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Data-Driven Engineering – Definitions and Insights from an Industrial Case Study for a New Approach in Technical Product Development

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The growing digitization affects all areas of engineering. Together with fast-paced trends, it drives complexity and uncertainty in many domains. Yet, its potentials are manifold and, in most cases, outweigh the disadvantages. Beneath terms such as “big data”, “digital twin”, the term “data-driven engineering” has evolved over the last years. However, neither in literature nor in industry, there is a unified definition or understanding of the term. The presented research is based on a literature review as well as an industrial case study. Several databases were screened systematically for the literature review and forward and backward searches were used additionally. The case study was conducted in a collaboration with a company in the climate system sector. First, a literature-based distinction between the terms model-based, model-driven, data-based, and data-driven as well as definitions of data-driven engineering were investigated. Representatives of the company then evaluated these findings in a workshop and together with the industry partner a consistent definition was developed. The authors define data-driven engineering as a framework for product development in which the goal-oriented collection and use of sufficiently connected product lifecycle data guides and drives decisions and applications in the product development process. Further, promising use cases for the industry partner regarding data-driven engineering were formulated. The use cases were initially evaluated and prioritized regarding their cost-benefit ratio. Symbioses with other strategies of the company such as Digital Twins, model-based engineering, and solution space engineering are outlined. For academia, the presented findings provide a consistent definition that can be used as a promising direction for future research. Especially a procedure model for the systematic conception and implementation of data-driven engineering would be beneficial. For industry, this paper provides insights on potentials of data-driven engineering, a differentiation from related concepts, and very concrete use-cases serving as a starting point for a company-specific implementation.
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NordDesign 2020
August 11-14, 2020
Kgs. Lyngby, Denmark
Data-Driven Engineering Definitions and Insights from
an Industrial Case Study for a New Approach in Technical
Product Development
Jakob Trauer1, Sebastian Schweigert-Recksiek1, Luis Onuma Okamoto1, Karsten
Spreitzer2, Markus Mörtl1, Markus Zimmermann1
1Laboratory for Product Development and Lightweight Design, Technical University of
Munich
jakob.trauer@tum.de
2Viessmann Werke Allendorf GmbH
Abstract
The growing digitization affects all areas of engineering. Together with fast-paced trends, it
drives complexity and uncertainty in many domains. Yet, its potentials are manifold and, in
most cases, outweigh the disadvantages. Beneath terms such as “big data”, “digital twin”, the
term data-driven engineering has evolved over the last years. However, neither in literature
nor in industry, there is a unified definition or understanding of the term. The presented research
is based on a literature review as well as an industrial case study. Several databases were
screened systematically for the literature review and forward and backward searches were used
additionally. The case study was conducted in a collaboration with a company in the climate
system sector. First, a literature-based distinction between the terms model-based, model-
driven, data-based, and data-driven as well as definitions of data-driven engineering were
investigated. Representatives of the company then evaluated these findings in a workshop and
together with the industry partner a consistent definition was developed. The authors define
data-driven engineering as a framework for product development in which the goal-oriented
collection and use of sufficiently connected product lifecycle data guides and drives decisions
and applications in the product development process. Further, promising use cases for the
industry partner regarding data-driven engineering were formulated. The use cases were
initially evaluated and prioritized regarding their cost-benefit ratio. Symbioses with other
strategies of the company such as Digital Twins, model-based engineering, and solution space
engineering are outlined. For academia, the presented findings provide a consistent definition
that can be used as a promising direction for future research. Especially a procedure model for
the systematic conception and implementation of data-driven engineering would be beneficial.
For industry, this paper provides insights on potentials of data-driven engineering, a
differentiation from related concepts, and very concrete use-cases serving as a starting point for
a company-specific implementation.
Keywords: data driven design, use-phase data, product development, design process, internet
of things (IoT)
1 Introduction
In the course of the digitization of products and processes of companies, more data is created
in the entire product life cycle than ever before (Hienz, 2014). This phenomenon is often
described as Big Data. Mauro, Greco, and Grimaldi (2016) describe Big Data as “[…] the
Information asset characterised by such a High Volume, Velocity and Variety to require specific
Technology and Analytical Methods for its transformation into Value.”. Thus, this term entails
three aspects the obstacles that occur due to the high complexity of data, the need for special
tools and procedures, and the potentials of this trend to create value. In recent years, academia
and industry primarily focused on potentials for service development and social media (e.g.
