Method for data inventory and classification

Abstract

One of the most notable drivers of the fourth industrial revolution is the availability of vast amounts of data along the entire lifecycle of a product. The analysis of product lifecycle data leads to promising potentials in strategic product planning. However, many companies are confronted with major challenges in identifying and specifying all the relevant heterogeneous and manifold data sources. This is due to the lack of simple approaches and methods for data inventory. In this paper we present a method for identifying and structuring data and their sources systematically by employing a framework to classify data sources and giving examples for application.

Full text

ScienceDirect
Available online at www.sciencedirect.com
Available online at www.sciencedirect.com
ScienceDirect
Procedia CIRP 00 (2017) 000–000
www.elsevier.com/locate/procedia
2212-8271 © 2017 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the scientific committee of the 28th C IRP Design Conference 2018.
28th CIRP Design Conference, May 2018, Nantes, France
A new methodology to analyze the functional and physical architecture of
existing products for an assembly oriented product family identification
Paul Stief *, Jean-Yves Dantan, Alain Etienne, Ali Siadat
École Nationale Supérieure d’Arts et Métiers, Arts et Métiers ParisTech, LCFC EA 4495, 4 Rue Augustin Fresnel, Metz 57078, France
* Corresponding author. Tel.: +33 3 87 37 54 30; E-mail address: paul.stief@ensam.eu
Abstract
In today’s business environment, the trend towards more product variety and customization is unbroken. Due to this development, the need of
agile and reconfigurable production systems emerged to cope with various products and product families. To design and optimize production
systems as well as to choose the optimal product matches, product analysis methods are needed. Indeed, most of the known methods aim to
analyze a product or one product family on the physical level. Different product families, however, may differ largely in terms of the number and
nature of components. This fact impedes an efficient comparison and choice of appropriate product family combinations for the production
system. A new methodology is proposed to analyze existing products in view of their functional and physical architecture. The aim is to cluster
these products in new assembly oriented product families for the optimization of existing assembly lines and the creation of future reconfigurable
assembly systems. Based on Datum Flow Chain, the physical structure of the products is analyzed. Functional subassemblies are identified, and
a functional analysis is performed. Moreover, a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the
similarity between product families by providing design support to both, production system planners and product designers. An illustrative
example of a nail-clipper is used to explain the proposed methodology. An industrial case study on two product families of steering columns of
thyssenkrupp Presta France is then carried out to give a first industrial evaluation of the proposed approach.
© 2017 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference 2018.
Keywords: Assembly; Design method; Family identification
1. Introduction
Due to the fast development in the domain of
communication and an ongoing trend of digitization and
digitalization, manufacturing enterprises are facing important
challenges in today’s market environments: a continuing
tendency towards reduction of product development times and
shortened product lifecycles. In addition, there is an increasing
demand of customization, being at the same time in a global
competition with competitors all over the world. This trend,
which is inducing the development from macro to micro
markets, results in diminished lot sizes due to augmenting
product varieties (high-volume to low-volume production) [1].
To cope with this augmenting variety as well as to be able to
identify possible optimization potentials in the existing
production system, it is important to have a precise knowledge
of the product range and characteristics manufactured and/or
assembled in this system. In this context, the main challenge in
modelling and analysis is now not only to cope with single
products, a limited product range or existing product families,
but also to be able to analyze and to compare products to define
new product families. It can be observed that classical existing
product families are regrouped in function of clients or features.
However, assembly oriented product families are hardly to find.
On the product family level, products differ mainly in two
main characteristics: (i) the number of components and (ii) the
type of components (e.g. mechanical, electrical, electronical).
