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Rooted in the automotive industry, this article discusses the topic of leveraging tacit knowledge through prototyping. After first providing an overview on learning and knowledge, the Socialization, Externalization, Combination and Internalization (SECI) model is discussed in detail, with a clear distinction between tacit and explicit knowledge. Based on this model, we propose a framework for using said reflective and affirmative prototyping in an external vs. internal learning/knowledge capturing and transfer setting. Contextual examples from select automotive manufacturing R&D projects are given to demonstrate the importance and potential in applying more effective strategies for knowledge transformation in engineering design.
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2212-8271 © 2016 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the scientific committee of the 6th CIRP Conference on Learning Factories.
6th CLF - 6th CIRP Conference on Learning Factories
Prototyping to Leverage Learning in Product Manufacturing Environments
Jorgen A. B. Erichsena*, Andreas L. Pedersena, Martin Steinerta, Torgeir Weloa
aNTNU - Norwegian University of Science and Technology, Richard Birkelands Veg 2B, Trondheim NO-7491, Norway
* Corresponding author. Tel.: +47-416-46-804; fax: +47-735-94-129. E-mail address:
Rooted in the automotive industry, this article discusses the topic of leveraging tacit knowledge through prototyping. After first providing an
overview on learning and knowledge, the Socialization, Externalization, Combination and Internalization (SECI) model is discussed in detail,
with a clear distinction between tacit and explicit knowledge. Based on this model, we propose a framework for using said reflective and
affirmative prototyping in an external vs. internal learning/knowledge capturing and transfer setting. Contextual examples from select automotive
manufacturing R&D projects are given to demonstrate the importance and potential in applying more effective strategies for knowledge
transformation in engineering design.
© 2016 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the scientific committee of the 6th CIRP Conference on Learning Factories.
Keywords: knowledge transfer; internal reflective prototyping; prototyping; tacit knowledge; integration events; product development
1. Introduction and Background
In this article, we argue for the use of explorative and
analytical approaches in product development processes by
discussing tacit knowledge accumulation and transfer through
prototypes. With this intention, we attempt to make several
contributions to current literature.
Firstly, we present a mapping of relevant literature on the
topic of knowledge, especially related to product development.
In this section, we are exploring organizational and individual
knowledge, the differentiation of tacit and explicit knowledge,
in addition to some current practices on the transfer of (tacit)
The second contribution is to present a model of prototyping
categories, with special emphasis on the differentiation
between learning and verification as the main intent for
prototyping activities. A model of four prototyping categories
is proposed, and discussed in relation to dealing with known
and unknown problems concerning tacit knowledge in product
The article closes by exemplifying the previous two sections
by providing insights from two industry cases. The use of
analytical and explorative approaches to prototyping are
discussed, and several possible research opportunities are
The automotive industryan industry with steadily
increasing demand for faster development cycles and higher
quality productsis subject to increasing competitive pressure.
Making mistakes is costly in an industry where product life
cycles are in the order of five to ten years, and late-stage design
changes have major implications for manufacturing planning
and processes. In addition, automakers need to rely on previous
experience, and cannot start from scratch in each development
project. The use of process and part standardization within the
product technology platforms is a well-established practice to
reduce the burden on the development teams. Hence, much
research is currently targeting knowledge and learning
mechanisms in new product development. Examples include
knowledge-based development (1)a method for extracting
basic principles of Toyota’s product development processes
In this paper, we focus on analytical and explorative
approaches, and their relation to both creation and transfer of
tacit knowledge in product development.
2 Author name / Procedia CIRP 00 (2016) 000000
2. Theory: Knowledge in Product Development
In (3), Ulonska presents numerous definitions of knowledge
found in product development. Rowley differentiates
knowledge and wisdom (4) by defining knowledge as
application of data and information (“know-how”), whereas
wisdom is defined as elevated understanding (“know-why”).
