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Killing the PLM Monolith - the Emergence of cloud-native System Lifecycle Management (SysLM)


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The rapid increase of complexity in modern products introduces numerous challenges for manufacturing companies around the globe. Model-Based Systems Engineering (MBSE) is seen as the best choice to handle this huge increase in complexity and as one cornerstone to realize the so called Digital Thread. In many industries MBSE constitutes an additional engineering discipline that needs to be established in organizations and that comes with sophisticated digital models which have not been used before. In fact, it also needs to be accompanied with the right set of tools, processes and methods – and with the right open, scalable and flexible IT-architecture to make it reality. On the one side MBSE needs mature Lifecycle and Configuration Management – on the other hand it must live within an open IT-environment satisfying the need to have the right set of engineering data always just a click away. This paper shows why legacy monolithic PLM tools cannot support the introduction of MBSE and are currently preventing the implementation of the Digital Thread vision. Instead, it postulates a modern cloud-native Web architecture based on Microservices and Linked Data that allows companies to introduce MBSE on the large scale and helps to avoid the establishment of just yet another silo. Only with Linked Data and a modern open Web architecture MBSE can unfold its full potential and is able to find its way into the daily work of engineers.
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Killing the PLM Monolith
the Emergence of cloud-native System
Lifecycle Management (SysLM)
Fraunhofer IESE
M.Sc. Oliver Bleisinger
M.Sc. Thomas Psota
Fraunhofer IAO
Dipl.-Ing. Jonathan Masior
University of Applied Sciences Esslingen
Dr.-Ing. Michael Pfenning
Accenture GmbH
M.Sc. Arnold Roth
Koneksys LLC
Dr.-Ing. Axel Reichwein
Mercedes-Benz AG
Dr.-Ing. Yousef Hooshmand
Contact Software GmbH
Dipl.-Wirtsch.-Ing. Christian Muggeo
xPLM Solution GmbH
Dipl.-Wirtsch.-Ing. Marc Hutsch
Version 1.0
May 30, 2022
A publication by Fraunhofer IESE
Copyright © Fraunhofer IESE 2022
Fraunhofer IESE is an institute of the
The institute transfers innovative software
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Fraunhofer IESE is directed by
Prof. Dr.-Ing. Peter Liggesmeyer
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The rapid increase of complexity in modern products introduces numerous challenges for manufac-
turing companies around the globe. Model-Based Systems Engineering (MBSE) is seen as the best
choice to handle this huge increase in complexity and as one cornerstone to realize the so called
Digital Thread. In many industries MBSE constitutes an additional engineering discipline that needs
to be established in organizations and that comes with sophisticated digital models which have not
been used before. In fact, it also needs to be accompanied with the right set of tools, processes
and methods and with the right open, scalable and flexible IT-architecture to make it reality. On
the one side MBSE needs mature Lifecycle and Configuration Management on the other hand it
must live within an open IT-environment satisfying the need to have the right set of engineering
data always just a click away.
This paper shows why legacy monolithic PLM tools cannot support the introduction of MBSE and
are currently preventing the implementation of the Digital Thread vision. Instead, it postulates a
modern cloud-native Web architecture based on Microservices and Linked Data that allows compa-
nies to introduce MBSE on the large scale and helps to avoid the establishment of just yet another
silo. Only with Linked Data and a modern open Web architecture MBSE can unfold its full potential
and is able to find its way into the daily work of engineers.
Keywords: MBSE, PLM, Microservices, Linked Data, Digital Thread, Monolith, Cloud-native,
Low Code Platform, Engineering 4.0, Industrie 4.0, Industry 4.0, DevOps, SaaS,
Semantic Web, Production Level 4.
Table of Contents
Copyright © Fraunhofer IESE 2022
Table of Contents
Table of Contents ....................................................................................... 1
1 Introduction .......................................................................... 2
2 Integrated vs. Federated, OOTB vs. Customizations ........... 5
3 The Emergence of modern Web Architectures .................... 7
3.1 CAP dilemma 7
3.2 Cloud native Microservices 10
3.3 The craft of scoping and implementing Microservices 16
4 Knowledge Graphs and Link-Enabling APIs ...................... 18
4.1 Linked Data for Engineering 18
4.2 Semantic Web technologies 25
4.3 To be truly Data-Driven we need Data-Centric Architectures 28
5 There is no Digital Thread without MBSE ......................... 31
6 The Concept: Cloud-native System Lifecycle Management
(SysLM) ................................................................................ 38
7 Outlook ............................................................................... 43
8 References ........................................................................... 45
Copyright © Fraunhofer IESE 2022
1 Introduction
With the start of the third decade of the 21st century we are in the midst of realizing the next
industrial revolution. The most complex system ever created by human beings the internet is still
growing and conquering new grounds where it not just connects people and businesses, enables
collaboration and the sharing of ideas and knowledge. It is now also connecting machines. This
creates a variety of new business opportunities for companies around the globe that are willing to
adapt their way of thinking. In fact, it enables people with ideas and a minimal set of programming
skills to be entrepreneurs as it tremendously lowers market entry costs.
For long established companies the Internet of Things and Services (IoTS) is also seen as a driving
factor to face current challenges. Be it the ever increasing need for highly customer-specific prod-
ucts (lot-size one) and therefore the ability to master the craft of variant management. Be it data-
driven optimization in maintenance of large-scale manufacturing systems (Predictive Maintenance).
Or be it the ever wished for reduction of Time to Market the time necessary to introduce a new
All this needs one enabler that is not within maintenance processes, IoT-platforms, IoT-ready sen-
sors in a factory or a Web portal that allows customers to configure a new product. This necessary
prerequisite is within the engineering phase. It is the way models are created, it is the way engi-
neering data is made accessible for later lifecycle phases and it is about the degree of formalization
achieved when describing the product under development. Model-Based Systems Engineering
(MBSE) is aiming to fill this gap. Its ultimate goal is to formalize the Systems Engineering process by
using a formal domain model and by doing so replacing document-driven approaches. This of
course goes to the heart and brain of every industrial company it urges us to rethink the way a
product is developed and illustrates the significant shortcomings of existing PLM tools.
The Industry 4.0 initiative that has been started quite some time ago, did see engineering and
development as one prominent field of action (see [1]). A little after that the term Engineering 4.0
(see [2]) came up to underline this fact even more. Industry 4.0 strongly needs accessible, high
quality and formalized engineering data. And most importantly: Accessible means that data must
be able to flow smoothly between applications even if they are not provided by the same tool
vendor. Without this openness every implementation of an industrial IoTS use case will fall far too
short and will never reach an enterprise scale. Data Analytics, Artificial Intelligence are truly prom-
ising fields of action for nearly every enterprise but to really make use of it we need to get data
out of its cage, out of the many silos that have been created over the past decades [3].
Copyright © Fraunhofer IESE 2022
Figure 1 - Industrial Revolutions and IT waves, taken from [4]
Besides the fact that we are in the middle of the 4th industrial revolution becoming a reality, we do
also see the third big wave of IT technology currently sloping over from Internet companies to
Enterprise IT (see Fig.1). Porter and Heppelmann described this 3rd wave in their article for the Har-
vard Business Review in 2014 “How Smart Connected Products are transforming Competition” (see
[5]). They stated that this wave is about how IT is revolutionizing products, what they ignore is the
fact that this 3rd wave also helped to create large scale cloud environments. With the establishment
of those environments cloud-native technologies and architectural principles have been invented
and validated over years. They are ready to use now in the engineering domain.
This architectural transformation finds its ideals in Amazon, Facebook and Google. But also wants
to adapt those lessons learned to the specific needs of the engineering domain. It can be said, that
the level of federation of data and services will increase and centralized, monolithic architectures
will get more and more obsolete. It started with Mainframe architectures in the 60’s and 70’s,
transformed to Client-Server Architectures and lately moved forward to Microservices. The problem
that many PLM vendors face is that just adding a Web client to their monolithic IT system only
modernizes the UI, it is not helpful with making their system’s internals future ready.
Monolithic Architectures were born in the 90’s where the database of choice has always been a
relational one, where the WWW has been around only a couple of years and Semantic Web tech-
nology has not been thought of. The classical monolithic architecture has been database applica-
tion rich client. With all data in one Relational Database Management System (RDBMS) with its
transactions based on the well-known ACID
principle. In the meantime, Web clients for PLM tools
atomicity, consistency, isolation and durability (ACID)
Copyright © Fraunhofer IESE 2022
have of course been introduced, but as stated before, this did not change the underlying architec-
After nearly 30 years of developing more and more functionality on top of this monolithic architec-
ture, PLM vendors are now struggling with changing it and moving forward with a more modern
approach. One of the problems they face is upgrading their customers to newer releases that offer
enhanced functionality. Another one is the lack of consistent architecture due to the fact that no
single individual oversees the complete architecture and especially the data model anymore lead-
ing to the need to communicate between a growing number of individuals only overseeing portions
of the whole and fragments of the dependencies in the PLM tool. The legacy that those tools carry
around with them is too complex to master the necessary leap forward. This may also be referred
to as “lost in Conway’s law”
where trying to change a minor thing in its architecture is bringing
the house of cards to collapse. To paraphrase Conway: Good organization and good processes lead
to good software. If communication and responsibilities in an organization are efficient and well-
defined, so is its IT-architecture. This is not just true for organizations using the software, but also
for organizations developing it. Every product and every application has its lifecycle and so do mon-
olithic PLM tools they reached the end of theirs and will be replaced.
Especially for PLM this monolithic architecture came along with a sales strategy aiming to lock cus-
tomers in ( Vendor Lock-in) and sell addon applications on top of the monolith that would not
be competitive as standalone solutions. As Semantic Web technology has not been around and
integration standards such as CORBA
failed for many different reasons, this monolithic but inte-
grated approach to PLM has been the dominant strategy for the last years. This slowly but effectively
is starting to change with Linked Data, Microservices and the experience that has been gained lately
with establishing huge federated cloud environments such as the SAP BTP
, Amazon Web-Services
(AWS), Azure, Alibaba or Google Cloud.
More than a decade ago PDM/PLM vendors followed different philosophies, not just the integrated
PLM approach based on a monolith. The alternative approach to that has been the federated ap-
proach that allowed to connect best in class engineering tools with a PLM backbone also called
“best-in-breed”. In that scenario the PLM tool focused strictly on data management and left the
authoring part to highly specialized tools that actually were able to solve the underlying engineering
The federated approach is currently experiencing a real renaissance as current Web-based technol-
ogies today support federation and integration of heterogeneous tool chains way better. In addition
the related but different discussion between Out of the box (OOTB) vs. Customizations is also on
the agenda again. Low Code Platforms help tremendously in digitalization projects and will also
start disrupting the PLM market, making customization easy, fast and upgradable.
