A Holistic View of the IoT Process from Sensors to the Business Value
Ateeq Khan, Matthias Pohl, Sascha Bosse, Stefan Willi Hart and Klaus Turowski
Magdeburg Research and Competence Cluster,
Faculty of Computer Science, University of Magdeburg, Magdeburg, Germany
Keywords:
IoT, Smart Systems, Cyber-physical Systems, IoT Process, Industry 4.0, Digital Manufacturing.
Abstract:
Internet of things (IoT) is the focus of research, and industries are investing heavily due to potential benefits
of IoT in various fields. This paper provides a holistic view of different phases in IoT covering all phases
from sensor data collection to the generation of business value. In this paper, we propose to use the proven
Six Sigma methodology for IoT projects. We describe each phase using a structured approach. We discuss
the consequences of each phase while selecting the phase as an entry or starting point. We use predictive
maintenance as a use case to demonstrate the practicability of our IoT process. Using these insights, IoT
project managers can identify required activities and competencies to increase success probability. In the end,
we summarise the paper findings and highlight the future work.
1 INTRODUCTION
Internet of Things (IoT), digitalization, are the terms
which got popularity due to potential benefits in va-
rious fields. Daily use products or things conside-
red useless so far from digitalization perspective are
becoming valuable and are used to improve services
and to generate new offerings in those areas. Some of
the examples are from manufacturing industry (digital
manufacturing or industry 4.0 terms are used in this
context), smart city, agriculture, and livestock areas.
IoT enables organisations and customers to make im-
proved decisions based on the data gathered directly
from the end devices or fields.
Due to the popularity of IoT, organisations need to
innovate faster and have to digitalise their processes
to take the benefit of digitalisation (Lucas Jr et al.,
2013). Organisations are under a constant threat to be
left behind if they do not follow or adapt the required
changes or the trend. Hence it is their focus.
On the one hand, organisations are already using sen-
sors and IoT devices in traditional products (things,
machines, and equipment), to sense the environment
for the overall benefits. On the other hand, such pro-
jects generate an enormous amount of data (big data)
and can be used to improve the existing services or to
offer entirely new services or business models.
However, management of such project is difficult due
to higher complexity, heterogeneity, cost explosion,
blurred boundaries between the physical and virtual
world and inter-disciplinary within and outside or-
ganisations. The majority of the projects are not
successful (other projects have a higher risk of failure
(Lee and Lee, 2015)).
Existing approaches only touch part of the problem or
specific use cases and do not provide a holistic over-
view of different phases required for the IoT project.
So, it is necessary to have a roadmap for the IoT pro-
ject with clearly defined phases and its descriptions.
In this paper, we address the issue of isolated parts,
and our approach is not limited to a single case in a
specific domain. We discuss why the existing data
mining and organisational change management stra-
tegies are not fully transferable or applicable to the
IoT projects.
We provide a complete overview of IoT process co-
vering from the business value perspective for the or-
ganisation to the sensors. We describe each phase
using a pre-defined structure naming, e.g., challenges
and disciplines, and the analogy of our process pha-
ses with the six Sigma sub-methodology phases. To
show the practicality of IoT process, we show how
our contribution can be used in a case study scenario
namely predictive maintenance which combines IoT
and Industry 4.0.
392
Khan, A., Pohl, M., Bosse, S., Hart, S. and Turowski, K.
A Holistic View of the IoT Process from Sensors to the Business Value.
DOI: 10.5220/0006362503920399
In Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security (IoTBDS 2017), pages 392-399
ISBN: 978-989-758-245-5
Copyright © 2017 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
2 RELATED WORK AND
DISRUPTIVE ASPECTS OF IoT
To handle an IoT project, which extends within dif-
ferent departments in an organisation and also across
organisations, a methodology, process or framework
is required to manage and coordinate throughout the
IoT landscape. There are already different methodo-
logies, frameworks, and processes discussed in the
literature that are considered as a candidate for IoT
projects. Few to name are 5S (Osada, 1991), Kai-
zen (Imai, 1986), Six Sigma methodologies (Smith,
1993), Kotter model (Kotter, 1996), SEMMA (sam-
ple, explore, modify, model, and assess) (Azevedo
and Santos, 2008), CRISP-DM (cross-industry stan-
dard process for data mining) (Shearer, 2000), and
KDD processes (Fayyad et al., 1996; Lodhi et al.,
2008).
