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Cite this article: Meyer, M., Wiederkehr, I., Koldewey, C., Dumitrescu, R. (2021) ‘Understanding Usage Data-Driven
Product Planning: A Systematic Literature Review’, in Proceedings of the International Conference on Engineering
Design (ICED21), Gothenburg, Sweden, 16-20 August 2021. DOI:10.1017/pds.2021.590
ICED21 3289
INTERNATIONAL CONFERENCE ON ENGINEERING DESIGN, ICED21
16-20 AUGUST 2021, GOTHENBURG, SWEDEN
ICED21 1
UNDERSTANDING USAGE DATA-DRIVEN PRODUCT
PLANNING: A SYSTEMATIC LITERATURE REVIEW
Meyer, Maurice (1);
Wiederkehr, Ingrid (1);
Koldewey, Christian (1);
Dumitrescu, Roman (1,2)
1: Heinz Nixdorf Institute, University of Paderborn;
2: Fraunhofer Institute for Mechatronic Systems Design IEM
ABSTRACT
Cyber-physical systems (CPS) are able the collect huge amounts of data about themselves, their users,
and their environment during their usage phase. By feeding these usage data back into product
planning, manufacturers can optimize their engineering and decision-making processes. Despite
promising potentials, most manufacturers still do not analyze usage data within product planning.
Also, research on usage data-driven product planning is scarce. Therefore, this paper aims to identify
the main concepts, advantages, success factors and challenges of usage data-driven product planning.
To answer the corresponding research questions, a comprehensive systematic literature review is
conducted. From its results, a detailed description of usage data-driven product planning consisting of
six main concepts is derived. Furthermore, taxonomies for the advantages, success factors and
challenges of usage data-driven product planning are presented. The six main concepts and the three
taxonomies allow for a deeper understanding of the topic while highlighting necessary future actions
and research needs.
Keywords: Data-Driven Product Planning, Big data, Machine learning, Early design phases, Product
Lifecycle Management (PLM)
Contact:
Meyer, Maurice
Heinz Nixdorf Institute, University of Paderborn
Advanced Systems Engineering
Germany
maurice.meyer@hni.uni-paderborn.de
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1 INTRODUCTION
The megatrend digitalization has turned mostly mechanical and electrical products into complex
systems integrating hardware, sensors, data storage, microprocessors, software and connectivity
(Porter and Heppelmann, 2014). These so-called Cyber-Physical Systems (CPS) integrate computation
and physical processes (Lee, 2008) enabling the collection, analysis and sharing of huge amounts of
data about the product and its environment (Porter and Heppelmann, 2014). As a consequence, CPS
turn their users into incessant generators of data (Erevelles et al., 2016). By feeding these data from
the usage phase back into product development and especially product planning, manufacturers can
optimize their engineering and decision-making processes (Erwin et al., 2015). As a result, these data
enable the companies to turn their customers and users into a powerful source of innovation (Holler et
al., 2016a) and – consequently – to develop a competitive advantage (Porter and Heppelmann, 2015).
2 SCIENTIFIC BACKGROUND OF USAGE DATA-DRIVEN PRODUCT
PLANNING
Product planning is the first activity in the product creation process. It aims at finding the success
potentials of the future to create a promising product design in the form of a principle solution
(Gausemeier et al., 2011). The results of product planning are the products to be developed by the
organization (Ulrich and Eppinger, 2016) and the corresponding requirements lists for the subsequent
product development (Pahl et al., 2007). The interconnections between product planning and product
development are strong: For Gausemeier et al., both are coupled by the conceptual design
(Gausemeier et al., 2011). Pahl et al. link product planning to the clarification of the task in product
development to underline the necessity of a content-related junction and a work-related integration of
the two (Pahl et al., 2007). Ulrich and Eppinger describe product planning as the initial phase or phase
zero of product development (Ulrich and Eppinger, 2016).
The success of product planning depends on the utilization of the available data which – esp. for CPS
– can be enormous. The process of accessing, aggregating and analyzing large amounts of data from
multiple sources is called data analytics (DA). It enables companies to extract knowledge from data to
understand historical and predict future events (Tyagi, 2003). Data analytics is based on mathematics,
computer science, and business analysis techniques (Porter and Heppelmann, 2015). It can be divided
into four types with increasing value and complexity: descriptive, diagnostic, predictive and
prescriptive analytics (Steenstrup et al., 2014).