Troilo, Luca, & Guenzi, 2017; Wilberg, Triep, Hollauer, & Omer, 2017; Zhan, Tan, Li, & Tse,
2018). However, there are numerous values for technical product development that could be
achieved by analysing data generated over the entire product lifecycle (Hienz, 2014; Zhan et
al., 2018). According to Eckert et al. (2019), over the next decades „all“ products will be cyber-
physical systems. In addition, product development will be affected by the increasing
interconnectivity of these product, providing analyses to drive design decisions (Eckert et al.,
2019; Spath & Dangelmaier, 2016). Multiple use cases and approaches to support this paradigm
shift are already known such as Digital Twins (Spath & Dangelmaier, 2016; Trauer,
Schweigert-Recksiek, Engel, Spreitzer, & Zimmermann, 2020). Yet, there is still no unified
definition, framework, or understanding of what could be described as “data-driven
engineering” (DDE). Consequently, the goal of this publication is to present an overarching
definition of DDE, which can be used to classify novel approaches, but also to guide the
previously described paradigm shift, supporting companies to compete in such complex and
fast-paced markets.
2 Methodology
2.1 Industrial Case-Study
The industrial case study partner is Viessmann, a medium-sized company with more than
10,000 employees. The company develops and designs climate systems for residential and
commercial applications. As many other companies, on the one hand the case study partner
strives to profit from the high potentials of the ongoing digitization, but on the other hand has
to face the challenges of the ambitious transition and the negative effects of an increasingly
complex environment. To overcome these threads, the company is undergoing an extensive
change process. Some first steps have already been taken, as more and more connectivity
services are included in the products. However, their purpose is primarily to increase the
customer satisfaction directly by providing possibilities e.g. to control the climate solution via
app. Nevertheless, more engineering-oriented approaches are in the planning, such as the
introduction of a Digital Twin (Schweigert-Recksiek, Trauer, Engel, Spreitzer, &
Zimmermann, 2020).
2.2 Systematic Literature Review
The research presented in this paper is based on the Design Research Methodology of Blessing
and Chakrabarti (2009). The impression of the literature-based research clarification indicated
an insufficient understanding of DDE. Consequently, type 2 of the research methodology
Comprehensive Study of the Existing Situation” was applied. Based on the research
clarification, a comprehensive descriptive study was conducted analysing the state of research
in literature, followed by an initial workshop-based prescriptive study. Two research questions
(RQ) guided the systematic literature review:
RQ1 What are the key characteristics of DDE?
RQ2 Where are the differences between DDE and other existing approaches?
Three search mechanisms delivered the results of the literature review. First, a keyword search
process was conducted. A research strategy was built to structure and guide the systematic
search process (cf. Figure 1).
Figure 1. Literature Research Strategy
Considering only data-driven engineering as search phrase, 24 results are indicated. In the
context of engineering there are only 14, twelve of which use it just once, or as a keyword. The
remaining two publications are in the field of biotechnology and thus are not in the scope of
this paper of mechatronic engineering. Therefore, the previously mentioned keywords were
used. Combining these keywords using Boolean operators as indicated in Figure 1 delivered
the search phrase. This phrase was entered in Scopus. Consequently, the results were filtered
down to 92 publications, as shown in Figure 2.
Figure 2. Keyword Search Process
The results of the keyword search process were accompanied by a targeted consultation of
textbooks, journals, and websites. Further, interesting references from identified literature were
traced back. Overall, the authors investigated 119 publications. The most relevant publications
are presented in the following section.
3 Literature Review
3.1 Distinction of Basic Terms
The adjective “data-driven” occurs in combination with many, very different terms in literature
in highly divergent topics. What does it actually mean? Often it is used synonymously with the
adjective “data-based” and in combination with adjectives such as “model-based”, or “model-
driven”. What is the distinction between those terms? Consequently, as a first step, those basic
questions need to be clarified.