Classical methodologies considering mainly single products
or solitary, already existing product families analyze the
product structure on a physical level (components level) which
causes difficulties regarding an efficient definition and
comparison of different product families. Addressing this
Procedia CIRP 93 (2020) 234–239
2212-8271 © 2020 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the 53rd CIRP Conference on Manufacturing Systems
10.1016/j.procir.2020.04.033
© 2020 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientic committee of the 53rd CIRP Conference on Manufacturing Systems
53rd CIRP Conference on Manufacturing Systems
Available online at www.sciencedirect.com
ScienceDirect
Procedia CIRP 00 (2019) 000–000
www.elsevier.com/locate/procedia
2212-8271 © 2019 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the 53rd CIRP Conference on Manufacturing Systems
53rd CIRP Conference on Manufacturing Systems
Method for data inventory and classification
Melina Massmanna,*, Maurice Meyera, Maximilian Franka, Sebastian von Enzbergb, Arno Kühnb,
Prof. Dr.-Ing. Roman Dumitrescua.b
aChair Advanced Systems Engineering, Heinz Nixdorf Institute, University of Paderborn, Fürstenallee 11, 33102 Paderborn, Germany
bFraunhofer IEM, Zukunftsmeile 1, 33102 Paderborn, Germany
* Corresponding author. Tel.: +49-5251-5465-346. E-mail address: melina.massmann@hni.uni-paderborn.de
Abstract
One of the most notable drivers of the fourth industrial revolution is the availability of vast amounts of data along the entire lifecycle of a
product. The analysis of product lifecycle data leads to promising potentials in strategic product planning. However, many companies are
confronted with major challenges in identifying and specifying all the relevant heterogeneous and manifold data sources. This is due to the lack
of simple approaches and methods for data inventory. In this paper we present a method for identifying and structuring data and their sources
systematically by employing a framework to classify data sources and giving examples for application.
© 2019 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the 53rd CIRP Conference on Manufacturing Systems
Keywords: Data-driven Product Planning; Industrial Data Analytics; Data Inventory; Data Sources; Product Life Cycle
1. Introduction
Industry 4.0 and the trend towards digitalization have
changed today's products significantly. So-called cyber-
physical systems are able to locally capture and process data
and to communicate this data to other systems or users. On the
other hand, concepts such as PLM and the digital twin allow
further data to be analysed along the life cycle of the product
[1]. This results in new possibilities for product planning. An
analysis of product data can enable conclusions to be drawn
about the use of the product and thus reveal potentials for
optimization. It allows receiving valuable and objective
information about the current state, failures and incorrect
operations. Using this information, it is possible to plan the next
product generation or new products, which are better adapted
to the actual needs and less error-prone. This field of research
is called data-driven product planning [2] and can be defined
as the intersection of 1) strategic product planning, 2) data
analytics and 3) (intelligent) complex products. Products
evolve from mechatronic systems into more complex cyber-
physical systems (CPS), which interact with the physical and
digital world by integrating sensors, actuators and advanced
information processing technology [3].
1) Strategic Product Planning is part of the process of Product
Engineering [4]. In this cycle the requirements for products are
systematically identified to capture the markets of tomorrow.
Three tasks are involved: foresight, product discovery and
business planning. Strategic product planning provides the
basis for the subsequent product development.
2) Data analytics is the examination of data with the purpose of
knowledge discovery. There are several standard models for
processing and analysing data. One is the Cross-Industry
Available online at www.sciencedirect.com
ScienceDirect
Procedia CIRP 00 (2019) 000–000
www.elsevier.com/locate/procedia
2212-8271 © 2019 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the 53rd CIRP Conference on Manufacturing Systems
53rd CIRP Conference on Manufacturing Systems
Method for data inventory and classification
Melina Massmanna,*, Maurice Meyera, Maximilian Franka, Sebastian von Enzbergb, Arno Kühnb,
Prof. Dr.-Ing. Roman Dumitrescua.b
aChair Advanced Systems Engineering, Heinz Nixdorf Institute, University of Paderborn, Fürstenallee 11, 33102 Paderborn, Germany
bFraunhofer IEM, Zukunftsmeile 1, 33102 Paderborn, Germany
* Corresponding author. Tel.: +49-5251-5465-346. E-mail address: melina.massmann@hni.uni-paderborn.de
Abstract
One of the most notable drivers of the fourth industrial revolution is the availability of vast amounts of data along the entire lifecycle of a
product. The analysis of product lifecycle data leads to promising potentials in strategic product planning. However, many companies are
confronted with major challenges in identifying and specifying all the relevant heterogeneous and manifold data sources. This is due to the lack
of simple approaches and methods for data inventory. In this paper we present a method for identifying and structuring data and their sources
systematically by employing a framework to classify data sources and giving examples for application.
© 2019 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the 53rd CIRP Conference on Manufacturing Systems
Keywords: Data-driven Product Planning; Industrial Data Analytics; Data Inventory; Data Sources; Product Life Cycle
1. Introduction
Industry 4.0 and the trend towards digitalization have
changed today's products significantly. So-called cyber-
physical systems are able to locally capture and process data
and to communicate this data to other systems or users. On the
other hand, concepts such as PLM and the digital twin allow
further data to be analysed along the life cycle of the product
[1]. This results in new possibilities for product planning. An
analysis of product data can enable conclusions to be drawn
about the use of the product and thus reveal potentials for
optimization. It allows receiving valuable and objective
information about the current state, failures and incorrect
operations. Using this information, it is possible to plan the next
product generation or new products, which are better adapted
to the actual needs and less error-prone. This field of research
is called data-driven product planning [2] and can be defined
as the intersection of 1) strategic product planning, 2) data
analytics and 3) (intelligent) complex products. Products
evolve from mechatronic systems into more complex cyber-
physical systems (CPS), which interact with the physical and
digital world by integrating sensors, actuators and advanced
information processing technology [3].