Conways Law states that an organizations chosen IT architecture follows its communication patterns. It can further be interpreted in
a way that for good software architecture a well-defined development process is necessary (which is quite common sense).
Common Object Request Broker Architecture (CORBA)
SAP Business Technology Platform, formerly known as SAP Cloud Platform
Integrated vs. Federated, OOTB vs. Customizations
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2 Integrated vs. Federated, OOTB vs. Customizations
The discussion about the best PLM philosophy is as old as the PLM market. The first and biggest
PDM implementations have been based on toolboxes (e.g. Metaphase) with a high amount of com-
pany specific customizations
. That helped companies to design exactly the processes and data
models they needed to support their business. The drawback has been the lack of upgradeability
and the relatively high costs to maintain such a highly customized PLM environment. Those envi-
ronments were also federated with best-in-class tools that solved the underlying engineering prob-
lems in depth. But on the downside, they lacked a standard integration technology because of the
vendors inability and unwillingness to agree on an integration standard.
The next evolution has been OOTB PLM tools, that were designed to contain predefined standard
processes. The aim has been to solve the upgrade issue, as data model and process model does not
change between implementations it is only enhanced, not changed. Connecting to external tools
has not been part of the concept as specific domain applications were just developed on top of the
monolith, so point to point connectors have not been necessary. In retrospective it can be said that
the OOTB approach to PLM has at best never been more than a dream or worse just a sales
OOTB PLM did not have customization capabilities built in, but had to introduce abilities to config-
ure and customize later on. Because realistically speaking in all PLM projects configuration and
certain customizations are always necessary and therefore those tools ran into the same upgrade
issues they were designed to avoid. Figure 2 shows that real implementations of PLM in Aerospace
and Defense are usually far behind the existing technology and the promised vision the situation
in other markets such as automotive is not much different. In the same report it is stated that at
least a third of all PLM implementations are more than 5 years behind the newest available release.
Upgrades of OOTB PLM tools are expensive and therefore where possible avoided.
And many of those implementations exist still today, as they were not able to be migrated to an OOTB PLM tool.
Integrated vs. Federated, OOTB vs. Customizations
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Figure 2 Real Implementations compared to Vision and Technology in legacy PLM
Good applications come from specializing in a domain this is also referred to as rule one of the
Unix philosophy: “Make each program do one thing well. To do a new job, build a fresh one rather
than complicate old programs by adding new features”
. PLM monoliths have not been built using
this philosophy. Most of the time the applications that run on such an architecture were just super-
ficially solving the underlying engineering problem, not competitive with standalone tools. The in-
corporation of acquired tools for example took years but mostly requirements changed in the
meantime. This created a situation where the so called “incorporation strategy” was just not keep-
ing up with the actual emergence of market requirements due to the complex nature of such en-
deavors. Connecting to external tools on the other hand (following an “integration” rather than an
“incorporation” strategy) has never had a priority with OOTB PLM. Thus, the field has been left to
IT service providers and IT departments leading to a situation with an uncountable number of con-
nector technologies existent and no such thing as a standard or framework has ever been developed
and largely adapted.
Picture ©ARAS Corp. Based on a CIMData report “Aerospace & Defense Industry PLM Value Gap Survey”, 2013.
The Unix philosophy has been documented by Doug McIlroy in the Bell System Technical Journal from 1978.
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3 The Emergence of modern Web Architectures
One fundamental principle of Industry 4.0 is the commitment to solve interoperability issues by
learning from the basic principles of the World Wide Web. The Industry IoT Consortium (IIC) made
that commitment even stronger when Richard Soley
described the foundation of the Industrial
Internet as the application of “Internet Thinking”. In other words, “use the way of thinking the
internet is built on to solve interoperability issues and apply it to other domains”, namely the Indus-
try and its Enterprise-IT and OT
The Internet and its architecture as we know it today can obviously be described as non-centralistic.
It is not controlled by a central instance. In contrast the Internet can just not be controlled as a
whole. It is the obvious role model for a decentralized system where federation of data and services
are the goto-approach. And in addition, the Internet itself of course is not static. It is constantly
changing, not just by its available services and data, but also by the constant change of available
and proven technologies. It is evolving quickly. Learning from it to find solutions for enterprise IT
not only means looking at the issue of interoperability and how it is handled, but also looking into
architectural patterns and technologies beyond the original HTTP, HTML and URL.
3.1 CAP dilemma
Decentralization and federation of data are the standard in cloud computing. One of its theoretical
underlying principles can be identified with the CAP- Theorem
illustrated in figure 3. It states that
it is impossible for a distributed computer system to provide simultaneously more than two out of
the following three guarantees: Consistency (C), Availability (A) and Partition Tolerance (P). All ex-
isting PLM tools are basically CA-Systems whereas modern cloud-services like google, Facebook,
LinkedIn are AP-systems guaranteeing availability and partition tolerance, but lacking consistency.
Chairman of the IIC and the Object Management Group (OMG).
Operational Technology
CAP-Theorem or Brewer’s Theorem appeared the first time in 1998 and has been presented at the Symposium on Principles for
distributed computing in 2000. In 2002 Seth Gilbert and Nancy Lynch of MIT proved Brewers initial conjecture and therefore rendered
it a theorem.
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Figure 3 The CAP- or Brewer’s Theorem for distributed computing, see also [6]
This lack in consistency means it cannot be guaranteed that two users asking an AP-system the
same question at the same time are always getting the same answer. CA-Systems on the other
hand and here especially RDBMS like all existing PLM-tools theoretically guarantee consistency
by only having a data set exactly once in one database. It may be referred here to the already
mentioned ACID-principles of relational databases. This creates the lack of partition tolerance. If
that instance is not available, there is no answer.
Partition Tolerance is highly related to the issue of scalability
. If you have only one database in-
stance scaling means adding resources (e.g. RAM, CPU, Bandwidth) this is referred to as vertical
scaling (scale up). Even today with the most modern hardware components that way of scaling
comes to an end with growing user numbers, growing amounts of data and network latency.
Figure 4 Generic Cloud Architecture
To learn more about scalability in web architectures see [23].
Courtesy of Cloud Application Architecture Guide Azure
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Horizontal scaling (scale out) in contrast does not have these hardware restrictions. People often
talk about the “Pets vs. Cattle” analogy when asked to describe the difference. In former days
hardware servers were treated like pets: they were given a name, if they were sick they were nursed
back to health. Should they become unavailable, everyone will notice. In a cattle approach each
instance is a virtual machine or container, scaling is done by creating more of them. If the underlying
hardware has a defect the instance is moved to another server all done via automation. No one
really cares about the underlying hardware anymore. The cattle approach is one of the core con-
cepts of the DevOps service model (see [7]). As software becomes more complex the one that is
developing it, is seen as the best operator of the software. It therefore brings together responsibil-
ities for developing, updating and operating the software most of the time in a cloud infrastruc-
ture. DevOps teams are interdisciplinary teams capable of providing a service via a cloud infrastruc-
ture from end to end with a maximum level of team autonomy.
This constitutes a major shift in the business model of PLM vendors. If PLM runs in an DevOps mode
in the cloud the vendor becomes responsible for everything needed to provide the service a cus-
tomer has subscribed to. For a manufacturing company this may mean to outsource a huge portion
of its IT operations to the PLM vendor or to invest in a company-wide cloud environment that will
be developed further in house. Both scenarios are imaginable and the implementations of both
strategies can be identified in the market already: PLM vendors offering SaaS
and Managed Ser-
vices and PLM vendors providing the platform with customers developing additional Application on
top in house. This also underlines that PLM may mean different things to a large enterprise and to
In a modern cloud-native approach often referred to as AP-approach in the CAP terminology
the classical ACID-principles are replaced by the so called BASE
-principles. It is to be discussed
later on what implications that has on PLM. In general it can be said that eventual consistency shifts
the paradigm from “always consistent” as in ACID to “probability to experience an inconsistent
state is statistically non-significant” – depending of course on the quality of the used cloud man-
In the PLM community the implementation of cloud computing has only cowardly been started.
Nearly all existing implementations in PLM are still on-premise with the drawbacks already described
and more to be identified. Most people will know the statement “There is no cloud it is just
someone else’s computer”. Looking at the available cloud technology in the market, this must be
characterized as a tremendous and unfortunate oversimplification of the situation.
In the PLM community most of the times the argument is made that PLM has higher requirements
on consistency because of its integrated configuration management capabilities and that’s why a
PLM-tool could never be based on an AP-system approach. In PLM this is referred to as “Single
Source of Truth”. This argument is currently challenged by academia and cloud PLM startups.
Software as a Service
Small and Medium-size enterprises
Basically Available, Soft state, Eventual consistency
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Also established PLM vendors have started offering PLM in the “cloud” – unfortunately that usually
only means hosting the same monolithic architecture outside the customers network and make it
available over the Internet. That approach is usually not able to leverage most of the features mod-
ern cloud environments are offering (e.g. horizontal scaling) not because those features are not
needed for PLM, but because transitioning from a legacy architecture built to operate on-premise
to a cloud-native architecture is a hard and potentially an impossible endeavor, that takes years to
transition. The architectural patterns are just too different to reuse most of the legacy technology
stack of a classical PLM tool and therefore “cloud-native PLM” requires a fresh start.
3.2 Cloud native Microservices
To better understand what is needed to realize cloud native environments in general, it is necessary
to look into a couple of definitions. “Cloud-native” itself, the so called “twelve factors”
, and the
architectural pattern of Microservices. An official definition of cloud native is given by the Cloud
Native Computing foundation:
Cloud-native technologies empower organizations to build and run scalable applications in modern,
dynamic environments such as public, private, and hybrid clouds. Containers, service meshes, Mi-
croservices, immutable infrastructure, and declarative APIs
exemplify this approach.
These techniques enable loosely coupled systems that are resilient, manageable, and observable.
Combined with robust automation, they allow engineers to make high-impact changes frequently
and predictably with minimal toil.
Scalability can be seen as one of the biggest advantages of cloud-native over the classical monoliths.
All RDBMS and especially PLM today create many challenges if those environments want to be
scaled over the level they originally have been designed for. With the concept of loosely coupled
systems running in containers scalability is no longer a challenge, it is built in and can to a large
extend be automated (Autoscaling).