The ISO International Organization for Standardization (2015) defines data as “reinterpretable
representation of information in a formalized manner suitable for communication,
interpretation, or processing”. Many publications equate data and information. However, data
is just a representation of information. Only if the context is known, it can be interpreted and
information is extracted (Bauer & Dangelmaier, 2016). For data-based or data-driven
approaches, especially data from after-sales phases of a product are of interest. According to
Wilberg et al. (2017), for this kind of data, the term use-phase data is used. Models are used in
Aspects
Synonyms
Data-Driven Engineering Digital*
Data-Based Design Virtual
Data-Enabled Product Development
AND
OR
8 Factors N = 2,000 N = 1,062 N = 92
Search
String Field of
Engineering Data-Driven
as Keyword
all fields of engineering and design (Eckert & Hillerbrand, 2019). According to Lindemann
(2009), models are, compared to the original, a simplified mental or physical object to draw
purpose-oriented analogies and conclusions about the real object. There is a plethora of different
models. process and product models are especially in engineering relevant, representing
specific aspects of the process or product in a simplified manner (Eckert & Hillerbrand, 2019;
Lindemann, 2009). Often models use data as an input. Thus, they can be used to decode the
entailed information of data. Consequently, information is formalized by data to enable
communication, processing, and interpretation, which can be done using models.
The distinction between -based” and “–driven” is more complicated. According to Cambridge
University Press (2020), the suffix -based” indicates that something is developed from the
prefix. “-driven” instead means that something is caused or influenced by something or to make
something happen (Cambridge University Press, 2020). Whereas the former suffix is passive,
the latter indicates an active influence. This is valid for example in software engineering. Here,
Model-Based Software Engineering entails just the systematic use of models as primary
engineering artefact. In Model-Driven Software Engineering instead, models are (semi-)
automatically transferred into other models, or (semi-)automatically generate code by
themselves (Nyßen, 2009). Regarding the terms data-based and data-driven no concrete
differentiation was found in literature. King, Churchill, and Tan (2017) introduce in addition to
data-driven, the terms data-informed and data-aware. In data-informed design, decisions are
prepared with insights from data, but other sources of information also affect the decision. In a
data-aware mindset the decision maker is not limited to data. It is rather about to be aware that
there are many sources to solve a problem and the best one is chosen, which does not have to
be data. Considering the definition of the adverb “-based”, the two additionally suggested terms
can be taken as subsets of “data-based”. There are several publications using “data-based” and
“data-driven” in combination with other terms, which will be presented in the following section.
3.2 Related Methods and Frameworks
From the literature review no consistent definition of the adjective data-driven was found.
Literature in the context of engineering is also sparse. However, literature provides some related
frameworks, procedure models, and methods that are indicating characteristics of DDE. These
publications are presented in the following.
In the context of computation and machine learning, especially the terms Data-Driven
Modelling or Data-Driven models appear. The former is an approach analysing data of a
system in order to model dependencies between system variables without exactly knowing the
physical relations (D. Solomatine, See, & Abrahart, 2008). This is especially required for very
complex products and can be an interesting use case in engineering as well, e.g. for solution-
space engineering (Zimmermann & Hoessle, 2013). Data-Driven Models” is a related
technique. It can be used predictive or descriptive either to predict values of variables, or to
reveal and interpret patterns in data (Anand & Büchner, 1998).
The latter application scenario can often be found in the field of data science and increasingly
finds its way into engineering. In this field, decisions often rely on data. An example is Data-
Driven Decision Making”. Here, data is gathered in order to support the decision-making. Thus,
decisions are expected to become more rational and less influenced by gut instincts
(Brynjolfsson, Hitt, & Kim, 2011). There is also evidence of the positive influences of this
technique (e.g. Provost & Fawcett, 2013).
In most cases, these methods are used for market analyses, or requirements engineering.
Consequently, data-driven concepts often focus on customers for instance “Cyber-Empathic
Design”. Ghosh, Olewnik, Lewis, Kim, and Lakshmanan (2017) describe it as a framework to
integrate data of user-product interactions into design activities. Thus, individual customer
perceptions and preferences can be tracked from the use phase using embedded sensors to drive
innovations and product portfolio decisions. A related framework is “Data-Driven Product
Design” with the difference that the focus is explicitly on the perception of a products visual
design (Chien, Kerh, Lin, & Yu, 2016). This term can be used interchangeable with Product
Design Analytics” (Ma, Kwak, & Kim, 2014). In another use case based on these frameworks,
product families can be designed in a data-driven way which is called “Predictive Data-
Driven Product Family Design” (Ma & Kim, 2016). Zheng, Feng, Gao, and Tan (2018)
describe these applications of product and use-phase in product development as the next
paradigm shift in engineering. Nowadays, instead of relying merely on expert knowledge,
“Data-Driven Product Development” can be used to derive good, robust, and feasible designs,
driven by product data (Zheng et al., 2018).