1) Strategic Product Planning is part of the process of Product
Engineering [4]. In this cycle the requirements for products are
systematically identified to capture the markets of tomorrow.
Three tasks are involved: foresight, product discovery and
business planning. Strategic product planning provides the
basis for the subsequent product development.
2) Data analytics is the examination of data with the purpose of
knowledge discovery. There are several standard models for
processing and analysing data. One is the Cross-Industry
Available online at www.sciencedirect.com
ScienceDirect
Procedia CIRP 00 (2019) 000–000
www.elsevier.com/locate/procedia
2212-8271 © 2019 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the 53rd CIRP Conference on Manufacturing Systems
53rd CIRP Conference on Manufacturing Systems
Method for data inventory and classification
Melina Massmanna,*, Maurice Meyera, Maximilian Franka, Sebastian von Enzbergb, Arno Kühnb,
Prof. Dr.-Ing. Roman Dumitrescua.b
aChair Advanced Systems Engineering, Heinz Nixdorf Institute, University of Paderborn, Fürstenallee 11, 33102 Paderborn, Germany
bFraunhofer IEM, Zukunftsmeile 1, 33102 Paderborn, Germany
* Corresponding author. Tel.: +49-5251-5465-346. E-mail address: melina.massmann@hni.uni-paderborn.de
Abstract
One of the most notable drivers of the fourth industrial revolution is the availability of vast amounts of data along the entire lifecycle of a
product. The analysis of product lifecycle data leads to promising potentials in strategic product planning. However, many companies are
confronted with major challenges in identifying and specifying all the relevant heterogeneous and manifold data sources. This is due to the lack
of simple approaches and methods for data inventory. In this paper we present a method for identifying and structuring data and their sources
systematically by employing a framework to classify data sources and giving examples for application.
© 2019 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the 53rd CIRP Conference on Manufacturing Systems
Keywords: Data-driven Product Planning; Industrial Data Analytics; Data Inventory; Data Sources; Product Life Cycle
1. Introduction
Industry 4.0 and the trend towards digitalization have
changed today's products significantly. So-called cyber-
physical systems are able to locally capture and process data
and to communicate this data to other systems or users. On the
other hand, concepts such as PLM and the digital twin allow
further data to be analysed along the life cycle of the product
[1]. This results in new possibilities for product planning. An
analysis of product data can enable conclusions to be drawn
about the use of the product and thus reveal potentials for
optimization. It allows receiving valuable and objective
information about the current state, failures and incorrect
operations. Using this information, it is possible to plan the next
product generation or new products, which are better adapted
to the actual needs and less error-prone. This field of research
is called data-driven product planning [2] and can be defined
as the intersection of 1) strategic product planning, 2) data
analytics and 3) (intelligent) complex products. Products
evolve from mechatronic systems into more complex cyber-
physical systems (CPS), which interact with the physical and
digital world by integrating sensors, actuators and advanced
information processing technology [3].
1) Strategic Product Planning is part of the process of Product
Engineering [4]. In this cycle the requirements for products are
systematically identified to capture the markets of tomorrow.
Three tasks are involved: foresight, product discovery and
business planning. Strategic product planning provides the
basis for the subsequent product development.
2) Data analytics is the examination of data with the purpose of
knowledge discovery. There are several standard models for
processing and analysing data. One is the Cross-Industry

Melina Massmann et al. / Procedia CIRP 93 (2020) 234–239 235
2 Author name / Procedia CIRP 00 (2019) 000–000
Standard Process for Data Mining (CRISP-DM) [5]. Figure 1
shows the extended CRISP-DM according to Reinhard et al.
[6]. Another one is the four-layer model to describe an
Analytics Use Case [7].
After understanding the problem, the second step in both
models is to understand the data and to analyse the data sources
to be able to collect the relevant data for the defined analytics
problem.