As shown, product planning and data analytics are two established and independent research areas.
Together, they span the new research area of usage data-driven product planning which still needs to
be thoroughly researched. Studies show that companies increasingly make decisions based on data
analysis results, seeing data analytics as a crucial building block for creating value; yet, most
companies do not utilize usage data within product creation processes (Erwin et al., 2015). Also,
research on the topic is scarce. From these considerations, four research questions are derived:
1. Main concepts: What concepts constitute usage data-driven product planning?
2. Advantages: Why should companies pursue usage data-driven product planning?
3. Success factors: What factors contribute to the success of usage data-driven product planning?
4. Challenges: What makes the implementation of usage data-driven product planning difficult?
3 RESEARCH DESIGN
To answer the research questions, we conducted a systematic literature review (SLR), following the
suggestions of Webster and Watson (2002), Brocke et al. (2009) and Rowley and Slack (2004). First,
we prepared a list of relevant journals on the basis of the Financial Times Research Rank by Ormans
(2016) and an additional extensive web search for topic-related journals (Webster and Watson, 2002).
We included journals which focus on disciplines like “Product Innovation”, “Innovation
Management”, “Engineering Management”, “R&D Management”, “Systems Engineering”, “Data and
Knowledge Engineering”, “Data Science”, “Big Data”, “Marketing Management”, “Operations
Research” etc. as usage data-driven product planning is an interdisciplinary field. All in all, the final
list included 44 journals. Second, we created a concept map to identify important concepts and their
synonyms (Rowley and Slack, 2004; Brocke et al., 2009). We used the concept map to iteratively
create and test search strings as a combination of the concepts in the concept map. Through iterative
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improvement, the following search string was formed: (“product development” OR “product design”
OR “product engineering”) AND (“usage data” OR “operational data” OR “lifecycle data” OR “big
data” OR “data analytics” OR “closed-loop” OR “feedback information” OR “feedback data”).
Since the number of logical connectors (OR and AND) was limited, we decided to not include the term
“product planning” as it is not widely used and our tests provided better results for the alternative
terms “product development”, “product design” and “product engineering”. Third, we searched for
articles within each journal (from 2005 to 2020) in the Google Scholar Database using the literature
research tool “Publish or Perish” (Harzing, 2007). The search resulted in 1870 articles which we
screened on a title and abstract basis. While hundreds of articles addressed topics like “big data” or
“data analytics” in different contexts, the abstracts of only 36 papers suggested a focus on usage data-
driven product planning. After reading these papers and conducting a backward and forward search
(Webster and Watson, 2002), we identified 12 papers which contained answers to our research
questions; the other papers were sorted out. Fourth, we examined papers from conference proceedings.
From prior research, we knew of 14 papers within the context of usage data-driven product planning.
We used these to conduct a backward and forward search, resulting in a list of 76 papers. From these,
12 papers contained answers to our research questions. Finally, following the suggestions of our
reviewers, we added 4 more articles.
In total, we identified 28 highly relevant articles. For the detailed analysis, we read each paper twice,
extracted all relevant information concerning our research questions and loosely clustered them into
thematic chunks. Thereafter, we iteratively improved the clustering by critically investigating each
cluster, splitting clusters up and building new clusters. As the result of this iterative process, we obtained
the main concepts, advantages, success factors and challenges of usage data-driven product planning.
4 RESULTS
4.1 Main Concepts
Usage data-driven product planning consists of six main concepts (see Figure 1):
1. The product’s sensors capture real-time readings of the product in its operating environment (Fathi
et al., 2011; Porter and Heppelmann, 2015; Hou and Jiao, 2020; Igba et al., 2015; Chowdhery et
al., 2020). The data include user-generated data (capturing user behavior), product operating data
(capturing product behavior) and environmental data (capturing environment behavior) (Hou and
Jiao, 2020, 2020). In addition, further data like manual reports can also be collected (Chowdhery
et al., 2020). The data collection approach can either be reactive and thus be driven by concrete
events (e.g. machine failures) or it can be proactive by collecting data on a large scale and
analyzing it exploratorily (Holler et al., 2016b; Holler et al., 2017).
