases. Improvements in an organization’s supply chain are used to specify added value. This is because IoT often brings operational improvements and organizations are less willing to provide their financial data, due to confidentiality issues. Third, only applications that fall in the definition of Internet of Things will be taken into perspective. This definition can be found in chapter 2, problem definition. Applicable cases for the case study must have an annual revenue of over 100 million euro’s, to ensure a sizable organization that is financially capable of implementing IoT. Finally, the appropriate cases must have an office in the Netherlands.
1.5. Thesis Outline The remaining part of this thesis is structured according to the research methodology explained above in paragraph 1.3. Chapter 2 explains the problem definition and the different topics covered in this thesis. Chapter 3 explains the research method that has been performed; giving detailed case study descriptions and the way the case study was executed and the way the data was analyzed. Chapter 4 discusses the plan of action. Validating the results is performed in chapter 5. Consequently, the results are discussed in chapter 6, after which a conclusion is given in chapter 7.
2. Problem definition To ensure the understanding of the central concept of this research project, this chapter describes the definition of the concept Internet of Things, adding value with Internet of Things, Internet of Things applications, -‐inhibitors and supply chain management. At the end of this chapter, the Enterprise Value map of Deloitte will be introduced and explained. The keywords used to search for the used articles and scholars can be found in Appendix B.1.
2.1. The Internet of Things Presently, multiple definitions for the concept Internet of Things exist. The reason for the various definitions is the consequence of the name: “Internet of Things” (Atzori, Iera and Morabito, 2010). LeHong & Velosa (2014) on behalf or Gartner, state IoT is “the network of physical objects that contains embedded technology to communicate and sense or interact with the objects' internal state or the external environment”. McKinsey Global Institute (2013) defines Internet of Things as “sensors, actuators, and data communications technology built into physical objects that enable those objects to be tracked, coordinated, or controlled across a data network or the Internet”. Atzori et al. (2010) describe that the definition of Internet of Things is actually a trait d’union between three IoT visions, see figure 2. In the first part ‘Internet’ pushes towards a network oriented vision while the second part ‘things’ pushes towards an objects oriented vision. Atzori et al. (2010) also state a semantic vision of IoT, when combining both visions, defined as: “a world-‐wide network of interconnected objects uniquely addressable, based on standard communications protocols” (Bassi & Horn, 2008). Finally, Atzori et al. (2010) define IoT as: “a world where things can automatically communicate to computers and each other providing services to the benefit of the human kind”. Because the interdisciplinary nature of the concept, this demarcation of the concept is required. Nevertheless, the usefulness of IoT can only be put to full use in an application domain where the three paradigms overlap (Gubbi, Buyya, Marusic & Palaniswami, 2013). The IoT concept augments
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connectivity from ‘any-‐time, any-‐place’ for ‘any-‐one’, into ‘any-‐time, any-‐place’ for ‘any-‐thing’. An enormous amount of devices will be connected to the Internet, each providing data, information or even services (Coetzee & Eksteen, 2011). Concluding: Internet of Things characterizes technologies to connect physical objects to the Internet. And in this way, connecting the physical world to the digital world.
Figure 2: IoT paradigm as a result of different visions (Atzuri et al., 2010)
The first example of the notion of the term “Internet of Things” was in Forbes magazine in 2002. Here, the co-‐founder and head of the Auto-‐ID Center at the Massachusetts Institute of Technology (MIT) was quoted saying: “We need an internet for things, a standardized way for computers to understand the real world”, this article was titled: “Internet of Things” (Schoenberger, 2002). A few years later in 2008, the first scientific conference was organized about the new concept (Mattern & Floerkemeier, 2010). Eventually, an action plan from the European Commission marked the Internet of Things as an evolution from the Internet, as they stated: “from a network of interconnected computers to a network of interconnected objects” (Mattern & Floerkemeier, 2010). That the concept is becoming increasingly more important can be derived from the US National Intelligence Council, which has listed IoT as one of the six “Disruptive Civil Technologies” with potential impacts on the US national power (National Intelligence Council, 2008). The council highlights the future opportunities that will arise from the adoption from IoT and that this could contribute greatly to the economic development. Research suggests that the number of interconnected devices will reach 24 billion devices by 2020, creating 1.3 trillion dollar in revenue opportunities (Gubbi, 2013).
2.2. Adding Value with Internet of Things Within the IoT vision, “smart” objects play a key role. These smart objects have the potential to transform the utility of these objects. Using sensors, the objects are able to communicate with each other and with people, by using networking capabilities. If these objects are upgraded by adding sensors to be “smart” in order to enhance their physical purpose, this could generate a substantial added value for the users of these ‘things’ (Mattern & Floerkemeier, 2010). Also the resulting expected increase in the performance of the IoT systems will help in the deployment of value-‐added services (Atzori et al. 2014). This digitally added value can both differentiate companies from competitors, lock-‐in customers into additional services using similar products and protect against counterfeit products (Mattern & Floerkemeier, 2010). Especially the integration of the Internet of Things with the cloud is a key aspect in adding value. In this way, the appropriate web-‐based services and applications, which are able to leverage data are made available by the smart objects to add value (Miorandi et al., 2012).
According to Fleisch (2010), IoT is applicable to every step in every value chain. This is due to the fact that trying to structure IoT applications is as impossible as modeling the whole world. Due to every industry on the globe, being embedded in the physical world. To structure this in another, and
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possible way, Fleisch (2010) formulated seven value drivers, which every possible application for Internet of Things retains. These drivers plus a short explanation are stated below.
1. Simplified manual proximity trigger: communicates a unique ID when manually moved into the roaming space of a proximity sensor. For example self-‐check-‐out in libraries.
2. Automatic proximity trigger: triggers a transaction automatically when the physical distance of two things drops below a threshold. For example car keys.
3. Automatic sensors trigger: expands value driver one and two by manually and automatically sensing and communicating the name of a thing. For example smoke detectors.
4. Automatic product security: the thing to be secured holds a minicomputer that is equipped with some security technology. For example anti-‐counterfeiting.
5. Simple direct user feedback: smart things feature simple mechanisms to give feedback to the humans who interact with them at the point and time of action. For example perishable good that shows quality status.
6. Extensive user feedback: extends the output from simple direct user feedback to rich services. For example a mobile operation and repair manual.
7. Mind changing feedback: this value driver is not based on technical features of the IoT, but says that the combination of the real-‐ and virtual world computing might generate a new level of manipulating people. For example save energy via smart meter applications.
A detailed description of the model of Fleisch (2010) including the value root, business value, consumer value and more example applications can be found in Appendix B.4.
2.3. Emerging Types of Internet of Things Applications The Internet of Things has the potential to impact to significantly influence all facets of society (Coetzee & Eksteen, 2011). In 2005, the International Telecommunications Union (2005) described four dimensions for IoT. Item identification, sensors and wireless sensor networks, embedded systems and nano-‐technology. Later, Chui, Löffler and Roberts (2010), expanded these four dimensions into six types of IoT applications. These six distinctive types of IoT applications, fall into two categories, namely ‘information and analysis’ and ‘automation and control’ (Chui et al., 2010). The first category consists of three types, which are ‘Tracking behavior’ (1), the monitoring of the behavior of persons, things or data through space and time. ‘Enhanced situational awareness’ (2), achieving real-‐time awareness of physical environment. And ‘Sensor-‐driven decision analytics’ (3), assisting human decision making through deep analysis and data visualization. Chui et al. (2010) indicate the remaining three applications in the automation and control category as ‘Process optimization’ (4), automated control of closed (self-‐contained) systems. ‘Optimized resource consumption’ (5), the control of consumption to optimize resource use across the network. And finally, ‘Complex autonomous systems’ (6), automated control in open environments with great uncertainty. An overview of the emerging IoT applications defined by Chui (2010) can be found in figure 3.
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Figure 3: emerging Internet of Things applications, adopted from Chui et al. (2010)
The present extensiveness of IoT potential, provides possibilities to develop a huge number of applications. However, only a small part of these applications are currently available (Atzori et al., 2010). Also Gubbi et al. (2013) identify numerous applications for IoT and the many application domains. All these applications can be classified based on the type of network, availability, coverage, scale, heterogeneity, repeatability, user involvement and impact (Gluhak, Krco, Nati, Pfisterer, Mitton & Razafindralambo, 2011). Also, there is an enormous crossover in the applications and use of data between the domains (Gubbi et al., 2013). The application domains that are identified in scholars by Atzori et al. (2014), Gubbi et al. (2013), Miorandi et al. (2012), Gluhak et al. (2011) and Atzori et al. (2010) generally involve applications in the domains transportation and logistics, healthcare, smart environment, personal, social and futuristic. Present day, the IoT applications are just starting to be visible in the industry, as can be seen in the roadmap of key technological developments in the context of IoT application domains, see Appendix B.3 (Gubbi et al., 2013). Gubbi et al. (2013) expect the full enfoldment of the Internet of Things to exceed the year 2025. This has to do with the fact that the current state-‐of-‐the-‐art is unlikely to be sufficient to enable the realization of the full IoT vision (Miorandi et al., 2012).