However, these methodologies have shortcomings,
for example, methodologies and frameworks like
CRISP-DM, KDD, SEMMA are more concerned
with the data mining process and do not include the
implication of business applications, business models
and scarcely describe the effect of one phase on the
other. Others, such as 5S, Kotter model, are very ab-
stract and rather suitable with the view to workplace
organisation or organisation change.
There are numerous works (Swan, 2012; Bonomi
et al., 2014) who address some parts of the IoT lands-
cape. Some of the works discuss only the IT per-
spective and others focus on the analytics perspective
and neglect other parts. There is a considerable
amount of literature which discuss IoT from the ap-
plications and future trends perspective (Curran and
Curran, 2014; Khan and Turowski, 2016b; Crowley
et al., 2014; Khan and Turowski, 2016a). In (Swan,
2012), the author reviews the eco-systems for IoT. It
covers only a part of the whole landscape. In (Bonomi
et al., 2014), authors propose a hierarchical distribu-
ted architecture and use a fog platform for analysis.
We argue that there are other factors which are inter-
related and have effects on other parts of the IoT pro-
ject or landscape. We describe these factors and pha-
ses and elaborate the whole process with the help of a
case study example.
3 AN ANALOGY OF THE IoT
PROCESS MODEL
For our IoT process, we advertise that we can use Six
Sigma methodology. Six Sigma methodology (Smith,
1993) was initially proposed to remove defects and
improve quality in 1986 but is now often used to me-
asure improvement in IT process execution and ser-
vices (George and George, 2003; Antony, 2006). Six
Sigma methodology is the best candidate to remove
defects, and we can use it for IoT project problems
described earlier. We propose to use Six Sigma sub-
methodology DMAIC because of its success in other
industries, and it has an analogy with the phases in our
process model. The sub-methodology DMAIC is an
acronym of the following steps, namely define, mea-
sure, analyse, improve, and control.
The brief description of these steps of DMAIC
methodology is as follows. In the define phase, we
define what is the problem; in the measure phase, we
find the areas of a problem; in the analyis phase, we
analyse the problem; in the improve phase, we take
necessary steps to remove the problem; and in the
control phases we control or check whether the pro-
blem is removed.
Our IoT process model consists of the following pha-
ses: sensors, pre-processing & analysis, business ap-
plications, and business value. The detailed descrip-
tion of these phases is described in Section 4.
The methodology steps of DMAIC have an analogy
with the following IoT process phases.
Define: This phase has an analogy with the business
value phase. In this phase, we define the scope and
objective of the IoT project. General questions in the
phase consider the nature of the problem or the aims
we want to achieve with this project. As this phase is
on the strategical level, we also identify the possible
stakeholder of the project, their roles, resources, and
what are the general requirements.
Measure: The sensors phase from IoT is associated
with this phase of methodology. In this phase, data or
measurements (raw data) are collected from the sen-
sors. We decide what kind of sensors we need and
how frequently measurement should be done.
Analyse: This phase is inline with the analysis phase
in the landscape. In this phase, we perform the ana-
lyses based on the data collected in an earlier phase.
We get insights from this phase which tell about the
behaviour or exhibit specific patterns.
Improve and Control: This is associated with busi-
ness application phase in the landscape. This phase
describes the necessary steps to improve the overall
situation or how we can use the outputs of other pha-
ses to improve business applications or business pro-
cesses of an organisation are. In the control phase, we
take measure to monitor the overall progress or per-
formance of the cycle.
We depict our process model phases and associ-
ated DMAIC steps in Figure 1. The organisation’s
vision serve as an input for the IoT project, and after
A Holistic View of the IoT Process from Sensors to the Business Value
393
Define
Measure Analyse
Improve &
Control
Organisation
Vision
Value for
Organisation
Sensors
Analysis
Business
Applications
Business
Value
Figure 1: Analogy of IoT landscape with DMAIC process.
the successful run, the value achieved from the pro-
ject is considered as an output. Due to the nature of
IoT projects and the characteristics described earlier,
the whole process is iterative.
4 IoT PROCESS PHASES
In this section, we elaborate the different phases of
the IoT landscape. We use the following structure to
describe different phases of our IoT landscape. The
structure is quite simple, self-explanatory regarding
the names, and consists of following parts, objecti-
ves and scope, challenges and requirements, tools and
methods, disciplines, interfaces, and influences on ot-
her phases. As shown in Figure 1, it is an iterative pro-
cess, so objectives and operations can be re-aligned
iteratively to optimize what is needed or possible in
current settings. In the following, we describe the
phases of IoT process.