The twelve-factor app is a methodology for building software-as-a-service (SaaS) apps, see
Swagger that uses the OpenAPI standard is one prominent example
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Figure 5 Monolithic vs. Microservice Architecture
The concept of loosely coupled systems is what the architectural pattern of Microservices is intend-
ing to establish. Instead of one clunky monolith providing all the features needed in a domain, it
federates the provided functionality into multiple instances of Microservices that communicate
loosely and are maintained and deployed separately. That helps to incrementally upgrade and even
migrate each Microservice by its own giving organizations the chance to enhance the architecture
and adapt it to new business challenges gradually without having to fund giant migration or up-
grade projects. In addition, organizations are also able to pick the best in class tools for their specific
needs and avoid too strong dependencies on a single software vendor ( Vendor Lock-in). A good
working definition of the term Microservices is given by Sam Newman:
Microservices are independently deployable services modeled around a business domain. They com-
municate with each other via networks, and as an architecture choice may offer many options for
solving the problems you may face. It follows that a Microservice architecture is based on multiple
collaborating Microservices. [8]
That means Microservices, unlike monoliths, are not set up in a classical three-tiered architecture,
but are designed to scale horizontally. That means they are modeled on the basis of a certain busi-
ness domain that is described by a certain bounded context.
A bounded context consists of functions that interact with each other frequently, whereas functions
that are rarely interacting belong to different bounded contexts if they need to communicate,
they can do so over the network. Figure 5 shows the different concepts and also highlights the fact
that Microservices can have different purposes Business Logic or Data Access and in addition
Picture taken and modified from,visited 03/06/2021
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each Microservice may have its own database in order to satisfy different requirements such as
scalability or independent deployability.
That creates the opportunity to implement the best suited database technology with each of the
Microservices ( Polyglot Persistence). On the one hand this especially is beneficial where NoSQL
databases are stronger than regular SQL
. Many use cases in the engineering domain can better
be supported with the use of a Graph, Object or Timeseries Database. On the other hand, there are
of course still use cases that are best supported by classical SQL. A Microservice Architecture gives
freedom to pick the best persistence layer for each business domain (or bounded context). It is
recommended to give each Microservice where possible its own database to make separate deploy-
ments easier. Also from a UI perspective Fig. 5 suggests to have one UI, but also here exceptions
are imaginable. Looking at the huge number of mobile apps with different UIs users interact with
frequently today, it is not always a hard requirement to standardize the UI in order to enhance
usability. Besides the freedom to choose the best suited database technology, the same level of
freedom applies to all other levels of the technology stack. Each Microservice should be set up in a
way that it is independently deployable and supports it’s business domain’s use cases optimally.
But most importantly Microservices are an architectural prerequisite to enable DevOps team’s au-
tonomy. Here again Conway’s law applies. Organization and architecture go always hand in hand.
Changing the way a development organization is set up without changing the architecture of the
software under development will cause a lot of friction. Why is that the case?
Classical monoliths were usually developed with a so called horizontal development organization.
Ideally speaking this has been a database team in charge of the underlying data model and trans-
actions (Persistence), an application team in charge of an applications business logic (Business) and
a UI team responsible for the UI (Presentation). In order to develop a new feature there is the need
to coordinate between all teams, leading to the fact that for minor feature enhancements a lot of
people need to be involved.
See [26]
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Figure 6 Monolithic vs. Microservice Architecture with Team allocation
Fig. 6 shows the difference in an applications architecture and corresponding team allocation. In a
Microservice approach those teams are interdisciplinary with database experts, application pro-
grammers, UI experts and product owners/managers all in one team. The main difference is they
are responsible for a rather small business domain / bounded context and can make decisions au-
tonomously. Once they start implementing a new feature they can work on their own, they are not
confronted with a lot of dependencies to other pieces of the architecture that they don’t have under
control. This leads to more pace and agility as it limits the number of people who need to be part
of the development or to state it differently the less people you need to communicate with, the
higher is the efficiency of this communication. Decisions can be made faster.
This can also be seen as lessons-learned from the introduction of the SAFe
methodology. SAFe
takes the concept of agile development and tries to scale it beyond just software development,
including product management and marketing. It defines interdisciplinary teams that are able to
develop an application with a high degree of team autonomy. And as stated before: way to often
here is the problem. Team autonomy can only work if there are little to no architectural dependen-
cies to other teams. If SAFe is introduced and the software platform is still a monolith the promised
agility and pace cannot be achieved, because the teams are usually highly dependent on enabler
work done by other teams. SAFe supports here with processes helping to identify dependencies
that constrain development of new features, but the actual solution to the problem is avoiding and
eliminating these dependencies in the first place with an architecture made for team autonomy.
Picture taken from, visited July 23rd 2021
Scaled-Agile Framework
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The Microservices themselves are operated in containers
where orchestration systems take care of
scaling, deployment and management of these containers. SAFe, DevOps Teams, and an open ar-
chitecture around microservices does not necessarily have to be hosted in the “Cloud”. It is just a
matter of fact that a lot of basic services to operate such architectures are already available in
existing cloud offerings. Besides the fact that many enterprises will benefit for business reasons
from “the Cloud” lower operating costs, lower invest or capital expense many classical PLM
vendors will move and cooperate with cloud companies just because there is infrastructure in place
that they cannot and don’t want to develop in house.
Different capabilities of selected public cloud service providers are shown in Figure 7. Besides the
core capabilities to provide services for networking, storage and compute, there are many other
more specialized and use case driven service categories. Generally, the market leaders are able to
cover most of the service categories within their corresponding cloud platforms. The major differ-
ence lies in the way the services are implemented and made available to users. Amazon Web Ser-
vices (AWS) for example, is known to provide the most cloud services for the most specialized needs
and new services are introduced in the fastest pace. The Google cloud platform (GCP) in contrast
has slightly less but more curated services to offer, with the major design goal to be the most
developer friendly cloud platform. Microsoft’s cloud platform (Azure) has its strengths in being well
integrated with the windows ecosystem (e.g. MS SQL, Windows Server) and therefore making a
migration of such resources more approachable.
Figure 7 Accenture Public Cloud Capability Canvas
For containers and orchestration systems the tools Docker and Kubernetes are currently the de facto standard.
23,3544678, visited 03/06/2021.
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The Accenture Cloud Capability Canvas captures these capabilities in available services and catego-
rizes them in the following way:
Services to build reliable and secure virtual private networks within cloud environments. Dedicated
or hybrid connectivity to on-premise networks can be enabled and webservice related functions like
content delivery or load balancing can be implemented.
Persistency & Storage
Services for different storage types like object storage, shared file storage or backups with high data
availability and durability.
Compute Services
Services to provision virtual servers or containers on cloud infrastructure with built-in scalability
features. Serverless technologies like Function as a Service (FaaS) are also included in this category
of cloud services.
Database Services
Services to provision, configure, and manage different database types like RDBMS, NoSQL or Graph
Enterprise Integration
Services to help large enterprises with public cloud data migration, ranging from simple data im-
ports, to VM imports and complex ETL scenarios or application integration. This includes also ex-
tensive support and solutions for large-scale migrations of mainframe environments (lift-and-shift
Security, Identity & Access Services
Services to protect and secure a cloud environment and associated applications including account
management, authentication and authorization, key and certificate management and firewall ser-
Management & Automation Services
Services for logging and monitoring cloud resources as well as automate the same with e.g. infra-
structure as a code (IaaC) capabilities.
Development Services
Services to support cloud developers with specialized cloud IDEs, build and deployment automation
tools as well as Continuous Delivery (CI/CD) or application testing.
App Services
Services to build and support integrated application services like media transcoding, push notifica-
tions or messaging services.
Mobile Services
Services supporting specifically mobile application development ranging from mobile analytics to
mobile application testing.
Analytics and Big Data
Services to build analytics and big data use cases, with data discovery and processing services as
well as orchestration and stream analytics functions.
Artificial Intelligence Services
Services for out-of-the-box AI use case support with speech, vision and language recognition but
also more sophisticated machine learning training models.
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IoT Services
Services for IoT use cases covering edge computing, gateway services and event handlers.
Enterprise Applications
Services for enterprise application use cases like content management, managed e-mail services or
desktop as a service.
Cloud offerings are usually accompanied by Managed Services and DevOps Teams. For a working
DevOps process it is key to establish efficient pipelining where development of new features, com-
piling, deploying and testing is effectively managed and the necessary traceability is achieved. That
requires a CI/CD
process which helps to automate the distribution of additional features to the
public cloud and private clouds as well. The mentioned “Twelve-factor App” methodology helps
developers to build SaaS-Apps with 12 guiding principles efficiently it can basically be seen as
“lessons learned” from developing Apps in modern Web architectures.
What can be identified here again is the fact that cloud offerings are based on a subscription model.
Customers pay a certain amount monthly, quarterly or yearly and receive the service they subscribed
with the guaranteed service level, basic enhancements, administration and operation. What they
do not pay for is licenses of the developed software. This raises an interesting point: As the vendor
of the software is not payed for licenses, he has no incentive to code a lot. In other words he is not
generating more revenue by developing more software he has the IP for. If a vendor wants to
maximize his profits and that of course can be assumed, as otherwise he would not be an actor
in a free market he will try to maximize revenue and simultaneously minimize costs. In this scenario
this means, he will do everything possible to attract more users to his service (more customers),
having less users/customers churn and offer that service in the most efficient way with a minimal
set of resources as an input. Again as he is not paid for the IP he owns, this creates a huge incentive
to use open source software. This change in current business model brings objectiveness in software
development software is not better just because a vendor owns its IP. Software is better because
it is better satisfying user requirements even if e.g. the library that has been used is based on an
open source license.
3.3 The craft of scoping and implementing Microservices
The term „Microservice“ has already been introduced. The question when looking at the PLM do-
main is what kind of Microservices can be identified and can there be any kind of guidance on how
to scope Microservices
. In other words what functionality should be implemented in one Micro-
service and which functions should be separated into two or more Microservices. It is important to
Continuous Integration / Continuous Deployment
Fig. 14 shows the defined Microservices for what is today „PLM“ in an IT-project at Mercedes-Benz.
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reiterate that Microservices are independently deployable services modeled around a business do-
main or so-called bounded context.
There are various approaches and methods for defining Microservices and domain contexts. How-
ever, in all these approaches, the business capabilities as well as the user-driven use cases are two
of the most important criteria when creating domain contexts. Naab introduce in [9] an addi-
tional important criterion for defining Microservices for a particular domain not just the consider-
ation of what business logic belongs to one bounded context, but also data needs to be part of the
consideration. This means: What kind of data is captured, stored and worked with in a particular
domain? How frequently is it changed? Does it need to flow between domains or is it specific to
one domain? How big is the conflict potential the risk when an inconsistent state appears?
The initial goal is here to minimize the movement of data between domains by mindfully defining
contexts and their boundaries, thereby reducing the potential for domain data inconsistency. New-
man [8] suggests, thus, keeping the data in the same database, thus in the same domain, if the
ACID principles are mandatory for the dataset. In other words, the criticality of the data transactions
affects the decision about the definition of domain contexts.