Bogers, Frens, van Kollenburg, Deckers, and Hummels (2016) present a framework, called
“Data-Enabled Design”, for the integration of use phase data in the design of a product-service
system. They integrated real-time use phase data as well as user interactions to iteratively
improve the product at hand. They also differentiate data-enabled from data driven data-
driven approaches use much larger data sets (big data) but less qualitative insights to
contextualize the data (Bogers et al., 2016).
A state-of-the-art approach in engineering is model-based systems engineering. As indicated
by the name, it is based upon models. Guardabasso, Lindblad, Witzmann, and Siarov (2019)
however propose a “Data-Driven Systems Engineering” methodology in order to improve the
traditional model-based procedure. By “data-driven” Guardabasso et al. (2019) mean the
consistent integration of engineering design data and its connections into the systems
engineering process. This approach is based on a single source of truth and seems to be closely
related to the concept of a digital thread, or digital twin. In this approach, the focus is on
engineering design data, not on use-phase data.
Another term that appears quite often in literature, especially in the field of decision-making
user experience and integration (UX/UI) is Data-Driven Design”. It is a much more
established research field than most of the approaches stated above. When searching for this
term on Scopus, 245 results are indicated. According to King et al. (2017), in data-driven design
decisions (especially in the context of user experience) are guided by the collected data. Liu
and Chen (2017) for example describe a data-driven design paradigm as a framework for
decision-making, where the decision is formulated as a mathematical model and can be
optimised. Kusiak (2006) presents different applications of data-driven design, where by
linking data to decision making product innovations are driven. In most of the described
application scenarios the analysed data led to decision, innovations, or product and process
improvements.
4 Results
4.1 Definition of Data-Driven Engineering
The previous section presented related terms, concepts, and definitions of DDE based on
literature. These findings were discussed also with five engineers from the industry partner in
order to come up with a consistent definition for DDE.
First, the adjective data-driven was discussed. The authors and the industry partner do see a
difference between data-based and data-driven. When for example a decision is made data-
based, data was just used to support, or to validate the human decision. However, when it is
made data-driven, an automated interpretation of the underlying data can prepare and indicate
a decision, which only needs to be approved by humans.
To make this clearer, an analogy was built on navigating in a car. In the basic version, the driver
would use a paper map, investigate a feasible route, and then navigate according to it.
Considering the map as data, he would only base all his decisions on the available data, but it
would need some effort to analyse it and it would not be given that it is the best (fastest, most
beautiful, shortest, etc.) route. The more elaborate version would be a data-driven system.
Consider a navigation system such as Google Maps or Apple Maps, integrating a map of traffic
data etc. With the underlying algorithms, the system itself can analyse the data and find the best
insights i.e. a route that is driven by the data to fulfil the requirements as well as possible.
However, there is still a person driving the car. The driver just does not need to make a decision
on the route. Consequently, data-based approaches are more “reactive”. When a decision needs
to be made, data is analysed. Data-driven instead is “proactive”. Proactively data is included
and implemented in the engineering process. Underlying models and algorithms deliver
directions for innovations or decisions more or less automatically. With this basic
understanding in mind a definition for DDE was developed:
Data-driven engineering is a framework for technical product development in which the use-
case-oriented collection and utilization of sufficiently connected product lifecycle data guides
and drives decisions and applications in the product development process.
The elements of the definition are described in the following to elaborate its characteristics. The
authors consider DDE to be a framework. Thus, it should include processes, methods, tools,
roles, and context factors, which support the implementation of DDE.
There should be a use-case-oriented gathering of data. Consequently, only required data points
in the right quality and the right format are transmitted and analysed. This is a prerequisite for
this framework and in line with other approaches, for example with the use-phase data strategy
of (Wilberg, Fahrmeier, Hollauer, & Omer, 2018). Thus, this is a central characteristic of DDE.
To achieve all potentials of this framework, engineers need to include the right technologies
and sensor in the products developed today in order to gather data tomorrow that is used to
improve the product development of future products. This is a new paradigm in engineering
and influences most parts of the product development process.
How the gathered data can be used is manifold. Use cases can range from predictive
maintenance, over production planning or innovation management to organizational
optimizations of the engineering process. The definition seems to be very ambitious and rigid.
Here, the authors suggest a model (based on Deshpande, Sharma, & Peddoju, 2019) as shown
in Figure 3. Starting with use cases of descriptive analytics DDE can be achieved stepwise by
elaborating the use cases further towards prescriptive analytics. Consequently, DDE is more
like a vision, which is formulated as: With data-driven engineering, decisions are made in every
phase of the product development process through the goal-oriented collection and use of data
from the entire product life cycle. Data is not just the basis of decision but drives the decision-
making and innovation processes through underlying analyses.