Additionally, it can be argued that knowledge can be further
divided into individual and organizational knowledge (5). The
sum of what is learned, experienced, discovered or perceived
(by individuals) during a project (in the organization) defines
organizational learning. The interactions of individuals are the
main ingredients of organizational knowledge, and the
knowledge of these individuals is called individual knowledge.
This is categorized in three categories; experience-based,
information-based and personal knowledge (6). Nonaka and
Takeuchi argue that the organizational knowledge exists
between (and not within) individuals (7).
2.1. Defining Integration Events and Knowledge Owners
Most product development organizations use stage-gates for
decision making. The stage-gate model is a financially-based
governance method, which leverages the importance of
financial decisions during development. However, this type of
process governance often makes event-based technological
decisions harder. Hence, there is a call for a more event-based
governance model in product development (8). An example on
such events can be the emerging trend of hosting ‘integration-
events’. These events are so-called learning cycle gates, and
aim at ensuring better insights and information while
preserving previous project know-how and learnings. This
way, large product development organizations aim at
transferring project (individual) knowledge into organizational
learning. Here, informal knowledge is formalized (made
explicit), and formal knowledge is interpreted (by the
individuals). The key to successful organizational learning is a
mutual exchange of these two kinds of knowledge.
Some companies employ key experts or learning facilitators
as catalysts for the exchange of knowledge within their
organization. These so-called knowledge owners are usually
technical or functional managers, who help preserve and
facilitate the learnings and insights. Examples of key experts
are Toyotas functional managers who owns the technology.
The functional managers employ existing knowledge within
projects, while so-called chief engineers challenge the existing
standard by being the customer representative. By spending
time with and on the development team, these key experts gain
experience and insights, which in turn will contribute to
organizational learning inside the company.
By taking a closer look at learning mechanisms in product
development in Fig. 1first introduced by Eris and Leifer (9),
and then further iterated by Leifer and Steinert (10)the
distinction between formal and informal knowledge is
clarified. Key experts are usually working in the informal area
(i.e. learning loops two and three), whereas the organization as
a whole operates in the formal area (i.e. learning loop one).
2.2. Tacit and Explicit Knowledge in PD
The terms tacit and explicit knowledge are closely linked to
formal and informal knowledge. Explicit knowledge consists
of information, facts and numbers that have been formalized
(learning loop one from Fig. 1) (11), and they can be
summarized into so-called ‘knowledge artifacts’ (12).
Examples on these knowledge artifacts include the widespread
use of A3 sheets in the Toyota product development system
(2,13), which usually contain condensed explicit information
about a project or system. Tacit (or informal) knowledge
includes everything non-explicit, hereunder learnings, know-
how, craft and skill of the product engineering individuals,
Figure 1 - Learning mechanisms in product development, adopted from (9) and (10).
Author name / Procedia CIRP 00 (2016) 000000 3
accumulated in learning loops two and three (14). We argue
that one key dimension of tacit knowledge is the interaction
with (and use of) objects and experiences in the product
engineering processes, often referred to as prototypes in one
form or another.
2.3. The SECI-model and Transfer of Knowledge in PD
First proposed by Nonaka, Toyama and Konno (15) as a
prevalent model for enhancement of knowledge creation
through conversion of tacit and explicit knowledge, the SECI
process (Fig. 2) can be used for describing the different stages
of knowledge transfer. The SECI model consists of four stages,
including socialization, externalization, combination and
internalization, and is used to describe how various knowledge
is transferred (in an organization) by spiraling through the four
stages. Four knowledge assets are presented as facilitators of
knowledge creation, and are categorized as experimental,
conceptual, systemic and routine. The latter has gotten
increasing support since its first appearance, and a study by
Chou and He (16) concludes conceptual knowledge assets (i.e.
PD insights) to have the most effect on knowledge creation.
By further studying the model, we can categorize the three
stages socialization (tacit-to-tacit), internalization (explicit-to-
tacit) and externalization (tacit-to-explicit) as forms of either
creation or transfer of tacit knowledge in development teams.