However, if for some reason the data has to be split or partly duplicated between different domains
and the ACID principles are still to be retained, approaches such as Sagas [10] can be used. Here,
transactions are divided into sub-transactions based on the respective business process. Although
the atomicity of the business transactions in their entirety is no longer guaranteed, each sub-trans-
action in different domains is still atomic. In this way, the sub-transactions are committed to differ-
ent domain databases after each business process step succeed. In case of a process failure, the
rollback scenarios are different. In a classic ACID transaction, a rollback is performed before a com-
mit. Thus, no trace exists in the database after the rollback. A Sagas-rollback, however, includes
many already committed transactions in different domain databases. This requires the elaboration
of rollback scenarios for each business process, ensuring consistency of data in all domains involved.
Although a Sagas-rollback leaves a trace in the touched databases, the rollback is semantically
equivalent to the rollback of ACID transactions. This helps to satisfy the requirement for semantic
replication between Microservices.
Communication and data flow between Microservices and domains can be both synchronous and
asynchronous. The simplest synchronous communication is if one Microservice calls the REST end-
point of another Microservice based on an HTTP method. In this case, the calling Microservice waits
for a response before sending the next message. This communication method is indeed a flexible
way to access data on demand. In asynchronous communication, on the other hand, messages are
sent without waiting for a response. This is usually suitable for writing operations and requires a
message broker
to manage the messages. Thus, each domain publishes its data, which can be
accessed by other domains.
For example, Apache Kafka, see [28].
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4 Knowledge Graphs and Link-Enabling APIs
The initiative “Plattform Industrie 4.0” underlined in its recommendations from 2013 (See [1]) the
demand for end-to-end digital integration for engineering across the entire value-chain. From an IT
perspective this addresses the lack of interoperability of engineering tools and especially PLM sys-
tems. There have been many different attempts to solve that problem or at least improve the situ-
ation. Today’s most promising cornerstone for a solution to this problem can be identified with
Linked Data a concept initially developed by the W3C
The automation of design and verification processes is key to improving product development speed
and risk reduction. The challenge is that automation workflows can only be established once the
relationships between engineering data are described formally. For example, it is necessary to know
which simulation result created by which simulation model is used to perform a specific test to
verify a requirement. Once these relationships are captured, then it is possible to perform auto-
mated workflows to check if new simulation results still satisfy the requirement after a change to
the simulation model. Existing automated workflows are currently discipline-specific, and not holis-
tic. For example, continuous integration and continuous deployment workflows for DevOps are
specific to software engineering. Impact analysis of requirements on model elements is specific to
systems and requirements engineering. Computing the product structure and performing new sim-
ulations based on a new lifecycle state of a 3D model is specific to mechanical engineering (e.g.
PLM). Creating a new machine learning model based on new data is specific to data analytics as
used in IoT. An important next step for improving productivity in engineering is to perform auto-
mated workflows at a global level, whereby workflows would execute based on relationships con-
necting data from all the various engineering disciplines including Application Lifecycle Manage-
ment (ALM), Product Lifecycle Management (PLM), Internet of Things (IoT), Simulation Data Man-
agement (SDM), Enterprise Resource Planning (ERP).
4.1 Linked Data for Engineering
The relationships between engineering data from different sources can be described in many dif-
ferent ways. A common idea would be to describe all the relationships in a central database, as in
one of the databases typically used for PLM or ERP. However, this approach does not scale. This
approach would be similar as to describing all the links between Web pages in one central database.
A distributed approach for saving links has advantages in terms of flexibility and effort. A distributed
approach for saving links allows each data source to evolve independently of the others. Each data
World Wide Web Consortium
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source can save its relationships on its preferred computer hardware, as long as the links are acces-
sible through the same HTTP protocol. Furthermore, the links can be created for each data source
using a different mechanism (e.g. simple text editor, custom HTML editor, WYSIWYG editor, etc.).
The same benefits of a distributed approach for saving links between Web pages also apply for
describing relationships between engineering data. For example, requirements will be edited using
a specific requirements management application using a specific user interface dialog to create new
relationships, such as between requirements or between a requirement and a test case. These re-
quirements-related relationships will be saved on a specific computer in a specific format. Another
application, such as a PLM solution will offer different user interface dialogs to create new relation-
ships and save the relationships on a different computer in a different database and in a different
The critical question is not in which central database the relationships need to be saved, but how
these relationships can be made accessible in a standard way through HTTP. As long as the rela-
tionships can be accessed and discovered through HTTP, then additional applications can collect
these relationships and decide to store the relationships in different “central” databases for differ-
ent purposes. For example, the relationships could be saved in an Online Analytical Processing
(OLAP) database to run the PageRank algorithm and determine the most relevant full-text search
results in the same way that Google indexes Web pages on the Web. Another application could
collect the relationships and save them in a graph database, or in a knowledge graph, to enable
graph queries and new graph-based applications. Another application could save the relationships
in a matrix for machine learning. A distributed approach to saving the relationships and to making
them accessible according to a standard protocol enables a scalable data integration strategy, as
demonstrated by the World Wide Web.
Figure 8 Linked Data Applications and Linked Data Principles
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Knowledge graphs and Semantic Web standards are often considered to be important technologies
to define and manage relationships between engineering data from different sources. It is important
to know how these promising technologies can be used to their fullest potential in engineering.
The Semantic Web can be considered an extension of the Web for machine readable data. Its pur-
pose was to allow a new generation of smart applications which could find answers to complex
questions such as for finding the cheapest flights, or automatically scheduling a doctor’s appoint-
ment. This vision was not realized as the public Web contains data which can be vague, inconsistent
and false. Automatic reasoners therefore could not make sense of bad data on the public Web.
The fundamental ideas to describe links between machine readable data on the Web, independent
of any advanced reasoning capabilities, were summarized by the inventor of the Web Tim Berners-
Lee in 2006 as the Linked Data principles. These principles actually help Web search engines to
better understand the content in Web pages and to offer improved search results.
defines a taxonomy for many common concepts such as movies, restaurants, recipes. Web page
creators add machine-readable data to their HTML pages according to the Linked Data principles
according to, so that Web search engines can in turn aggregate data from multiple
Web pages and improve search results. For example, a Web search for a movie will return the movie
ratings from multiple sources such as IMBD and RottenTomatoes, in addition to returning the nor-
mal search results. The principles of Linked Data have been adopted at very large scale, however
primarily in the context of Web pages pointing to the standardized concepts.
The adoption of Linked Data principles within the enterprise with private data is limited. Many
applications consume RDF data by importing RDF in the form of files (e.g. through bulk import) and
by offering a SPARQL endpoint. Very few applications consume RDF data through HTTP requests,
as is required by Linked Data applications and even less applications expose RDF data as derefer-
enceable HTTP resources as required by the Linked Data principles. The main exception are applica-
tions complying with Open Services for Lifecycle Collaboration (OSLC) which are used primarily for
requirements, test case management and task management. This is unfortunate as private enter-
prise data is of high quality, and private enterprise Linked Data could be the basis for many enter-
prise Linked Data applications as shown below.
Figure 9 Traditional vs. Link-enabling APIs
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While traditional APIs support the exchange of data between a server application and clients, an
API complying with OSLC supports the creation of links between API resources from different APIs.
As a result, links between data from different silos can be created ad-hoc by API clients, in other
words by engineers themselves. OSLC relies on the Linked Data principles and provides additional
standards to support easy ad-hoc linking of Web resources by human users, as well as data interop-
erability standards for important engineering data aspects such as versions, change events, access
rules, schemas, and containers.
Figure 10 - Example for Hypermedia
However, the creation of links between engineering data, such as between a specific requirement
and a test case, can only be done by engineers manually. The engineer needs to define the link by
choosing a link source, a link type, and a link target. Even though the link source and link target
are typically created in different applications, the engineer wants to create the link without switch-
ing applications. Ideally, from a user experience perspective, the engineer can create the link by
staying in one application. A challenge in this scenario is for the engineer to discover the HTTP URL,
in other words the unique global identifier, of the other API resource he wants to create a link to.
Human users can easily discover the URL of Web pages by using a search engine. However, API
resources in the context of an enterprise are private and not indexed by public search engines. The
engineer therefore needs to discover the HTTP URL of the resource he wants to link to by using a
different technique called hypermedia. We use it all the time when browsing the Web. The idea is
that the user only needs to know the URL of an “entry point” resource which will help the user
discover other available resources linked to the entry point resource. For example, I know that a
website like will have a Web page containing the report on a specific soccer game but I
don’t know the specific URL of that Web page. I can discover that URL by first accessing the entry
point resource, then selecting a category like Bundesliga, and then selecting the specific
Bundesliga game I’m interested in among the games that have been reported.
Similarly, an API can provide an entry point resource, which an API client can use to discover on his
own available resources provided by this API such as API resources describing categories (e.g. sim-
ulation model) and specific resource types (e.g. parameter) and specific resources (e.g. parame-
ter123). OSLC APIs support this notion of hypermedia and enable resource discovery based on an
Knowledge Graphs and Link-Enabling APIs
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entry point resource called ServiceProviderCatalog. An application #1 can then communicate with
another application #2 having an OSLC API and discover possible link targets to be displayed to the
user in application #1.
In order to find answers to complex questions, voice assistants are currently in part realizing the
vision of the Semantic Web. These voice assistants use in the background knowledge graphs com-
posed of trusted data maintained by private organizations such as Google and Amazon. The term
knowledge graph was coined in 2012 by Google. Existing knowledge graphs are currently mainly
used to answer questions about general knowledge such as who the president of a certain country
is. General knowledge is found typically in Wikipedia articles or in documents resulting from a Web
search. These documents contain natural language, which is then converted automatically into
knowledge graphs, which in turn can be queried to find the answer to a complex question. The
same approach could be used in an engineering context in which an engineer would ask his voice
assistant on his smartphone which parts satisfy a certain requirement which costs less than 100
EUR. As a result, the engineer would then see the answers to his question presented on his
smartphone. This scenario can be achieved if the engineering data originating from different
sources, as well as the relationships between the data from different sources, can be accessed easily,
for example through a common HTTP protocol as it is the case with REST APIs, in order to build a
knowledge graph.
Figure 11 - OSLC Core
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The following aspects make the adoption of knowledge graphs for engineering data more chal-
lenging than for general knowledge: data changes, access rights, versions of data, and the defini-
tion of relationships. Engineering data changes much more often than general knowledge. This
means that each data provider needs to expose changes to data in a standard way according to a
standard protocol (see e.g. OSLC TRS
). Engineering data is only visible to certain users whereas
general knowledge is visible to everyone. This means that each data provider needs to expose its
data access rules in a standard way according to a standard protocol (see e.g. OSLC TRS). Engineer-
ing data is very often versioned whereas general knowledge is not. This means that each data
provider needs to expose the different versions of data in a standard way according to a standard
protocol (see e.g. OSLC Configuration Management).