Fig. 1. Extended Cross-Industry Standard Process for Data Mining (CRISP-
DM) [6]
The process of data discovery, collection and specification
within the scope of data acquisition we call data inventory. In
order to identify existing data in technical systems and to make
it successfully accessible for data analysis, data sources and
processing units have to be included in a data inventory,
evaluated according to the specific application and structured.
This step of data inventory is also an essential part in data-
driven product planning since this field has many and
heterogeneous data along the product life cycle to consider
which form the essential basis. Often, however, companies or
data analysts do not have an overview of the existing data
because it is distributed throughout the company.
That quickly presents a first hurdle to the application of data
analytics. Finally, data preprocessing is specified for already
existing data with a development effort of about 50-70% [8].
The effort for upstream data collection is not specified.
Therefore, the research question arising for us is: How can data
inventory be supported within data-driven product planning, so
that all relevant data is identified and the effort is minimized?
To the best of our knowledge, there currently is no assistance
for identifying relevant data sources that would enable
companies to carry out a systematic data inventory. Therefore,
we propose a method for data inventory, which is based on a
concept for a knowledge base about typical data sources. It
classifies and structures data and their sources along the phases
of the product life cycle, frequent tasks and activities and
relevant functional areas in the company. By doing so
important areas where data may occur are taken into account
during data identification, which helps data-interested
employees to find and access relevant data. This allows
successful implementation of data-driven product planning in
companies.
The paper is structured as follows: After a problem analysis
where we examine the foundations and backgrounds, we give
an overview over the state of research (section 3). Afterwards
we present the concept for a knowledge base as method for data
inventory (section 4) and possible applications of the
framework (section 5).
2. Analysis of the Problem
2.1. Foundations and Definitions
In the context of data analytics it is important to differentiate
between terms “data”, “information” and “knowledge” and to
understand the relationships between these terms. In order to
represent the connections, the model of the knowledge pyramid
is appropriate (see fig. 2) [9]. Data can be understood as a
collection of objective, mostly measurable or observable facts
in a raw form such as numbers or symbols. For data various
further definitions can be found [11,12].
Processing data and bringing them in a relevant context results
in information. Information is characterized by the fact that the
underlying data has been given a certain reference. Information
can be turned into knowledge by understanding how to apply
data to achieve relevant goals. All the information is organized
in a way that it can be useful and we are able to generate
insights. In the last step, wisdom can be reached if we
comprehend the “why” behind all the patterns.
In the following, we understand data as recorded interpretable
signs and signals, which potentially provide information in a
given context or for a specific purpose.
By data source we mean the following: “A data source may be
the initial location where data is born or where physical
information is first digitized, however even the most refined
data may serve as a source, as long as another process accesses
and utilizes it. Concretely, a data source may be a database, a
flat file, live measurements from physical devices, … [13]”.
Fig. 2. Knowledge pyramid [9,10]
2.2. Structuring Frameworks
A structuring framework has the objective of creating basic
structure to set a focus to a problem at hand. Such a framework
provides ideas for solving the problem by giving the context to
collect the necessary information. In its simplest form, the
foundation of a framework could be a list of important
categories. Structuring frameworks are important because they
provide a framing to help others quickly understand and
organize the aspects of a problem. This enables a systematic
analysis. Such an analysis is even easier when using a visual
form for presenting the framework.
As basis for our knowledge base and structure for our
framework, we differentiate between 1) a process/ product life
cycle centric and 2) company-structure centric classification
(see Fig. 3). We consider these views in order to understand

236 Melina Massmann et al. / Procedia CIRP 93 (2020) 234–239
Author name / Procedia CIRP 00 (2019) 000–000 3
and analyse the involved processes and structural aspects in
data-driven product planning for manufacturing companies.
For one, the product orientation with its whole life cycle from
the ideation to the end of the life of a product and the associated
processes is essential. Especially for product planning and
development it must be ensured, that in every phase the
respective processes are coordinated and all information is
integrated. Every phase can be set deeper by breaking down the
process into finer steps, e.g. the development phase with the V-
model (see section 3).
Fig. 3. (a) Process/Product Life Cycle View; (b) Company-Structure View
Another view is the company structure. A manufacturing
company can be structured according to the functional areas.
All these functional areas contribute to the production and
distribution of a product and structure the value creation. The
areas have different tasks that generate, involve and require
data.
The analysis of the life cycle of a product with respect to its
data sources and data requires frameworks and methods for
representing the relevant data and its origin in a view that
allows a structured and systematic classification as
comprehensive as possible. Nevertheless, the overview has to
be intuitively understandable and flexible in individual
adaption by the company.