The last stage, combination (explicit-to-explicit), can be
described as an implemented knowledge repository, where the
formalized knowledge within the organization might be
distributed to sub-groups that require this knowledge. In the
context of transferring tacit knowledge, socialization includes
creating a work environment that encourages understanding of
expertise and skills through practice and demonstrators.
Externalization, or the act of formalizing the tacit knowledge,
aims at feeding this into the organization. Similarly,
internalization aims at interpretation of formal knowledge, and
includes conducting experiments, sharing results, and
facilitating prototyping as a means of knowledge acquisition
(15). Chou and He (16) also conclude that conceptual
knowledge assetsi.e. “knowledge articulated through
images, symbols and language” (15)are the most efficient
tool for facilitating externalization and internalization.
2.4. A Proposed Model of Prototyping Categories
In (17), prototypes are defined as “An approximation of the
product along one or more dimensions of interest”, thus
including both physical and non-physical models. Examples
include (but are not limited to) sketches, mathematical models,
simulations, test components and fully functional pre-
production versions of the concept (18).
We argue that prototyping can be divided into four different
categories (Fig. 3) (19). The horizontal axisthe intent of the
prototypeis split into two sub-categories; “reflective” and
“affirmative”. The vertical axis, displaying the target audience
of the prototype, is spit into “internal” and “external”. This two-
by-two matrix gives four different prototyping categories
which will be briefly explained below.
2.4.1. External, affirmative prototyping
Typically used for making pre-production models, this kind
of prototyping approximate a nearly finished model, and are
often termed alpha and/or beta prototypes (20) intended for
validation or showcase purposes. These prototypes are high
fidelity (i.e. highly detailed) models, used for external
validation (e.g. certification test etc.), marketing, or in-depth
customer interaction. In an automotive setting, these may be
the cars subject to road testing, being pre-production cars tested
on closed test circuits by external users.
2.4.2. Internal, affirmative prototyping
Focused in terms of function, this type of prototyping is
intended for function, reliability and feasibility testing.
Examples include combinations of subsystems, fatigue testing
of conceptual prototypes or project milestones to validate team
progression. Although high in fidelity (regarding function and
complexity), these prototypes are still rarely shown to public
audiences. Automotive examples on this kind of prototyping
includes running lifecycle testing of components, like shock
absorbers, axles and other moving parts.
2.4.3. External, reflective prototyping
Companies often seek feedback from external sources by
showing off concepts. User interaction is carefully observed
and recorded for further study, and responses and reactions are
used for further improving other concepts. This kind of
prototyping is used for observing interaction with external
sources, enabling the design team to take a step back and learn
from the observations. In the automotive industry, automakers
often show off one-of-a-kind concept car projects at large
automotive venues to gather external feedback and reactions.
2.4.4. Internal, reflective prototyping
Internal, reflective prototyping is a learning activity, used by
the product development team to learn and conceptualize ideas.
These prototypes are rough, made for exploring, understanding
Figure 2 - The SECI-model, with blue areas highlighted as areas of interest,
adopted from (15).
4 Author name / Procedia CIRP 00 (2016) 000000
and experimenting with functionalities that are essential for
product success, with the aim of creating new insights within
the product development team (21). Typically, internal,
reflective prototypes have low fidelity (22), and therefore
regarded as waste after a project is finished. These prototypes
may prove especially useful when facing high complex
problems, like the component layout of an automotive engine
By using terminology from the Tacit Knowledge
Framework (23,24), we use the terms ‘knowns’ and
‘unknowns’; Both affirmative prototyping categories are linked
to analysis, as they are dealing with known problems and
requirementsthe ‘known knowns’ (i.e. known articulated
problems with known possible solutions). Adversely, reflective
prototyping categories aim at exploration, and thus at dealing
with unknown problems—the ‘unknown unknowns’ (i.e. non-
articulated problems with unknown solutions). Coming from
this perspective, we argue that known problems are best solved
analytically, while unknown problems are best solved
3. Examples: Learning from Prototyping
In the following subsections, the theory presented in the
previous section will be accentuated to show the influence of
internal, reflective prototyping in product development. The
first case considers applying a physical prototype to an analysis
for evaluating the numerical method and consequentially
learning about the method and saving time in the process. The
second case presents a failed crash box, once designed for a
new car model that was well analyzedbut still failed due to
an overlooked design-manufacturing detail. A discussion of the
mistakes is made in light of the theory presented.