4.1 Sensors:
In this phase, we sense the environment and measure
the surroundings. These measurements of the sur-
rounding can be properties of technical devices, envi-
ronmental situations or even human activities which
are the main focus of monitoring. However, there are
other types of sensors that are also used in IoT en-
vironments to receive executable commands and take
actions.
Objectives & Scope: The main aim of this phase is
to collect data for analysis. Such data can be collected
from physical sensors in the environment, e.g., tempe-
rature, pressure, humidity in the environment or it can
be a virtual data, e.g., from computing resources, e.g.,
disk space, rotation, memory. Physical sensors, vir-
tual sensors, and control entities are either organized
in sensor networks with a communicating link nodes,
or they are able to send data on their own to data cen-
ter. The form of an IoT landscape of sensors depends
on a given use case and requirements.
Challenges and Requirements: The challenges and
requirements in this phase are to identify what kind of
data should be collected and how frequently it should
be gathered. This identification depends on the sce-
narios and requirements, whether the data collected
from the sensors should be in real time, every se-
cond, or aggregated from the sensors. The design
of power-optimized micro-controllers or microcom-
puters in combination with sensors and network devi-
ces can follow with attention to IT security and safety
issues as much as legal considerations. All devices
must be part of a well-structured network to provide
the desired data for a data center or storage database.
Tools and Methods: Next to standard network con-
nections like Ethernet, Wi-Fi or GPRS there are a lot
of other network technologies coming up. ZigBee, Z-
Wave and LoRa are only some of them. Nevertheless,
RFID and NFC are suitable for short-distance soluti-
ons. In the course of optimizing the data transfer, ap-
plication layer protocols like MQTT, CoAP or XMPP
are used primarily although new network technolo-
gies bring their own protocol bundles. For the same
reason, these devices are becoming smaller. While
Arduinos and Raspberry Pis are popular for tinkering,
one can get completely operative devices by Z-Wave
or LoRa, so one is not forced to think about opera-
ting systems and applications scripting. In most ca-
ses, the linkage to a data center is not made directly.
All data streams that are sent by devices will be bun-
ched on linking platforms. Depending on the size of
a platform the data preprocessing or the data analysis
can be realised on these IoT platforms (on-premise or
cloud solutions).
Disciplines: The sensor layer is strongly characteri-
sed by the discipline of electrical engineering consi-
dering the micro controllers and sensors that are nee-
ded for IoT projects. But of no less importance is the
IoTBDS 2017 - 2nd International Conference on Internet of Things, Big Data and Security
394
Figure 2: An overview of IoT process.
experience in computer system engineering and com-
puter networking.
Interfaces: Business decisions can drive the con-
struction, the extension or the improvement of a sen-
sor network, so one sees the obvious link to the bu-
siness value layer. In some scenarios, the sensors
layer and the analytics layer are not clearly separable,
because technical components are overlapping (e.g.,
SmartHomes). However, this interface is definitely
visible, especially industrial, application systems.
Influences: A proper definition of required sensors,
sensor networks and data streams is necessary such
that one has to ensure the quality of the sensors, an
efficient network and processable data. Otherwise,
it would have an effect on the interfaces or the out-
come. For instance, short error intervals of an ob-
served machine or technical component will not be
detected with a long sampling rate of a sensor. Ho-
wever, an extremely short sampling rate will produce
a lot of data and could lead to system crashes in data
preprocessing especially in a huge sensor network.
4.2 Preprocessing & Analysis
In this phase, we describe both the preprocessing and
analysis phase of the IoT project, because preproces-
sing is a necessary step for the analysis. In preproces-
sing, we prepare the data for anlysis purpose.
Objectives & Scope: The main objective of this
phase is to obtain a data model or prediction model
for knowledge generation or decision making by data
analysis. Before performing data analysis, data pre-
paration is necessary to increase the effectiveness and
efficiency of the analysis. The preparation of data
spread over data selection, where one identifies avai-
lable and required data and provides it in a utilisable
way, and a data cleansing, where one handles mis-
sing data, noise and outliers and also verify data re-
liability. After data quality is checked one continues
with data transformation, that includes data transfor-
mation and data dimension reduction. The separation
between important and less important data also invol-
ves algorithms for clustering, classification or com-
ponent analysis, so one sees intersections with data
analysis methods. The final part of this process step
is data analysis. The prepared data will be the input of
the descriptive statistical analysis or the machine lear-
ning algorithm to obtain prediction models and pres-
criptive statements.