2. Using cyber-infrastructure (van Horn et al., 2012), the captured usage data are fed back into the
product creation process (Porter and Heppelmann, 2014; Jun et al., 2007), where they are valuable
in all stages (Holler et al., 2017; Hou and Jiao, 2020; Jun et al., 2007), but offer the highest value
in the early stages like product planning as these are characterized by lots of uncertainties and the
determination of lifecycle costs (Holler et al., 2016b; Holler et al., 2017). Here, the data are used
to objectively quantify product performance and usage profiles (van Horn et al., 2012) to find
usage-centric improvements for the product under consideration (Holler et al., 2016b; Holmström
Olsson and Bosch, 2013; Jun et al., 2007; Hou and Jiao, 2020; van Horn et al., 2012).
3. To identify improvements, statistical analysis, data mining and machine learning techniques must be
applied (Hou and Jiao, 2020; Igba et al., 2015). The data analysis can (a) build upon the available
data in a bottom-up approach (less effort and faster implementation) or (b) start with a predefined
objective in a top-down approach (more effort, clear future-focus) (Wilberg et al., 2017b).
4. The results of the data analysis enable developers to make decisions based on facts instead of
assumptions only (Hou and Jiao, 2020; Chowdhery et al., 2020), thus improving the decision-
making process (Jun et al., 2007; Wu et al., 2020; Fathi et al., 2011).
5. Identified improvements can be implemented in existing and future products (Jun et al., 2007;
Wilberg et al., 2017b; Abramovici et al., 2017; Wu et al., 2020; Xu et al., 2016; van Horn et al.,
2012). For future products, especially the development of new product generations is suited for the
analysis of feedback data (Holmström Olsson and Bosch, 2013; Igba et al., 2015; Fathi et al., 2011;
Chowdhery et al., 2020).
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Figure 1: The six main concepts of usage data-driven product planning
6. By feeding back usage data into product planning, usage data-driven product planning represents
an inverse design approach which will not replace, but complement the traditional, assumption-
based forward design (Hou and Jiao, 2020; Wilberg et al., 2017a). In conjunction, inverse and
forward design form a reinforcing loop for the continuous and iterative improvement of product
design (Hou and Jiao, 2020).
4.2 Advantages
Usage data-driven product planning offers a variety of advantages which are listed in a taxonomy in
Table 1 and explained below. The advantages apply on three levels: Analysis, Process and Business.
Table 1. Taxonomy of the advantages of usage data-driven product planning
Level 1: Analysis
Level 2: Process
Level 3: Business
(1.1) Finding hidden
information
(2.1) Improved customer- and
user-involvement
(3.1) Improved decision-making
processes
(1.2) Better product
understanding
(2.2) Continuous requirements
analysis
(3.2) Usage-centric product
portfolio
(1.3) Better understanding of
customer and user needs
(2.3) Reduction of hardware-
prototyping and field-testing
(3.3) Higher delivery frequency
of functionality
(1.4) Contextualize and
evaluate qualitative and
subjective data
(2.4) Faster product
development
(3.4) Higher innovative strength
Analysis-Level: (1.1) The utilization of analytics approaches like data mining makes it possible to
find hidden information within the data that would be impossible to find manually (Menon et al.,
2005). This is especially true when comparing many products and thousands of sensor-readings over
time (Porter and Heppelmann, 2015). (1.2) The data lead to an improved understanding of the product
in operation (Menon et al., 2005; Holmström Olsson and Bosch, 2013). Usage data-driven product
planning uses these insights to improve product design and thus eliminate failures instead of using
data to predict them (Holler et al., 2016a). (1.3) Analyzing usage data leads to a better understanding
of customer and user needs and is likely to deliver better results than traditional approaches
(Timoshenko and Hauser, 2019; Porter and Heppelmann, 2014). The data can be used to analyze
customer behavior and preferences (Hou and Jiao, 2020; Porter and Heppelmann, 2015) and derive
customer segments (Cantamessa et al., 2020). For these, individual (Hou and Jiao, 2020; Holler et al.,
2016b), future (Holmström Olsson and Bosch, 2013), unspoken (Li et al., 2015; Timoshenko and
Hauser, 2019) and latent needs (Hou and Jiao, 2020; van Horn et al., 2012) can be identified. (1.4)
Furthermore, quantitative product usage data helps to contextualize and evaluate qualitative and
subjective data (Holler et al., 2016b).