Relationships between general knowledge is often described through links between Wikipedia ar-
ticles. When content from Wikipedia is transformed into a knowledge graph, links between Wik-
ipedia articles are transformed into edges of the generated knowledge graph. Relationships be-
tween Web pages or relationships between Wikipedia articles are defined in a markup language
using for example HTML anchor elements, or doubled square brackets in the Wikipedia editor. Each
link points to a target Web page or Wikipedia article identified by a unique global identifier in the
form of an HTTP URL. It is easy to find the URL of a Web page using a Web search engine. It is easy
to find the URL of a link target. On the other hand, engineering data is not public and it is not
indexed by public Web search engines. There is no widely used equivalent of a Web search engine
for private company data. As a result, it is much harder for engineers to find the identifier of engi-
neering data to be used for link targets, and thus to define relationships between engineering data.
In order to overcome this handicap, each data provider needs to facilitate the discovery of its data.
A lot of engineering data belongs to data containers in the form of projects or repositories. And
engineering data is typically associated with one or several data types. So, each data provider needs
to expose in a standard way its data containers and its type-specific data access methods (see e.g.
OSLC Core). Then engineers can discover possible link targets using the same approach independ-
ent of the specific data provider.
OSLC Tracked Resource Set
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Figure 12 Knowledge Graph with Change Events, Access Rules and Version Managed Data
Overall, the use of knowledge graphs in engineering is very promising. However, engineering data
is not general knowledge data. Engineering data is private data which changes often until released
and whose access is restricted to certain users. By adopting standards to expose engineering data,
each data provider can facilitate the linking of engineering data, and in turn the creation of an
engineering knowledge graph. APIs which adopt standards to facilitate the linking of data can be
called link-enabling APIs, in contrast to the traditional producer-oriented or consumer-oriented APIs.
Right now, link-enabling APIs are already used in engineering in the form of Open Services for
Lifecycle Collaboration (OSLC) APIs.
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4.2 Semantic Web technologies
The origin of the WWW can be traced back to solving the interoperability issue between Hypertext
editors at CERN
where Tim Berners-Lee (see [11]) basically invented the Internet as we know it
today. Back then, research results were captured in a variety of hypertext tools and distributed
amongst many computers. To exchange research data faster and more efficiently Berners-Lee used
the idea of existing Hypertext editors where linking meant relating passages or lines inside of a
hypertext document to allow to jump back and forth. He combined that with the idea of retrieving
a document from another computer connected to the network. To achieve this, he invented the
protocol HTTP
, the global address room URL
and the standardized markup language HTML
exchange hypertext documents between server and browser.
Since then the WWW has of course developed further. From Web 2.0
that refers to websites that
emphasize user-created content, Semantic Web as a critical component of what is understood to
be Web 3.0, to the newest result of the Internet’s evolution: Linked Data. Linked Data builds upon
existing Web technologies but emphasizes the point that Semantic Web should not just help a
human being to better make sense of a website, but extends it to make websites be readable by
computers as well. With Linked Data not just HTML documents are shared between server and
browser as it has been the case for Web 1.0, now it is about exchanging data fulfilling the vision
to make the internet itself a global database allowing computers and users to retrieve any captured
information from it.
Conseil européen pour la recherche nucléaire (former name), today: Organisation européenne pour la recherche nucléaire. It is
based in Geneva, Switzerland.
Hypertext Transfer Protocol
Uniform Resource Locator, in more general terms URI is used, which stands for Uniform Resource Identifier.
Hypertext Markup Language
Also known as Social Web
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Figure 13 Semantic Web Technology Stack, see [12]
The Semantic Web attempts to make the Web "intelligent" by using various technologies that build
the Semantic Web Stack (see Fig. 13). As illustrated in the figure, the established Web technologies
form the basis of the Semantic Web. Linked Data is seen as a subset of this stack. Communication
mechanisms such as HTTP, encoding standards such as Unicode, identification mechanisms such as
IRI/URI, and authentication mechanisms are implemented in this layer. On top of that, representa-
tion syntaxes and formats such as TURTLE, JSON-LD, RDFa and μFormats as well as the Resource
Description Framework (RDF) are built. RDF is indeed one of the main building blocks of the Seman-
tic Web, standardizing the description and exchange of data across the Web. RDF also makes the
data queryable via SPARQL
, which is another important building block for both querying and
handling data. The technologies described so far are truly essential for standardized data exchange
and data integration on the Web. However, the real Semantics is enforced only by the introduction
of modeling languages (such as OWL and RDFS) and rule languages (such as RIF and SHACL). These
languages are crucial for creating sophisticated models of real-world phenomena and systems, in
order to breathe the Semantic soul into the data and make it machine-interpretable. The core prom-
ise of the Semantic Web is achieved by using the aforementioned highly standardized technologies
as we create applications. Top layers of the stack (including Logic and Trust) are not yet standardized
and are mainly still in academic stages.
SPARQL Protocol And RDF Query Language
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During work on the initial HTTP protocol the foundation of the internet Roy Fielding identified
a certain architectural style all interactions on the Web are based on (see [13]). The so called Rep-
resentational State Transfer (REST)
describes a set of constraints to be used for creating Web
services. REST defines a set of rules and constraints on how to serve and operate on resources
available on but not restricted to the internet. Those resources are uniquely identifiable through
a Uniform Resource Identifier (URI) and can be accessed by standard HTTP methods, like any com-
mon internet browser offers. Additionally, REST defines a strict separation between the data and
its representation. This means that different applications can receive the same data in different
formats. For instance, a human readable form might be displayed inside a Web browser, whereas
an application trying to integrate the data into its workflow might receive it in an interchangeable
format, like JSON. In the context of Microservices one of these constraints „Statelessness“ is most
critical as it enables loosely coupled systems. It does that by not storing any kind of information
relating to a connection on the server side, but keeping it all encoded in the message. This means
that only the clients need to keep their state, and the server can handle an indefinite number of
requests, without having to worry about internal resources. An added side-effect of not having the
server bound to individual client connections is an enhanced scalability. RESTful APIs are therefore
an additional prerequisite to enable an IT-architecture based on Microservices.
By design, data on the internet has no given semantic meaning. To be able to describe date en-
countered on the net, efforts of the Semantic Web resulted in the Resource Description Framework
(RDF). RDF takes two central properties of the internet into account: that everything on the internet
can be reached under an unique address and that everything is connected through “links”. RDF
utilizes these two properties by first declaring every “object” as a resource, which can be uniquely
identified by its URI. It is then possible to semantically describe a resource, by naming the “link”
together with its origin and destination, which results in a so called “triple”. By using this approach,
structured and semi-structured data on the internet can be semantically described and used be-
tween different applications. Not only objects themselves, but also the objects they link to, as well
as the relationship itself, can be part of other triples. That way a Web of relationships gets created,
which results in a natural graph.
A combination of both technologies RDF as a semantic description about what the data represents
and REST as a means of how to access the data is thus a powerful and scalable approach to build
futureproof applications in the form of Microservices.
See [25].
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4.3 To be truly Data-Driven we need Data-Centric Architectures
The fragmented IT-Landscape in enterprises is one of the main impediments of being Data-Centric.
The common practice to satisfy the needs of existing and emerging processes and methods in the
company is often to build or buy a new IT-Solution, which is often an application with tightly cou-
pled database and a fixed data-schema. Thus, a new silo is created with lots of redundant data and
lots of redundant functionalities, and the technical debt continues to grow. This leads, inter alia, to
high costs for the management and synchronization of redundant data, high maintenance costs of
interfaces for data and tool integration, high manual effort to handle data in different data sources
and increasing possibility of using wrong or outdated data. These tightly coupled applications and
repositories also make the development of new methods and solutions significantly more cost- and
Coping with the already mentioned industrial challenges needs however a seamless integration of
tools and full traceability across the system life cycle no matter where the data is stored. Indeed, a
data-centric architecture is a prerequisite for a data-driven enterprise. To achieve this, the applica-
tions need to be decoupled from the databases and data repositories. This is especially necessary
for reading and consuming data from different data sources with standard Web-based interfaces.
As illustrated in figure 14, a data-centric architecture
divides the enterprise IT landscape into three
layers, namely the Application layer, the Semantic layer and the Data layer. In this architecture, the
semantic layer is the key building block in providing a unified view of business objects and their
connections across different domains in the enterprise, regardless of where the data is stored. The
technologies of Semantic Web play a crucial role both in building and in connecting ontology mod-
els (based on OWL, RDFS, SHACL …) as well as in seamless integration of different layers (based on
OSLC APIs, SPARQL-Endpoints …).
This data-centric architecture focuses on the knowledge workers and domain experts in the enter-
prise and tries to provide them with all the data they need to do their jobs. Thus, the users receive
their desired data from various data sources in their predefined context and in their favorite appli-
cation. For example a designer who works with a CAD application, can access and even modify the
requirements related to the components in development directly in the CAD environment and does
not need to use a second application just because the requirements data is stored in a separate
repository. Furthermore, this architecture is an important enabler for a cross discipline impact anal-
ysis based on various interconnected and cross-linked (ontology) models both at the Semantic layer
as well as at the Data layer. This transforms indeed the fragmented data silos into a live enterprise
knowledge graph.
The illustrated architecture is a result of a concept study carried out at Mercedes-Benz AG.
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Figure 14 Data-Centric Architecture for a Data-Driven Enterprise (Source: Mercedes-Benz AG)
The proposed architecture based on Semantic Web technologies enables and fosters significantly
the emergence of Microservices in the enterprise. As shown in Figure 14, each Microservice covers
only a subset of functionalities and a subset of data belonging to a domain context. The holistic
picture can then be built simply by linking the Microservices at the metadata and data levels. This
paves also the way for a long desired federalized PLM landscape with data scattered in various
databases with theoretically different technologies, necessary to satisfy best the requirements of
the corresponding Microservice or use-case. It is only important to use standard semantic tools and
languages to ensure inherent interoperability at both the model and data levels. Thus, data in one
Microservice can be handled as first-class citizen in other Microservices.
The API schema of link-enabling OSLC APIs adopts the Open World Assumption. API clients can
change resources exposed by an OSLC API, as long as they don’t violate any constraints. These
constraints are used to ensure that resources of the same type have a minimum common set of
properties in order to support interoperability. For example, OSLC requirement resources should be
described according to the OSLC requirements management specification. An OSLC specification
consists of an RDF vocabulary and a separate set of constraints defined as OSLC resource shapes or
alternatively as W3C SHACL. The RDF vocabulary defines a set of URLs for identifying resource types
(e.g. Requirement type identified by and resource
properties (e.g. verifiedBy identified by The constraints
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relate to the multiplicity of properties for example to indicate that a requirement can only have one
title property. Domain-specific OSLC specification, such as related to requirements or change man-
agement, are likely to evolve over time, especially in order to be better aligned with domain-specific
standards such as ReqIF, SysML, or STEP Application Protocols defined by traditional standardization
organizations. It is technically simple to convert an existing domain-specific schema into an equiva-
lent Web-compatible OSLC specification. The officially adopted domain-specific OSLC specifications
only cover a few domains related to systems and software engineering. Many non-official domain-
specific OSLC specifications have been created by developers of OSLC APIs to describe domains
related to simulation (e.g. FMI) and MBSE. While the domain-specific OSLC specifications are likely
to evolve over time, similarly as domain-specific schemas (e.g. SysML v1, and then v2 etc.), the
domain-independent OSLC specifications related to resource discovery, global configuration man-
agement, change events, and other generic data aspects are expected to change less over time.