2.4. Inhibitors for Internet of Things Currently, there are several projects on different aspects of Internet of Things. However, an open and accessible infrastructure is missing, inhibiting further adoption (Uckelmann, Harrison & Michahelles, 2011). One of the main challenges for IoT is to transform connected things into real actors of the Internet by developing appropriate design methodologies (Sundmaeker, Guillemin, Friess, Woelfflé, 2010). Hence, this will provide connected devices with a higher degree of smartness enabling their autonomous behavior (Atzuri et al., 2010). This involves considerable societal and ethical challenges on both European and global level (Sundmaeker et al., 2010). Next to this technological challenges, Bandyopadhyay & Sen (2011) identify three key challenges for IoT. These challenges involve the foundation of the network, as also identified by Sundmaeker et al. (2010). The issues considering security, privacy and thrust is the second challenge as also confirmed by Atzuri et al. (2010) and Weber (2010). There are the challenges for the security of the network against e.g. DDOS attacks and malicious software. Second, the user privacy must be assured so the user remains in control of personal information. The thrust domain consist of the natural exchange of critical data as ‘things’ will communicate on behalf of users (Bandyopadhyay & Sen, 2011). The third major challenge is managing the heterogeneity of the network as indicated by, among other Miorandi et al. (2012) Bandyopadhyay & Sen (2011), Atzuri et al. (2010). This challenge connects to managing large amounts of data, designing an efficient architecture for networking of the nodes and designing
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sensor data communication protocols (Bandyopadhyay & Sen, 2011). Summarizing, the key challenges for IoT to succeed are network foundation, security, privacy and trust, and managing heterogeneity.
2.5. Supply Chain Management For this master thesis, added value of IoT will be studied using supply chain management (SCM) as a guideline. Hence, supply chain management will be defined in this paragraph. The term Supply Chain Management can be split up in ‘supply chain’ on one side, and ‘supply chain management’ on the other. First, supply chain will be defined after which supply chain management will be demarcated. Mentzer, DeWitt, Keebler, Min, Nix, Smith & Zacharia (2001) define a supply chain as: “a set of three or more entities (organizations or individuals) directly involved in the upstream and downstream flows of products, services, finances and/or information from a source to a customer”. A definition of supply chain management is less general, as Mentzer et al. (2001) discuss with six possible definitions, ranging from scholars from 1985 until 1998. This implies that the term SCM has changed during the years. When combining the different definitions, Mentzer et al. (2001) finally define SCM as: “the systemic, strategic coordination of the traditional business functions and the tactics across these business functions within a particular company and across businesses within the supply chain, for purposes of improving the long-‐term performance of the individual companies and the supply chain as a whole”. This definition of SCM will be used for this thesis.
2.6. Shareholder Value In this thesis, defining shareholder value is achieved by using the Enterprise Value Map2 (EVM) from Deloitte. This value map was created in 2003 and is designated from the client’s point of view. This framework shows the relationship between shareholder value and business operations. It maps where maximum value can be delivered at an organization. When printed, the simplified version EVM covers a full A3 page to be readable. The full version consists of 847 possible actions to improve shareholder value at organizations and is generally used at Deloitte to identify points to increase shareholder value at clients. The EVM helps organizations to organize, discuss and prioritize improvement opportunities that deliver maximum value in terms of revenue growth, operating margin, asset efficiency and market expectations of future growth. This research will use a simplified version of the EVM due to time constraints and because the simplified version offers enough depth to identify where the Internet of Things can add value. A representation of the EVM can be seen in figure 4.
Figure 4: Revised Deloitte Enterprise Value Map.
2 Deloitte’s Enterprise Value Map, see: http://www2.deloitte.com/us/en/pages/operations/articles/enterprise-‐value-‐map.html
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3. Research Method In this chapter, the used research method is discussed. First, the design of the framework is elaborated in 3.1. Second, the case study selection is elaborated in 3.2 and the case study execution in paragraph 3.3. At the end of this chapter, the analysis is performed in paragraph 3.4.
3.1. Framework design This paragraph explains how the presented framework has been created, consisting of the theory input, structure, discussion and purpose of the framework. Thereafter, the plan of action is explained. This will be done by following the structure of van Aken (2007) and Eisenhardt (1989). Also, the existing value drivers, what IoT applications apply and what inhibitors occur is explained. The design of the framework is derived from the research questions of this thesis.
3.1.1. Input The input for the design of this framework is based on the analysis of the qualitative case studies. Also, the value drivers that Deloitte mapped are used. Furthermore, the inputs where derived from the research questions of this thesis, which makes the framework more clear and enables structured answering of the research questions. Summarizing, the input consists of the following contributions, the text between brackets represents the research questions addressed.
1. Where IoT adds the most value in an organization, using Deloitte’s Enterprise Value Map (research question 3).
2. How is this value added, using the IoT value drivers of Fleisch (2010) (research question 3). 3. What kind of applications are required, using the distinct types of IoT applications by Chui et
al. (2010) (research question 2). 4. Why IoT is not yet implemented in the cases, so what are the challenges or inhibitors for IoT
adoption (research question 4)
The framework gives insight in where an organization could implement IoT first, to have the most impact for the organization. The different inputs are explained in the next paragraph.
3.1.2. Structure As described in the previous paragraph, the input for the framework consists of four types of input. These inputs will be discussed in this paragraph, after which the design of the framework will be presented. The structure of the framework is designed so it has a matrix-‐style layout.
The framework has a Y-‐axle that indicates practical paths to increase shareholder value. This axle is adapted from the Enterprise Value Map™ of Deloitte. This map is designed to “accelerate the connection between actions you can take and shareholder value”. An adapted representation of the Enterprise Value Map can be seen below. The EVM, as shown in figure 4, has been adapted due to the size of the complete EVM, because complete EVM requires to be printed on A3 format to be properly readable. A condensed version of the EVM can be found in Appendix B.6.
The x-‐axle consists of the IoT value drivers as presented by Fleisch (2010). These value drivers look at the value that applications can add for both businesses and customers. Fleisch (2010) analyzed a list of about hundred existing and emerging applications and concluded that every single one of these applications corresponded with one of the seven main value drivers he identified. The resulting preliminary framework, with corresponding axles can be seen in figure 5.
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Figure 5: Preliminary framework
On each relevant enterprise value map section, the type of IoT application is identified, which interviewees said to be applicable to this situation. The possible choices of applications are defined by Chui et al. (2010) and consist of six possible types of emerging applications. Also, the general inhibitors for IoT are identified during each interview. These inhibitors help identify why IoT is not yet implemented and what needs to happen to enable the adoption of the concept.
On the cross-‐sections, the interviewees marked where they thought IoT could add value. If respondents saw value on a cross-‐section, respondents were asked to mark it with a score from one to ten. The number one, indicating the least amount value. The number ten, indicating the maximum amount of value that they thought could be added by adopting IoT. By using this concept, a heat map can be constructed on where the respondents saw the most value and how many respondents scored a particular cross-‐section on the framework.
Finally, the framework is created twice. Once for short term, from now until three years from now and once for long term, from three to ten years from now. The length of these two timeframes have been selected by consulting several Deloitte consultants about the average implementation time of IT projects. The consultants indicated that a period of three years is about the time an IT implementation lasts, taking in perspective the start initial idea for an IT project, all the way through the completed implementation of a project. That is why respondents are most likely to be able to identify processes in which IoT can provide value in those two timeframes. By segmenting the interviews into two timeframes, the difference can be observed between short term factors and long term factors influenced by IoT.
3.1.3. Purpose The purpose of the framework is to help identify where implementation of IoT has the most added value for an organization and helps to give insight in the value drivers, type of IoT application and factors that have inhibited the use of IoT until now.
The framework will help organizations gain insight in where IoT will have the highest impact in their organization. This complements the data that organizations need for strategic decisions about how to apply the IoT concept and what value drivers will create this added value.
Furthermore, the framework will help organizations to gain insight in the kind of IoT application that would suit their business best. This can help to create the perception of employees in twofold. First, organizations have a complete image of possible IoT applications and secondly, it gives an indication of the applications that could have the largest impact for their organization.
1 2 3 4 5 6 7Simplified Manual Proximity Trigger
Automatic Proximity Trigger
Automatic Sensors Trigger
Automatic Product Security
Simple Direct User Feedback
Extensive User Feedback
Mind Changing Feedback
Volume 1 Acquire new customersPrice Realization
2Retain and Grow Current Customers
3Leverage Income-‐Generating Assets
4 Strengthen PricingSelling, General & Administrative
5Improve Customer Interaction Efficiency
6Improve Corporate/ Shared Service Efficiency
Cost of Goods Sold7
Improve Development & Production Efficiency
8Improve Logistics & Service Provision Efficiency
9Improve income tax efficiency
Property, Plant & Equipment
10Improve Property, Plant & Equipment Efficiency
Inventory11 Improve Inventory Efficiency
Receivables & Payables
12Improve Receivables & Payables Efficiency
Company Strenghts
13Improve Managerial & Governance Effectiveness
14Improve Execution Capabilities
External Factors 15 External Factors
Internet of Things Value Drivers
Shareh
olde
r Value
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Finally, the framework gives an indication on how many respondents found a particular part of their company suitable to adopt IoT, and how much value they thought could be added by using the score map from one to ten.
3.2. Case Study Selection In order to create a robust case study for this master thesis, the concept of ‘population’ is crucial. Since selection of an appropriate population controls extraneous variation and helps to define the limits for generalizing the findings (Eisenhardt, 1989). Cases can be chosen to fill theoretical categories and while cases can be chosen arbitrarily, random selection is neither necessary nor preferable (Eisenhardt, 1989). This is why, before starting to find usable case studies, some guidelines where created to ensure a replicable case study. Recapitulating from paragraph 1.4, these guidelines are that the organization must have an annual revenue of over 100 million euro’s, to ensure a sizable company. Also, the organization must have an office in the Netherlands. Furthermore, the organization must have affinity supply chain management. Finally, for the scope and due to the limited time frame of this thesis the decision was made to do a case study consisting of three organizations.