3.1. Case I: Applying Physical Computation for a Rotational
Spiral Spring
In (25), a case illustrates the effects of combining numerical
computations with testing a physical representation of the
design. The time required to design a concept by using
analytical tools in complex cases can be greatly reduced by
applying a physical prototype for testing and comparison, as
proposed in the article.
The case studies a rotational spiral spring that is analyzed by
setting up a numerical model (using mechanical spring theory),
predicting stiffness and maximum stress of the rotational spiral
spring. Meanwhile, a physical model is made with MDF
(Medium Density Fibreboard) and tested (Fig. 4). The output
data reveals a striking similarity, though the stiffness is
somewhat overestimated in the analysis. Although the results
are not identical, the combination of the physical and numerical
computations shows the numerical analysis to be transferable
to the physical dimension and may be scaled further.
Combined, these methods yield satisfactory results in a very
short time.
This case shows very well how time can be saved by
applying internal, reflective prototyping early in the product
development process to facilitate faster learning. This approach
may prove especially applicable for complex cases, reducing
complexity by understanding which analytical tools might be
appropriateand saving time by doing so. As for all internal,
reflective prototyping, the prototype used for the physical part
of the computation is not applicable in the finished product.
However, it facilitates the designers’ learning of how their
analytical problem transfers into the physical domain. Internal,
reflective prototyping is used to learn from internally, either
individually or as a collaborative group, as they typically are
low fidelity in nature, but educational and time saving.
3.2. Case II: Crash Box Failure Due to Lack of Variability
In this case, we use an example from a large European
automaker, which had designed a crash box for topological
optimization, to be fit into a new car model. Crash boxes,
separate deformation elements between the front bumper and
the front longitudinal rail, are designed to deform on low-speed
impact to prevent damage to the rest of the car to reduce the
repair cost. The production method of the crash box was
extrusion of one open cross-section that was bent, cut, pierced,
and welded into a closed box configuration with an integrated
foot plate mounted to the rails.
The Danner crash test (26) rates cars at the impact of
collision in their ability to minimize costs of repair at 0-
15km/h, for the purpose of evaluating the car’s properties to set
an insurance premium base. In the Danner test, the crash box
Figure 4 - MDF prototype with markings used to estimate the flex of the
rotational spring (25).
Figure 3 - A proposed model of four prototyping categories.
Author name / Procedia CIRP 00 (2016) 000000 5
of the said model was expected to crush in a controlled manner
upon collision test impact without damaging expensive
components or activate the air bags, which are the costliest to
replace. In the numerous FEA simulations done to optimize the
system, the welding configuration was assumed to be
geometrically perfect, starting at the very end of the box.
However, in production (MIG) welding, start and stop of the
weld seam tend to create minor groove of varying magnitude at
the very end, depending on dimensional accuracy of the
individual part, and other control parameters. Hence, the
accuracy of the FEA model was not capable of capturing the
local stress state in the vicinity of the grove (as illustrated in
Fig. 5). Instead of failing by controlled crushing as predicted in
the FEA model, occasionally, the weld seam failed like a zipper
starting from the very end of the box once the bumper folded
and contacted the very end of the crash box. The fluctuations
(in the force deformation curve) triggered the air bag sensors,
resulting in the airbags deploying in low speed tests at 15 km/h.
This type of failure is considered catastrophic as a consequence
of the repair costs associated with replacing the airbags.