Challenges and Requirements: A definition of pro-
cessable data sets and output variables is not required,
but a postulation could simplify the data analysis. In
consequence of the assumption that data collection
is error-prone and 40% of collected data is “dirty”
(Fayyad et al., 1996), a solution is crucial, especially
in real-time IoT cases. Due to needed context infor-
mation, human interactions are sometimes necessary
but should be minimized as much as possible to avoid
additional error sources. The main challenge at all is
the choice of the best algorithm or method for spe-
cific problems. It is not just that one is interested to
gain knowledge out of data, one is encouraged to find
a suitable trade-off between complexity and resource
consumption or between accuracy and comprehensi-
bility. That does not only refer to data analysis met-
hods, handling huge amounts of data also requires
suitable strategies that regard data management and
data processing, e.g., store raw or aggregated data, ef-
ficient storage, and parallelization.
Tools and Methods: There is a considerable amount
of tools and methods, that could be used for analy-
tics. Therefore, we can differentiate between the tools
required for data processing and data analysis pur-
pose. On the one hand Apache Storm, Samza, SAP
Smart Data Streaming are used for data processing
and preprocessing, and on the other hand, Apache
Spark Mlib, R, SAP PAL are used for data analysis
purpose. The choice of certain tools depends on the
chosen system landscape. However, the methods are
mostly similar. Data integration, feature filters, wrap-
pers, clustering, pattern recognition, principal compo-
nent analysis, descriptive and predictive statistics are
rough terms that one will use in the analytics phase.
Disciplines: The mass of machine learning methods
A Holistic View of the IoT Process from Sensors to the Business Value
395
and statistical methods require expertise in mathema-
tical statistics, algorithmics, and data science. At the
latest, when one is compelled to adjust and require de-
velopment of methods for individual use cases. Ho-
wever, data management is fundamental to handle
data streams and storage.
Interfaces: This phase has interfaces on the adjacent
phases. Data streams are obviously the connection to
sensor networks and furthermore the processed and
analysed data will be provided for business applicati-
ons.
Influences: This phase has an influence on business
applications and business value. Although, a useful
outcome of data analysis is not guaranteed. A suitable
data preprocessing can affect the task of data analysis.
However, to ensure a reliable and valuable result, one
has to verify them with appropriate testing procedu-
res. Misleading statements as an input for business
applications are certainly undesired.
4.3 Business Applications
Information systems are used to support business.
An information system (IS) “consists of people and
machines that generates and/or uses information and
which are interconnected by communications. An in-
formation system in the narrow sense is an application
system or application software for performing opera-
tional tasks” (Gabriel, 2012). In general information
systems can be classified based on different criteria.
One possible classification for IoT applications can
be made by means of the business intelligence con-
cept because Business Intelligence solutions play an
important role in the area of planning, analysis and
help in decision making. Gabriel defines the busi-
ness intelligence system as: Applications based on
data warehousing, OLAP and data mining concepts,
as well as a modern reporting and portal system with
which a diverse and powerful IS can be used in vari-
ous application areas” (Gabriel, 2012).
Objectives & Scope: The objective of an IoT appli-
cation is to improve the decision quality, optimise the
business processes, and support the user in their daily
operations. This objective is achieved by utilising the
outcomes of the project and using them for analysis
and reporting.
Challenges and Requirements: IoT applications are
highly complex and interconnected as described in
(Vermesan, 2014). The single truth of information is
missing because of information silos, and correct de-
cisions can not be made on such data. Another chal-
lenge is an exponential growth of data in data silos
which lead to inconsistent and redundant data. The
key requirements in this phase are a high informative
value (usability of information, e.g., within a business
process), availability (important reports receive more
resources and available when required), and stability
(low failure rate). Another challenge in the data ware-
house architecture is to link data that historically grew
into separate data silos.
Tools and Methods: There are various tools and met-
hodologies available for this phase. From the integra-
tion of applications perspective, integration tools and
platforms are available, e.g., SAP Process Integration,
integration services to integrate the data. Change ma-
nagement and configuration management methodolo-
gies can be used to manage the change and configure
the system.