In order to find suitable cases, 28 employees of Deloitte Consulting -‐ranging from consultant to partner-‐ where contacted. All these employees have knowhow of IoT and/or worked on projects which included IoT. From these contacts, 13 meetings where organized in which the research was discussed in depth and the several possible cases where discussed. Next, several organizations that fitted the set requirements where approached.
From the approached organizations a total three organizations where selected that best fitted the scope. The choice for three organizations was made because of the limited time available to conduct this research. Two organizations can contradict each other, but three organizations will always ensure that a conclusive decision can be made by reaching a two vs. one majority. For the same reason, three respondents per company where selected. Again, to be able to prevent a possible stalemate in which no conclusion can be reached.
The selected organizations are:
-‐ Canon Europa N.V. (Canon) -‐ Havenbedrijf Amsterdam N.V. (Port of Amsterdam) -‐ Koninklijke KPN N.V. (KPN)
A short explanation of each organization is given in the next three paragraphs.
3.2.1. Case Description Canon Europa N.V. Canon is a Japanese multinational corporation, which specializes in the manufacturing of imaging and optical products. The company was founded in 1937, has its headquarters in Tokyo, Japan. Canon started to produce a 35mm camera and since then, started to sell products like (office) printers, scanners, digital cameras, video recorders, binoculars and the software to support these products. This results in an annual revenue of 26,5 billion euro’s, a profit of 2,4 billion euro’s and more than 198.000 employees worldwide. The company has two offices in the Netherlands, in Amstelveen and Den Bosch. In 2011, Canon acquired the Dutch printing and copying hardware manufacturer Océ to become the world’s largest hardware supplier of printing equipment.
For some time, Canon is offering its eMaintenance system3 to let customers manage their Canon devices more efficiently. This system automatically gathers diagnostic data from multifunction devices of Canon’s clients. The data is transferred to Canon’s servers and can be analyzed. This eMaintenance system has several advantages for the client. Examples are that the toner is 3 Canon’s eMaintenance system, see: http://www.canon-‐europe.com/for_work/solutions/solutions/office_software/emaintenance/
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automatically replenished, when running low. So the client never has to run out of toner. Also, data concerning the mechanical status of the device is collected by Canon. This means that preventive maintenance can take place before the machine fails, ensuring minimal (unexpected) downtime. Finally, firmware updates can be pushed to the multifunction devices, ensuring up-‐to-‐date software. In essence, this is an example of IoT, since sensors are placed on a thing, e.g. a printer. Not only does the eMaintenance system has significant advantages for the customers, also the advantages for Canon and their supply chain and maintenance department are numerous.
3.2.2. Case Description Havenbedrijf Amsterdam N.V. The first port activities in Amsterdam date back to the 13th century. The Port of Amsterdam was one of the main ports of the Dutch East India Company (VOC), during the Dutch Golden Age. Present day, the port is the fourth largest port in Western Europe, measured by transshipment. From April 1st 2013, the Port of Amsterdam is privatized by the municipality of Amsterdam. Havenbedrijf Amsterdam N.V. is designated for the management, operation and development of the port. The port is located on the banks of the North Sea Canal and the IJ and is connected to the North Sea and the Markermeer. The Port of Amsterdam has over 5000 vessel arrivals per year and an annual revenue of 133 million euro. Around 361 employees work at the Port of Amsterdam, but the number of jobs directly and indirectly created by the companies in the port region is around 55.000. Next to being a port for bulk goods and containers, the port also functions as an important harbor for cruise ships. The Port of Amsterdam is the biggest petrol harbor in the world.
The new business department of the Port of Amsterdam is testing a new system which can be described as a ‘smart’ quay wall. This quay wall has sensors build inside, that check if there is a ship docked at the quay. This is the first example of an IoT application at the Port of Amsterdam, but there are many possibilities for new and interesting IoT applications at the port. This smart quay wall is piloted at a small part of the harbor, at the Houthaven. A separate project that the Port of Amsterdam is running, relates to sniffers. This is a type of sensor that monitors the surrounding air quality. In this way, the Port of Amsterdam can check if ships are expelling hazardous materials and can notice if there is a gas leak, for example. Step by step, the Port of Amsterdam is gradually expending the capability of their IoT network and applications. Also, the organization is planning more pilot projects in the area of IoT in the future.
3.2.3. Case Description Koninklijke KPN N.V. KPN is a supplier of telecommunication and ICT services for the consumer and business market. The company provides fixed and mobile telephony, internet and television for the consumer market. For the business market, the company provides complete telecommunications and IT solutions. KPN originates from Staatsbedrijf der Posterijen, Telegrafie en Telefonie (PTT), which was founded in 1928 and was a publicly owned company. KPN was had its Initial Public Offering (IPO) in 1994 and the privatization of the company was completed in 1996. Currently, the company has an annual revenue of almost 8.5 billion euro’s.
KPN is working with different solution-‐ and business partners to offer IoT solutions to customers. The company is offering services in ranges of RFID, LoRa, security and Machine 2 Machine (M2M) connectivity. The company is identified by Gartner as visionary in the area of M2M. KPN is currently unfolding several initiatives in regard to smart cities, which is essentially an IoT within a city to offer services ranging from public transport connectivity to smart lighting on the streets. Initiatives include for example the Amsterdam Smart City project4.
3.3. Case Study Execution In addition to talking to the appropriate organizations, talking with the right people is essential to extract the right data from the interviews. Respondents should have sufficient know-‐how about IoT,
4 Amsterdam Smart City, see: http://amsterdamsmartcity.com
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before they qualify as a proper interviewee. In the phone call before every interview, the respondent was asked about their know-‐how about the subject, to be sure that the interview got the right results. Only respondents were considered who have a function in IT strategy, Enterprise Architecture and/or are dealing with CTO/CIO subjects. A list of functions of all respondents can be found in Confidential Appendix E.2.
The interviews are based on the theories Deloitte’s Enterprise Value Map -‐which describes how to add shareholder value-‐, the IoT value drivers by Fleisch (2010), and the distinct types of IoT applications by Chui et al. (2010). This results in a structured interview, covering all three theoretic models, described in the problem definition chapter. This can be seen below at the numbers four, five and six. A semi-‐structured interview approach was chosen to allow deviations in the interviews, in order to allow the interviewee to elaborate more on certain topics. In addition, this gives the opportunity for the interviewer to ask more specifically about certain topics. While, a specific set of topics was addressed in every interview. Every interview was structured as specified below:
1. Introduction of both the interviewee and interviewer 2. Introduction of the research and purpose of the interview 3. General questions about Internet of Things 4. Questions about the Internet of Things value framework 5. Closing / final remarks
To prepare the respondents for each interview, a 15 minute phone call was scheduled prior to every interview. During this phone call, both the interviewer and the interviewee introduced themselves first. After an introduction, the interviewer explained the research, research goal, used framework and interview procedure to the interviewee. By preparing the interviewees for the interview, it was made sure that interviewees understood the research goal and interview procedure that was going to take place. This prior introductory phone call ensures that the available interview time is used as efficiently as possible. After the phone call, an additional e-‐mail was sent to the respondents, in which a document was attached, explaining the framework used during the interview. For each interview, the framework and list to mark type of applications and inhibitors was printed on A3 format and given to the interviewee. In this way, the interviewee could fill in the framework to ensure a correct translation of their IoT value perceptions. To facilitate the interviewee, the interviewer filled-‐in the list of application types and inhibitors during the interview, whilst the interviewee filled-‐in the framework. To make sure that each interview was processed the same, the processing procedure of Reijers (2006) was used for each interview. This method consists of the following steps:
1. If permission is given, the interview is recorded. Else, only notes will be taken. 2. The interview is transcribed. 3. The interview notes are presented to the interviewee for review. 4. Remarks are processed into final interview results. 5. Recordings are erased once the case studies have been completed.
The full list of interview questions can be found in Appendix E. On average, the interview report is about 9 pages in length and the interview time averaged 50 minutes. The transcription of the interview was usually completed on the same day or the day after, which was finalized as soon as possible in order to facilitate the validation of the report with the interviewee. All transcriptions where e-‐mailed to the interviewees as soon as possible to assess for the correctness of the report. This resulted in a high response rate (10/11), which resulted in some minor corrections to the interview report.
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3.4. Analysis After the fieldwork had been completed, the analysis of the data is the next step according to the model of Eisenhardt (1998). In section 3.4.1 the motivation behind the analysis is explained. In 3.4.2 the case study analysis is discussed.
3.4.1. Motivation The analysis of the gathered data was conducted in two steps. First, this involves a detailed case study, with case descriptions for every individual case, so called: ‘within-‐case analysis’. These case descriptions are central in the generation of insight (Eisenhardt, 1989). There is no standard format for within-‐case analysis. However, the general idea is to become familiar with each case as a stand-‐alone entity. This allows for unique patterns of each case to emerge, before generalizing patterns across cases (Eisenhardt, 1989). Secondly, ‘across case analysis’ was performed to check for emerging patterns to see if similar results were found at the other organizations.
3.4.2. Analysis of case studies After a conducted interview, the voice recorder data file was back upped. Next, the interview was typed-‐out and set in a common format, to be able to transcribe the data. No special software program was used to transcribe the interviews. Instead, two columns, one for the interview text and one for the identified codes, where generated. The results can be found in Confidential Appendix F. Secondly, the interviews where transcribed, coded and analysis was performed in two steps. First, ‘within-‐case analysis’ was performed to understand case specific issues, characteristics and business cases (Eisenhardt, 1989). For example, Canon has a totally different business case than the Port of Amsterdam. So in order to understand the complexity of every case, the within-‐case analysis is necessary. After the within-‐case analysis, the across-‐case analysis was performed to research patterns across cases and plot the resulting framework (Eisenhardt, 1989).
