2023 | 009
DPLE – Digital product lifecycle
engineering for hydrodynamic bearings
Engine Component Developments - Tribology

Falk Nickel, Miba Bearing
Gunther Hager, Miba Bearing
Pascal Strassl, Miba Bearing
Xenia Kottulinsky, Miba Bearing

This paper has been presented and published at the 30th CIMAC World Congress 2023 in Busan,
Korea. The CIMAC Congress is held every three years, each time in a different member country. The
Congress program centres around the presentation of Technical Papers on engine research anddevelopment, application engineering on the original equipment side and engine operation andmaintenance on the end-user side. The themes of the 2023 event included Digitalization &Connectivity for different applications, System Integration & Hybridization, Electrification & Fuel Cells
Development, Emission Reduction Technologies, Conventional and New Fuels, Dual Fuel Engines,
Lubricants, Product Development of Gas and Diesel Engines, Components & Tribology,
Turbochargers, Controls & Automation, Engine Thermondynamis, Simulation Technologies as well as
Basic Research & Advanced Engineering. The copyright of this paper is with CIMAC. For further
information please visit https://www.cimac.com.

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ABSTRACTAs competition levels increase continuously in invest markets for energy and transportation every
component must be optimized to the essential requirements to be successful. This applies to thehydrodynamic bearings as well which must be customized, balancing function, reliability and cost to
the specific optimum. Opportunities from digitalization are increasingly employed to realize theoptimum product for an application more precise and faster. DPLE - Digital Product Lifeycycle
Engineering is the comprehensive tool chain interacting and interfacing to drive this. The paper will
explain the current status of DPLE implementation, development and expectations for the future. The
paper will focus on the following elements of DPLE.
Customized Bearing Solution
Consequently DPLE results in customized products to the individual application. While this has been
the aspiration and need within the industry for quite some time the additions of the digital revolution
allows major steps to get there.
Continuous Data Flow following the maturity level of the product in development including a feed-backloop
Generating, storing and sharing product data over the full product lifecycle starting with therequirements and closing with the end-of-life analysis is the prerequisite for the method of DPLE. A
feed-back loop is continuously reaching from end to start.
SOR – Specification of Requirements
The SOR is set-up in a standardized digital format and environment to set and ensure format, content
and interdependencies. It is the very important interface and joint platform for the engine and bearing
engineering experts.
Product Design and Engineering
From layout until final product design engineering is developing based on one consistent and evolving
data set. The engineering toolchain is pulling from and feeding into the one data stream assuring thepermanent validity of inputs and outputs.
Single Product Data documentation
Production data management over the complete value chain enables tracing of process and productcharacteristics at any step. Artificial intelligent methods allow complex model generation which maylead to engineering and production measures in consideration of functional consequences.
Application Data Monitoring in testing and real world
Smart bearings with plug and play features offer new insides of application conditions and needs.
These insides support the validation of engineering and product design. This is especially a greatchance to increase the quality, speed and capability of simulation.
End-Of-Life Data evaluation
Intended bearing operation and failure modes can be investigated in test-bench operation to a large
extent. Accelerated durability testing is an even higher challenge. Real world application durability datafrom end-of-life evaluation therefore is a very effective addition for calibrating the test and simulation
profiles.
Data Security and Accessibility
Data security and accessibility are non-comprisable must haves to protect all contributions andcontributors and assure the effectiveness of the engineering environment.

1

INTRODUCTION

As a supplier for high loaded bearings for the
combustion engine industry the major task is not
only providing a bearing product but to accompany
the component over the whole application life cycle.
To be and stay a valued partner of the applicating
industry besides a reduction of time to market andadequate product costs, lean and efficient
processes along the whole value chain arenecessary.
On the work force side avoidance of overload withpermanent repeated routine tasks (MUDA) is
needed. Instead using people’s expertiseaccompanied by statistics (data mining of productand process values) for 1st time right and productand process optimization using digitization is thestate-of-the-art opportunity. Besides the direct
efficiency gains employee’s engagement will raise
resulting in substantial engineering performanceincrease.
DPLE - Digital Product Life Cycle Engineering is
the comprehensive interacting and interfacing tool
chain to drive this. The paper will explain thecurrent
statusofDPLE
implementation,
development and expectations for the future.

knowledge is stored unstructured “somewhere” on
network drives or local drives only.
It is obvious that such a situation is suboptimal for
know-how build-up, knowledge distribution and an
efficientapplication
optimized
product
development.
The Product development process (PDP) as aformal procedure is handled via the so called IMS(integrated management system) available via the
Miba “intranet”. The formal process steps to be
fulfilled are well defined but especially the technicalresults and data are neither visible within the IMS
nor has it been considered to cover technicaldetails.
To improve the situation, an overall approach incooperating working processes and dealing with
the interfaces of several software solutions has tobe taken. As shown in Figure 1 a holistic closed
cycle approach along the product development iskey. Although all of the phases are in place alreadynow, a structured data storage and interfaces are
missing to exploit the full potential of data mining
with minimum resources.