Disciplines: In this phase, following disciplines from
business economics, and roles are determining factors
in the implementation and development of such IoT
applications. For example the domain expert provi-
des the technical input for the business application,
the business analyst covers the business perspective,
and the IT consultant provides the necessary link bet-
ween business and IT. The IT consultant also provides
systematically and formally presents the requirements
for the IT system so that they can be implemented in
design and implementation.
Interfaces: The information generated in the applica-
tion can serve then as input for other applications or
further analysis (Vermesan, 2014). Although an in-
terface or integration is required to access or provide
the results to other applications.Ideally, the output of
the application is a standardized format, which can be
used in other applications and systems.
Influences: The application has a direct influence on
the business value. A well-designed application can
clearly visualize the measured or analyzed data for
the end user. A well-designed application is an ap-
plication which has a good usability, availability or
actuality.
4.4 Business Value
The concept of business models and generating busi-
ness value is the focus of management in the research
literature (DaSilva and Trkman, 2014; Fielt, 2014; Pe-
trikina et al., 2014). Here we provide two definitions
of business models. A business model is defined as A
business model describes the rationale of how an or-
ganisation creates, delivers, and captures value” (Os-
terwalder and Pigneur, 2010). In (Teece, 2010), it is
defined as “The essence of a business model is in de-
fining the manner by which the enterprise delivers va-
lue to customers, entices customers to pay for value,
and converts those payments to profit”. As in the case
of IoT projects, where customers or different orga-
IoTBDS 2017 - 2nd International Conference on Internet of Things, Big Data and Security
396
nisations are involved, business value can be defined
from the project’s and participant’s perspective. This
outcome can be a new business model or improve the
existing business processes, offering new services to
the customers.
Objective and Scope: From the organisational per-
spective, it is the important phase of the IoT project.
In this phase, organisations define the objective of the
IoT project. The objectives of the project can gene-
rate new business models to be offer in a new market,
improving an existing process, understanding custo-
mer needs and requirements where the product is in
operation, solving existing problems or offering com-
pletely new business models. The scope of the project
not only defines items included or targets of the itera-
tion, but also the business value that a project brings
to the organisation as well as the problems the aims
of an organisation.
Challenges and Requirements: For the IoT project
in this phase, higher management commitment played
an important role and is mentioned as a challenge in
literature (Sundmaeker et al., 2010; Khan and Turow-
ski, 2016b). IoT projects involve other departments
of an organisation, without a clear directive from hig-
her management, departments or individuals may not
be interested in participating in the project. Simi-
larly, the decision maker’s vision is also important to
prepare the organisation for the future. Tools and
Methods: There are various tools and methodologies
available for business value. From a methodological
perspective, following methods are commonly used
e.g., business canvas, design thinking workshops, and
strategic information management techniques.
Disciplines: The involved disciplines are business
economics and project management. The overall un-
derstanding of business and project management ex-
perience is vital for a successful completion of a pro-
ject. From a role perspective, project managers and
an overall understanding of the whole process is a re-
quirement.
Interfaces: The outcome of the project can be used in
business applications, offering new services, or busi-
ness models to generate revenue directly from the cu-
stomers. In the case of business applications, overall
business processes are improved, e.g., in CRM, Sales.
New offerings are made to the customer to generate
the revenue for the organisation.
Influences: This phase influences different phases of
the process. Depending on the objectives, require-
ments in other field are decided, e.g., what kind of
data we have to measure using the sensors, business
processes or applications which we can improve, or
the whole process.
Production
Test
Proof of
Concept
Feasibility
study
Figure 3: A cycle of an IoT project.
5 DISCUSSION
For an IoT project, an entry point can be any of the
shown five phases although, the considerations are
different for each entry point. Generally, require-
ments for different phases come from a business per-
spective. Then the business case is defined, and requi-
rements for each phase are decomposed. We show an
example in our case study for such entry point. Alt-
hough, in the ideal case, it is suggested to start from
the business value phase. However, in practice, some
of the organisational settings, devices already recor-
ded data (historical) in different locations, e.g., pres-
sure, temperature. Organisations start from the exis-
ting data and then enhance the scenarios or combine
the existing data with other data. An organisation may
take sensors as a starting point for business value or
better business decisions.
Different perspectives also play an important role
for the selection of an entry point. In (Khan and Tu-
rowski, 2016a), the authors discuss two inside-out and
outside-in perspectives.