The coding was done using a set of pre-‐determined codes, which were altered and updated continuously in the process of analyzing all case studies. This created a consistent set of codes throughout all different interview analyses. Codes where created for identifying points in the Enterprise Value Map (EVM) where IoT can add value, to identify IoT value drivers that add value, to identify applications and to identify inhibitors. Other contextual data was noticed, but not coded. This was done to apply focus to the results and to prevent getting lost in a tangle of codes and data.
Because the interviewees where given a printed A3 copy of the framework, it was ensured that the correct interpretation of the interviewee’s response was noted. The documentation is available in Confidential Appendix D. After the coding and transcription, the filled-‐in A3 frameworks, application and inhibitor lists where combined to a frequency table and anonymized. This confidentiality was needed, since some of the respondents required anonymity for the data obtained from their participation. The data collection took place in a timeframe of 1 month.
After the data collection and analysis of the cases had been completed, two more interviews were been conducted with IoT experts at Deloitte. The purpose of these two interviews is to verify if the case study results are a good representation of their experiences with IoT projects. The results of these interviews are presented in the validation chapter. A description of the background of these IoT experts can be found in chapter 6.
4. Plan of Action The point has come in which the gathered data and framework design are being put into an actual plan of action. This plan of action is presented in the form of an IoT value framework, which is presented at the end of this paragraph. Section 4.1 describes the structure of the framework, 4.2 presents the framework and section 4.3 the state-‐of-‐the-‐art of IoT. In section 4.4 and 4.5, the
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emerging types of IoT applications and inhibitors for IoT are discussed. Finally, the purpose and framework are presented in section 4.6.
4.1. Structure The structure of the framework used in this study is generally the same as the preliminary framework, discussed earlier. Respondents see no added value for ‘Income taxes’. Therefore, this part of the EVM is left out of the framework. Furthermore, only points that where identified by two or more respondents are displayed in the final framework. This is to increase validity of the framework, by preventing the solo opinion of a respondent to be included in the framework. Before the framework was constructed, the grades that respondents allocated to the value drivers where normalized. This was done in order to assign the proper colors, to indicate value and size, to indicate the number of respondents. These two variables are displayed using circles varying in size and color.
4.2. Framework The resulting graphical representation from the preliminary framework is presented in this section. This framework incorporates the results of all case study interviews. First, the results derived from the framework are discussed, next the framework is presented for the short term value (0-‐3 years) and long term value (3-‐10 years).
4.2.1. Short term framework, within 0 – 3 years The results of the case study are given as five key points, these points are specified below. These key points are that IoT has value for revenue growth, operating margin, ‘property, plant & equipment efficiency’ and ‘inventory efficiency’. Also the value driver ‘automated sensors trigger’ is seen as valuable and respondents see no value for ‘improve income tax efficiency’.
1. First, IoT value drivers across the range have high values and a high response rate for ‘acquiring new customers’ and ‘retaining and growing current customers’. This implies that between now and three years, companies want to use IoT to grow their customer base. When the number of customers increases at an organization, so does the turnover. Organizations want to attract new customers by adding innovative and value added services to their portfolio. The question is if organizations can actually create the applications to successfully add these new services. Customers want high quality, attractive applications that work, as quoted by one of the respondents: “(applications) should reflect the needs of customers, (…) they want the best and they want it now”.
2. Second, a clear presence of IoT value drivers can be seen at the Operating Margin of the EVM. IoT value drivers can be used to add value to ‘customer interaction efficiency’, ‘improve corporate/shared service efficiency’, ‘improve development & production efficiency’ and ‘improve logistics & service provision efficiency’. In turn, this adds value to ‘Selling, General & Administrative’ and ‘Cost of Goods Sold’ and thus increases the ‘Operating Margin’. The highest response is given to the ‘Automatic Sensors Trigger’ value driver, this IoT value driver increases the value proposition, by smart things that monitor its local surroundings and self-‐triggers actions if required (Fleisch, 2010).
3. Third, organizations see potential for IoT to increase ‘property, plant & equipment efficiency’ and ‘inventory efficiency’. Foremost, organizations hope to increase efficiency by sticking sensors on their products and inventory in order to monitor them. However all value drivers where marked by respondents, the most distinctive is the ‘automatic sensors trigger’, which self-‐triggers action of a thing when required (Fleisch, 2010). So, for example, when a product leaves a warehouse or enters a subassembly line and automatically signals this to a software program or application. Such an application can make supply chains more lean and enable improved operational excellence, as named by respondents.
4. Fourth, the value driver ‘automatic sensors trigger’ is identified in the framework at each point on Deloitte’s Enterprise Value Map (EVM). This indicates that this IoT value proposition is the
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most important in short term, also, the average value that is given to this value driver is higher than any other value driver. This gives an indication that organizations that want to adopt IoT, should focus on implementing this value driver for the next couple of years.
5. In addition, respondents see no potential for value at ‘improve income tax efficiency’. Potentially, this is because this EVM part is primarily focused on reduce spending on taxes. However this does not mean that there is no added value to be gained by using IoT to improve tax efficiency. The short term framework can be found at the end of this paragraph.
Concluding, in short term, IoT adds value for revenue growth, increasing the operating margin and increasing value for parts of the asset efficiency.
4.2.2 Long term framework, within 3 – 10 years Looking at the long term value framework, three distinctive points can be distinguished, which are in the ‘revenue growth’, ‘operating margin’ and for the value driver ‘automatic sensors trigger’. These are summarized below.
1. For the long term vision, so from 3 to 10 years from now, this case study identified a lot of added value at the ‘Acquiring New Customers’ and ‘Retain and Grow Current Customers’ points of the EVM. Implying that organizations see IoT as a tool to grow their customer base in the long term. This trend is also shown at short term vision of the framework. So in the short term as well as in the long term, using IoT to acquire and retain customers is seen as important.
2. Secondly, the number of respondents that see value for these two EVM categories increase for the three value drivers that act as a trigger, with the highest response at the ‘automatic sensors trigger’. This increase in interest and expected high value can be seen at the feedback side of Fleisch’s (2010) model. As sensors have more functions when looking at the right side of the model, this suggests that more advanced sensors will become more relevant within three to ten years from now.
3. Finally, the added value that could be seen for increasing the operating margin shifts mainly towards improving the customer interaction efficiency. For the short term version of the model, the added value for IoT was identified for the whole operating margin part of the EVM. Many respondents indicated value at this part of the EVM. Also the expected value increases when looking at the feedback side of the model by Fleisch (2010). The long term framework can be found at the end of this paragraph.
Concluding, IoT adds long term value for growing revenue, increasing the operating margin and for the ‘automatic sensors trigger’ value driver.
4.3 State-‐of-‐the-‐Art The current state-‐of-‐the-‐art of IoT at the three different case studies is discussed in this paragraph. In order to improve the quality of the answer, the respondents were asked which IoT applications exist at their organization, instead of asking respondents the current state-‐of-‐the-‐art, which could have led to biased answers. All cases have some sort of IoT application running at their company or have created an application for a client. The detailed analysis of the identified applications can be seen in Confidential Appendix C.5. A summary of the results is displayed in table 1. In this table, the identified applications of each case is projected. Also, the identified applications are translated to the emerging type of IoT applications by Chui et al. (2010).
Looking at a category level, all cases have identified applications from the ‘Information & analysis’ section model by Chui et al. (2010). Zooming in at this category, KPN realized an application related to tracking behavior, with sensors in cars for the purpose of fleet management analysis. Also, the company recently was involved a project with connected fridges. The Port of Amsterdam has started a pilot in which they have placed sensors at the quays. These proximity sensors are able to detect if a ship has docked at the quay. In the future, this enables ships that visit the harbor, to check for a free
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quay to dock. Canon has integrated multiple sensors in their copy machines to be able to track the status of the machines. This enables the company to stream their supply chain and offer more and extended services to their customers.
Case Identified IoT applications IoT application (Chui et al., 2010) Canon Built-‐in sensor technology in printers Sensor-‐driven decision analytics KPN Involved in projects for customers in
terms tracking behavior Tracking behavior
Port of Amsterdam Quays that monitor if a ship is docked Enhanced situational awareness Table 1: IoT state-‐of-‐the-‐art of the case studies
All current applications of this case study can be mapped to the ‘Automation and Control’ section of the framework of Chui et al. (2010). Gathering information and performing analysis has to take place first, before automation and control can be performed. This seems logical, as IoT is a relatively new concept. This gives an indication that the sensors required to facilitate IoT are available, but that actually automating and controlling ‘things’ with software is the next step for companies. Also, respondents indicated that the applications and software for IoT, still have to be written. This gives an indication that the state-‐of-‐the-‐art can be found in using IoT to gain information and perform analyses on this information.
4.4 Emerging types of Internet of Things applications After discussing the value drivers and current state-‐of-‐the-‐art, this paragraph formulates the emerging types of IoT applications as identified during the case study interviews. To recall the types of applications; Chui et al. (2010) identified six types of emerging IoT applications, which are divided in two subcategories, namely ‘information and analysis’ and ‘automation and control’. The emerging applications are described in two timeframes: short term and long term. A detailed description, including tables and graphs can be found in Appendix C.6.