The key elements of DPLE are:•Application layout configurator and data base•

Bearing test rig references•

Digitalization of production process and quality
data management•

Bearing judgement and application data
monitoring•

Data mining and interpretation

A further section will deal with the prerequisites as:
Figure 1. The generic concept of DPLE•

Software Interfaces•

Data access and security•

System performance•

Legal aspects

Therefore, Miba is working on several aspects,
processes and interfaces to improve this situation.
In the following major elements towards this goal
are described, sharing insights, showing alreadysuccessful running systems and also pointing out
challenges.

3
2

THE CONCEPT OF DPLE

3.1

Already now many digital systems are in use
dealing with the different aspects of product layout
product design, production quality and validation.
Additionally, a significant amount of data and

CIMAC Congress 2023, BusanBUILDING BLOCKS
Application layout solutions

Miba’s Bearing Division is developing andconstantly improving so called Configurators. A
Configurator is a customized software solution that

Paper No. 009

Page 1connects the customer and the applicationengineering departments during initial phases ofthe layout and design process.

speed during the actual software development andit helps to bring future users on board and into thediscussion.

These valuable tools for both the engine businessas well as the heavily evolving wind business havea strong impact on time to market reduction.

This kind of prototype is a simple clickable online
screen that is easy to adjust and to access. The
visualization is key when discussing the look andfeel with the users and developers.

The solutions support the standardization and help
the internationally acting teams to follow similar
routines, share the same data set worldwide andproduce aligned reports (e.g., for feasibility).
The digitalization of these first steps of the solution
process for a highly engineered product reduces
the response time but it is a long and intense
process to bring working routines, gut feelings,
excel lists into a simplified and user-friendly frontend.
The steps that have been proven successful in the
Bearing Division as well as the main features will
be described under the following bullet points:
3.1.1

Customer Journey Mapping (CJM) as
basis for software solution need

3.1.3

Software development in scrum
framework

The software development of the Bearing
configurators is performed by Miba IT and
Digitalization Department. The entire department isworking with these methods.
Scrum [2] is a lightweight framework that helps
people, teams and organizations generate value
through adaptive solutions for complex problems.
Following this framework during the developmentof the Bearing Configurators supports an iterativeand flexible but value driven approach that
integrates the users from the start.
3.1.4

Main Features of the Engine
Configurators

Customers are invited to use the online
Configurator, to fill in the relevant technical data
structured in several steps.

Figure 2. Example for Customer Journey Mappingat Miba
The fundamental idea behind CJM [1] is relatively
simple; it is a visual depiction of the sequence of
events through which customers may interact withan organization during an entire engineering
process. It represents a graphical schematic of all
the possible organizational touchpoints that
customers may encounter during an entire
development process.

During these steps the customer is required toprovide information regarding all layout relevant
data as Crank Train geometry, mass and material
data, Cylinder Bank definition and details in regard
to Crank Pin as well as Main Bearing Pin surface
quality. In addition, operation specific data asengine speed and load tables will be provided.
Guiding the external user through these featuresand making sure the workload stays self
explanatory was the main aim during development.
Possibilities to copy and auto fill data are given;drawings can be uploaded; many aspects havebeen translated into simple graphs that avoid
confusion and the usual back and forth forclarification.

In Miba the mapping showed a need for digital
touchpoints with the customers to increase speed
and quality of data alignment.
3.1.2

Development of UI /UX Prototypes

To fill those digital touchpoints mentioned under
3.1.1. with usable software features it has become
very common to work with so called UI/UX
Prototypes. Their development increases the

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After completion of the data by input of the engine
configuration parameters the user can inform therespective Miba Application Engineer who will thencreate an automated feasibility report including alldata and graphs.
3.2

Figure 3. Visualization of Crank Train and Cylinder
Bank; Coupling Side

Figure 4. Visualization of Crank Train and Cylinder
Bank; Coupling SideOne example is shown under Figure 3 and 4. To
define the view onto the Crank Train and Cylinder
Bank the graphic clarifies the rotation direction as
well as the view direction.
For immediate feedback and verification of the data
input a bearing force plot is generated based on theengine specification in combination with a selectedfiring pressure curve and rotational speed. An
Example of a MB #2 force plot of a V8 engine isdepicted in Figure 5.