The influence of small variations imposed by manufacturing
(welding) is a very complex matter. Sensitivity testing of the
crash box with the same production-intent premises as the
serial produced product would have prevented encountering a
failure such a long time after launch. This clearly demonstrates
the risk of failing to integrate the product development process
and the manufacturing process. The design engineers did not
know this would be an issue, and the unspecified ‘parameter’
related to end configuration (of the weld) remained an
unknown until several vehicles were retested after launch.
If the team had engaged in internal reflective prototyping
activities, the influence of such critical design features could
have been uncovered. The learning outcome in this case could
have led the team members to acquire the necessary knowledge
to see the disconnection between the manufacturing process
and the intended design, possibly identifying a low-cost
solution (process or design change) to such a fairly fixable
In this case, properly done internal, affirmative prototyping
could have uncovered the problem. However, we would argue
that doing internal, reflective prototyping in the early stages of
the development process would have facilitated important
learning. As a result, the early development process would be
less complex, and problems not otherwise perceived as
problems would be uncovered. Hence the value of prototyping
and testing to learnnot only to verifycould have
significantly saved time, money and averted the ultimate failure
of the design.
4. Research Potential of Using Explorative and Analytical
Methods for Learning in Product Development
Furthermore, the insights, experience and learnings present
a unique research opportunity, since improved understanding
of the creation and transfer of tacit knowledge will alter how
we facilitate the product development process. Hence, there is
a call for more research concerning how tacit knowledge
influences the development of products with high levels of
complexity, especially when dealing with many unknown
As identified in (27), there is a gap between professional
knowledge and real-world practice. In his works, Simon
applies methods of optimization from statistical decision
theory, thus laying a foundation for a scientific approach to
treating knowledge. Adversely, Schön (28) argues that the real
challenge lies not within the treatment of well-formed
requirements, but rather the extraction of such requirements
practically unknown unknownsfrom real world situations. In
(29), Schön presents reflective iteration rounds as a learning
tool of great potential. Taking this perspective, we argue that
reflective prototyping may be used as a learning tool in
handling unknown unknowns in product development.
Ultimately, we argue that, in reality, product development
requires balancing of the tacit and the explicit, the explorative
and the analytical. We have seen that disconnection between
product development and manufacturing processes cause major
implications for entire value chains. In hindsight, exploration
and experience of manufacturing techniques and challenges
could have led to the discovery of potential risks and problems
in the product development process (unknown unknowns),
andif sohow to best balance analysis and exploration for
uncovering these unknowns in a cost and resource efficient
5. Conclusion
The purpose of this paper has been to accentuate the
possibilities of using prototyping in product development for
manufacturing settings. An attempt has been made to map
future opportunities, both for industry and academia, and a call
for the recognition of prototyping as a time saving learning
tool. The potential of applying exploration by interaction with
prototypes related to knowledge capture, transfer and learning
is demonstrated in the context of the automotive industry. Thus,
a call for increased focus on mixing analytical (e.g.
simulations) and explorative (e.g. prototyping) approaches is
presented as a viable direction for further efforts in both
industry and academic communities.
Altogether, the importance of understanding the interplay
between (tacit) knowledge, explorative and analytical
Figure 5 - Exemplification of a crash box, with highlighted area of interest.
6 Author name / Procedia CIRP 00 (2016) 000000
approaches to problems in product development and
manufacturing, and the role of prototyping for learning are
topics that require further pursuit.
This research is supported by the Research Council of
Norway through its user-driven research (BIA) funding
scheme, project number 236739/O30.
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... The 'why' of a prototype is often referred to as purpose (or intent)-the reason for building the prototype (J. A. B. Erichsen, Pedersen, Steinert, & Welo, 2016c, 2016bM. B. Jensen et al., 2015;Lim, Stolterman, & Tenenberg, 2008). ...
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... According to [6], the output of most design activity is twofold; one part being information (explicit knowledge), and the other being experience (tacit knowledge). The output might be formalized in terms of text, numbers or simulation data, or crude, reflective prototypes used within the team [7,8], which we summarize as 'artifacts'. ...