4.4.2 Short term emerging applications, within 0 – 3 years Within a period from now to three years, the respondents identified a total of 52 emerging applications. 83% of these applications where identified in the sub-‐category information and analysis, representing a vast majority of the emerging applications.
The most identified types of emerging applications are ‘enhanced situational awareness’ and ‘sensor-‐driven decision analytics’. These two types of applications represent over 60% of the identified emerging applications. Enhanced situational awareness helps to achieve real-‐time awareness of physical environment (Chui et al., 2010). Respondents saw applications in monitoring the outside environment with sensors like cameras, sniffers and proximity sensors. For sensor-‐driven decision analytics, respondents named dashboards to help customers make decisions and gain understanding about things and/or the environment, as desired applications.
A notable trend can be seen at emerging applications at the EVM category: revenue growth. The number of identified applications for that part of the EVM is 24, so this represents almost half of the applications. Foremost, these applications are mapped for acquiring new customers and retaining and growing the current customer base. This implies that respondents see the value of applying IoT for use of information and analysis, to acquire new customers and expand the current customer base.
The ‘complex autonomous systems’ application type was named only one time by respondents for short term applications. This might be because it represents automated control in open environments with great uncertainty, for example autonomous drones making their own decisions. This technology is cutting-‐edge at the moment and only used for test purposes, for example in autonomous cars.
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4.4.3 Long term emerging applications, within 3 – 10 years For the long term -‐a period within 3 – 10 years from now-‐, significantly less applications than in the short term where identified by respondents, namely 18 emerging applications in total. The lower number of identified applications has to do with respondents being unable to grasp what the long term applications for IoT can be, all but one application were identified in the sub-‐category ‘Information and analysis’
Similar as for the long term emerging types of applications, the EVM category ‘Revenue growth’ is best represented. The exception is ‘Leverage income-‐generating assets’, of which no emerging types of applications where mapped by respondents. So respondents want applications that help them acquire new customers, retain current customers and help them strengthen pricing.
Half of the emerging types of applications can be found at ‘Sensor-‐driven decision analytics’. This emerging type of application involves assisting human decision making through deep analysis and data visualization (Chui et al., 2010). This has to do with creating dashboards and applications for people to visualize the feedback from sensors.
It seems that companies that want to integrate IoT in their supply chain, need to start with a pilot and on a small scale, in order to properly test the concept. When placing sensors, these sensors need to be as advanced as possible, in order to enable the use of future IT developments. However, companies need to pilot a limited set of functions and gradually expand the functions of the sensors.
4.5 Inhibitors for Internet of Things adoption In this paragraph, the most important inhibitors that where identified during the different case studies are discussed. First, the short term inhibitors are given and next the long term. The comprehensive ranking of all short-‐ and long term inhibiting factors, including the number of respondents that identified a factor, can be found in Appendix C.7 & C.8.
4.5.1 Short term inhibitors In total, seventeen short term inhibitors for IoT where identified by respondents. The short term inhibitors that where characterized the most are displayed below. These inhibitors where identified by four out of the nine respondents. The inhibitors are: ‘cost of technology / investments’, ‘privacy’, ‘lack of technology’ and ‘capacity’.
1. ‘Cost of technology / investments’, the cost of the technology or investments required to kick-‐start IoT adoption is conceived as high by many respondents. Not only the present-‐day cost of IoT devices, but also the spin offs that IoT creates, such as the cost of creating applications and software is perceived as high. This has to do with software that has to be written or adapted to be able to analyze the mountain of data that IoT devices can generate. After this analysis, something has to be done with the data, so some sort of Artificial Intelligence has to be built into the software to empower the ‘things’ to control themselves and make decisions for themselves.
2. ‘Privacy’ is the second key inhibitor. Privacy concerns are well represented for the adoption of IoT. Employees, organizations and customers have to be willing to give up some privacy in order for the concept to gain maximum potential, but are they willing to? Also, personal privacy is argued as an inhibitor because when things are able to follow peoples’ every move, it could have major consequences for their lives. ‘
3. Lack of technology’ is the third major inhibitor. Respondents believe that the technology that is able to energize IoT, will not be present between now and three years. Technology like a sufficient network connectivity and data transfer capabilities, software and capable hardware.
4. The last key inhibitor is ‘capacity’. This inhibitor refers to the capacity of companies to implement the concept into their companies and products. This is due to other, large improvement IT projects within the studied cases and as a result of the merger of IT systems. It raises the question if the analyzed cases are able to have the manpower to make full use of IoT.
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4.5.2 Long term inhibitors The list of identified long term inhibitors is significantly shorter than the list of short term inhibitors. The respondents identified a number of nine long term inhibitors for IoT adoption. One respondent was not able to identify any long term inhibitors.
1. ‘Privacy’ was named by five of the nine respondents as an inhibiting factor. Respondents identified privacy on a corporate level as being highly important for organizations in order to protect their company secrets. IoT offers a tremendous opportunity to track things and gather data, this data must be protected. Personal privacy is regarded as important as well. Due to the many sensor’s that will deployed in the long term, computers will be able to track almost everything a person does during the day. ´Investments’ and ‘Technology’ where both named twice as inhibitor. This is because a proper deployment of IoT devices requires a large investment from companies who want to harness the real power of the concept.
2. Secondly, technology must be developed to be able to make use of the outputs of the billions of sensors. The hard-‐ and software required to make full use of IoT is not present yet, also in the connectivity are some technology gap. This refers to the possibility to transfer huge amount of data from billions of these devices.
4.6 Purpose The purpose of these frameworks is to present important parts of companies, where value can be added using IoT. Also, the framework indicates which value drivers might be best suitable to achieve the desired value at these points. Furthermore, the framework identifies the current state-‐of-‐the-‐art and which type of emerging IoT applications are suitable to generate the desired value at organizations. This framework helps IT strategists and CIOs to determine where to apply IoT first at their company, so that most value can be added.
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5. Validation Next to the conducted interviews, two employees of Deloitte where interviewed to validate the results. The experts are a senior-‐consultant and director from Deloitte Digital. The Deloitte Digital service line focusses to support organizations device their digital strategy, mobile, social/web, content management and managed services. The department has already completed multiple IoT project’s for customers. Both experts have completed multiple strategic projects for clients that wanted an advice from Deloitte in how to proceed with IoT as an emerging concept. These projects where mostly conducted for clients in the ‘Telecom, Multimedia and Telecommunication’ branch from Deloitte. This branch deals with the connectivity and data transfer part of IoT, which is essentially the backbone of IoT. Only if the connectivity of IoT devices is facilitated, IoT is able to function. That is why, for some years, this branch is working on giving strategic advice to clients who want to provide this connectivity to facilitate IoT. Because of their experience with strategic IoT projects, the experts where selected to validate the results of the case study.
Because the novelty of the concept, a comparison was made between the results of the case study and the opinion of the experts. In this way, an assessment can be made if the case study results reflect the results of experts in the field of IoT. In order to facilitate further research, no factors, inhibitors or applications will be deleted. So the full overview is presented.
5.1 Validation value framework To validate the results, the same interviews were conducted as were done at the different case studies. The only deviation was that no questions were asked about the specific type of IoT applications, this is because of the broad applicability of the IoT concept. So the experts could come up with a countless list of applications. The results of this validation are shown below and the corresponding model can be found in Appendix C.2 & C.4.
5.1.1 Short term framework validation For validation of the short term vision of the value of IoT, the Deloitte consultants identified essentially the same points of value as the case study did. However the amount of value differs, the areas on the framework where value can be added are in line with the results of the case study. For improve income tax efficiency, no value was identified. Therefore, this section of the EVM was left out of the framework. The differences and similarities are discussed below.
1. During the case study, the respondents identified four key points of value. For the EVM, these are at ‘acquiring new customers’ and ‘retaining current customers’, at creating more value at the ‘operating margin’ of the EVM and at ‘asset efficiency’. For the value drivers, the ‘automatic sensors trigger’ stands out as a value driver with value at every part of the EVM. Now, looking at the results of the Deloitte consultants, the first value quadrants for the first three points can be seen as well.
2. In general for the ‘revenue growth’ part of the EVM, value was identified at almost all IoT value drivers. Though, a notable difference is the high value identified at ‘leverage income assets’, for ‘automatic product security’ during the validation sessions.
3. Subsequent, the value on short term for the ‘operating margin’ part of the EVM, was also seen during the validations, except for the improve ‘income tax efficiency’ section. This shows that the constructed framework is valid for this section.
4. At the ‘Asset efficiency’ section of the EVM, the value for ‘improve property, plant & equipment efficiency’ and ‘improve inventory efficiency’ where also identified. Representing the same value as was identified at the IoT value framework.
Concluding, the identified added value for IoT for the validation in the short term framework are largely the same as the case study respondents identified. This suggests that the constructed framework on short term is valid.
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5.1.2 Long term framework validation During the expert sessions, the ‘mind changing feedback’ and ‘automatic product security’ value drivers were identified for the long term vision, these are elaborated below.
1. Looking at the long term framework validation, the thing that stands out is the value that is seen at the ‘mind changing feedback’ value driver. This IoT value driver, which influences the behavior of users (Fleisch, 2010), is seen as highly valuable by the experts. This is because the value driver is the most advanced of all value drivers and provides active feedback to users. During the validation sessions, this value driver was identified as being valuable for all parts of the EVM except ‘income taxes’. Also during the case study, this value driver was identified by many respondents as being able to add the most value for IoT. However, the most value is identified by the experts for ‘acquiring new customers’, ‘retaining current costumers’, ‘improving interaction efficiency’ and ‘managerial & governance effectiveness’. Indicating that IoT will be a concept that will remain to attract new customers in long term, providing revenue growth.
