A Reference Process Model for Usage Data-Driven Product Planning

Abstract

Cyber-physical systems generate and collect huge amounts of usage data during operation. Analyzing these data may enable manufacturing companies to identify weaknesses and learn about the users of their products. Such insights are valuable in the early phases of product development like product planning, as they facilitate decision-making for product improvement. The analysis and exploitation of usage data in product planning, however, is a new task for manufacturing companies. To reduce mistakes and improve the results, companies should build upon a suitable reference process model. Unfortunately, established models for analyzing data cannot be easily applied for product planning. In this paper, we propose a reference process model for usage data-driven product planning. It builds on three well-established models for analyzing data and addresses the unique characteristics of usage data-driven product planning. Finally, we customize the model for a manufacturing company and demonstrate how it could be implemented in practice.

Full text

A Reference Process Model for Usage Data-Driven Product Planning
Maurice Meyer
Paderborn University
Maurice.Meyer@hni.upb.de
Ingrid Wiederkehr
Paderborn University
Ingrid.Wiederkehr@hni.upb.de
Melina Panzner
Paderborn University
Melina.Panzner@hni.upb.de
Dr.-Ing. Christian Koldewey
Paderborn University
Christian.Koldewey@hni.upb.de
Prof. Dr.-Ing. Roman Dumitrescu
Paderborn University
Roman.Dumitrescu@hni.upb.de
Abstract
Cyber-physical systems generate and collect huge
amounts of usage data during operation. Analyzing
these data may enable manufacturing companies to
identify weaknesses and learn about the users of their
products. Such insights are valuable in the early phases
of product development like product planning, as they
facilitate decision-making for product improvement.
The analysis and exploitation of usage data in product
planning, however, is a new task for manufacturing
companies. To reduce mistakes and improve the results,
companies should build upon a suitable reference
process model. Unfortunately, established models for
analyzing data cannot be easily applied for product
planning. In this paper, we propose a reference process
model for usage data-driven product planning. It builds
on three well-established models for analyzing data and
addresses the unique characteristics of usage data-
driven product planning. Finally, we customize the
model for a manufacturing company and demonstrate
how it could be implemented in practice.
1. Introduction
The further development of products is the main
development focus of engineers. In a survey with 247
engineers, ALBERS et al. found that only 7% of
developments are true new developments without carry
overs or adjustments of existing products. In contrast,
93% of developments represent further developments of
existing products [1]. To further develop a product,
engineers need to answer questions like: How does the
product perform in the field? What are its strengths and
weaknesses? How do users utilize the product? What are
new requirements for the product? Answering such
questions is not trivial, as companies often lack high
quality information about the product usage phase [2].
A new solution for this problem emerges from the
progressive digitalization of products. In recent years,
the digitalization has transformed mechatronic products
into so-called cyber-physical systems that integrate
hardware, sensors, data storage, microprocessors,
software, and connectivity. These systems can collect
data about themselves and their environment during
their utilization [3].
The analysis of such usage data promises to be
especially valuable during the early stages of product
development like product planning [4]. As the initial
phase or phase zero of product development, product
planning aims at finding success potentials of the future
[5] and promising products to be developed [6]. In this
regard, usage data analytics can help exploit insights
about predecessor products and their users that lead to
new success potentials and set the agenda for product
improvements. In conjunction, usage data analytics and
product planning span the new research area usage data-
driven product planning [7].
At present, the analysis of usage data in product
planning is not widely researched. For example,
BERTONI found that research on analyzing data to
identify customer needs mainly focuses on data from
social media and online reviews [8]. In addition,
approaches that address usage data often have a broader
perspective than product planning. For example,
WILBERG et al. present a stakeholder-oriented procedure
for the development of a use phase data strategy [10].
The approach addresses stakeholders in different
business functions, e.g., service or marketing. While
such approaches help to identify the potential value of
usage data for the different stakeholders of a company,
they do not help analyze usage data in product planning.