Process-Level: (2.1) By building on usage data, the customer and user involvement in the
development process is markedly improved (Hou and Jiao, 2020), leading to better collaboration
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(Holler et al., 2016b). (2.2) Feedback data enable a continuous requirements analysis (Timoshenko
and Hauser, 2019) (e. g. through the continuous live elicitation of customer needs (Cantamessa et al.,
2020)) eliminating momentary biases. (2.3) As the feedback of usage data promotes a usage-centric
and fact-based decision-making process, hardware-prototyping and field-testing necessities can be
reduced significantly (Holler et al., 2016b; Holler et al., 2017). (2.4) Consequently, usage data-driven
product planning allows for a faster product development process (Menon et al., 2005).
Business-Level: (3.1) The integration of data analytics methods into product planning enables data-
and fact-based decisions (Wuest et al., 2014; Holler et al., 2016a) while reducing assumption- and
experience-based ones (Holler et al., 2017; Holler et al., 2016a), thus improving decision-making
processes (Xu et al., 2016; Menon et al., 2005). (3.2) Analyzing usage data promotes a usage-centric
product portfolio (Holler et al., 2016a; Holler et al., 2017; Holmström Olsson and Bosch, 2013).
(3.3) The continuous requirement analysis and the faster development process help companies to
increase their delivery frequency of functionality (Holmström Olsson and Bosch, 2013; Jun et al.,
2007) and thus react on insights not anticipated by the previous design (Cantamessa et al., 2020). (3.4)
Lastly, analyzing usage data enables companies to create high-quality innovations (Li et al., 2015;
Kiron et al., 2014) as it pushes them towards new ideas at a higher speed (Erevelles et al., 2016) and
facilitates their acceptance by keeping the companies open-minded (Kiron et al., 2014).
4.3 Success Factors
To exploit the advantages mentioned, the factors contributing significantly to the success of usage data-
driven product planning must be identified. Table 2 shows a taxonomy of these success factors within four
classes: Organization, Product, Data Analysis and Evaluation. The factors are described below.
Table 2. Taxonomy of the success factors for usage data-driven product planning
Class 1: Organization
Class 2: Product
Class 3: Data Analysis
Class 4: Evaluation
(1.1) Data strategy
(2.1) High number of
intelligent components
(3.1) Standardized data
and systems
(4.1) Comprehensible
data analysis results
(1.2) Use cases
(2.2) Long operating
times
(3.2) Effective data col-
lection and feedback
(4.2) Decision support
systems
(1.3) Ignorance-b.
view & induct.
reasoning
(2.3) High similarity
with other products
(3.3) Proactive data
collection and analysis
(1.4) Cooperation of
product & data experts
(2.4) Data access
(3.4) Joint analysis of
heterogeneous data
(1.5) Integration of DA
with tradition. methods
(3.5) Complex data
analyses
(1.6) Turning consu-
mers into prosumers
(3.6) Considering the
product context
Organization: (1.1) A data strategy is critical for success (Wilberg et al., 2017a; Erwin et al., 2015).
It is the result of (a) an in-depth analysis of the product under consideration, the corporate strategy etc.
and (b) a conceptional phase (Wilberg et al., 2017b) which includes the definition of use cases among
others (Wilberg et al., 2017a). (1.2) Use cases create value by e. g. defining the analysis goal, asking
open questions, outlining its potential benefits and deriving the data that need to be collected (Menon
et al., 2005; Wilberg et al., 2017a; Wilberg et al., 2017b). In order to identify opportunities and
limitations, all relevant stakeholders must be involved (Wilberg et al., 2017b). (1.3) An ignorance-
based view and inductive reasoning promote the definition of a successful use case (Erevelles et al.,
2016). The ignorance-based view triggers questions with a high potential for hidden insights while
inductive reasoning will create a greater understanding of the investigated object (Erevelles et
al., 2016). (1.4) As use cases require product knowledge, a close cooperation of product and
data experts is crucial (Shahbaz et al., 2006; Fathi et al., 2011; Li et al., 2015; Cantamessa et al.,
2020). Especially at the start of common projects, interaction points between product and data
experts are numerous, making a direct communication and a common language a necessity (Li et
al., 2019). In the evaluation phase, multiple perspectives from a heterogeneous team may capture more
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valuable insights from within the data (van Horn et al., 2012). Hence, this so-called knowledge fusion is
critical when evaluating use cases and analysis results (Xu et al., 2016). (1.5) The integration of data
analytics with traditional methods promises the highest new product success and knowledge fusion (Xu
et al., 2016; Hou and Jiao, 2020). (1.6) Lastly, for data feedback, companies need to renew their
customer relationships by turning consumers into prosumers (Cantamessa et al., 2020).