2. The consultants see added value for the ‘automatic product security’ value driver. In regard to privacy and security, this value driver is marked as more important by the consultants than in the case study. Security is seen as an important value driver to prevent counterfeit products and protect products and assets.
Concluding, the expert sessions identified the most value for the most advanced value driver, namely: ‘mind changing feedback’. Also, the ‘revenue growth’ part of the EVM is identified as valuable. Finally, automatic product security is valuable in regard to security and privacy.
5.2 Validation of inhibitors In this section, the inhibitors that were identified by the two consultants of Deloitte will be discussed. First, the inhibitors for the upcoming three years will be discussed, after which the longer term inhibitors for three to ten years are debated.
The framework can be seen in Confidential Appendix C.7 & C.8.
5.2.1 Short term inhibitors As short term inhibitors, the inhibitors ‘interoperability’, ‘user acceptation’, ‘security’ and ‘privacy’ were named during both expert interviews and are discussed below. Also their relationship in regard to the identified inhibitors during the case study is debated. All, in total nine, identified inhibitors for IoT adoption can be found in Confidential Appendix C.7.
1. The interoperability between IoT systems, IoT suppliers and technology will become an inhibitor on short term. Also because there is no standard yet, that provides guidelines in how IoT devices should operate and communicate with each other.
2. A lack of a standard is the next inhibitor, identified during the validation sessions. Because IoT is still in its infancy, there is no clear standard that helps the sensors to communicate with each other. The lack of a standard and bad interoperability is also named as inhibitor during the case study, but was marked with a more general term as lack of technology. So these two inhibitors are validated.
3. User acceptation: IoT users will have to accept that their daily lives is being monitored by sensors. This is an inhibitor because not all users want to sacrifice their privacy. This inhibitor was not identified as one of the most important inhibitors during the case study, but was mentioned a few times.
4. Finally, privacy was identified as inhibitor. As explained in the previous inhibitor, users of IoT have to be willing to give up some of their privacy in order for the concept to fully function. This inhibitor was also identified during the case study and thus validated as important.
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Summarizing, all short term inhibitors identified during the expert sessions can be linked to the inhibitors identified in the case study. Because of the relative novelty of the concept, no inhibiting factors will be deleted. In this way, a complete overview is given to facilitate future research.
5.2.2 Long term inhibitors For the long term inhibitors, from three to ten years, the expert sessions provided the following four inhibitors. The long term inhibitors ‘security’ and ‘social aspect’ were named in both interviews. Also, ‘privacy’ and the ‘vulnerability of the internet’ where identified. The inhibitors are compared to the full list of inhibiting factors for long term, which can be found in Appendix C.8.
1. Security: because cybercrime is increasing and the IoT sensors provide countless access points to the network, a good way of securing IoT has to be devised, this creates an inhibiting factor. Security can also be found in the full list of inhibitors and is thus validated.
2. Social aspect: right now, IoT is thought of as a gadget. When people realize that everything they do is tracked by sensors, people can realize that the big brother effect is taking place. Inhibiting IoT adoption. This inhibitor is not explicitly named in the case study and therefore is not validated.
3. Privacy: also on long term, privacy remains an inhibitor. According to the experts, this is because everything is going to be digitalized and therefore will be visible. People’s movements can be tracked by the minute, creating privacy issues. The privacy issue is also noted during the case study and validated.
4. Vulnerability of the Internet: if there is a power-‐cut, all connected things and thus the IoT will stop working, or work partially. This exposure is an inhibitor for IoT adoption according to the experts. This factor is not identified at the case study and therefore not validated. However, the factor could be interesting to integrate in further research.
Summarizing, the long term inhibiting factors that the experts identified can be grouped two groups, the first group is the ‘security’ part, including the factors: security and vulnerability of the internet. The second group relates to ‘privacy’, including the social aspect and privacy issues that IoT creates.
6. Discussion An essential part of theory building is a comparison of the emergent concepts, theory or hypotheses with existent literature. Also, conflicting literature represents an opportunity, because it forces researchers to a more creative way of thinking (Eisenhardt, 1989). Literature that presents similar findings is relevant as well, because it connects underlying resemblances in singularities which would normally not be associated. Linking the case study results to the literature is vital, because the findings often originate from a limited number of cases (Eisenhardt, 1989). Van Aken (2007) calls this step ‘literature enfoldment’.
Due to the approach of this research, a comparison with literature can only be performed for the state-‐of-‐the-‐art-‐ and the inhibiting IoT factors. While the presented framework for added value and emerging applications are attained from scholars. These scholars do not provide an outlook in where value can be added best and what kind of applications are emerging. Consequently, an approach has been chosen in which the case study results are discussed with a focus on future research suggestions. If literature is existent about a topic, it is mentioned in the discussion.
6.1 Internet of Things added value Framework In this paragraph, the most important outcomes of the IoT framework will be discussed.
For both timeframes of the IoT value framework, the EVM sections ‘Acquire new customers’ and ‘Retain and grow current customers’ stand out as providing opportunities to generate value. Every IoT value driver is identified as valuable for these two sections. In the long term, the number of responses and perceived importance shift right, towards the value drivers that provide feedback to
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users. This shift towards the right of the framework provides a richer experience for customers, as things are able to give feedback to users about their status. This added value for acquiring-‐ and retaining current customers seems reasonable because IoT is expected to grow tremendously, this creates opportunities to acquire new customers.
Observing the ‘improve customer interaction efficiency’ part of the EVM, the difference of the ‘Mind changing feedback’ IoT value driver stands out. The value driver is slightly present at short term, but emerges for the long term vision with many respondents and a higher value. This trend corresponds with the fact that organizations want to provide rich information that influences the behavior of users in a positive way.
Another interesting trend is the change in value at the ‘asset efficiency’ branch of the EVM. For the short term, the case study revealed value for all IoT value drivers at ‘improve property, plant & equipment efficiency’ as well as ‘improve inventory efficiency’. When looking at the long term predictions, the value almost disappears, with only the ‘automatic sensors trigger’, ‘automatic sensors trigger’ and ‘automatic feedback’ providing a significant score. For this part of the EVM, added IoT value in factories or production facilities seem evident. Placing sensors on products and inventory seems like something that can add value in supply chain management. But respondents mostly observe this value in the short term, this is probably caused by the fact that this case study has not interviewed persons who work in a production facility. KPN and the Port of Amsterdam produce no products themselves and Canon Europe only distributes and markets the products. The development and production is done overseas in Japan and the USA. Future research might be able to verify if IoT really does not add value in de long term for asset efficiency. Also, the value for managerial & governance effectiveness, as well as execution capabilities is greatly reduced in comparison with the short term vision.
Finally, ‘improve income tax efficiency’ is not identified by any respondent as being able to add value by using IoT. However, a way that IoT can add value for this part of the EVM might be hard to conceive, this does not necessarily mean that there is no value to be added using IoT. Because of the small scope and limited time for this research, it is possible that IoT is able to add value to this part of the EVM. However, this study does not identify this value, future research may conclude that IoT can add value for improving the income tax efficiency.
6.2 State-‐of-‐the-‐art As identified earlier, the IoT state-‐the-‐art that develops from this case study is related to acquiring information and analyzing this information. The emergent applications all relate to the Information and analysis section of Chiu et al. (2010). As described earlier, automation and control is easier to perform then the other section of Chui et al. (2010), which is automation and control. Broadening our view by reviewing literature about IoT applications reveals that automation and control applications do exist (Atzuri et al., 2014). But the applications that actually perform automation and control tasks are limited. In the research of Atzuri et al. (2014), only one of the seven researched applications has the capability to perform an autonomous establishment of social relationships, and therefore is capable of automation and control. Thus, possibly because of the narrow scope of this research, only information and analysis applications where identified.
6.3 Emerging types of applications The category ‘information and analysis’ of the types of emerging applications by Chui et al. (2010), represent 83% of the applications found in this case study (1). Also, the section revenue growth and in particular acquire new customers and retain current customers are named often in both short-‐ and long term, in the short term in particular, where the two EVM sections represent almost half of the identified applications (2). Looking at the transition from short term to long term vision, the automation and control section almost completely disappears when looking at the identified
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applications (3), but why? Finally, for income taxes, no applications are identified (4). In this paragraph, these points are discussed.
1. Over 80% of the applications are identified in the ‘information and analysis’ section. This section includes applications that generate better information and analysis, and significantly enhance decision making (Chui et al., 2010. Reviewing the case study, all selected cases have applications that are also in the information and analysis section. Therefore, the results of the cases could be biased because of the view the respondents have when looking at IoT applications at their company. Chances are, when studying a manufacturing organization, that the response for applications in the ‘automation and control’ domain will be significantly higher.
2. The two parts of the EVM that stand out in the short-‐ and long term prognosis are ‘acquire new customers’ and ‘retain and grow current customers’. Almost half of the identified applications correspond to this two sections. This corresponds with the results of the constructed IoT value framework. This case study identified IoT as a way to attract new customers to an organization and by doing so, pushing the revenue growth. Because IoT is still in its infancy, this assumption could be right. Also, literature and advisory firms are suggesting that the number of IoT devices will grow enormously to 50 billion by the year 2020 (Cisco, 2011), adding an economic value of $1.9 trillion per year (Gartner, 2013), creating a valid result of the case study.