There are numerous challenges which cross cut
the phases in IoT landscape. One of the challenges is
the security. The security of sensors means that sen-
sors are generating and providing real data and it is
not breached or corrupted. Another aspect concerns
the ownership of the data. Regarding this, it is impor-
tant to know who is responsible- the organisation that
collects the data or the one that generates the data?
In Figure 3, we show the lifecycle of an IoT project.
The project usually starts with a feasibility study. Af-
terwards, proof of concept is made to show the practi-
cality of the project. After an initial test, the actual
project will be rolled out in the real production envi-
ronment.
A Holistic View of the IoT Process from Sensors to the Business Value
397
6 PREDICTIVE MAINTENANCE
We use predictive maintenance as a use case to show
the importance and implication of the IoT landscape
process. This example also shows how a change in
one phase will have an impact on the whole lands-
cape. Generally, requirements come from a business
strategy or business level. Then these requirements
are further analysed or classified in each phase accor-
dingly. All phases contribute in reaching the objective
of the project.
First of all, we describe the associated IoT landscape
process with this case.
For predictive or preventive monitoring, data is
collected from numerous kind of sensors, e.g., from
pressure, temperature, humidity, and motion sensors.
These data are then processed for further analysis. It
results in solution where the company can avoid fai-
lures and avoid cases where break down occurs in the
organisation. Using sensors in the machine or at loca-
tion helps to sense the situation early, and actions can
be performed rapidly or reported at a higher level in
real time. Such data is further analysed to diagnose
potential threats, and necessary actions can be taken
before equipment breakdown or inefficiency occurs.
Services processes can be constructed in such a way
that error-prone parts can be replaced before they ac-
tually break.
We also discuss a few challenges and future sce-
narios in this use case. In some systems, machine
condition data is stored in summarized form in row-
oriented database systems. Such summarization can
hide vital information regarding the machine state.
Analysing raw data over time or correlation of such
data with other attributes (like temperature, vibration,
quality of operation performed on material) will help
to find new insight information on machine conditi-
ons in which they are operated and will increase the
reliability of a machine while predictive maintenance
can be performed. In future, a huge amount of data
can be stored without summarization and such data
can be analysed for future scenarios.
Predictive maintenance can lead to savings in
maintenance costs (up to 30%) and up to 75% fewer
failures, inducing an increase in productivity of up to
25% (Smith, 2008) (relevance proved by a study of
the US energy department). Now we discuss the vari-
ous entry points of our phases for this scenario.
Business value: Costs of machinery downtime can be
very severe to a business’ performance (e.g., loss of
reputation, costs for additional taff, replacement ma-
chines, additional stock capacities, opportunity costs)
. In order to optimize cost-effectiveness of e.g., pro-
duction processes, predictive maintenance initiative
may be introduced by the management.
Business Applications: A predictive maintenance
initiative may also be started from an application
point of view. An example could be the improvement
of supplier relations management in order to mini-
mize replacement time for equipment. In addition, the
success control for a predictive maintenance initiative
would be done on an application level (cost-benefit
analysis). Thus, an identified need for further impro-
vements in existing predictive maintenance contexts
states another entry point. Also optimisation attempts
for existing processes may lead to a PM initiative.
Preprocessing and Analysis: An already performed
analysis can serve as an entry point, too. Besides
an existing, but maybe not very effective predictive
maintenance analysis (e.g., alarms could be too late,
root cause analysis does not lead to improvements),
also other analyses that utilize sensor data from equip-
ment can be used in the predictive maintenance con-
text.
Sensors: The equipment already in use may pro-
vide information about maintenance-relevant statis-
tics that are not used in a predictive maintenance con-
text. From this starting point, processing opportuni-
ties may be discussed. Another entry point could be
the availability of some sensors for an existing pre-
dictive maintenance initiative and the planned intro-
duction of new sensors to improve its quality.
7 SUMMARY AND OUTLOOK
This paper deals with the IoT process. We provide a
holistic view of different phases in IoT projects cove-
ring from sensor perspective of data collection to the
generation of business value for an organisation. We
emphasise to use proven Six Sigma methodology for
IoT projects. We discuss the consequences of each
phase while selecting the phase as an entry or starting
point. We use predictive maintenance as a use case
to demonstrate the practicability of our IoT process.
Using these insights, IoT project managers can iden-
tify required activities and competencies to increase
success probability. In the end, we summarise the pa-
per findings and highlight the future work.
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