As there are no existing approaches, the creation of
a reference process model for usage data-driven product
planning is required. In this paper, the term reference
process model describes a generic process model that
formalizes recommended practices for a certain domain
[11]. Our research question is as follows: How does a
reference process model for usage data-driven product
planning have to be designed?
Proceedings of the 55th Hawaii International Conference on System Sciences | 2022
Page 6105
URI: https://hdl.handle.net/10125/80081
978-0-9981331-5-7
(CC BY-NC-ND 4.0)

Utilizing a design science approach, the result of
this paper is a reference process model for usage data-
driven product planning. It guides companies with a
stepwise procedure and helps structure projects in usage
data-driven product planning. It is especially useful for
manufacturing companies that want to start exploiting
usage data in product planning.
The paper is structured as follows: In section 2, we
describe our research design for the construction of the
reference process model. Section 3 presents the
reference process model for usage data-driven product
planning in detail. In section 4, we show the
customization of the model for a manufacturing
company. Section 5 concludes the paper.
2. Research design
For the development of the reference process model
for usage data-driven product planning, we followed the
design science research (DSR) guidelines. According to
HEVNER et al., DSR aims at developing viable artifacts
(e.g., models or methods) for relevant business
problems. These artifacts must be novel and provide a
clear contribution through solving so far unsolved
problems. They are created and evaluated by applying
rigorous methods and utilizing the knowledge base [12].
While design science research represents the
foundation of our research design, our concrete
procedure broadly derives from the Process Model for
an Empirically Grounded Reference Model
Construction by AHLEMANN and GASTL. This process
model emphasizes the importance of consulting domain
experts when constructing and validating reference
process models [13]. In addition to that, we analyzed
established models following DE LA HIDALGA et al. [14]
and FRANK et al. [15]. To carve out a suitable reference
process model from all the input data, we utilized VOM
BROCKE’s design principles [16]. Overall, our
procedure comprises four phases: Domain analysis,
Model construction, Empirical validation, and
Customization (see Figure 1). Subsequently, the phases
are described in detail.
Figure 1: Procedure model for the development of the
reference process model
2.1. Domain analysis
In the domain analysis phase, relevant knowledge
about the domain shall be captured and prepared for the
model construction [13].
For the considered domain, MEYER et al. provide a
recent comprehensive systematic literature review [7].
In their work, the authors derive the main concepts,
advantages, success factors and challenges of usage
data-driven product planning [7] und thus present a
substantial overview of the topic. Therefore, we used the
literature review as our knowledge base.
Next, we investigated our knowledge base in search
of necessary process steps for the reference process
model. AHLEMANN and GASTL propose this step as a
suitable way to structure and aggregate the knowledge
available [13]. We analyzed the knowledge base using
the question: Which process steps does the desired
reference process model need to include? Table 1 shows
the derived process steps for the reference process
model. They represent domain requirements for the
model construction and conclude our domain analysis.
Table 1: Necessary process steps for the reference process
model derived from the knowledge base
ID
Process step
P-1
Analysis of the product and strategy [10, 17]
P-2
Analysis of the data analytics capabilities [10]
P-3
Definition of use cases [4, 17, 18]
P-4
Definition of data needs [3, 19]
P-5
Collection of usage data [19–21]
P-6
Pre-processing of usage data [19, 21–23]
P-7
Analysis of usage data, i.a. [19, 22, 24, 25]
P-8
Validity-check of data analysis results [19, 26]
P-9
Interpretation of the analysis results [19, 27]
P-10
Creation of new ideas [28]
P-11
Identification of requirements [4, 27, 29, 30]
P-12
Planning the improvement of existing and
future products, i.a. [17, 21, 25, 26, 28, 31, 32]
2.2. Model construction
The model construction phase aims at developing a
first version of the reference process model [13]. This
version is based on the necessary process steps from the
knowledge base and on existing process models.
As our desired reference process model shall
describe how to analyze usage data of existing products
to find improvement potentials, we focused our analysis
of existing process models on models for analyzing data.