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In recent years, studies on efficient strategies in prototyping have been accelerated in number. Among them, studies on the economies of prototyping, which analyze the relationship between the gained value of prototyping and its fidelity, are being conducted. However, the types of prototyping that improve the economies of prototyping are yet to be revealed. Also, it has been indicated that generating communication in prototyping can lead to performance improvement. Therefore, this study focused on communication during the prototyping process and analyzed the economies of prototyping at a private company. First, the economies of prototyping were explored using 27 prototyping and their gained values and fidelity for the private company. Next, each prototyping was categorized using a prototyping category centered on communication to indicate trends in economies of prototyping in each category. Furthermore, Pearson’s product-moment correlation coefficient was calculated to grasp the data relationship between the economies of prototyping and communication. Considering the results of the above analyses, we proposed a prototyping method that improves the economies of prototyping. Specifically, we proposed the technique of “prototyping that involves external stakeholders early on in the development stage and shortens expended time” as this leads to enhanced economies of prototyping. This study suggests the possibility of improving the economies of prototyping by consciously implementing “prototyping in a way that reduces the time required while involving external stakeholders at an early stage.”
Context Knowledge transfer plays an important role in digital Service Creation Projects where information should flow through service design, Agile UX, and software implementation phases. One context for these handovers exists in projects where the service designers participate in the early phases of exploring and scoping the service, while agile user experience specialists take over the digital parts of service design and programmers the software implementation. Objective The purpose of this study is to summarise scientific knowledge into best practices for effective information flow in real world Service Creation Projects. Special attention is paid on an important and understudied project phase, knowledge handover from service design to software implementation, which is referred as Service Design Handover in this study. Method A systematic literature review was conducted to analyse the current scientific knowledge on knowledge transfer in digital Service Creation Projects. PRISMA 2020 statement was used for reporting the review, which also influenced planning and execution of the systematic review process. SCOPUS search brought up 773 publications, and the full content analysis was done for the 41 most relevant publications. Results Based on the literature analysis, the best practices for effective knowledge transfer are related to communication quality and quantity, circumventing the need for communication, and verifying successful communication. To provide an overview of effective knowledge transfer, frameworks of Service Creation Project information flow and Service Design Handover are proposed. Conclusion The existing knowledge transfer literature is voluminous, but this literature review is the first to study knowledge transfer in Service Creation Project context. The framework, best practices, and list of potential problem sources in knowledge transfer provide new knowledge for managing the information flow in service creation. The research gaps found in this literature review show the need for future research, such as empirical studies on service creation practice.
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This thesis is addressed to engineering design researchers; aiming to strengthen and improve research by providing better methods and tools for researching prototyping in early-stage Product Development (PD) projects. It argues that there is a need for a new method that allows for capturing more observations with less effort required by researchers, and that this need comes from the limitations of methods, tools and resources available to the PD researchers. The main contribution of this thesis is to propose a new method for capturing prototypes from ongoing early-stage PD projects that enables initial analysis of large datasets on prototypes, which can be used for deciding when and where to apply the existing, more resource demanding methods. Essentially, this thesis argues that capturing physical prototypes (as output from prototyping activity) provides a feasible solution for gathering larger datasets from ongoing PD projects with lower effort required of the PD researcher compared to the existing methods. In order to test and evaluate the proposed method, a system for digitally capturing physical prototypes has been developed, aiming to fulfil a set of identified functional requirements for implementing the proposed method. This system has been deployed in two locations, the R&D department of a company and in a prototyping workshop facility at TrollLABS, NTNU. A dataset of over 950 physical prototypes have been captured digitally through multi-view images and metadata during this PhD project—demonstrating that the proposed method could feasibly be used to gather research data from ongoing early-stage PD projects. This thesis argues that the proposed method can be used for both quantitative and qualitative investigations of early-stage PD projects and demonstrates how this could be done using the captured prototypes from one project. A challenge that emerges from gathering larger datasets of captured prototypes is the resources required for analysing the data. This thesis shows several possible solutions for this problem by automatically classifying various properties from images of prototypes by retraining pre-trained models for object detection with custom datasets—showing that, if researched further, such solutions may reduce the effort required for analysing prototypes in engineering design research substantially.