Product: (2.1) Products with a high number of intelligent components are especially suited for
usage data-driven product planning as these generate the data necessary for a large scale data
analysis (Holler et al., 2016a; Jun et al., 2007). (2.2) In order to generate enough data for the
algorithms to work properly, long operating times are a prerequisite (Holler et al., 2016a). (2.3)
When comparing the data of multiple products, a high similarity between these products is essential
(Holler et al., 2016a). Due to this, usage data-driven product planning works best in product
generation planning (Holler et al., 2017) and when using the principles of design modularity and
platforms (Cantamessa et al., 2020). (2.4) Finally, data access must be achieved through providing
customers transparency concerning data usage and incentives in form of a clear value proposition
for sharing their data (Porter and Heppelmann, 2014; Wilberg et al., 2017a).
Data Analysis: (3.1) Standardized data and ecosystems facilitate data exchange and analysis (Wilberg et
al., 2017a; Holler et al., 2016a). For qualitative data, formal taxonomies and standardized information
structures improve reuse (Goh and McMahon, 2009). (3.2) An effective data collection and feedback
process is mandatory for usage data-driven product planning to work. It can be achieved through (a)
smart sensing and advanced data-collection methods (Hou and Jiao, 2020) and through (b) clearly
defined points of data capture and reuse as well as incentives within the people’s work processes (Goh
and McMahon, 2009; Menon et al., 2005). The data must be mapped to concrete product instances as
well as the product type (Abramovici et al., 2017). (3.3) In general, a proactive data collection and
analysis promises to deliver the most value (Holler et al., 2016b). (3.4) In order to achieve valuable
analysis results, heterogeneous data (e. g. structured and unstructured data) need to be jointly analysed
(Porter and Heppelmann, 2015; Igba et al., 2015; Abramovici et al., 2017) in (3.5) sophisticated data
analyses as these promise to achieve deeper insights and more satisfied users (Porter and Heppelmann,
2015; Erwin et al., 2015; Cantamessa et al., 2020). (3.6) Before analyzing data, it is important to
consider the context of the product and its data: These contextual information need to be evaluated when
deciding about the data analysis approach (Shahbaz et al., 2006). Furthermore, they help to understand
the analysis results, potentially leading to deeper insights (Wilberg et al., 2017a).
Evaluation: (4.1) The results of the data analysis must be easily comprehensible as decision-makers are
not involved in all analysis steps (Li et al., 2015). Managers and product developers need to be presented
aggregated and accurate knowledge instead of vast amounts of data (Abramovici et al., 2017).
(4.2) Furthermore, they need decision support systems which give them suitable advice (Jun et al., 2007)
and help them make deliberate decisions based on the information available.
4.4 Challenges
While the success factors help companies to exploit the advantages of usage data-driven product planning,
they will still face numerous challenges. Table 3 shows a taxonomy of challenges using the four classes
Organization, Product, Data Analysis and Evaluation. The challenges are described below.