The domain-independent OSLC specifications are therefore considered more stable and more im-
portant than the domain-specific OSLC specifications.
OSLC as a specific implementation for Linked Data in the engineering context also has some draw-
backs that need to be handled, most importantly if there are legacy tools with proprietary APIs that
do not allow to develop an OSLC adapter quickly, there needs to be an alternative way of getting
data out of its cage. Especially in the MCAD/ECAD space there is little to no alternative than to
create connectors that are able to talk to the legacy proprietary APIs. Ideally these connectors are
not just point-to-point connectors but all the connectors are part of a bigger framework that helps
to neutralize legacy APIs. One example is the XPLM connector framework that supports multiple
ECAD, MCAD and PLM-Systems
There is no Digital Thread without MBSE
Copyright © Fraunhofer IESE 2022
5 There is no Digital Thread without MBSE
The art of product development has gone through tremendous changes over the last century.
Where it started with pencil and drawing board we now face a situation where product data is
created in an uncountable number of software tools that help to formalize, simulate and therefore
automate the development of a product in order to bring it faster to the market, satisfy more
customer needs, improve quality and maintainability.
Figure 15 History of Engineering Tools [14]
Model-Based Systems Engineering (MBSE) is just the latest methodology and tool set that aims to
conquer the challenges modern product development is facing: From autonomous driving cars to
self-assembling manufacturing systems, from smart cities to electric airplanes. The main challenge
is not the introduction of a system modeling tool. It is how the introduction of MBSE methodology
and tools can fit into the existing landscape of tools and processes.
Model-Based Systems Engineering
There is no Digital Thread without MBSE
Copyright © Fraunhofer IESE 2022
Looking back to the history of product development we can see several phases (see Fig. 15). Before
there was any computer support in product development, engineers worked with physical drawing
boards. As there has not been much electronics nor software in their products and the complexity
of parts in terms of their geometry has been manageable, this tool set was sufficient at times.
Drawings were managed in an archive and they received numbers, title and name of creator -
essentially meta data.
The next phase can be characterized by the introduction of 2D CAD
. The first time that computers
helped to create drawings. This phase basically replaced the physical drawing board by a computer
that ran a 2D CAD tool a change in authoring system, as they are usually categorized. With the
introduction of 3D CAD phase 3 the necessity occurred to manage the created models virtually
and therefore PDM/PLM tools were invented. In the meantime, of course ECAD
, EDA and CASE
tools were introduced to also help the non-mechanical engineers to develop their respective com-
ponents more efficiently. Although some of those systems’ implementations are using direct con-
nectors to manage their data in PDM/PLM or connect their respective TDM
to a PDM backbone,
most of PDM/PLM implementations in the world still focus to a large extend on the mechanical
domain. ECAD and CASE is seen as separated silos in terms of IT architecture and organization-
From todays perspective it gets clear that the number of tools and models that are used to describe
a product under development is overwhelmingly large. Those models have not been implemented
to interoperate amongst each other and the corresponding tools do have a lot of overlapping func-
tionality. For instance structural information is described in any of the tools unfortunately in its
own language.
MBSE is aiming to bridge this gap between the engineering disciplines and tear down the walls
between those silos. It therefore formalizes established Systems Engineering processes and best-
case practices in so called system models. Rather than working with documents and spreadsheets
system engineers started to work with formal descriptive models of the system under development
(e.g. in a semi-formal language like SysML
). Those models capture more information than just
structural (what has essentially been the strength of PDM/PLM tools) in addition system models
are able to capture behavioral, parametric information and requirements. That is why the next dec-
ade of development is about developing systems rather than products and capture additional be-
havioral information the existing tool set consisting of CAD/PLM is not able to do.
A very exciting reply to today’s challenges is given by the new version of the modeling language
SysML v2 (see [15]). A fundamentally new system architecture is not based on UML anymore but
on a semantic meta object facility and on the kernel modeling language (KerML). The new archi-
tectural approach is essentially more simple and parsimonious. But the main advantage lies in the
Computer-Aided Design
Electrical/Electronical CAD
Computer-Aided Software Engineering
Team Data Management
Systems Modeling Language, standardized by the Object Management Group (OMG).
There is no Digital Thread without MBSE
Copyright © Fraunhofer IESE 2022
extensibility and the leveraged model library support. The models are more unified. Structure and
behavior models are deeper integrated and the terminology is cleaner and simpler. All this provides
an ideal approach how the challenges in today’s landscapes should be tackled.
Todays’ system models are purely descriptive which means in order to be executed they need to
integrate with simulation models. There are many examples of such integration efforts. It can be
done on the basis of 0D/1D-simulation tools, e.g. in a language like Modelica or MATLAB/Simulink
it can also be further supported by integrating with 3D-simulation tools, be it CFD, FEM or MBS
In this context especially OMG’s SysPISF
specification and Modelica Associations’ Project “System
Structure and Parameterization” need to be mentioned.
One main problem that MBSE currently faces is its adaption amongst non-systems engineers that
need to consume and work with data that is initially created in the system model. The steep learning
curve that is characteristic for systems modeling leads to the creation of a “MBSE silo” next to the
existing engineering silos. In order to make MBSE successful it needs to be connected to the Digital
Thread. And because MBSE is aiming to formalize early-phase development processes that were till
now only document-driven there is no complete Digital Thread if it does not include MBSE.
The Digital Thread itself is not a tool chain where content from one model is transformed into
content of the next model. This could be the understanding of it from an MDA
-perspective where
Platform Independent Model (PIM) is transformed into Platform Specific Model (PSM)
. The Digital
Thread purely wants to connect all relevant artifacts that are part of the development of a system,
trace between them, ensure their configuration and help organizations to identify what is affected
in case of a change.
The Digital Thread runs through an organization and connects the digital artifacts that an organi-
zation produces. It is as organization-specific as it sounds and therefore it is just impossible to im-
plement the Digital Thread vision on top of an OOTB, inflexible, clunky monolith. The point for low
code platforms which do not ignore the fact that customizations are necessary, but have those
capabilities built in and by doing so bring them under control, cannot be stressed enough.
Making MBSE be part of the Digital Thread needs prerequisite conceptual work. There have been
a couple of research projects and academic publications on the topic of MBSE/PLM integration that
are worth looking at. In [4] Dickopf discusses a holistic system development methodology consisting
of process, method and tool. Pfenning describes in [14] a way to map, synchronize and manage
system models in a PLM system with his System Concretization Model. Both publications are based
on previous concepts by Gilz. He showed in [16] a way to move system model data to PLM with a
© The Mathworks, Inc.
Computational Fluid Dynamics, Finite-Element Method, Multi-Body Simulation.
Object Management Group, SysML extension for Physical Interaction and Signal Flow Simulation
Model-driven Architecture, see [27].
This may be an interesting area of research for the next years to come: Adapting the MDA for Systems Engineering. The real world
implementation of the digital thread is the more pragmatic scenario. The digital thread on the other hand can be seen as an initial
prerequisite to work on this so called “Systems-MDA”.
There is no Digital Thread without MBSE
Copyright © Fraunhofer IESE 2022
focus on profile, methods and visualization of the result in a PLM backbone. All of that lead to the
Kaiserslautern’ System Concretization Model (KSCM) described in [17] and shown in Figure 16
see also [18]. Hooshmand et. al. proposes in [19] and [20] a landscape for the integration of PLM
and MBSE and the semi-automatic creation of system models based on ontology-based model li-
Lessons learned from academia is that integrating MBSE with PLM needs a huge amount of flexi-
bility not just in terms of the PLM data model, but also in the ability to map and synchronize any
element (be it object or relationship) of a system model. This is needed, because used methodolo-
gies and moreover used stereotypes in system modeling languages
differ tremendously between
Figure 16 Kaiserslautern’ System Concretization Model (KSCM)
Pfenning introduces in [14] the term anchor element to illustrate the problem. As stated before the
implementation of the Digital Thread needs to be based on a set of digital artifacts (or work items)
that are created by an organization to capture and document decisions. This could be for example
requirements, use cases, activities, issues, builds, CAD models, parameters, features, libraries, sche-
matics, layouts, part lists, test protocols and so on. Obviously, this can lead to an endless list uniquely
defined for each organization. The Anchor Elements concept does not want to restrict that list to a
standard set of this artifacts. The concept wants to find a subset of all of those artifacts that are
needed to communicate interdisciplinary meaning between the engineering disciplines. This nec-
essary abstraction needs to be done in order to make interdisciplinary collaboration successful. An
initial compromise for a standard set of anchor elements can be seen in the Kaiserslautern’ system
concretion model.
More specifically in SysML
There is no Digital Thread without MBSE
Copyright © Fraunhofer IESE 2022
It consists of several so called spaces: The requirements space that captures the Requirement Model,
the verification and validation space that captures the model necessary to test and simulate the
developed systems in all its different digital models, the administration space that captures the
necessary meta data, processes, rules and so on to manage models and data, and finally in the
middle the so called solution space where all models can be found that describe the system under
development from every viewpoint and viewpoint here means again in any necessary type of
digital model, be it MCAD, ECAD, or the system model itself.
The solution space knows 4 abstraction layers 4 ways to describe the solution, getting more con-
crete from the top to the bottom: from a contextual viewpoint (what is my system’s context/envi-
ronment? Is it part of a bigger system of systems?), from a functional viewpoint (what is my systems
functionality without looking at its subsystems, components?) from a logical viewpoint (what are
my systems’ subsystems? Which system elements can I define in order to lay out its architecture?)
and finally from a physical viewpoint where the link to existing product structures is made.
All structures on each of the layers can of course be subject to variation and therefor there is a
dimension called “variability” that allows for definition of alternatives and is therefore a prerequisite
to support tradeoff analysis. Furthermore, there are several links between elements that help to
trace: verify
to find the right test case to verify a requirement, derive to break requirements down
(cascading requirements), satisfy to show which solution element can satisfy a requirement, allocate
to show what element on a lower layer of the solution space is implementing which element of a
higher layer.
On each layer also flows can be found that describe behavior. Flows can in general be of type
energy, information and matter. Behavior is therefore described by flows between elements, not so
much by elements themselves.