Conference Paper
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This article is rooted in the automotive industry as starting point, and discusses the topic of leveraging tacit knowledge through prototypes. The aim of this study is to make the case of using reflective and affirmative prototypes for knowledge creating and transferal in the product development process. After providing an overview on learning and knowledge, the Socialization, Externalization, Combination and Internalization (SECI) model is discussed in detail, with a clear distinction between tacit and explicit knowledge. Based on this model, we propose a framework of using said reflective and affirmative prototypes in an external vs. internal learning/knowledge capturing and transferal setting. Rounded by two case examples from the automotive industry we end by identifying the emergent research questions and areas. Using prototypes and prototyping may hold a monumental potential to better capture and transfer knowledge in product development, thus leveraging existing integration events in engineering as a basis for knowledge transformation.
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The search for effective and reliable innovation processes has concerned companies for decades. Stage-Gate (SG) is one of the most common models employed to structure new product development (NPD) efforts. Despite its popularity along with other efforts made to enhance NPD performance, overall innovation failure rates are still reported high. From the perspective of a NPD team SG is not a process at all, it is essentially a series of checkpoints introduced to ensure compliance between resource allocation and perceived business potential. Therefore, NPD teams need a more dynamic environment, focusing on problem-solving and risk mitigation at a more knowledge-based level. The present paper seeks to establish an event-driven NPD process within existing business processes, and determine its applicability in a real-world case study in a company that develops advanced products. An incremental implementation strategy was chosen, introducing the concept in the front-end of a single project, rather than across the entire company. The first experiences show that team performance is improved across three dimensions: outcome (effectiveness); process (efficiency); execution environment.
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Many companies have introduced Lean practices in product development and innovation processes to gain competitive advantage. However, the application of Lean outside the factory floor is not straightforward, where principles and practices may be translated and contextualized when introduced in functional areas that differ significantly from manufacturing. Especially in such multifaceted contexts as to be found in System Engineering (SE) companies. In this article, we investigate the extent to which SE companies relate their practices to the knowledge component of Lean product development, and the degree to which such lean practices and capabilities are implemented. The overall goal is to determine how SE companies compare to companies in other industrial sectors in this regard, and thereby gaining new insights into strategies for more contextual implementation of lean in engineering functions. A survey is conducted in the Norwegian manufacturing industry to determine Lean practices from the construct of a generic model as basis. The survey was answered by 257 respondents from 50 companies, providing the opinion of individuals as to where they place their current practices and capabilities on the lean maturity scale for each question, including a supplemental set of performance and productivity related assessment items. Results indicate that there seems to be significant differences between perceived LPD-performance in Systems Engineering versus the other sectors when talking about knowledge management. This study may reveal a potential for the Systems Engineering industry to improve upon the way organizational learning is managed to develop and sustain a culture for continuous improvement.
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Physical prototypes have always been important in engineering design. However, little is known about the role that prototypes play in the development of complex physical products. This paper investigates the role of prototypes and prototyping in the development of two novel product innovations recently launched by an automotive original equipment manufacturer (OEM). Through an exploratory case study, prototypes are found to provide the capability to aid learning and communication both within the development teams and across the organization. Actual prototype usage was found to encompass activities beyond merely the verification and validation purposes covered in traditional engineering design literature.