3. Respondents see much less applications in the long term than on short term. Respondents indicated that they find it hard to picture a clear image of the future of IoT. They see a big future for IoT, but especially how this future is going to look like is uncertain. This has to do with the current inhibitors for IoT, such as the absense of a standard. For example, Google only just revealed the first IoT Operating System, named: Brillo5. This paves the way for a future standard, in which sensors can talk with each other and data is exchanged.
4. Finally, at short term and long term visions, applications the EVM part ‘Improve income tax efficiency’ is not identified. This is in line with the proposed framework for IoT added value. It could be that there are no conceivable applications for this section, more likely is that there are examples but they are not identified due to the scope of this thesis.
A full list emerging types of applications can be found in Appendix C.6.
6.4 Inhibitors for Internet of Things Recapitulating from the problem definition section, the four key inhibitors for IoT adoption are ‘network foundation’, ‘security’, ‘privacy and trust’ and ‘managing heterogeneity’. Looking at the case study, the major emerging inhibitors are ‘investments’, ‘privacy’, ‘lack of technology’ and ‘capacity’. Also the ‘choice of a standard’, ‘security’ and ‘awareness’ where named often for short term. Looking at long term, privacy was named the most. See Appendix C.7 & C.8.
Comparing the literature with the case study results, all issues identified by scholars are reflected in the case study. Vice versa, new inhibiting factors that arise from this case study are ‘investments’ and ‘capacity’. These two new inhibitors are elaborated in the next section.
The investments that are required for IoT to function is seen as an inhibitor by this case study. High investments come in twofold. First, respondents indicate that the cost to connect a thing to the internet are high, as the sensors and equipment for IoT to function are still relatively expensive. The sensors have to be integrated in things. If this are newly manufactured products, the sensors can simply be integrated into the new product. But if the things are already present, such as ships, manufacturing equipment etc., the sensors have to be connected to the device. The cost of labor to perform this operation is perceived as high, as the breakeven point will take a long time. Secondly, additional software and hardware is required to connect the ‘things’. This extra soft-‐ and hardware, increases the investments of IoT, according to this case study. IoT requires software to analyze and
5 Google Brillo, see: https://developers.google.com/brillo
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make use of the data from sensors, and additional hardware is required to perform the necessary calculations to analyze data and provide connectivity. Few scholars write about IoT investments, in particular Gubbi et al. (2013) and Mattern & Floerkemeier (2010) write about the topic. But it seems that very few papers write about the investments that IoT requires in order to function.
Secondly, capacity is a new inhibiting factor. This case study reveals that companies expect to lack the capacity that is needed to integrate IoT in the future. IoT integration requires companies to draw employees from other, important, projects at the analyzed cases. Thus, the companies expect to lack the necessary manpower in order to successfully integrate IoT. In the existing literature, the lack capacity or manpower is discussed in terms of reducing the required capacity or manpower at a company. No papers have been found that describe the lack of capacity at a company to integrate IoT. But nevertheless, this could be an inhibitor, particularly in short term. Further research is needed to verify this emergent inhibitor.
The insight from these two new inhibiting factors is that financial motivation plays a big role in inhibiting the adoption of IoT, as it relates to both factors. Because of the novelty of the subject, the investments are high and employees have to be moved from other IT projects in order to have the capacity needed to implement the concept.
7. Conclusion This chapter concludes this thesis. First, the proposed research questions are answered, then the research contributions, limitations and suggestions for further research are presented.
7.1 Research questions 1. What is the current state-‐of-‐the-‐art of Internet of Things?
The current state-‐of-‐the-‐art according to this case study are applications that gather information and analyze this information. Applications identified during this case study are related to tracking the behavior of objects, scanning the environment to achieve real-‐time awareness and to assist human decision making through analysis. Organizations are able to analyze the gathered data and make adjustments to their supply chain using this data.
2. What are the possible applications using Internet of Things?
1. The applications that are suitable for Internet of Things are divided in two categories. These application categories are ‘information and analysis’ and ‘automation and control’. Especially the ‘information and analysis’ category seems well suited for IoT within now and three years. Over 80% of the IoT applications identified during this case study fall into this category. This gives an indication that organizations desire IoT applications that are able to gather information and analyze the data.
2. A closer look at the ‘information and analysis’ category of IoT applications, reveals that over 75% of these applications relate to ‘Enhanced situational awareness’ and ‘Sensor-‐driven decision analytics’. These application types relate to achieving a real-‐time awareness of the physical environment and to assist decision making through analysis and data visualization.
3. Next to this, at the short term vision of the Internet of Things value framework applications for automation and controlling processes are identified. The ‘Automation and control’ section almost completely disappears from the long term vision, which is surprising. Regrettably, the time available for this thesis is too short to study a possible cause for this trend, but should be studied in future research.
4. Finally, companies need to start with a pilot to test IoT applications. But immediately start to integrate sensors that are state-‐of-‐the-‐art, in order to include IT developments later on. During the pilot, organizations can gradually expand the capabilities of the sensors and steadily unlock
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their full potential. This enables a step-‐by-‐step integration of the concept and enables organizations to properly test the IoT possibilities.
3. In which processes can IoT generate the most value?
Processes where Internet of Things can add the most value are identified using Deloitte’s Enterprise Value Map (EVM). Three distinct points of value can be distinguished for Internet of Things adoption. The framework created to show the added value of Internet of Things can be seen in chapter 5.
1. First, the most important value addition can be seen in the processes of ‘acquiring new customers’, as well as ‘retaining and growing current customers’. Internet of Things is a new concept, thus a lot of value can be added to an organization by acquiring new customers using Internet of Things. In the meantime, retaining and growing the current customer base with innovative and good quality Internet of Things applications also adds value. Especially for the revenue growth of an organization, because these two processes influence the volume of products sold.
2. Second, Internet of Things can be used to add value to the operating margin of a company. This can be done by using Internet of Things to enhance customer interaction efficiency and improving corporate service efficiency. In addition, Internet of Things adds value for ‘development & production’ and ‘logistics & service provisioning’ sections of the operating margin. The added value is the most on short term and becomes lower in long term.
3. A shift of value when moving from the short-‐ to long term vision is the lower value for the asset efficiency part of the EVM. This involves property, plant & equipment efficiency and inventory efficiency. Internet of Things sensors and capabilities to track assets and inventory will improve in the future. So, value decline of these processes was not expected as you would expect the value Internet of Things brings to improve asset efficiency to grow in the future.
4. A general trend can be noticed for all processes of the EVM. The value drivers for Internet of Things move from the more simple trigger-‐ and feedback sections, towards ‘Automated sensors trigger’ and ‘Mind changing feedback’ for the long term vision. These two value drivers are the most advanced form of their respected categories, when taking the trigger section and the feedback section into account. So the shift towards these two value drivers seems justified, as they offer the most possibilities.
4. What are the current inhibiting factors for IoT adoption in these processes? To enable the actual integration of Internet of Things, some factors have to be overcome that inhibit adoption. During this research, four main factors came to light that are going to inhibit the adoption of Internet of Things, which are: ‘privacy’, ‘technology’, ‘investments’ and ‘capacity’. These inhibitors are inhibiting for the short term and long term vision of the concept. A full list of inhibiting factors can be found in Appendix C.7 & C.8.
1. Privacy is the foremost and biggest inhibitor for Internet of Things. The logic behind this, is that Internet of Things is going to make use of countless sensors that are attached to things. These sensors are able to track almost every part of human’s daily behavior. Because the human behavior is so accurately tracked, privacy is easily compromised when the data falls into the wrong hands. One of the many examples is if an insurance company is going to charge you more for your health insurance, because the company has acquired your shopping data that shows you are buying unhealthy food. This is an advantage for the insurance company, because it can adapt pricing. But for the affected user, this is a bad development.
2. The second inhibitor that emerged from this case study is technology. This involves technology that is able to handle the data, like connectivity, data transfer. But also hardware like sensors and computing power. Finally, technology as an inhibitor also involves the software that is able to handle and analyze the data. If the technology is not on par for IoT, then the concept is not able to unlock its full potential.
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Until now, the identified inhibitors all have been identified by previous scholars. The next two inhibitors where not found in the literature, these are: ‘investments’ and ‘capacity’. These two newly identified inhibitors are discussed in the next two sections.
3. The third inhibitor is investments. This study shows that organizations see the high costs of hardware and software and labor as an inhibitor for Internet of Things adoption. Internet of Things sensors are becoming cheaper, but the costs to install sensors on ‘things’ is perceived as high. Creating a low return on investment. Next to the cost of hardware and installing the sensors, the software to handle the vast amounts of data needs to be created. Analytics software is currently available, in order to handle the data in real-‐time, organizations expect to acquire software that is capable to run on their own IT systems.
4. The final inhibitor only applies to the short term vision for Internet of Things. This inhibitor is capacity. This study indicates that companies fear they lack the capacity to integrate IoT in short term. Organizations often deal large scale IT projects. These projects apply to the improvement of legacy systems or as a result of a merger with another company. IT projects have a big impact for an organization and often need a lot of manpower in order to succeed. Therefore, the capacity or manpower required to implement Internet of Things within now and three years is not fully available. This could result in a slower adoption of the concept.
7.2 Research contributions The contributions that this research delivers for research can be split up into practical contributions and academic contributions.
In the field of practical contributions, this research has mapped the value of IoT in a structured and organized manner in a framework. Using this framework, IT strategists and C-‐level management are able to identify in which processes IoT can add the most value for their organization. Also, the associated IoT value drivers can be identified to help them identify the value of IoT. Strategists are given a tool and trend of emerging IoT applications and are given a complete overview of all possible IoT applications. This ensures a comprehensive understanding of the concept and enables them to make decisions based on complete information. Finally, due to the identification of IoT inhibitors, C-‐level managers and IT strategists can view factors that need extra attention. In this way, the chance of a successful IoT integration increases. Also, the inhibitors that are identified in this study can be tackled right from the start of an IoT integration project, instead of emerging during the project. This can prevent complications during the integration process.
