Information Quality in PLM: A Product Design Perspective

Abstract

Recent approaches for Product Lifecycle Management (PLM) aim for the efficient utilization of the available product information. A reason for this is that the amount of information is growing, due to the increasing complexity of products, and concurrent, collaborative processes along the lifecycle. Additional information flows are continuously explored by industry and academia – a recent example is the backflow of information from the usage phase. The large amount of information that has to be handled by companies nowadays and even more in the future, makes it important to separate “fitting” from “unfitting” information. A way to distinguish both is to explore the characteristics of the information, in order to find those information that are “fit for purpose” (information quality). Since the amount of information is so large and the processes along the lifecycle are diverse in terms of their expectations about the information, the problem is similar to finding a needle in a hay stack.
This paper is one of two papers aiming to address this problem by giving examples why information quality matters in PLM. It focuses on one particular lifecycle process, in this case product design. An existing approach, describing information quality by 15 dimensions, is applied to the selected design process.

Full text

Preprint. Please cite as follows: Wellsandt, Stefan, et al. "Information Quality in
PLM: A Product Design Perspective." Advances in Production Management
Systems: Innovative Production Management Towards Sustainable Growth.
Springer International Publishing, 2015. 515-523.
DOI: 10.1007/978-3-319-22756-6_63
adfa, p. 1, 2011.
© Springer-Verlag Berlin Heidelberg 2011
Information Quality in PLM:
A product design perspective
Stefan Wellsandt
1,3
, Thorsten Wuest
2
, Karl Hribernik
1
, Klaus-Dieter Thoben
3,1
1
BIBA - Bremer Institut für Produktion und Logistik GmbH, Bremen, Germany
2
Industrial and Management Systems Engineering, Benjamin M. Statler College of
Engineering and Mineral Resources, West Virginia University, Morgantown, USA
thwuest@mail.wvu.edu
3
Faculty of Production Engineering, University of Bremen, Germany
{wel, hri, tho}@biba.uni-bremen.de
Abstract.
Recent approaches for Product Lifecycle Management (PLM) aim for the effi-
cient utilization of the available product information. A reason for this is that the
amount of information is growing, due to the increasing complexity of products,
and concurrent, collaborative processes along the lifecycle. Additional infor-
mation flows are continuously explored by industry and academia – a recent ex-
ample is the backflow of information from the usage phase. The large amount of
information that has to be handled by companies nowadays and even more in the
future, makes it important to separate “fitting” from “unfitting” information. A
way to distinguish both is to explore the characteristics of the information, in
order to find those information that are “fit for purpose” (information quality).
Since the amount of information is so large and the processes along the lifecycle
are diverse in terms of their expectations about the information, the problem is
similar to finding a needle in a hay stack.
This paper is one of two papers aiming to address this problem by giving exam-
ples why information quality matters in PLM. It focuses on one particular lifecy-
cle process, in this case product design. An existing approach, describing infor-
mation quality by 15 dimensions, is applied to the selected design process.
Keywords: Product Lifecycle Management; quality management; usage data;
product design; data quality; usage information
1 Introduction and problem description
Closing the information loops along the product lifecycle is a recent effort under-
taken by research projects [1], [2]. One of the reasons why closing information loops is