For this, numerous process models exist. MARISCAL et
al. and PLOTNIKOVA et al. provide an overview about
existing process models and show that almost all
identified approaches are based on two original models:
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the Cross Industry Standard Process for Data Mining
(CRISP-DM) and the Knowledge Discovery in
Databases model (KDD) [33, 34]. Therefore, these two
models are subsequently described in more detail.
CRISP-DM is an industry-, tool- and application-
neutral process model for data mining. It was created by
the CRISP-DM CONSORTIUM and is based on the
experience of numerous data mining practitioners [35,
36]. For several years, it has been considered the
de facto standard for data mining projects.
The process model consists of six iterative phases:
(1) Business understanding aims at understanding the
business objectives and requirements and converting
them into a concrete data mining problem definition.
(2) In data understanding, the data are collected and
investigated to spot, for example, data quality problems.
(3) The data preparation includes tasks like the
transformation and cleaning of the data to construct the
final dataset. (4) During modeling, models are built and
refined with the data. (5) In the evaluation, the model
must be evaluated regarding the business objectives.
(6) Finally, in deployment, the model is put into
operation and a report is generated [35].
KDD refers to the overall process of discovering
useful knowledge from data [37]. FAYYAD et al. define
the KDD process as “the nontrivial process of
identifying valid, novel, potentially useful, and
ultimately understandable patterns in data” [38].
The process consists of nine iterative steps: (1) In
learning the application domain, relevant domain
knowledge and the desired goals are captured.
(2) Creating a target dataset aims at selecting the
dataset to be analyzed. (3) In data cleaning and pre-
processing, basic operations like noise reduction and
outlier removal take place. (4) Data reduction and
projection focuses on tasks like feature engineering and
dimensionality reduction. (5) In choosing the function of
data mining, the purpose of the desired model is decided
(e.g., classification, regression). (6) Choosing the data
mining algorithm(s) addresses the selection of suitable
method(s) for searching patterns in the data, e.g., by
comparing different models and parameters. (7) Data
mining describes the search for patterns in the data. (8)
In interpretation of the results, the patterns discovered
are interpreted and translated into the domain language.
(9) The final step usage of the discovered knowledge
covers documenting the new knowledge, reporting it
and taking action [38].
In addition to these industry-neutral models like
CRISP-DM, KDD, and their derivatives, we decided to
also analyze one manufacturing-focused approach to
account for any industry-specific aspects. The
VDI/VDE 3714 guideline presents a standard for the
implementation and operation of big data applications in
the manufacturing industry. It aims at aggregating the
numerous contributions towards big data analytics in the
manufacturing industry and at unifying them into one
model [39].
The guideline describes seven iterative phases:
(1) In the definition phase, the questions and objectives
to be answered or achieved need to be specified.
(2) Next, exploring the data situation aims at describing
and structuring the available data and defining
additionally required data. (3) In data management, data
from different sources are merged. (4) Modeling deals
with creating an evaluable model from the data.
(5) Subsequently, an initial evaluation of the data
analysis results with respect to the project goals is
necessary. (6) Implementation and rollout aims at
transferring the big data application into continuous
operation. (7) The final phase sustainability addresses
the project documentation as well as an assessment of
economic, technical and social aspects to ensure a
sustainable impact of the big data project [39].
To construct our first version of the reference
process model, first, we analyzed the three existing
process models CRISP-DM, KDD, and the VDI/VDE
3714 guideline. We followed DE LA HIDALGA et al. and
FRANK et al. and created a detailed process model for
each, including all tasks and steps mentioned in their
descriptions [14, 15]. We also synchronized their steps,
thereby highlighting gaps and similarities in the models.
Second, we aggregated the established reference
models to build one exhaustive model from the three
original models [16]. We found that we could sort all
process steps into four main processes: (1) Planning of
the data analysis, (2) Analytics and data preparation,
(3) Analytics workflow design and modeling and
(4) Exploitation of the data analysis results. In detail:
(1) Planning of the data analysis contains process
steps related to the early business perspective on the
project: Parts of CRISP-DM’s business understanding
(e.g., the determination of business objectives and
requirements); KDD’s learning the application domain;
VDI/VDE 3714 guideline’s definition.