3. State of research
3.1. Structuring frameworks
We group existing frameworks according to the
aforementioned views (see section 2).
1) Product life cycle centric frameworks
The concept of product life cycle management (PLM) can be
regarded as foundational for our work. The approach aims at
managing the product related information efficiently during the
whole product lifecycle. The product life cycle constitutes the
basis of PLM. There are many approaches, which model the
product life cycle. We describe some of these briefly in the
following. The VDI guideline 2221 considers the system
phases preliminary study, development, production, launch,
operation and change, which are assigned to the roughly
described tasks [14]. Another standard, ISO 15288, which
looks at the life cycle from the perspective of systems
engineering, lists the concept, development, production, use,
maintenance and decommissioning phases as components of
the product life cycle [15]. In the concept of product life cycle
management the PLM reference process [16] considers all
phases, in which the usage of IT systems is an essential
component.
The 4-cycle model according to Gausemeier et al. focuses on
the product development process and is a concept for the
integrative development of market services [4]. The model
ranges from the business idea to series production and is
divided into four cycles: Strategic product planning, product
development, service development and production system
development.
Eigner and Stelzer provide an assignment of tasks and activities
to the individual product life phases [17].
The V-Modell allocates a more development centered view of
the product [18]. It was originally designed as a process model
for software development. Due to the increasing complexity of
products and the integration of different domains, this
interdisciplinary approach was adapted for complex or
mechatronic development tasks.
2) Company-centric frameworks
Gausemeier et al. present a generic model of a manufacturing
company with its essential functional areas [19]. As essential,
they consider product planning, development/construction,
work preparation, distribution, purchasing, production, service
and quality assurance.
In addition to the two types of frameworks, other views on
these two orientations exist. A prominent representative of such
a view is the automation pyramid [20]. It represents the
different levels of automation in a factory and allows the
structuring of technologies into different functional layers of
industrial manufacturing. This is useful to describe types of
communication within a company and typical internal IT
systems that are important data sources [21].
Those data sources along the automation pyramid contain
many different categories of data.
Data in a production-oriented company can be divided into
organizational and technical operational data [22]. The
organizational operational data includes order data and
personnel data. Technical operational data are machine data,
tool data and material data. Machine data is differentiated into
product and process data. The latter include all data that is
generated during the operation of a machine. Product data
describes the condition of the manufactured part. In
combination with process data, they provide information about
the production process.
According to Schäfer et al., data sources can be roughly divided
into three groups according to the origin of the data: machine-
generated, human-generated content and business data [23].
Another way to classify data is to differentiate between direct
and indirect data sources [24].
3.2. Specification techniques
To the best of our knowledge, standard models and
specification techniques for data inventory or data collection
don’t exist. Some approaches and more general methods are
presented in the following.
A model that combines a machine data view and a process
data view represents the data map [25]. It visualizes the data
flow between data sources and software systems with their
properties and (business) process contexts. In contrast, data

Melina Massmann et al. / Procedia CIRP 93 (2020) 234–239 237
4 Author name / Procedia CIRP 00 (2019) 000–000
flow diagrams have a stronger focus on technical aspects. They
offer a graphical overview of data flows and data processing in
IT systems [26].
The data landscape canvas [27] represents a tool for the
systematic exploitation of data sources and classifies data
sources according to their origin: owned data, earned data, paid
data and public data. Additionally the canvas differentiates
between various forms of data: raw data, derived data and
linking data.
The CONSENS specification technique (CONceptual
design Specification technique for the ENgineering of complex
Systems) serves the cross-domain description of mechatronic
systems on the level of a principle solution and considers
different aspects or partial models for the description [28]. This
technique also takes information flows into account and is able
to represent data and data sources. The effect relationships
specified in the functional structure show dependencies and
interactions between the system elements. They are thus
variables and flows that potentially occur and can be absorbed
within a system. Due to the general formulation, they can be
understood as generic product data. Data can be located and
identified by deriving the corresponding data points.
In the context of the digital twin data modelling techniques
are used to aggregate the different kind of models and data that
make up the digital twin model. Schroeder et al. give a good
overview of concepts and modelling methods [29].
In summary, there is a number of approaches and methods
that are able to model data and data sources. None of these
offers a combined view on the product, its entire life cycle
including the usage phase and on standard processes or
activities. In addition, these methods do not offer any kind of
tool which supports the first search and collection of data. This
is where a knowledge base could provide added value by
providing good starting points.