For academics, the results of this case study help to fill the current gap regarding specifically in which processes Internet of Things can add the most value. Academics are also given an indication as to what kind of value drivers are applicable to these processes. Outcomes of this framework can be used in order to initiate further research in determining why certain trends emerge from the framework, e.g. the change of short term to long term value perceptions of several processes. The research also identifies which types of emerging applications are the most applicable to real life cases and could be subject to another research for their applicability in other sectors. Also, the outcome of this study in regard to the inhibiting factors of IoT can help academics to study why initiatives to integrate IoT are succeeding or failing.
7.3 Limitations The most important limitations for this study are as follows:
1. Due to time constraints, the number of cases for this case study is relatively low. Because of this, the results of this thesis might not represent the general opinion of companies about IoT and where value can be added. Replication of this thesis should be performed to verify the results.
2. All cases had some affinity with supply chain management, as was pre-‐defined in the scope. This choice can affect the results of this thesis, because it might be that supply chain management is further ahead in the field of Internet of Things. And as a result, much is published about the
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sector. Also, companies related to supply chain management might have a different opinion about IoT, because the concept seems to be most applicable to them. This factors could bias the results of this thesis.
3. When validating the results, two consultants of Deloitte where asked for their vision of IoT. Although, the consultants are experienced in the field of Internet of Things and have a track record in assignments for clients, this provides a narrow sample, which could result in an incomplete result.
7.4 Suggestions for further research The field of IoT is relatively unexplored. This is why quite some research is still left to be done to unravel the Internet of Things. In fact, this study raised many more questions about IoT and related topics. In the discussion part of this thesis, some recommendations have been given for future research. The key suggestions for further research, which can contribute to the results in this thesis are:
1. This thesis was done in an explorative and qualitative way because the concept of IoT is relatively new. Due to the scope, no quantitative research has been performed in order to verify the results of this study. Future quantitative research should be performed, to verify if the identified processes, value drivers, emerging applications and inhibiting factors are correct.
2. Only organizations that have affinity with supply chain management have been taken into account for this thesis. Further research that identifies more sectors could strengthen and add to the results of this thesis.
3. Many of the inhibiting factors for short-‐ and long term are still unexplored by literature. For example the privacy issues that were identified and how to cope with them? And how to properly secure this network of sensors? Also, the rights of people’s privacy and the law in regard to Internet of Things need to be studied. Internet of Things is a multidisciplinary concept, which still needs to be studied in many ways. A needless fear in the general public about Internet of Things could prevent adoption. Hence, this subject needs to be further examined.
4. When observing the value framework and specifically looking at the difference between short-‐ and long term visions, two distinct trends can be seen in value reduction. First, at the asset efficiency side of the framework, the value for development & production efficiency and logistics & service provision efficiency is greatly reduced with significantly less respondents and perceived value. Second, at the expectations section the managerial & governance effectiveness and also the execution capabilities reduce in value and respondents as well. The cause why these parts of the EVM reduce in value is out of scope for this thesis, so further research could untangle the reason for this value drop.
5. The emerging type of applications are oriented towards the Information and Analysis section of the types of emerging applications identified by Chui et al. (2010). This is the case for both timeframes. According to this study, information and analysis seems like the most important section of Chui’s (2010) model. However, the low number of responses for the other section is remarkable. One would expect that Automation and control must have countless applications, because IoT is such a broad topic. Further research is needed to study if this tendency for information and analysis, is correct.
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References Accenture. (2014). Driving unconventional growth through the industrial Internet of Things. Retrieved April 22, 2015, from Accenture: http://www.accenture.com/us-‐en/technology/technology-‐labs/Pages/insight-‐industrial-‐internet-‐of-‐things.aspx
Akyildiz, I. F., Brunetti, F., & Blázquez, C. (2008). Nanonetworks: A new communication paradigm. Computer Networks, 52(12), 2260-‐2279.
Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. Computer networks, 54(15), 2787-‐2805.
Atzori, L., Iera, A., & Morabito, G. (2014). From" smart objects" to" social objects": The next evolutionary step of the internet of things. Communications Magazine, IEEE, 52(1), 97-‐105.
Bandyopadhyay, D., & Sen, J. (2011). Internet of things: Applications and challenges in technology and standardization. Wireless Personal Communications, 58(1), 49-‐69.
Bassi, A., & Horn, G. (2008). Internet of Things in 2020: A Roadmap for the Future. European Commission: Information Society and Media.
Benbasat, I., Goldstein, D. K., & Mead, M. (1987). The case research strategy in studies of information systems. MIS quarterly, 369-‐386.
Chui, M., Löffler, M., & Roberts, R. (2010). The internet of things. McKinsey Quarterly, 2(2010), 1-‐9.
Cisco. (2011). The Internet of Things: How the Next Evolution of the Internet Is Changing Everything. Retrieved April 21, 2015, from http://www.cisco.com/web/about/ac79/docs/innov/IoT_IBSG_0411FINAL.pdf
Coetzee, L., & Eksteen, J. (2011). The Internet of Things-‐promise for the future? An introduction. In IST-‐Africa Conference Proceedings, 2011 (pp. 1-‐9). IEEE.
Corbin, J., & Strauss, A. (1994). Grounded theory methodology. Handbook of qualitative research, 273-‐285.
Deloitte. (2014). The Internet of Things Ecosystem: Unlocking the Business Value of Connected Devices. Retrieved April 22, 2015, from Deloitte: http://www2.deloitte.com/us/en/pages/technology-‐media-‐and-‐telecommunications/articles/internet-‐of-‐things-‐iot-‐enterprise-‐value-‐report.html
Dickson-‐Swift, V., James, E. L., Kippen, S., & Liamputtong, P. (2007). Doing sensitive research: what challenges do qualitative researchers face?. Qualitative Research, 7(3), 327-‐353.
Eisenhardt, K. M. (1989). Building theories from case study research. Academy of management review, 14(4), 532-‐550.
Flick, U. (2009). An introduction to qualitative research. Sage.
Fleisch, E. (2010). What is the internet of things? An economic perspective. Economics, Management, and Financial Markets, (2), 125-‐157.
Gartner. (2013). Gartner Says It's the Beginning of a New Era: The Digital Industrial Economy. Retrieved April 3, 2015, from Gartner: http://www.gartner.com/newsroom/id/2602817
Gartner. (2015). Internet of Things Scenario: When Things Become Customers. Retrieved April 3, 2015, from Gartner: http://blogs.gartner.com/don-‐scheibenreif/2015/04/03/what-‐happens-‐when-‐things-‐become-‐customers/
32
Gluhak, A., Krco, S., Nati, M., Pfisterer, D., Mitton, N., & Razafindralambo, T. (2011). A survey on facilities for experimental internet of things research. Communications Magazine, IEEE, 49(11), 58-‐67.
Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645-‐1660.
LeHong, H., & Velosa, A. (2014). Hype Cycle for the Internet of Things, 2014. Gartner.
Mattern, F., & Floerkemeier, C. (2010). From the Internet of Computers to the Internet of Things. In From active data management to event-‐based systems and more (pp. 242-‐259). Springer Berlin Heidelberg.
McKinsey Global Institute. (2013). Disruptive technologies: Advances that will transform life, business, and global economy. Retrieved April 21, 2015, from McKinsey & Company: http://www.mckinsey.com/insights/business_technology/disruptive_technologies
Mentzer, J. T., DeWitt, W., Keebler, J. S., Min, S., Nix, N. W., Smith, C. D., & Zacharia, Z. G. (2001). Defining supply chain management. Journal of Business logistics, 22(2), 1-‐25.
Microsoft. (2015). Creating the Internet of Your Things. Retrieved April 21, 2015, from Microsoft: http://www.microsoft.com/en-‐us/server-‐cloud/internet-‐of-‐things.aspx
Miorandi, D., Sicari, S., De Pellegrini, F., & Chlamtac, I. (2012). Internet of things: Vision, applications and research challenges. Ad Hoc Networks, 10(7), 1497-‐1516.
Myers, M. D., & Avison, D. (1997). Qualitative research in information systems. Management Information Systems Quarterly, 21, 241-‐242.
National Intelligence Council. (2008). Disruptive Civil Technologies: six technologies with potential impacts on US interests out to 2025. National Intelligence Council.
Reijers, H. A. (2006). Implementing BPM systems: the role of process orientation. Business Process Management Journal, 12(4), 389-‐409.
Schoenberger, C. R. (2002). The internet of things. Forbes Magazine, 6.
Sprenkels, B. (2014). Internet of Things Business Models and Applications. Eindhoven: Eindhoven University of Technology.
Sundmaeker, H., Guillemin, P., Friess, P., & Woelfflé, S. (2010). Vision and challenges for realising the Internet of Things.
Uckelmann, D., Harrison, M., & Michahelles, F. (2011). An architectural approach towards the future internet of things. In Architecting the internet of things (pp. 1-‐24). Springer Berlin Heidelberg.
Van Aken, J., Berends, H., & Van der Bij, H. (2012). Problem solving in organizations: A methodological handbook for business and management students. Cambridge University Press.
Weber, R. H. (2010). Internet of Things–New security and privacy challenges. Computer Law & Security Review, 26(1), 23-‐30.
Yin, R. K. (2013). Case study research: Design and methods. Sage publications.