(2) Analytics and data preparation includes
process steps concerning the clarification of the
analytics task as well as the selection and collection of
the data: Parts of CRISP-DM’s business understanding
(e.g., the determination of data mining goals), CRISP-
DM’s data understanding; KDD’s creating a target
dataset; VDI/VDE 3714 guideline’s exploring the data
situation and data management.
(3) Analytics workflow design and modeling
summarizes the process steps addressing data pre-
processing and analysis: CRISP-DM’s data preparation
and modelling; KDD’s data cleaning and pre-
processing, data reduction and projection, choosing the
function of data mining, choosing the data mining
algorithm(s), and data mining; VDI/VDE 3714
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guideline’s modeling and parts of the evaluation of the
data analysis results.
(4) Exploitation of the data analysis results
consists of process steps for the interpretation and
utilization of the data analysis results: CRISP-DM’s
evaluation; KDD’s interpretation of the results and
usage of the discovered knowledge; Parts of VDI/VDE
3714 guideline’s evaluation of the data analysis results.
We omitted the following process steps, as they aim
at the continuous operation of the built models and
therefore do not contribute to the purpose of our
reference process model: CRISP-DM’s deployment;
VDI/VDE 3714 guideline’s implementation and rollout
and sustainability.
Third, again following VOM BROCKE’s design
principles, we specialized the aggregated model for our
considered domain [16]. For each necessary process
step from Table 1, we analyzed if it was already
sufficiently addressed in the model. If not, we added a
new process step (e.g., derivation of use cases)
following DE LA HIDALGA et al. and FRANK et al. [14,
15]. As a result, we obtained a first version of the
reference process model with four main and 16 sub
processes. Figure 2 shows the schematic procedure.
Figure 2: Schematic procedure for the model construction
2.3. Empirical validation
For the validation of the reference process model,
AHLEMANN and GASTL recommend conducting
interviews with topic experts [13]. We performed three
semi-structured interviews of 60-90 mins duration. Our
interview partners are characterized in Table 2.
Table 2: Overview about interview partners
ID
Position
Experience
I-1
Innovation and process consultant
6 years
I-2
Digitalization consultant and
entrepreneur
15 years
I-3
Head of data science department
11 years
The interviews were aimed at answering the following
questions: Is the reference process model complete or is
something missing? Are the sub processes structured in
a logical sequence? Are the sub processes clearly
separated from each other?
We discussed each main and each sub-process with
all interviewees and protocolled all remarks and
questions raised in the interviews.
After each interview, we refined the reference
process model according to the remarks of the experts.
For example, we added the sub-process Update product
strategy in the Exploitation main process. After all
interviews were conducted, we showed the updated
reference process model to all experts again. We asked
for further remarks, but all experts were satisfied and
confident that it fulfills its requirements. Section 3
presents the resulting model.
2.4. Customization
After the validation, AHLEMANN and GASTL
suggest to test the reference process model [13]. We
performed this practical test with a machinery and plant
engineering company, which produces machine tools
and production equipment in the field of forming
technology.
In the test, we customized the reference process
model for the company. We used the RACI method,
which is also known as RAM (Responsibility
Assignment Matrix) [40]. The method’s goal is to assign
tasks and responsibilities. For that, it uses a matrix with
processes in the rows and roles in the columns [40].
For the assignment of tasks and responsibilities,
four options are possible. They are derived from the
name of the method: R, A, C, and I. The letter R stands
for responsible and describes which person is
responsible for a task. The letter A stands for
accountable: It shows who decides whether a task has
been performed correctly. For example, this could be a
supervisor. The letter C describes that a person should
be consulted when the task is performed. Lastly, the
letter I names all people who need to be informed about
the events and the results for that specific task [41].
While working with the matrix, several assignment rules
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must be respected, e.g., only a single person can be
responsible for a given task [41].