Digitalization of production process andquality data management

A few years ago, the need for a change to a digitaltransformation initiative was born. It was obviousthat digitalization is a key success factor which canbe seen as a differentiator. All our processes havebeen analysed with the support of leading experts.
This stress test took more than half a year and theoutcome was a powerful gap analysis with very
good roadmap indications. For sure these analyses
were just an indication how the organization could
develop and what recommendation could bringwhich benefit. Overall, it was clear to establish aninternal organizational initiative together with the IT
department to be sustainable and successful on thelong run. With this drive from the customer needs
and IT support an out of the box thinking wasestablished. Overall, a lot of good ideas were born,and pillars have been set for a sustainable digital
footprint.
3.2.1

Planning

In a highly dynamic production environment, aneffective and flexible material flow is necessary to
improve business productivity and process
efficiency. By dealing with all this complexity atsales and operations planning, production
planning, and procurement planning the rightbalance will help to secure a healthy Economic
Value Added (EVA), as shown in Figure 6. Miba’s
vision is to bring all these interactions together andfind - with suitable algorithms - the right demandplanning and production program.

Figure 6: EVA Triangle of Interactions
Figure 5: “Rigid” Force plot as instant data input
verification

CIMAC Congress 2023, BusanPaper No. 009

Page 33.2.2

Smart Factory

Smart factory, frequently referred as Industry 4.0,
uses the integration of advanced technologies,
such as IoT, robotics, and AI, in manufacturing tocreate a more connected, automated, and datadriven
manufacturing
process.
In
ourunderstanding we want to use these digitaltechnologies to increase efficiency, reduce costs,
improve quality, and increase flexibility in
manufacturing.
Miba´s approach of a smart factory should be tocreate a self-monitoring and self-optimizing systemthat can make decisions and take actions with lesshuman intervention.
A very important component on the so-called digitallandscape is the examination with the digital twin
approach. A digital twin is a virtual representationof an object, system or task. This digital model
should represent the real-world with its
characteristics inclusive data and assets. Our main
expectation is to combine all these layers and do
simulations to increase efficiency at our time tomarket process with balanced production costs.

3.2.3

One of the most difficult questions at this stage was
the development of the right IIoT architecture.
There were a lot of question marks at theconnection, database, interface, device etc.
Moreover, the huge variety of machines, sensors
with their applications must be considered.
Finally, with a lot of exchange with other industrial
leaders and experts we found out that we have toestablish our own architecture with theconsideration of certain standards like OPC Unified
Architecture.

MES (Machine Executive System)

Key functionalities like production scheduling, datacollection, quality management and performance
analysis have to interact in relation with machinedata in order to automate certain process task andbeing a functional layer between the ERP(Enterprise Resource Planning) system.
The understanding is to bring transparence andvisibility to all departments by increasing the
uptime, reduction of inventory, improved quality
controls and a paperless shop floor.
There are a lot of positive examples and changes
which bring value to the shop floor. The utilizationof the production assets by improving the overall
equipment effectiveness (OEE) is one majorachievement. Moreover, nearly no printers arenecessary anymore at the shopfloor. All relevant
documents can be displayed at the production
terminals. All necessary records are immediately
available to decision takers across all integrated
systems.
3.2.4

In our interpretation it allows real-time datacollection, analysis and visualization, which leads
toimproveddecision-making,predictive
maintenance and optimization of production
processes.

Figure 7: Miba IIoT Architecture
By having all this data in a structure form, analysis
and interfaces to other systems easily can beestablished. With these possibilities new businessmodels can bring value for the entire production
process. Now for example specialist bring all data
in relation to the production processes to findpatterns and irregularities to reduce scrap rate.
One other use case how IIoT in combination withpredictive maintenance will help, is to avoid
unplanned downtime or achieve better planning ofour production support processes. For example
critical components have to be monitored
constantly and replacements should be kept instock.

IIoT (Industrial Internet of Things)

IIoT is an application of IoT (Internet of Things) inindustrial settings, which enables the connectivityof machines, equipment, and other industrial
assets with the internet, allowing them tocommunicate with each other and with people.