Conference Paper
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Over the past decades, the finite element method (FEM) has helped to accelerate design processes. However, when a problem is highly nonlinear, e.g. systems with changing unilateral contacts, numerical methods often struggle or fail completely to solve the design problem. We propose the concept of physical computation (pC) as a tool to help circumvent these numerical problems and thereby accelerate the design process. pC denotes the process of using physical systems to compute the answer to a specific part of a problem, which is hard to solve using numerical or analytical methods. pC complements said methods. Additionally, the use of rapid prototyping (RP) allows to quickly manufacture the pC setups. The application of pC in a design process is shown on the case study of a highly progressive spring. The combination of pC and numerical methods is shown to be efficient in the case study. Based on the results of this case study we see considerable potential to reduce the effort needed for the design evaluation of diverse design problems through the application of the concept of pC.
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Over the past thirty years, a powerful methodology for innovation has emerged from engineering and design thinkers in Silicon Valley. It integrates human, business and technical factors in problem forming, solving and design: "Design Thinking." This human-centric methodology integrates expertise from design, social sciences, business and engineering. It is best implemented by high performance project teams applying diverse points-of-view simultaneously. It creates a vibrant interaction environment that promotes iterative learning cycles driven by rapid conceptual prototyping. The methodology has proven successful in the creation of innovative products, systems, and services. Through courting ambiguity, we can let invention happen even if we cannot make it happen. We can nurture a corpus of behaviors that increase the probability of finding a path to innovation in the face of uncertainty. Emphasis is placed on balance of the questions we ask, and the decisions made. A suite of application examples and research finding will be used to illustrate the concepts in principal and in action.''
Conference Paper
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Requirements elicitation research is reviewed using a framework categorising the relative 'knowness' of requirements specification and Common Ground discourse theory. The main contribution of this survey is to review requirements elicitation from the perspective of this framework and propose a road map of research to tackle outstanding elicitation problems involving tacit knowledge. Elicitation techniques (interviews, scenarios, prototypes, etc.) are investigated, followed by representations, models and support tools. The survey results suggest that elicitation techniques appear to be relatively mature, although new areas of creative requirements are emerging. Representations and models are also well established although there is potential for more sophisticated modelling of domain knowledge. While model-checking tools continue to become more elaborate, more growth is apparent in NL tools such as text mining and IR which help to categorize and disambiguate requirements. Social collaboration support is a relatively new area that facilitates categorisation, prioritisation and matching collections of requirements for product line versions. A road map for future requirements elicitation research is proposed investigating the prospects for techniques, models and tools in green-field domains where few solutions exist, contrasted with brown-field domains where collections of requirements and products already exist. The paper concludes with remarks on the possibility of elicitation tackling the most difficult question of 'unknown unknown' requirements.
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This article presents a comprehensive framework for the analysis of value creation and knowledge development in general and, in particular, for professional service firms (PSFs). The framework integrates the relationship between the domain choice and the resource (or knowledge) base, and argues that the bridge between the two is best explained as value creation processes (VCPs) with two interrelated dimensions: direct value creation for the clients, and indirect value creation in terms of enhancing the knowledge base.
The use of tacit knowledge is a common feature in everyday communication. It allows people to communicate effectively without forcing them to make everything tediously and painstakingly explicit, provided they all share a common understanding of whatever is not made explicit. If this latter criterion does not hold, confusion and misunderstanding will ensue. Tacit knowledge is also commonplace in requirements where it also affords economy of expression. However, the use of tacit knowledge also suffers from the same risk of misunderstanding, with the associated problems of anticipating where it has the potential for confusion and of unravelling where it has played an actual role in misunderstanding. Thus, the effective communication of requirements knowledge (whether verbally, through a document or some other medium) requires an understanding of what knowledge is and isn’t (necessarily) held in common. This is very hard to get right as people from different professional and cultural backgrounds are typically involved. At its worst, tacit requirements knowledge may lead to software that fails to satisfy the customer’s requirements. In this chapter, we review the diverse views of tacit knowledge discussed in the literature from a wide range of disciplines, reflect on their commonalities and differences and propose a conceptual framework for requirements engineering that characterises the different facets of tacit knowledge that distinguish the different views. We then identify methodological and technical challenges for future research on the role of tacit knowledge in requirements engineering.