At the start of the workshop, we collected the
company’s relevant roles for usage data-driven product
planning. Then, we successively assigned the roles to
each sub-process of the reference process model. The
results are shown in section 4.
3. Reference process model for usage data-
driven product planning
The reference process model for usage data-driven
product planning consists of four main processes:
(1) Planning of the usage data analysis, (2) Analytics
and data preparation, (3) Analytics workflow design
and modeling and (4) Exploitation of the data analysis
results. Each main process comprises four sub-
processes. In the following, all sub-processes are
described. The sources are given with the corresponding
IDs, e.g., [I-1] and [P-1] (see Table 1 and Table 2), and
abbreviations, e.g., [CRISP-DM]. Figure 3 shows the
reference process model. Please note that the arrows
represent sequence flows and not information flows.
(1) Planning of the usage data analysis
(1.1) Identification of investigation needs:
Input: Product strategy
Procedure: This sub-process aims at finding critical
needs for investigation concerning the product or its
users. For this, the business objectives specified in the
product strategy are analyzed [CRISP-DM, I-1, P-1].
This strategy contains details about the product program
design, e.g., about the product’s strategic focus and its
planned evolution [42, 43]. In addition to the strategy,
the status quo of the product under investigation is
examined [KDD, CRISP-DM, P-1]. From these
analyses, critical investigation needs are derived.
Output: Investigation needs
(1.2) Analysis of data analytics capabilities:
Input: -
Procedure: In this sub-process, the capabilities of the
organization and the product concerning usage data-
driven product planning are assessed [P-2]. For
example, the available resources (e.g., data mining
experts, usage data) need to be analyzed [CRISP-DM].
Output: Data analytics capabilities
(1.3) Definition of boundary conditions and goals:
Input: Investigation needs (1.1); Data analytics
capabilities (1.2)
Procedure: Next, boundary conditions and goals need
to be defined [CRISP-DM, KDD, VDI/VDE]. Boundary
conditions include requirements, prerequisites,
assumptions, and constraints of the project. Just as the
goals, they can refer to the organization or the product
context. Goals help achieve the business objectives and
guide the analytics activities. The boundary conditions
and goals set the direction for the subsequent steps.
Output: Boundary conditions and goals
(1.4) Derivation of use cases:
Input: Boundary conditions and goals (1.3)
Procedure: Use cases address the previously defined
content goals. They link possible influential variables to
a goal or ask questions about the relationships of goals
and influential variables [VDI/VDE, I-2, P-3]. For each
use case, costs and benefits must be estimated
[CRISP-DM].
Output: Use cases
(2) Analytics and data preparation
(2.1) Specification of analytics objectives:
Input: Data analytics capabilities (1.2); Use case (1.4)
Procedure: This sub-process transforms a use case into
concrete data analytics objectives [CRISP-DM]. If
domain knowledge is missing, it needs to be captured to
derive relevant variables and adequate data analytics
approaches (e.g., clustering or association rule mining)
[KDD]. For this, the data scientists stay in close contact
with the domain experts [I-3].
Output: Data analytics objectives
(2.2) Definition of data needs:
Input: Data analytics objectives (2.1)
Procedure: This sub-process specifies the data and their
sources [P-4]. Also, it defines specific measurements or
data objects [VDI/VDE]. This includes the
transformation of the defined physical variables into
concrete data attributes in the target dataset. For
example, from the domain knowledge and demand (e.g.,
noise behavior), raw data and data sources (e.g., sound
vibrations via a sound level meter) need to be derived.
Output: Data needs
(2.3) Collection of data:
Input: Data analytics capabilities (1.2); Data needs
(2.2)
Procedure: In this sub-process, the data needs are
compared with the actual existing data in the company.
If the required data are not available, an acquisition
concept must be developed and implemented [P-5].