Table 3: Taxonomy of the challenges of usage data-driven product planning
Class 1: Organization
Class 2: Product
Class 3: Data Analysis
Class 4: Evaluation
(1.1) Definition of a
data-strategy
(2.1) Highly individual
products
(3.1) Selection of the
data to be collected
(4.1) Validity-check of
the analysis results
(1.2) Definition and
selection of use cases
(2.2) Short-cyclical
product improvements
(3.2) Overviewing data
availability and usage
(4.2) Interpretation of
data analysis results
(1.3) Positive cost-
benefit-ratio
(2.3) Capturing of the
product context
(3.3) Data pre-
processing
(4.3) Creation of new
ideas
(1.4) Integr. of DA into
traditional processes
(2.4) Missing sensors
(3.4) Analysis of large,
multimodal data
(2.5) Prohibition of data
access
(3.5) Choice of analysis
methods
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Organization: (1.1) Defining a data-strategy is a major challenge many companies struggle with (Erwin
et al., 2015; Wilberg et al., 2017b). (1.2) This includes the definition and selection of promising use
cases as initially it is often difficult to define use cases at all and later there may be too many to
implement (Wilberg et al., 2017a; Wilberg et al., 2017b). (1.3) Use cases must offer a positive cost-
benefit-ratio (Holler et al., 2016a; Wilberg et al., 2017b), remain within a company’s capacities (Wilberg
et al., 2017a) and show their usefulness (Holmström Olsson and Bosch, 2013; van Horn et al., 2012).
(1.4) Another challenge is the integration of data analytics into traditional product creation and
decision-making processes (Hou and Jiao, 2020; Wilberg et al., 2017b), both technically and
organizationally (Wilberg et al., 2017a).
Product: (2.1) Highly individual products represent an obstacle as data analysis insights may not
apply to other instances in the product class and thus may not be transferable (Holler et al., 2016a;
Holler et al., 2017; Cantamessa et al., 2020). (2.2) Furthermore, short-cyclical product improvements
contradict the necessity of long operating times within a given configuration (Holler et al., 2016a).
(2.3) Facing these challenges and the reduced comparability of products, capturing the product usage
context becomes even more important for data reuse (Igba et al., 2015), but it is a major challenge
(Hou and Jiao, 2020; Menon et al., 2005). (2.4) Due to the added costs, products may also miss
sensors and thus be unable to collect the data necessary (Wilberg et al., 2017a). When retrofitting
sensors, functional and economic aspects must be considered (van Horn et al., 2012). (2.5) Moreover,
customers may prohibit data access (Wilberg et al., 2017a), especially to sensitive data (van Horn et
al., 2012), exposing companies to additional complexity and costs to obtain rights to the data (Porter
and Heppelmann, 2014).
Data Analysis: (3.1) While sensors generate lots of data, user-generated data are also extensive for
complex products (Timoshenko and Hauser, 2019). But as more collected data do not necessarily lead
to better results (Hou and Jiao, 2020), the selection of the data to be collected must be consistent with
the strategy (Porter and Heppelmann, 2014) and carefully planned (Menon et al., 2005; Hou and Jiao,
2020). (3.2) Here, another challenge arises: Companies often lack an overview of data availability and
usage (Holmström Olsson and Bosch, 2013; Wilberg et al., 2017b) which prevents information reuse
(Goh and McMahon, 2009). (3.3) Once the data are collected, pre-processing confronts companies
with multiple obstacles. Data validity must be checked (Wilberg et al., 2017a) and multimodal data,
structured and unstructured, must be transformed and integrated (Porter and Heppelmann, 2015; Hou
and Jiao, 2020; Shahbaz et al., 2006; Abramovici et al., 2017) which can be overwhelming for
companies (Kiron et al., 2014). Here, especially textual data are difficult to work with because of their
heterogeneity (Menon et al., 2005). (3.4) In complex industries, analyzing large, multimodal data is
necessary to investigate the product from diverse perspectives (Xu et al., 2016; Hou and Jiao, 2020).
However, companies struggle with a substantial lack of experience (Wilberg et al., 2017b) and seldom
use complex analyses (Erwin et al., 2015). (3.5) Lastly, the choice and appropriateness of data
analysis methods largely affect the quality of the solutions (Cantamessa et al., 2020).