4. Method for data inventory and classification
The method for data inventory and classification consists of
a framework for a structured knowledge base, which can be
integrated into a larger process of data requirements analysis
and collection within data-driven product planning. It
structures various data (objects) into a two-dimensional matrix
in order to consider both the product-oriented and the
organization-oriented view. On the vertical axis a classification
according to the life cycle phases is given, which in turn is
divided into tasks and activities in the respective phases. This
gives a more precise overview over possible sources of data in
a company. On the horizontal axis a classification according to
the functional areas in a company is made. An overview is
given in fig. 4.
The method aims at an extensive collection of data sources
and further information as metadata and thus can be applied in
a workshop, interview or survey format with data controllers
and domain experts.
In the following, we describe the conception of the
knowledge base.
4.1. Horizontal layer of the framework/matrix
In an empirical study with four different German
manufacturing companies, a forming technology specialist, an
industrial connectivity expert, a financial and retail ATM
producer and a housing technology manufacturer, we
interviewed 3-5 experts each from different functional areas
such as development, marketing and service. Based on the
generic model of a company’s functional structure (see section
3.1 and fig. 5) and some guiding questions, as “In which
functional areas is data available that can be used to plan
products?”, we identified functional areas, in which relevant
data for the use case of data-driven product planning can occur.
We identified up to three different categories of relevance of
functional areas according to data availability: Service, Product
Planning and Sales are functional areas, where relevant data for
analyzing product usage, weaknesses and to uncover
optimization potentials could be found. In development and
construction division as well as in quality management relevant
data can occur, but it is depending on the product and company
under consideration. Generally speaking, the production, IT
and purchasing department are no areas to start collecting data
for a use case in data-driven product planning. The functional
areas in the first two categories form the horizontal dimension
of the matrix.
4.2. Vertical layer of the framework/matrix
On the vertical dimension the life cycle phases are listed.
We consider the phases product planning, development/
construction, production, operation/usage and revocation. In
section 3 different approaches of the product life cycle were
presented. In essence, all models are similar in their life cycle
phases. The two introduced approaches overlap in the phases
development, production and operation or use. For the first and
Fig. 4 Framework for a knowledge base for data inventory and classification

238 Melina Massmann et al. / Procedia CIRP 93 (2020) 234–239
Author name / Procedia CIRP 00 (2019) 000–000 5
last phase they mainly differentiate in naming. For preliminary
studies and concept we utilize the phase of product planning as
both are main tasks in this cycle of product engineering (see
section 3.1). Again, instead of change and decommissioning we
integrate the phase of revocation in our product life cycle,
which can include both of these steps. There are some
approaches that take additional phases, such as Process
Planning [e.g. 30] into account, but most models neglect this
phase or include it in production. Others differentiate between
Distribution and Service, which we assign to the usage phase.
We subdivided every phase into further tasks and activities,
which we identified as standard ones. For the standard tasks in
product planning we oriented to the 4-cycle model of product
engineering and considered foresight, product discovery and
business planning. All these tasks can again be broken down
into individual activities, e.g. product discovery can contain the
activities of generating product ideas and deriving
requirements. For subdividing the phase of
development/construction we used the process steps of the V-
model, since it is considered a standard in product
development. Production contains production preparation,
manufacturing and assembly. The usage phase we divided
according to the standard tasks by [17]. Last phase can be split
into disposal and recycling as suggested by DIN ISO 9004 -1
[31].
5. Towards an Application
Based on a literature analysis and an interview with domain
experts, we illustrate the potential applications of the
framework and the knowledge base. For application, we
consider two main options: 1) Usage of the framework as an
empty canvas for identifying and collecting data sources and
data, which can be relevant for data-driven product planning.
2) Usage of the structuring framework as basis for a knowledge
base in form of a checklist.
1) For the first option, different scenarios are conceivable.
One possibility can be the application of the framework within
a workshop with representatives from different functional areas
in a company. With the help of a moderator, they can fill the
matrix. We suggest the following procedure: if different
employees from different functional areas are represented, the
areas can be analyzed in detail by looking at all life cycle
phases of the product under consideration and asking for
available data. Key questions of the moderator could be “What
data you come into contact with in marketing?”, “Which are
generated or used in the early phases as Product Planning of the
product?”, “Which tasks do you have in Product Planning and
which methods and artefacts are used where data is involved?”,
“Which data is relevant for your use case in product planning,
i.e. what findings can be expected?”. For example, the
Marketing identifies some market studies, customer
satisfaction surveys and risk assessment data in the task of
foresight in product planning as available data in their area.