Output: Raw data set
(2.4) Description of data:
Input: Raw data set (2.3)
Procedure: This sub-process is about understanding the
collected data and analyzing them regarding their
processing options [CRISP-DM]. Here, the goal is data
literacy. Steps are a first exploration, a holistic
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description of the data with relevant meta data, and a
classification of the data [VDI/VDE]. If data quality is
not sufficient, it is necessary to go back to the previous
step and acquire the data with the required quality [I-3].
Output: Data characteristics
(3) Analytics workflow design and modeling
(3.1) Workflow design:
Input: Data analytics objectives (2.1); Data
characteristics (2.4)
Procedure: In this sub-process, pre-processing and
modeling techniques are selected and composed into
analytics workflows [I-3]. Typically, there are several
techniques for the same problem type. Some modeling
techniques need specific data formats or have certain
model assumptions. This requires a close link between
data preparation and modeling as well as an alignment
of these steps as a coherent workflow [I-3].
Output: Conceptual workflows
(3.2) Data pre-processing:
Input: Raw data set (2.3); Conceptual workflows (3.1)
Procedure: In this sub-process, analysts need to refine
the data to prepare it for modeling [CRISP-DM, KDD,
P-6]. Here, the pre-processing methods defined in the
workflows must be implemented (e.g., record and
attribute selection, integration of different data sets, data
cleaning, data transformation, and feature engineering).
Output: Pre-processed data
Figure 3: Reference process model for usage data-driven product planning
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(3.3) Model building:
Input: Conceptual workflows (3.1); Pre-processed
data (3.2)
Procedure: This sub-process is about implementing the
algorithms of the workflows together with an adequate
test design [CRISP-DM, KDD, VDI/VDE, P-7]. The
latter examines and compares the workflows and their
parameter settings.
Output: Analysis results
(3.4) Model validation:
Input: Analysis results (3.3)
Procedure: The models and their results need to be
validated [CRISP-DM, VDI/VDE, P-8]. For this,
suitable technical metrics and evaluation criteria must
be selected. Model building and validation iterate until
results no longer improve and reach satisfaction.
Output: Model performance
(4) Exploitation of the data analysis results
(4.1) Results interpretation:
Input: Use case (1.4); Analysis results (3.3); Model
performance (3.4)
Procedure: The interpretation of the analysis results
seeks to reveal unknown, but valuable insights about the
product and its users [P-9]. It covers the evaluation of
the analysis results and their transfer into the product
context, e.g., the description, explanation, and
verification of the results by product experts [KDD,
VDI/VDE]. The insights form the starting point for the
identification of new success potentials for product
improvement [I-1].
Output: Success potentials for product improvement
(4.2) Idea generation:
Input: Investigation needs (1.1); Success potentials for
product improvement (4.1)
Procedure: In this step, promising ideas for product
improvement are generated [P-10]. They are aimed at
exploiting the identified success potentials. From all
ideas, the most promising ones are selected [I-1].
Output: Ideas for product improvement
(4.3) Requirements derivation:
Input: Success potentials for product improvement
(4.1); Ideas for product improvement (4.2)
Procedure: Building on the success potentials and ideas
for product improvement, this sub-process aims at
translating the new ideas into requirements for product
development [P-11].
Output: Requirements
(4.4) Product strategy update:
Input: Requirements (4.3)
Procedure: In the last sub-process, the product strategy
is updated based on the new requirements [I-1]. It
specifies the changes planned for new product
generations and existing products [P-12].
Output: Updated product strategy
4. Customized process model
To demonstrate how it could be implemented in
practice, we customized the reference process model
with a machinery and plant engineering company,
which produces machine tools and production
equipment in the field of forming technology (see
section 2.4). The company links nine roles to the
implementation of the process: Purchasing agent, sales
engineer, service technician, pre-developer, mechanical
developer, electrical developer, virtual developer, head
of development and CEO. In the following, tasks and
responsibilities of each role are described. The resulting
RACI-matrix is shown in Figure 4.
Of all roles, the purchasing agent is the least
involved. He only supports during the identification of
investigation needs by presenting current and future
purchasing challenges.