so important is the expectation that designers and manufacturers will be able to create
products (and services) of higher quality. This expected increase in product quality is
largely based on the assumption that information about the products’ in-use behavior
(‘usage information’) will lead to better decisions in processes like product design. Us-
age information can substantiate decisions and thus increase their transparency within
collaborative working environments. Recent research on information about product us-
age typically focuses on the capabilities and general appropriateness of different ap-
proaches, methodologies or solutions that make usage information available to certain
decision-makers (e.g. [3] and [16]). Important for the actual integration of the infor-
mation is the technical capability and a use/business case, as well as the adequacy of
the information for the given case. Due to the heterogeneity of usage information, it is
difficult to decide what information is actually relevant for a certain decision process –
currently, the quality dimensions for usage information are largely unknown.
This paper will discuss the importance of information quality in PLM. For reasons
of complexity the scope of the paper has to be significantly limited. This is done by
focusing on one exemplary information loop (i.e. from usage to design). Furthermore,
only one decision-process is selected for the following discussion.
2 Related work
2.1 Information flows in PLM
Handling product data and information along the complete product lifecycle is stated
as PLM [4]. A product’s lifecycle can be structured into three subsequent phases stated
as ‘beginning of life’ (BOL), ‘middle of life’ (MOL) and ‘end of life’ (EOL). The con-
cept of PLM was further extended during the EU-funded large-scale research project
PROMISE – it demonstrated the possibilities of closing information loops among dif-
ferent processes of the lifecycle [5]. The recent concept of PLM is illustrated in Figure
1. Internal information flows within the phases are not covered in the illustration.
Fig. 1. A product lifecycle model and its major information flows [6]
Among the three lifecycle phases, two types of information flows can be established.
The forward-directed flows are the ones that are typically mandatory to design, pro-
duce, service and dismiss the product. Backwards-directed flows are typically optional
and allow optimization and control of processes. One recent example for optimization
is the improvement of product design through the integration of usage information from
the MOL phase – this approach is sometimes called ‘fact-based design’ [7].

Following the working-definition argued by Wellsandt et al., usage information is
“[…] any product-related information that is created after the product is sold to the
end customer and before the product is no longer useful for a user” [6]. Usage infor-
mation can originate from sources like product embedded sensors, maintenance reports,
shopping websites, social networks, product reviews and discussion forums [8]. Infor-
mation from these heterogeneous sources feature very different characteristics concern-
ing their format (e.g. structured vs. unstructured data), scope (e.g. plain data vs. multi-
media) and the lifecycle activities covered in the content (e.g. use, maintenance and
repair).
2.2 Information Quality (IQ)
The topic of IQ has been intensely discussed for at least two decades; several sophisti-
cated definitions for ‘information quality’ exist. Since the purpose of this paper is not
to discuss these fundamental concepts, a thoroughly discussed definition is selected for
this paper. From a general perspective, the quality of information can be defined as the
degree that the characteristics of specific information meet the requirements of the in-
formation user (derived from ISO 9000:2005 [9]). Based on this understanding,
Rohweder et al. propose a framework for information quality that is an extension of the
work conducted by Wang and Strong [10] – it contains 15 information quality dimen-
sions that are assigned to four categories as summarized in Table 1.
Table 1. Dimensions of information quality [11]
Quality category
Scope
Quality dimensions
Inherent
Content
Reputation; free of error; objectivity; believability
Representation Appearance Understandability; interpretability; concise representation; con-
sistent representation
Purpose-dependent Use Timeliness; value-added; completeness; appropriate amount of
data; relevancy
System support
System
Accessibility; Ease of manipulation
The selected definition of information quality is split into four categories, i.e. inherent,
representation, purpose-dependent and system support. Each category has dimensions
that characterize information by two to five dimensions. A description of some defini-
tions of these quality dimensions is provided in Table 2.
Table 2. Selection of quality dimensions and their description (based on [11])
Quality dimension
Description
Reputation
Credibility of information from the information user’s perspective
Free of error
Not erroneous; consistent with reality
Objectivity
Based on fact; without judgement
Believability
Follows quality standards; significant effort for collection and processing
Understandability
Meaning can be derived easily by information user
Interpretability
No ambiguity concerning the actual meaning; wording and terminology
Concise representation
Clear representation; only relevant information; suitable format
Consistent representation
Same way of representing different information items
Accessibility
Simple tools and methods to reach information
Ease of manipulation
Easy to modify; reusable in other contexts