First two sources can reveal relevant insights about market and
customer requirements, from which new product functions for
product planning could be derived. In addition to that, the
respective data sources can be included: a database and file
system. Employees from the development department have
knowledge about data in the usage phase structured as machine
sensor measurement data, environmental information and
important process data during the usage period. The data could
come from different sources such as IoT databases or in a cloud
based data store. If data occurs which cannot be sorted into the
product life cycle or the functional areas, it can be assigned to
an additional field. Additional information, such as the data
category can directly be integrated by marking the identified
data as technical operational data. Besides a workshop, other
formats as a survey can also be used. For a survey, we suggest
to transfer the matrix and its fields into questions, similar to the
moderators’ key questions, such that it is self-explainable and
the experts and company representatives are able to answer
them independently. Afterwards the results of the surveys can
be transformed, such that it results in an overview of data and
their sources. This leads us to the second option.
2) After filling out the matrix, it can be used as blueprint and
knowledge base for other companies who want to inventory
their data. For one thing, the matrix can be filled based on a
literature analysis, for another it can be based on workshops
and surveys with domain experts as described before. The
knowledge base can then be used to support the data inventory
by tagging the data accordingly. Here we suggest to
differentiate between the status of “existing” and “non-
existing” (see fig. 6). Furthermore, it can be very helpful to
distinguish between “relevant” and “not relevant”, so that
relevant, but non-existing data can be identified easily, but this
requires a concrete use case.
Both options result in a structured overview of the
companies’ data and data sources usable for data-driven
product planning, which can be stored centrally. Analytics
Fig. 5. Data in functional areas of the company
Fig. 6. Extract from a filled knowledge base

Melina Massmann et al. / Procedia CIRP 93 (2020) 234–239 239
6 Author name / Procedia CIRP 00 (2019) 000–000
experts, project and data managers can use this information to
quickly start data acquisition and analysis or address a concrete
analytics problem.
6. Conclusion and future research
We have presented the structural framework of a knowledge
base as a method for data inventory in data-driven product
planning. In contrast to existing approaches and methods for
identifying data and data sources, our method combines a
holistic view on the company and the product by structuring
along the product life cycle phases and their main tasks and the
functional areas in a manufacturing company. This promises a
comprehensive and systematic data inventory and further
possibilities for application. The framework can be applied in
a workshop or survey or if filled out, used as knowledge base
and checklist. All options result in a structured overview of the
company data.
As method considering the entire manufacturing process it
also offers potentials in other fields of Industrial Analytics and
is not only limited to the application in data-driven product
planning. The method could also be used, for example, in a top-
down approach to analyze existing data before deciding on an
analytics project in production.
The development of the knowledge base and the application
as checklist requires further research.
Further steps are the integration of the method into an
overall process of data inventory and data analysis in data-
driven product planning which also considers the data needs of
the use case and the subsequent specification of the data to have
a good basis for analysis.
7. Acknowledgement
This research and development project is funded by the
German Federal Ministry of Education and Research (BMBF)
within the “Innovations for Tomorrow’s Production, Services,
and Work” Program and implemented by the Project
Management Agency Karlsruhe (PTKA). The author is
responsible for the content of this publication.
For more details about the project, visit www.dizrupt.de.
References
[1] Kitain, L.: Digital Twin – The new age of manufacturing. Seebo. 2018.
https://www.seebo.com/digital-twin-technology/
[2] Massmann, M. et al. (2019): Significance and Challenges of Data-driven
Product Generation and Retrofit. Procedia CIRP, 84, 992 - 997.
[3] Alur, R.: Principles of Cyber-Physical Systems. The MIT Press,
Cambridge, London 2015.
[4] Gausemeier J, Czaja A, Dülme C. Innovationspotentiale auf dem Weg zu
Industrie 4.0. In: Wissenschafts- und Industrieforum Intelligente
Technische Systeme 2015, Heinz Nixdorf Institut, (2015).
[5] Shearer, C.: The CRISP-DM Model: The new blueprint for data mining.
Journal of Data Warehousing, volume 5, pp. 13-22, 2000.
[6] Reinhart, F.: Industrial Data Science – Data Science in der industriellen
Anwendung. Industrie 4.0 Management, 32:27-30, 2016.
[7] Kühn, A., Joppen, R., Reinhart, F., Röltgen, D., von Enzberg, S., &
Dumitrescu, R. (2018). Analytics Canvas–A Framework for the Design and
Specification of Data Analytics Projects. Procedia CIRP, 70, 162-167.