CIMAC Congress 2023, Busan

3.2.5

Sputter Technology in combination
with Artificial Intelligence

In collaboration between the Sputter Technology,
the production and the IT team, an AI (Artificial

Paper No. 009

Page 4

Intelligence) system has been set up. This system
is already operative on most of the serial coating
devices to support preventive decision making inreal time for destructive testing avoidance.
Sputtering (PVD) technology is used in Miba todeposit the final running layer onto the surface ofbearings and gears. To ensure a high productquality, destructive testing is carried out for all
produced batches and therefore a significant
number of parts are destroyed per year.
Due to the complexity of the process withnumerous influence parameters an analytic
correlation approach to identify out of range
batches must be supported by high effort
destructive testing after the coating process.
Therefore, it has been decided to develop a datadriven solution. AI-based models have beendeveloped to predict the quality of the parts. Thesemodels were trained from the historical data byusing machine learning techniques. The AI
predictive quality system is up and running, and it
will be improved, evaluated and monitoredcontinuously. The described Architecture in shownin Figure 8.

decisions. In this regards the prescriptive analytics
is the branch which provides for a specific situation
recommendation.
According to Gartner there are four layers which
are helping to classify common use cases.

Figure 9: Gartner chart: From Hindsight toForesight
At Miba we are focusing on all of these four layers.
Nevertheless, we see a high potential in certain
areas where we want to concentrate our activities
e.g., identifying trends and insights, improving
fraud detections and optimizing the supply chain.
Finally, the goal is, using data as an asset,
minimizing human reaction, but predictive andproactive decision, with a high grade ofautomatization. An overview of the different gradsof so-called analytic capabilities is shown on Figure
10.

Figure 8: Sputter/AI Architecture
3.2.6

Automatization

Automatization is one of our key elements by our
digital transformation. For instance, all processes
will be reviewed regularly by the usage of data
mining. It is easily possible to find out if processes
are very complicated and not stable. For some ofthese insights Miba uses an ongoing approach forautomating these processes by the use oftechnology to perform tasks without the need ofhuman intervention. This can include the use of
RPA, automated machines and computer software
to perform tasks such as assembly, inspection,
packing and inbound logistics. Automation can also
include the use of control systems to monitor andorchestrate our appliances.
3.2.7

Data Analytics

Data analytics is a very essential ability not justproviding information, but on a next level to suggest

CIMAC Congress 2023, Busan

Figure 10: Gartner: Four types of analytical
Capability
3.3

Bearing test rig references

For bearing type qualification and internalbenchmarking for product development as well as
product quality assurance bearing test rigs areused. The Miba Bearing testing department is
operating 8 test rigs. For efficiency reasons andprocess optimization an oracle data base with aPaper No. 009

Page 5

Web interface has been set up covering the whole
testing process from:•the testing request by internal customers•

the test part production orders•

quality inspection documentation•

the test preparation (shaft / counterpart)
preparation & documentation•

storage of testing results incl. recorder files
and post testing inspection report

Today the established workflow / process has
become a very essential asset. The data input
is embedded in the testing workflow. A screen
shot of a single process step is shown in Figure
11.

3.4

Bearing judgment and application data
monitoring

All the efforts taken on process digitalization on
layout, simulation and production side are open
loop as long as the loop has not been closed
consequently. In Miba´s case as component
supplier the field experience of the products within
the end-users applications is this loop closure.
During engine and bearing type development a
DVP is set up in close cooperation with engine
OEMs. This is of course essential for reaching atarget validation level before high volume serial
release. Field inspection of components are done
rarely as costs for disassembly and down time are
significant. Inspections during service andespecially end of life are an option but most of theservice staff is neither trained in bearing judgment
nor do they have an easy to use tool for assistance.
As a prerequisite for an inspection App Miba
investigated the options of an image recognition
system for the bearing judgment. For training of themachine learning algorithm experts pre-evaluatedseveral hundred bearings.

Figure 11: Web interface for quality check requests
Access with predefined roles is possible for all
involved employees and internal customers, within
the VPN. Needless to say that data as well asprocess mining is therefore easily possible.
Depicted in Figure 12 an interactive result data
evaluation is shown.

Figure 12:Test rig result, Data visualization
Up to date the benchmarking and multi test run
result evaluation is done manually, which limits thedata sets to be monitored. As there are more than
16.000 data sets available a machine learning
approach for exploitation of the full data pool is thelogical next step.