Evaluation: (4.1) Analyzed data forms the basis for product improvement. But before impactful decisions
are made, the validity of the data analysis results must be checked (van Horn et al., 2012) as the data
samples analyzed are typically limited (e. g. due to small sample sizes) (Hou and Jiao, 2020). Here, even
small errors could lead to major mistakes in the solution (Hou and Jiao, 2020). Therefore, it is necessary
that product experts verify data quality and analysis results (Li et al., 2019). (4.2) The interpretation of the
data analysis results shall turn data into valuable insights. However, as inverse problems do not have a
unique solution (Hou and Jiao, 2020), the correct interpretation represents a major challenge (Hou and Jiao,
2020; Xu et al., 2016). To extract valuable insights, knowledge in both engineering and data analytics are
required. Also, contextual information needs to be considered to avoid wrong interpretations (Wilberg et
al., 2017a). (4.3) Lastly, the creation of new ideas based on the analysis and interpretation of existing data
is not trivial (Wu et al., 2020). Also, the derivation of improvements for a whole product class from
(partially) subjective information about one instance is difficult (Abramovici et al., 2017).
5 DISCUSSION AND CONCLUSION
5.1 Key Insights
1. Usage data-driven product planning consists of six main concepts (see section 4.1). (1) The
captured user-generated, product operating and environmental data (2) are fed back into product
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planning and (3) analyzed with statistical analysis, data mining and machine learning techniques.
(4) The results enable fact-based decisions (5) to improve existing and future products. (6) The
feedback process represents an inverse design approach and forms a reinforcing loop with
traditional forward design.
2. Usage data-driven product planning offers advantages on three levels (see section 4.2). On the
analysis-level, the analysis of products, customers and users is improved. On the process-level,
planning and development processes are enhanced while reducing time and costs. On the business-
level, fact-based decisions enable a usage-centric product portfolio and a higher innovative strength.
3. Success factors and challenges can be structured using taxonomies with the classes organization,
product, data analysis and evaluation (see sections 4.3 and 4.4). While some success factors and
challenges are independent of each other, a significant number of success factors correspond
directly with challenges: data strategy, use cases, integration of data analytics with traditional
methods, long operating times, high similarity with other products, data access, effective data
collection and feedback, joint analysis of heterogeneous data and complex data analyses. Their
realization is decisive for the successful implementation of usage data-driven product planning.
5.2 Limitations
The limitations of our study are related to our research design. First, our journal selection process may
have excluded further journals with relevant articles. Yet, we believe that by conducting a thorough
backward and forward search, we found the most relevant articles. Second, due to high number of
related concepts, the construction of our search string may not be optimal. We tested multiple options
on a sample basis, but we did not investigate all papers for every option. Search strings including
different concepts (like product planning, data mining) may yield results not found with our search
string. Third, we searched only within the Google Scholar Database, setting up our dataset based on
titles, abstracts, and keywords. Fourth, the selection of relevant articles as well as the clustering
process were subjective. Other scholars might have included different articles and clustered the
elements in another way.
5.3 Implications for Managers and Future Research
Managers can use the six main concepts to create a better understanding of the topic within their
companies. Likewise, the listed advantages can be utilized to achieve a high level of acceptance and
promote future projects. Using the success factors taxonomy, managers can assess the current and
needed maturity of their companies for usage data-driven product planning (e. g. with a capability
maturity model). Lastly, managers should analyze the challenges and create plans to overcome them.
As usage data-driven product planning is a new research area, various future research is needed.
From our results, three research needs stand out: (1) The creation of a data strategy with promising use
cases is crucial as it guides all following steps. Here, the data strategy as well as the use cases must
align with the companies’ corporate strategy and its current situation. (2) The integration of data
analytics into traditional forward design processes is also critical as usage data-driven product
planning as a form of inverse design will not replace but complement traditional forward design. (3)
The utilization of data analytics must be made accessible to small and medium-sized enterprises. In
contrast to large companies, they mostly lack resources to build up their own sophisticated solutions.
Finally, as our research focuses on product usage data only, the integration of non-usage data should
be analysed, e.g., how can data from sources like customer reviews complement the usage phase data?
Especially for the identification of customer needs, there is already a high number of approaches using
text mining techniques on social media and online reviews (Bertoni, 2020).
ACKNOWLEDGEMENTS
The research and development project DizRuPt is funded by the German Federal Ministry of
Education and Research (BMBF) within the “Innovations for Tomorrow’s Production, Services and
Work” Program. The author is responsible for the content of this publication. Thanks to the reviewers
for their detailed feedback and their valuable literature suggestions.
ICED21 3297
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