Figure 3. Levels of Data Analytics towards DDE (based on Deshpande et al., 2019)
Intelligence
Descriptive Analytics
Diagnostic Analytics
Predictive Analytics
Prescriptive Analytics
Data-Driven Engineering
In order to use the data, it needs to be connected sufficiently. In the highest level of DDE, data
from all phases of a products lifecycle should be used. Thus, the product lifecycle circle can
be closed and already produced and sold products deliver data that can be used to design the
products of the next generation. To do so, they need to be connected. However, connecting all
data would be a utopia. Consequently, data need to be connected sufficiently to achieve the
envisaged use case or vision. Therefore, a strong enabler of DDE could be the concept of a
digital twin, entailing information, models, and data from all phases of the lifecycle (Trauer et
al., 2020).
The most important characteristic is that decisions and applications in the product development
process can be driven by data not only based on but also driven. For example, in an automated
way, requirements can be updated, or concept decisions can be made. Instead of just validating
or strengthening the gut feeling of an engineer regarding a decision, the system itself would
give a suggestion, which then needs to be checked by the engineer. Not only the subjective bias
of experts is reduced decisions depend not only on the current state of information, but also
on potential future information. With a data-driven approach, predictions can be made on a
rational basis and updated regularly.
To conclude, the following values of data-driven are suggested. While the other side is still
important, DDE should put emphasis on:
Prescriptive over descriptive approaches,
Proactive over reactive approaches,
Dynamic over static data,
Real-time over historic data.
4.2 Use Cases at the Industry Partner
To elaborate the definition further and to find a starting point for the further development of
DDE, promising use cases were developed in a four-hour long workshop together with the
industry partner. First, in a mute brainstorming session the attendants filled out use case
templates consisting of a title, a user story, a problem description, expected value, and expected
effort of the use case. Further, contextual information on product, data source etc. were
collected. Next, each representative of the company presented their use cases to the others,
before the attendees evaluated the use cases qualitatively regarding value and effort on a scale
from one to five. The rating resulted in the following portfolio, depicted in Figure 4. The
portfolio contains the averages of the attendee’s voting.
Figure 4. Evaluation of Use Cases Related to Data-Driven-Engineering
1
2
3
4
6
7
8
9
10
11
1
2
3
4
5
1 2 3 4 5
Value
Effort
Malfuntion of the
system
2
Specification of the
combustion unit
3
Predictive
maintenance
4
Necessity of a hot
water
storage tank
5
Heating cycles
6
Acceleration of
production
7
Reduction of sensor
components
8
Number of hot water
preparations
9
Flameout
Benefit of Features
Reliability
Engineering
With this portfolio at hand, it was possible to select the four most promising use cases namely
use case 4, 6, 8, and 11. Use case 8 regards heat pumps. Heat pumps have a valve that directs
the hot water from the heat pump either into the house for heating or into the hot water tank for
drinking water. Currently, there are frequent hot water preparations, the cause of which are
unclear. A possible cause could be a leaking valve or simply a high house consumption of water.
So far, the reason is not exactly known. With use phase data of the whole heat pump and more
specifically of the valve, it would be possible to identify the real cause for this issue. The
analysed data could indicate a new specification for the valve, or adjusted requirements for the
whole system. With this use case it would be possible to improve the efficiency of the climate
system, increase the customer satisfaction, and reduce maintenance costs. The effort was rated
medium to low, as some of the contextual data is already gathered by service systems.
Use case 4 also tackles a problem for heat pumps. In order to be able to make currently unused
heat from a heat pump available on demand at a later date, a buffer tank is positioned between
the heat generator and the heat sink. This is also important as it decreases the need of frequent
compressor launches which increases the compressor’s lifetime. However, with novel
compressor technologies, a hot water tank as storage might not be needed anymore. To prove
this hypothesis, just data from the compressor would be needed. Some of this data is already
collected and the analysis would be relatively easy, so the effort was rated low. As it would be
possible to save some components and thus reduce costs, the value was rated medium to high.
Use case 6 deals with the reduction of production and maintenance time. With the specific (de-)
assembly times from production and service, it would be possible to indicate potentials for
improvement for the components within the heating system. A model could be filled with that
data resulting in concrete hints for improvement in the design of the components. On the one
hand, the gathering of data would be relatively easy, however, building a model to analyse the
data might be more complicated. Thus, the effort was rated medium. On the other hand, the
value would be quite high it could be possible to reduce production and maintenance time,
reduce costs, and improve the efficiency of the production line.