Figure 13: Development of the Algorithm stages
from expert judgment on top to transfer learning in
combination with customized loss function (bottom)
As visible in Figure 13 the correlation between the
optical judgment done by experts to the final image
recognition algorithm used is quite impressive forhigh quality pictures with uniform illumination. A

CIMAC Congress 2023, BusanPaper No. 009

Page 6sensitivity analysis with different resolutions andlighting settings turned out to significantly influence
the AI based judgement result. As in service
environment an adequate illumination and picture
quality can´t be expected no further efforts havebeen taken for a bearing judgment app for the time
being.
A different path to gain more application related
data - to be correlated with production data - is theuse of measurement data of condition monitoring
systems. Actually, for the connection rod thesesystems are mainly used within the engine
development (for risk mitigation and shortening
development time) or for special applications, forhealthmonitoring.
In
addition,
indirectmeasurements are done with splash oil
temperature sensors for medium speed engines.
With the maturity of all these monitoring systems,
application related data volume will increasesignificantly. In combination with subsystem /component layout and production data down to thesingle part a holistic data picture of single bearing
systems should be deduced. Anomaly detection,
health monitoring and TbO prediction could be
improved significantly.
Data exchange coordination can be executed via
independent, non-profit platforms (as Mýa) for
promotion of industry collaboration in realizing thebenefits of digital technology [8].

4TECHNICAL AND ORGANIZATIONAL
PREREQUISITESTo optimally support the digital product life cycle
engineering both technical and organizational
prerequisites must be fulfilled. As the product life
cycle spans multiple systems, they must be
interconnected, and the involved people must be
equipped with the right skills and tooling to work
efficiently together.
4.1

Organizational prerequisites

the software development process become more
expensive and harder to accomplish.
4.2

Technical prerequisites

Interconnectivity of systems and the global
distribution of the users create certain challenges
that must be addressed on a technical level toenable not only great user experience but also agood flow in the development process.
4.2.1

Software Interfaces

The first question to be answered during system
integration is finding the right way to connect two
applications. To ensure the toolchain can beproperly integrated over the entire process, thesystems involved need to support modern interface
technologies.
Custom applications must expose their functions &data via application programming interface (API)standards such as REST [3] or ODATA [4]. Thisway the tools can easily be interconnected andavoid manual data duplication. This has huge
impact on the efficiency of the development team,as they don’t have to think too much about how thesystems get connected, but which data should beexchanged or which function to be called.
As modern software solution usually consists of a
JavaScript based frontend and an API based
backend which brings a great deal of flexibility.
The user interfaces are all built on modern web
frameworks such as Angular. These frameworks
connect to a REST API in the backend andtherefore you always build an application with an
integration layer by default.
Connectivity to existing standard solutions also
must be considered. If they offer othercommunication protocols an API based integration
layer should be considered.
4.2.2

Data access and security

With resource constraints on local markets, a shift
to the global pool of software development experts
is crucial. A diverse team, working in an agile way,with short sprint cycles is essential to identify
issues early and spark new ideas.

To enable our customers - anywhere in the world to directly access our application layout frontend
and collaborate with our engineers in a state-of-theart workflow, we must follow a zero-trust [5]approach.

To be truly successful, a different way of working isneeded. Ideas need to be quickly prototyped andchallenged by the domain experts in the applicationengineering area.

Zero-trust means we treat every request to theapplication as potentially bad. Hence every request
must be authenticated, inspected by a firewall andinput data validated.

The exchange between the domains is important to
identify necessary changes as early in the process
as possible. Like physical products, changes late inSecure coding guidelines are a necessity to ensurehigh code quality and proper release workflows into
production. The secure coding guidelines are

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Page 7

continuously reviewed and adapted. For example,
if changes to standard documents such as OWASPTop Ten [6] are announced.
Data Access also follows the lease privilege
principle, so data is not excessively exposed.
Inside the application the Customer Data isseparated and via a strict roles & rights
management module ensured that no accidental
data leaks occur.
To access our applications. a common Identity
provider is used that also allows federated user
logins from our partner companies. This means ourcustomer’s users login with their own company
credentials. This has two major benefits. First theuser doesn’t need to remember a new password.
Second, if they leave their company and theiraccount is locked, they automatically loose accessto our portal. Technically, we leverage Azure Active
Directory four our internal users and B2B Guest
accounts to grant customer’s access to oursystems.

page or the JavaScript files that are used by thebrowser to render the page. This way if you load apage for a second time only a fraction of data needs
to be loaded again.
For long running calculations on the other hand,asynchronous jobs are created with instantfeedback to the user that the calculation has
started. This allows the user to still navigate thepage, without the need to wait until a calculation
has been done. After the calculation is done theuser is notified that he can now review the results.
The high-level architecture of our customer facing
configuration solutions is shown in figure 14.