From a practical IT-architecture perspective it is obvious that all of this information cannot live in
just one software tool. It is necessary to have several tools capturing the described partial models
communicate with each other. Linked Data is one cornerstone for this communication and it is
obvious that the semantics on each trace link (verify, validate, allocate, satisfy, derive, …) fit quite
nicely into a RDF
-triple definition.
What is not covered in the KSCM is the instance level, and therefore the lifecycle phases of opera-
tion/maintenance and production. This is where Digital Twins also need to be connected to the
digital thread.
In addition validate not shown here, but a necessary additional relationship to trace for validation.
Resource description framework
There is no Digital Thread without MBSE
Copyright © Fraunhofer IESE 2022
Figure 17 Digital Master and Digital Twin
The Digital Twin lives on an instance level as the computer-science community would say. For
other engineers the difference between Digital Master
and Digital Twin
can be explained by just
saying the digital twin corresponds to a real twin that can carry a serial number it corresponds to
an actual build system a customer can buy whereas the digital master is every discipline model
and every piece of data necessary to define a type of product and therefore it can carry a part
The KSCM (as illustrated in Figure 16) only shows the inner structure of the Digital Master. There
has been additional work on bringing this type level together with the instance level, represented
by the Digital Twin. In [21] Muggeo and Pfenning show a high-level process on how the Digital
Twin can be instanciated from its Digital Master. Fig. 17 shows this process.
There is the real world reserved for real manufactured and operated systems and there is the virtual
world that captures all data and models necessary to describe the system over its lifecycle. In the
development phase there is usually not yet an existing twin corresponding to the real world at
least when it is about the initial development. The Digital Twin gets instanciated from its Digital
Master which most of the times (but not always) means that the Real Twin gets produced. The
Master potentially knows variability this is why the 150% system model can be found here. After
production the real twin is operated, and the digital twin can be used to make sense of collected
field and operation data this may help to optimize the design or even optimize the operated real
twin e.g. less energy consumption by using a better engine control configuration. It is also possible
The term digital master originates from the iViP research project. This is also the reason why Prostep iViP Association has iViP as its
Industry 4.0 Initiative came up with the term „Verwaltungsschale“ (eng: administrative shell) to express the need to digitally de-
scribe a component in a factory. Both terms can be understood to be very similar.
There is no Digital Thread without MBSE
Copyright © Fraunhofer IESE 2022
to virtually first verify software updates on the digital twin before updating the real twin over the
air. This can be referred to as closed loop engineering.
The main question to answer in this context is potentially what kind of data is needed for the digital
twin and what data is only referenced or inherited from the type level. The answer to this question
may as well be company specific and cannot always be standardized that easily.
It goes without saying that any implementation of a System Lifecycle Management System (SysLM)
must include not just the digital master on a type level with all the artifacts described above, it must
also include the Digital Twin concept. That’s why IoT platforms must also be part of any IT-
architecture aiming to implement a System Lifecycle Management System.
The Concept: Cloud-native System Lifecycle Management (SysLM)
Copyright © Fraunhofer IESE 2022
6 The Concept: Cloud-native System Lifecycle Management (SysLM)
The System Lifecycle Management (SysLM) concept isn’t really new, but its implementation in the
industry has not yet been achieved due to the obvious amount of work necessary to fulfill this
. The authors of this paper agree that any implementation of a SysLM concept can only be
realized by the usage of a decentralized IT-architecture. This means any implementation effort must
find answers to the question how the three main building blocks described above Microservices,
Linked Data and MBSE can be brought together and how those technologies can operate jointly
in a holistic concept. On the following pages, a possible concept is presented. This is not the only
valid concept for solving this problem, but it can certainly serve as an illustration of the necessary
problem-solving process.
As illustrated in figure 14, on the highest layer (the application layer) the involved Microservices are
defined. In this example, 18 such Microservices are defined, showing that roughly 1-2 dozens Mi-
croservices are required to have the same functionality as in a PLM monolith. Each Microservice is,
as stated before, independently deployable and possesses its own persistence layer. The Micro-
services or bounded contexts cover the entire lifecycle of a product or system. Starting from Re-
quirements Engineering, to (M)CAD Data Management, to ECAD data management, including
Meta Data / Master Data, Bill of Material Management, but also of course including the Software
domain to include things like source code management
. The scope of each Microservice is defined
by the underlying business context.
In a more generic concept, all Microservices will have their own endpoints and API to communicate
with each other via semantic links. Figure 18 shows a generic cloud-native Microservice. As stated
before, it will run in a container (here Docker), it will have its own operating system as it is running
in a container this layer will only have the most relevant capabilities in there. Above we see its own
persistence layer, depending on the best data base technology for the business context. It should
be noted that when scaling, the Microservices are cloned, but this rarely means that the database
is also cloned. In other words, each Microservice in the bounded context has its own database, but
the database is shared among the clones of the same Microservice when scaling. Thus, the persis-
tence layer is shared between the clones
To ensure the needed flexibility there needs to be built-in low code capabilities. This makes it pos-
sible to easily change data and process model. Repository and modeling engine help to easily
achieve customizations without having to hard code on the application layer nor having to directly
change the persistence layer. After roughly two decades of experience with data modeling in PLM
See [24].
To read the full list, please see Figure 14.
See also fig. 18.
The Concept: Cloud-native System Lifecycle Management (SysLM)
Copyright © Fraunhofer IESE 2022
systems, the low-code data modeling functionality should include additional object-oriented con-
cepts that legacy PLM-systems only support to a certain level. This could mean (multi-) inheritance
and instanciation, property libraries, auto-generated forms for the UI. The OMG’s modeling lan-
guages stack around UML/SysML
and for the process model BPMN
can serve as a role model
how to include graphical modeling into a low-code engine
. It does not mean that SysML and
BPMN are the only possible standards out there. Most of the time the steep learning curve especially
for SysML will hinder its adaption and therefor some light-weight graphical modeling needs to be
in place.
Figure 18 Generic Microservice
In addition to the definition of the Microservices, one of the most important aspects is the interop-
erability and communication between these services. Linked Data, as shown in the middle layer of
Figure 14, plays an important role here that can be used as a blueprint for data exchange, an
obvious example technology as already introduced is OSLC
. What needs to happen is that addi-
tional domains must be included in the shared ontology. This helps to set up the described
knowledge graph and helps to query the whole network rather than each database.
Unified Modeling Language / System Modeling Language
Business Process Model Notation
A good example here is Mendix Studio / Mendix Studio Pro for rapid app development. See
Open Services for Lifecycle Collaboration
The Concept: Cloud-native System Lifecycle Management (SysLM)
Copyright © Fraunhofer IESE 2022
Figure 19 Generic Cloud Architecture
As shown in Figure 19, Microservices are cloud-native, which not only increases the scalability of
services, but also facilitates flexibility in addressing new requirements at different lifecycle stages of
a system and in different business contexts. By breaking down a monolithic software into smaller
bounded contexts, the scalability of each Microservice can be increased independently and as
needed. In addition, the release cycle of Microservices can be handled pretty much independently,
allowing to respond more quickly to new needs and requirements and giving the DevOps team the
necessary level of autonomy.
To ensure that data is synchronized between the many Microservices the messaging broker will help
to distribute data and changes through the network of connected data bases. This is especially
important when datasets are needed at more than just one Microservice. Linking via REST-APIs
and semantic technologies such as OSLC helps to build the knowledge graph. Brokers are needed
to help distribute data sets among the network in order to make more detailed operations running
See also [30]
The Concept: Cloud-native System Lifecycle Management (SysLM)
Copyright © Fraunhofer IESE 2022
on that data work sufficiently. It should be understood that it is never a decision about linking or
synchronization, it must be ensured that both concepts are used where they are needed in combi-
Depending on the persistence strategy there can be multiple different types of databases (one per
Microservice) this of course also means that the principles they run on can differ. Where for many
classical PLM tasks ACID principles (see also Sagas in [10]) apply, BASE principles may play an im-
portant role when it comes to IoT use cases with a tremendously higher amount of data and update
Docker Containers are managed by Container Orchestration systems such as Kubernetes (see [22]).
There are of course multiple possible clients imaginable: Web clients and Mobile Apps. But it may
also be required to connect to existing desktop tools using proprietary connectors. This is needed
as long as not all authoring systems have moved to a cloud infrastructure. There is also the necessity
to balance the load coming from the clients e.g. via Load Balancers.
Figure 20 Technology stacks for certain example Microservices
Fig. 20 gives an overview of six example Microservices and their corresponding technology stack.
Where the managed data structures are complex and not easily to be brought into a hierarchy,
The Concept: Cloud-native System Lifecycle Management (SysLM)
Copyright © Fraunhofer IESE 2022
graph databases are best suited. Querying a complex graph with such a database will deliver an-
swers fast because it will not need to join tables as regular SQL databases would need to do. This
applies to a so called graph visualization Microservice
as well as to a systems architecture Micro-
service. There are already PLM start-ups making use of graph databases
When there is a lot of data to be collected and analyzed Time Series Database are a possible best
fit. The most obvious use case in the engineering domain is around IoT. Collecting data from the
field to find correlation in it to drive system enhancements is probably the most cited use case
File Management will also probably never stop playing an imported role. Moving existing PLM-
concepts for distributed file management (DFM) to a cloud infrastructure will mean making use of
object storages such as AWS’ simple object storage service Amazon S3
. This is a cloud platform
service that can be adopted to fulfill the need to distribute large files (such as CAD models) globally.
For other pretty well-established services such as BOM Management and defining and executing
workflows it may just mean to stick to regular SQL databases.
An optional component and something that needs additional discussion is the introduction of a
central link repository. Currently the shown concept does not manage links centrally but rather lets
users create, update and delete those links manually. It is imaginable that in the future there will
be the need for a central instance that manages links in order to achieve a higher level of con-
sistency. This discussion originates in the initial creation of the WWW, where the introduction of a
central link database wasn’t agreed on. Back then consistency has been sacrificed in order to create
a highly scalable system (see [11]). For an enterprise use case it may be necessary to rethink that
concept as configuration management also for product liability purposes plays a tremendously more
important role in the engineering domain than it plays in the public internet.
In addition, the assumption is that a lot of needed capabilities that are not engineering specific may
just come from cloud platform services. The most obvious examples would be file management,
access control, authentication, reporting and a couple of other capabilities. These may differ from
cloud-to-cloud provider but will in general be nothing specific to engineering use cases and can
therefore be seen as available.
See [29].
Ganister PLM is a good example of graph-based PLM, see
Predictive Maintenance and Closed Loop Engineering should be mentioned here.
In other cloud environments there are similar services available.
Copyright © Fraunhofer IESE 2022
7 Outlook
Nothing is certain, except for change itself. The enterprise IT industry is changing rapidly. The intro-
duction of Cloud Computing and its adaption constitutes a major shift, not just in software terms
but more importantly in the way business is done. The authors of this paper wanted to illustrate
what challenges are currently faced by the industry and what solution concepts are being discussed.