Use case 11 is the most ambitious but also most promising one. It is difficult to predict how the
components will be stressed in the field. Thus, the component designs are subject to great
uncertainty, for example with regard to the collectives of loads. This problem is even worse for
supplier parts, as there is also uncertainty as to whether the components have been developed
and tested in accordance with the requirements. Therefore, if use-phase data on these load
collectives would be available already in the design phase, specifications could be formulated
more accurate, the components fit for intended use could be increased and the reliability
engineering would be improved. To achieve this goal elaborate analyses and a huge amount of
data would be required. Thus, the effort was rated as high. However, this use case can be
achieved stepwise by implementing smaller use cases such as use case 4 and 8, which directly
contribute to this goal.
5 Conclusion
5.1 Discussion
This paper presented a definition of DDE. Therefore, related literature was analysed and
discussed with the industry partner. It was hard to draw the line on potentially interesting related
literature. Thus, there might be aspects, which might not have been addressed. The selection
was made together with the industry partner. The intention of this paper was not to develop a
completely new concept. The perception was rather, that there is an ongoing paradigm shift in
engineering, yet there is no term or description for it. Consequently, many publications
presented in section 3 do not stand in contradiction to our definition but can be seen as a subset
of it. The presented definition was also evaluated with four representatives of the industry
partner (cf. Figure 5). To assure that the attendees understood the definition, also the
understanding was evaluated. The results were documented in a portfolio. Each small circle
represents the rating of one participant. The big dot in the middle is the average score. In total,
the understanding was quite high. The overall relevance was rated as high as well and especially
for their company as a promising new approach.
Figure 5. Evaluation of the Definition of DDE
However, there are also some limitations to overcome. The biggest limitation was seen in the
use-case oriented collection of data. It seems hard to evaluate potential use cases and specially
to assess their business case in order to get the approval to implement required technologies to
achieve the use case. Further, before implementing use cases the basic conditions need to be
assured, e.g. a consistent data format, software tools for the analyses, etc. Further, the definition
was perceived as quite strict and ambitious. However as already stated earlier, it should serve
as a vision as an ambitious goal that can be achieved step by step through smaller projects. In
terms of the vision, there are of course further limitations specially as innovation an decision
processes depend on a plethora of other factors, which makes it harder fully cover these aspects
with DDE.
5.2 Outlook
In the future collaboration a procedure model for the implementation of DDE will be developed.
This will be done based on the use cases presented in section 4.2. Combining our approach with
the use-phase data strategy of Wilberg et al. (2018) should help to overcome the challenge of
the use-case oriented collection of data. To evaluate the potential use cases, also methods and
approaches to develop valid business cases will be investigated. This project will be connected
with an ongoing project on digital twins (Schweigert-Recksiek et al., 2020) as well as an project
on solution space engineering.
Acknowledgments
The authors thank Viessmann for the very fruitful collaboration.
1
2
3
4
5
1 2 3 4 5
Relevance
Understanding
Data-Driven Engineering
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Background: A critical task in product design is mapping information from consumer to design space. Currently, this process largely depends on designers identifying and mapping psychological and consumer level factors to engineered attributes. In this way, current methodologies lack provision to test a designer's cognitive reasoning and could introduce bias when mapping from consumer to design space. In addition, current dominant frameworks do not include user-product interaction data in design decision making, nor do they assist designers in understanding why a consumer has a particular perception about a product. Method of approach: This paper proposes a new framework - Cyber-Empathic Design - where user-product interaction data is acquired via embedded sensors. To understand the motivations behind consumer perceptions, a network of latent constructs forms a causal model framework. Structural Equation Modeling (SEM) is used as the parameter estimation and hypothesis testing technique, making the framework falsifiable in nature. Results: To demonstrate the framework, a case study of sensor-integrated shoes is presented, where two models are compared - one Survey-based and one using the Cyber-Empathic framework model. Two methods are used to estimate the parameters and the fit indices - Covariance based SEM and Partial Least Square SEM. It is shown that the Cyber-Empathic framework results in improved fit using both estimation techniques over survey-only SEM. Conclusion: This work demonstrates how low level user-product interaction data can be used to understand and model user perceptions in a way that can support falsifiable design inference.