[8] Kurgan, L.A.; Musilek, P.: A survey of Knowledge Discovery and Data
Mining process models. In: The Knowledge Engineering Review 21
(2006), S. 1-24.
[9] C. Zins. Conceptual approaches for defining data, information, and
knowledge. Journal of the American Society for Information Science and
Technology, 58(4):479-493, 2007.
[10] Grazzini, J. & Pantisano, F. (2015). Guidelines for scientific evidence
provision for policy support based on Big Data and open technologies.
10.2788/329540.
[11] ISO/IEC 11179: Metadata Registry (MDR) standard.
[12] Butterfield, A., Ekembe Ngondi, G., Kerr, A. 2016. A Dictionary of
Computer Science (7th. ed.). Oxford University Press, Inc., USA
[13] McDaniel, S. (2019): What is a Data Source? Talend.
https://www.talend.com/resources/data-source/
[14] VDI 2221: Methodik zum Entwickeln und Konstruieren technischer
Systeme und Produkte, 1993.
[15] ISO 15288: Systems and software engineering - System life cycle
processes, 2008.
[16] Schuh, G., Bender, D., Bohl, A., Hatfield, S., Lenders, M., Mueller, J.,
Schlick, C.M., Mütze-Niewöhner, S., Schmitt, R., & Gärtner, T. (2008).
Systemunabhängige Referenzprozesse für das PLM: Handbuch ; TFB – 57.
[17] Eigner, M.; Stelzer, R.: Product Lifecycle Management - Ein Leitfaden für
Product Development und Life Cycle Management. Springer, Dordrecht,
2. Auflage, 2013.
[18] VDI 2206: Entwicklungsmethodik für mechatronische Systeme, 2004.
[19] Gausemeier, J.; Plass, C.: Zukunftsorientierte Unternehmensgestaltung -
Strategien, Geschäftsprozesse und IT-Systeme für die Produktion von
morgen. 2. Auflage, Carl Hanser Verlag, München, 2014.
[20] Dumitrescu, R.; Gausemeier, J.; Kühn, A.; Luckey, M.; Plass, C.;
Schneider, M.; Westermann, T.: Auf dem Weg zu Industrie 4.0:
Erfolgsfaktor Referenzarchitektur. it‘s OWL Clustermanagement, 2015.
[21] Laudon, K. C.; Laudon, J. P.: Management of information systems.
Pearson 2019.
[22] Kurbel, Karl: Produktionsplanung und –steuerung im Enterprise Resource
Planning and Supply Chain Management. 6. Auflage. München, Wien :
Oldenbourg Verlag, 2005.
[23] Schäfer, A.; Knapp, M.; May, M.; Voß, A.: Big Data, Vorsprung durch
Wissen, Innovationspotenzialanalyse, Fraunhofer Institut für intelligente
Analys- und Informationssysteme IAIS, 2012
[24] Cambridge International AS & A Level Information Technology 9626
Topic 1.2 Sources of Data. Cambridge International Examinations 2015.
[25] Joppen, R., Von Enzberg, S., Kühn, A., Dumitrescu, R.: Data Map –
Method for the specification of data flows within production. In:
Conference on Intelligent Computation in Manufacturing Engineering, 18-
20 July 2018, Gulf of Naples, Italy, 2018.
[26] Blokdyk, G.: Data Flow Diagram A Complete Guide. Emereo Pty Limited,
2020.
[27] Raffeiner, M.: Erkunden Sie Ihre Datenlandschaft. Datentreiber 2019.
https://www.datentreiber.de/blog/erkunden-sie-ihre-datenlandschaft/
[28] Gausemeier, J.; Lanza, G.; Lindemann, U. (Hrsg.): Produkte und
Produktionssysteme integrativ konzipieren. Carl Hanser Verlag, München,
2012.
[29] Schroeder, G., Steinmetz, C. Pereira, C., Espindola, D.: Digital Twin Data
Modeling with AutomationML and a Communication Methodology for
Data Exchange, IFAC-PapersOnLine, Volume 49, Issue 30, 2016.
[30] Tschirner, N. C.: Rahmenwerk zur Integration des modellbasierten
Systems Engineering in die Produktentstehung mechatronischer Systeme.
Dissertation, Fakultät Maschinenbau, Universität Paderborn, 2016.
[31] Technische Produktdokumentation - Lebenszyklusmodell und Zuordnung
von Dokumenten, 1997.