The introduced concept of cloud-native Systems Lifecycle Management has been discussed to illus-
trate the ongoing struggle to find a solution and how different components of a possible solution
may fit together. It is with no means the only possible outcome. Additional things need to be eval-
uated, dependencies to existing legacy systems will probably play also a huge role. All eventualities
can just not be foreseen in totality. Still, we wanted to leave the reader with a couple of statements
we were able to condense out of the intense discussions we had when working on this paper.
10 Statements for the future:
1. Microservices are an architectural prerequisite for agile DevOps processes due to the fact that team
autonomy can only be achieved if dependencies within a software’s architecture can be brought
under control.
2. Without openness of the involved IT applications digitalization cannot be scaled to an enterprise
level. Most use cases currently discussed for AI, Data analytics and IOT will just not work if this
openness isn’t ensured.
3. MBSE needs access to engineering data and mature lifecycle, configuration and data management
capabilities. Configuration management is way more than just versioning and baselining.
4. Microservices give freedom to choose the best technology be it UI, persistence, or programming
language to solve the domain problem at hand in the best way.
5. Decentralized Environments need semantic technologies to query not just one database, but a net-
work of databases. Linked Data in contrast to the original Semantic Web is a pragmatic approach
focusing on easy to understand and therefor easy to scale principles.
6. A Microservice-based Architecture needs in addition to Linked Data technologies also asynchronous
data exchange for example by using message brokers. Linked data and messaging brokers may
serve as the necessary middleware for cloud-native SysLM .
7. Model-Based Systems Engineering and the system model can serve as an interdisciplinary meta-
model. It cannot achieve anything beyond a pilot-project if it is not connected to existing engineer-
ing data that lives within distributed engineering tools.
8. Systems Modeling itself will eventually also move to a cloud infrastructure, making it necessary to
support the authoring of a system model within the web client.
9. Data Protection and Privacy laws will require the establishment of local data centers as moving
sensitive (and personal) data between jurisdictions will be inhibited. For example between the US
and the EU.
Copyright © Fraunhofer IESE 2022
10. The open API philosophy will be strengthened in the years to come also by legislation and precedent
cases. Closing APIs to prevent competition can already be seen as distortion of competition. Legis-
lation is slowly but effectively moving into a direction where openness and interoperability is more
important and of higher economical and social value than copyright protection of API terminology.
Copyright © Fraunhofer IESE 2022
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Title: Killing the PLM Monolith
the Emergence of cloud-
native System Lifecycle
Management (SysLM)
Date: May 30, 2022
Status: Final
Classification: Public
This chapter explains the implementation of the trends and methodologies of digitalization presented in Chap. 3, specifically in the application area of engineering. Digitalization of the product and the connected service developed in the framework of service-orientated business models are presented here. A further significant point of digitalization is the horizontal and vertical integration of the technical and administrative work processes along the product lifecycle. Vertical integration concerns the integration of the authoring systems along the lifecycle phases, requirements management, system architecture, CAD in mechanics, electronics and computer-aided software engineering (CASE), and simulation. Horizontal integration focuses on the administrative functions such as release, change, and configuration management across the entire product lifecycle (PLC) and the technical integration of the information created in the individual partial models of the PLC phases. The typical application functions along the product lifecycle are represented on the basis of a market leading SysLM system. The chapter is rounded off with a requirements analysis of SysLM and a presentation of a maturity model for the evaluation of the digitalization of engineering.
Dieses Kapitel erläutert die Umsetzung der in Kapitel drei vorgestellten Trends und Methodiken der Digitalisierung speziell im Anwendungsgebiet des Engineerings. Dabei wird die Digitalisierung sowohl des Produktes als auch der im Rahmen von Service-orientierten Geschäftsmodellen entwickelten Dienstleistungsprodukte vorgestellt. Ein weiterer wesentlicher Punkt der Digitalisierung sind die horizontale und vertikale Integration der technischen und administrativen Arbeitsprozesse entlang des Produktlebenszyklus. Bei den vertikalen Integrationen handelt es sich um die Integrationen der Autorensysteme entlang den Lebenszyklusphasen Anforderungsmanagement, Systemarchitektur, CAD in Mechanik, Elektronik und CASE (Computer Aided Software Engineering) und Simulation. Bei der horizontalen Integration stehen die administrativen Funktionen wie Freigabe-, Änderungs- und Konfigurationsmanagement über den gesamten Produktlebenszyklus und die technische Integration der in den einzelnen Phasen erzeugten Informationen im Vordergrund. Die typischen Anwendungsfunktionen entlang des Produktlebenszyklus werden am Beispiel eines marktführenden SysLM-Systems dargestellt. Das Kapitel wird durch eine Anforderungsanalyse an SysLM und einer Vorstellung eines Reifegradmodells zur Bewertung der Digitalisierung des Engineerings abgerundet.
Full-text available
Inhalt: Der unternehmerische Erfolg hängt letztlich davon ab, Ideen möglichst effizient und effektiv in technologisch hochwertige Produkte für globale Märkte zu überführen. Vor diesem Hintergrund kommt dem Produktentwicklungsprozess (PEP) eine zentrale Bedeutung zu. Verfolgt man die Produktentwicklung in den letzten 50 Jahren, so haben der Funktions- und damit auch der Komplexitätsumfang dramatisch zugenommen. Virtualisierung, Integration und Interdisziplinarität zwischen den Disziplinen Mechanik, Elektrik/Elektronik, Software und Dienstleistung sowie Zusammenarbeit zwischen den einzelnen Phasen des Produktlebenszyklus werden zur Grundlage eines modernen PEP. Dieser Artikel soll eine offene Diskussion über die Gestaltung eines solchen interdisziplinären PEP eröffnen.
Full-text available
Current processes for highly interdisciplinary and complex smart products rely on the support of a big variety of IT systems. At the lowest level, authoring systems are used to create digital models and engineering data objects. A multitude of data management systems which in turn have to be integrated into comprehensive Product Lifecycle Management (PLM) and Enterprise Resource Planning (ERP) solutions enable the management of this product information and engineering processes (e.g. for engineering change and release management) along the entire product lifecycle. Engineers use these information management approaches for their daily work processes like engineering change or release management. In industrial environments, several heterogeneous IT systems coexist but cannot easily be connected and thus provide critical barriers for engineering collaboration. Being able to flexible access required engineering information from these IT systems supported by a platform that provides integrated metadata repository engineering could improve engineering workflows like change management dramatically. The approach introduced in this paper describes a Metadata Repository for Semantic Product Lifecycle Management (SP²IDER) which provides an additional information management layer that uses an IT architecture based on a minimalistic core to view and access data from a multitude of IT source systems. Instead of storing data from these source systems in SP²IDER, open web technologies like Linked Data principles and JSON-LD allow providing real-time access to the source systems.
Conference Paper
Full-text available
The increasing complexity of industrial plants is reflected in the system development and system modelling process. However, the complexity of the system model increases generally over proportionally. This makes the development, the administration and above all the reuse of the system models very difficult. This paper presents a method for semi-automatic creation of industrial plant models by using model libraries. For this purpose, an integrated IT infrastructure and an ontology based model library has been set up. The library is developed based on SysML notation, covers all RFLP abstraction levels and links the elements at all levels, which enables bidirectional impact analysis. The library is project-neutral and forms the basis for building project-specific system models based on the developed IT infrastructure. This increases, inter alia, the reuse of knowledge in companies. In addition to the industrial plant sector, the method presented is applicable to other domains.
Conference Paper
Full-text available
Systems Engineering has been proven to be an effective approach to cope with the increasing complexity of systems, consisting of mechanical, electrical, electronic and software components. However, as the systems getting more interdisciplinary and in particular more software-intensive, classical document based methods cannot efficiently master the growing complexity. In contrast, model-based approaches look promising in gaining the control over the complexity of systems and the development processes. For this, a formalized system model, integrated in the IT landscape, is indispensable. Thus a pragmatic approach has been developed, which enables a (semi-)automatic creation of formalized system models in SysML. Furthermore, an ontology has been developed to define the relationships and to enrich the system model with more semantic. The system model and the ontology will be then used to connect the domain models and artefacts along the development process. This ensures the required traceability and enables, inter alia, an automatic impact analysis, if any change arises. The federalized solution for the heterogeneous model landscape of industrial plants is applicable to other domains.
Technical Report
Full-text available
Während sich das Konzept der „Industrie 4.0“ mittlerweile sehr konkret in Pilotprojekten, Referenzmodellen, Standards, Normen und Technologien niederschlägt, ist noch weitgehend unklar, wie das Engineering von Industrieprodukten und produktnahen Dienstleistungen künftig gestaltet werden muss. In Interviews und einem Workshop mit Industrieexperten hat das Institut für Innovation und Technik (iit) Grundzüge eines künftigen „Engineering 4.0“ herausgearbeitet. Dessen bestimmenden Elemente sind eine Feedback-Architektur, die simultane Betrachtung aller beteiligten Ingenieursdisziplinen sowie partizipative und agile Kooperationsmodelle.
The internal and external complexity of today's systems is continuously increasing. These smart cybertronic systems consist of more and more components, are subject to a higher number of variants, and the distribution of disciplines is also subject to the increasing dominance of the system software. Besides, these systems temporarily connect to other systems or devices to fulfill one or more objectives, using the rising digitalization possibilities. To make this increasing complexity manageable and understandable for developers and end-users, the development approaches of today's systems have to change in parallel to the change of the systems themselves. Concepts and aspects from Product Lifecycle Management (PLM), Model-based Systems Engineering (MBSE) and Closed-loop Engineering (CLE) can provide positive input in this context The thesis "A holistic Methodology for the Development of Cybertronic Systems in the Context of the Internet of Things," written by Thomas Dickopf, pursues the goal of integrating development processes, methods, and tools under intensive consideration of system usage and the possibilities of system optimization. Current trends in digitalization, as well as well-established procedures for the development and management of products, form the basis of a two-step development approach. This approach consists of a macro-methodology (VPESystemDevelopmentMethodology) for the entire development process and a micro-methodology (VPEmecPro²methodology) which is proposed for the early phase of system analysis and conceptual system design. The whole procedure is supplemented by a loop-based improvement concept, which supports the optimization of the system under development, the system during its operational use, as well as subsequent product generations.
Increasingly, big data is viewed as the most strategic resource of the 21st century, similar in importance to that of gold and oil. While sitting on these vast pools of data, many organizations are simply not ready to take advantage of this new strategic resource. Embracing big data requires addressing a number of barriers that fall into the domains of technology, people, and organization. A holistic, socio-technical approach is required to overcome these barriers. This article introduces the specific tactics we recommend for addressing big data barriers, which involve changes to technology infrastructure, a focus on privacy, promotion of big data and analytic skills development, and the creation of a clear organizational vision related to big data.