The activities of the sales engineer are limited to
the first main process. He is responsible for the
identification of investigation needs. After that, he is
consulted because of his close customer relationships.
Later, he receives information about the results
interpretation and the product strategy update.
The role of the service technician is especially
important in the first two main processes. As he is in
close contact with customers, he is consulted during the
planning and preparation main processes. Furthermore,
he supports in the results interpretation.
The pre-developer helps in the identification of
investigation needs. At the end, he is informed about the
product strategy update as it affects his future work.
Mechanical and electrical developers accompany
the process from the derivation of use cases to the
product strategy update. Early on, their expertise is
needed for the analytics and data preparation. While
they are only informed about the analytics workflow
design and modeling, they are again consulted for
results interpretation and idea generation.
The virtual developer is the most important role
for performing usage data-driven product planning in
the considered company. He is responsible for all sub-
processes of the first three main processes except the
identification of investigation needs (see sales
engineer). For the second and third main process, he also
oversees the results obtained. While he has a consulting
function for results interpretation, he is only informed
about the results of the sub-processes idea generation,
requirements derivation, and product strategy update.
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The head of development has a consulting role
during the planning main process. While he is only
informed about the second and third main process, he is
responsible to exploit the identified insights and find
promising product improvements.
The last role is the CEO. He is informed about all
process activities. Furthermore, he oversees the
planning and exploitation main processes.
5. Conclusion
The result of this paper is a reference process model
for usage data-driven product planning. It consists of
four main processes: (1) Planning of the usage data
analysis, (2) Analytics and data preparation,
(3) Analytics workflow design and modeling and
(4) Exploitation of the data analysis results. Each main
process is divided into four sub-processes, which further
explain the tasks to be performed. The model’s
customization with a machinery and plant engineering
company shows how the reference process model could
be implemented in practice. In the following, our work’s
contributions to theory and practice are described.
Finally, its limitations as well as recommendations for
future research are presented.
5.1 Contributions to theory and practice
Usage data-driven product planning is a new and
promising research field. While there are numerous
established reference process models for performing
data mining or big data analyses, none of them can
easily be applied to product planning. The reference
process model presented in this paper addresses and fills
this gap. Thereby, the developed artifact represents a
valuable addition to the knowledge base as requested in
design science research [12]. The reference process
model contributes to the scientific discourse by
describing the utilization of data analytics approaches in
the early phase of innovation.
For the practical contribution, the model provides
managers an overview about how to perform usage data-
driven product planning. Managers can confidently
build on the model as it is based on three process models
that are widely used in practice. For the implementation,
our paper lays out how to customize the model by
assigning responsibilities to each sub-process.
5.2 Limitations and recommendations for
future research
The result of this work is subject to four main
limitations. (1) When analyzing existing reference
models, we focused on three models: CRISP-DM,
KDD, and VDI/VDE 3714. However, there are
numerous further reference models, many of which are
derivatives of the original CRISP-DM and KDD models
[34]. These models might have included further aspects
for our process. Yet, we are convinced that we captured
all critical aspects with our selection. (2) The validation
was performed with three interviews. A higher number
of interviews would probably have generated further
Figure 4: Results of the customization in a RACI-matrix
Page 6112

valuable suggestions. Yet, as our interviewees have
diverse experiences and perspectives, we are confident
that they pointed out the most important points. (3) The
reference process model conceptually describes the
required steps and tasks of usage data-driven product
planning. However, concrete methodical approaches are
not yet included in the reference process model.
(4) Until now, we have not yet fully applied the
reference process model in practice, but only parts of it.
Therefore, some real-world challenges could still be
unaddressed.
Considering the limitations, future research should
focus on the following three recommendations:
(1) Methodical approaches need to be developed for
each sub-process. (2) The whole process should be
performed with multiple companies to find more
practical challenges. (3) The reference process model
should be regularly updated and improved, e.g., after its
first complete application in practice.
Acknowledgements
This work is funded by the German Federal Ministry of
Education and Research (BMBF). The authors are
responsible for the content of this publication.
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