Besides managing the access to data, also it’s
integrity must be ensured. Changes to data islogged in a change log and can be audited. To
prevent data loss, regular backups are taken, andsecurity measures established that allow a restore
to a previous point in time.
4.2.3

Figure 14: High-level
configuration engine

System performance

The expectations of users have changed over theyears when it comes to loading times of an
application. In the distributed world, this challenge
hasn’t gotten easier as data still needs to travel theworld. Global cloud providers, offer technicalsolutions that help tackle this challenge.
Hosting the applications in the cloud and using theirbackbones with solutions like Content Delivery
Networks (CDN) [7] to shorten the distance from auser to the application is one step.
Further important steps to boost loading times areproper system design with features, such as lazyloading of components or smart caching.
Lazy loading for example only loads the data fromthe backend which is currently needed by the user.
For example, if you show a table of data, you only
load 30 rows which can be shown at once and if theuser scrolls additional data is loaded from thebackend.
Caching is also an effective method to reduce theamount of data travelling over the line. This is
usually applied to static files such as images on the

CIMAC Congress 2023, Busanarchitecture

of

the

Important is that all access flows through the
Network & Security infrastructure in the cloud. The
Frontend & Backend are hosted on scalable cloudservices and the Data layer is using platform as aservice database modules.

5

CONCLUSIONS

Beside the focus on technology innovation, thedigital transformation is a key element in Miba´sstrategy to create Customer value in terms oftailored bearing solutions supporting leading
application system. This strategy consists of two
pillars, business model development and internal
processes. DPLE tackles the aspects of both, asinternal processes are optimized and streamlined
using various digital approaches, but also thebusiness aspects are covered using engineering
platforms for time to market reduction. As
described in the main sections the actual focus ison several “independent” projects, in the
background the approach is to interlink the
databases and use data mining tools for additional
value creation. The systems developed andintegrated are evolving to continuously increase thecapability and benefit. However, individual digital

Paper No. 009

Page 8elements accomplished years of operation, like thetest rig database and the MES already and there is
no doubt that they truly pay off. In certain productfamilies the time to first product sample was
reduced to 50%. Data consistency especially forcomplex global project organizations is guaranteedavoiding ineffective alignment loops. New productfamilies can be based on AI driven virtual quality
monitoring
avoiding
resource
consuming
destructive testing.

60, Issue 1, January–February 2017, Pages 143150[2] Schwaber and Sutherland KS&JS. 2010 The
scrum guide. Creative Commons.org[3] REST – ROY FIELDING DISSERTATION 2000
Architectural Styles and the Design of Networkbased Software Architectures (uci.edu) (Chapter 5)[4] ODATA: OData - the Best Way to REST

6

DEFINITIONS, ACRONYMS,
ABBREVIATIONS[5] Department of Defense Zero Trust Reference
Architecture July 2022, Page 9

VPN: Virtual private network[6] OWASP Top 10: OWASP Top Ten | OWASPFoundation

OEE: Overall equipment efficiency
PVD: Physical vapour deposition[7] What is Azure Content Delivery Networkhttps://learn.microsoft.com/en-us/azure/cdn/cdnoverview

DVP: Design verification plan
RPA: Robotic Process Automatization
OPC: Open Platform Communications[8]https://man-es.com/company/pressreleases/press-details/2020/07/22/rolls-roycesigns-mou-for-mya-platform

IoT: Internet of things

Bibliography

IIoT: Industrial internet of things

https://blogs.gartner.com/jasonmcnellis/2019/11/05/youre-likely-investing-lotmarketing-analytics-getting-right-insights/

AI: Artificial intelligence
EVA: economic value added

https://www.gartner.com/en/newsroom/pressreleases/2014-10-21-gartner-says-advancedanalytics-is-a-top-business-priority

OEM: Original equipment manufacturer
CJM: Customer Journey Mapping
UI/UX: User Interface User Experience
TbO: Time between overhaul
MES: Maschine execution system
API: applicatoin programming interface
REST: REpresentational State Transfer
ODATA: Open Data Protocol
CDN: Content Delivery Network
B2B: Business 2 Business
OWASP: Open Web Application Security Project

7

REFERENCES AND BIBLIOGRAPHY[1] Rosenbaum, MSR. 2016. How to create acustomer journey map. Business Horizon. Volume

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