In order to receive a specific statement about the actual quality of an information item,
the as-is characteristics of the item must be compared with the required characteristics.
The better the matching is, the higher the information quality is considered.
3 Methodology and Scope
In order to substantiate the framework proposed by Rohweder et al., the require-
ments of the information users (decision-makers) must be identified and compared with
the proposed IQ dimensions (see Table 1). For this purpose, the information flow from
MOL to BOL is targeted in this paper – the main subject is usage information. The
targeted decision-making process is ‘requirements elicitation’, an information-inten-
sive decision-making processes conducted early in product design.
Process description. Requirements elicitation (REL) is a systematic, and oftentimes
iterative, process aiming to retrieve information from users (and other stakeholders) –
the main result of this process is a list of explicit user requirements [12]. Techniques
for information retrieval include surveys, questionnaires and observation. Recent ap-
proaches, like fact-based-design, aim for the retrieval of actual product usage infor-
mation, in order to improve, for instance, the requirements list (see section 2.1).
Required characteristics. In literature, quality dimensions for the results of the elic-
itation activity, i.e. documented requirements, are readily available (e.g. IEEE 830
standard and [13]). A non-comprehensive list of requirements quality (RQ) dimensions
is provided in Table 3. It is valid both for individual requirements and whole lists of
requirements. RQ dimensions and IQ dimensions have overlapping in some areas.
Table 3. Quality dimensions for requirements according to IEEE 830 and [13]
Criterion
Description
Valuable
Has a specific economic value or benefit for the business
Correct
Free from errors and in accordance with facts
Clear
Only one way to understand the requirements; no ambiguity
Understandable
Target audience can perceive the intended meaning
Complete
All relevant requirements are captured
Consistent
Not containing any logical contradictions
Assessable
Importance or relevance can be estimated
Verifiable
Fulfillment is testable in limited time
Modifiable
Variable components and the influences in case of changes are clearly identifiable
Traceable
Identifiable; connected to other requirements and documents
Relevancy
Fulfills a specific stakeholder need or goal (e.g. needed for realization)
Realizable
Can be realized in practice; resource efforts are estimated
Additional, but more general, information requirements result from decision-making
in business environments, e.g. cost-efficiency of collection and use of information.
4 Discussion
The quality dimensions in Table 3 describe desired characteristics of documented
requirements, i.e. an output of the REL activity. In a PLM scenario, like fact-based-
design, the requirements can be derived from usage information effectively serving as

an input of the REL activity. Deriving requirements from usage information requires
some kind of analysis and interpretation of the information, in order to get valuable
design knowledge. Since usage information and requirements are involved in the elici-
tation activity, their quality characteristics might be related to each other. The relation
between the two sets of quality dimensions will be substantiated in the following. For
reasons of complexity, each IQ dimension will be put into the context of one RQ di-
mension at most. Therefore, the given examples for relations among RQ dimensions
and IQ dimensions are not meant to be comprehensive – there are, most likely, other
influences that are not covered in this paper. Since this paper lacks a specific use case
for REL, the ‘use’-scope of IQ dimensions is not further covered in this paper. The
discussion is structured according to the four categories summarized in Table 1.
4.1 Content scope
Reputation (rep). Quality dimensions, like the ones in Table 1, can be difficult to in-
stantiate for a company, for instance when expertise or resources are lacking. In these
cases, previous positive experience with usage information sources can outweigh the
lack of precise estimations for the IQ. The reputation is relevant for decision-making in
general, thus also relevant for REL.
Free of error (foe). An error is something produced by mistake [14]. Concerning usage
information, errors can occur in at least two areas, i.e. measurements and human-au-
thored contents. In case of measurements, errors can be caused by, for instance inap-
propriate calibration of sensors, poorly placed sensors and measuring wrong events. In
case of human-authored information, errors can be a result of, e.g. unskilled authors
(e.g. typos and wording) and limited knowledge of authors (e.g. wrong statements and
conclusions). When deriving requirements from erroneous usage information, the re-
sulting requirements might be corrupted (e.g. reflecting a non-existent user need) –
therefore, the correctness of requirements benefits from correct usage information.
Objectivity (obj). A characteristic of human-authored feedback information is its sub-
jectivity – also stated as response-bias [15]. When dealing with user responses (e.g. in
online discussions) information users generally need to take response-bias into account.
Measurements, on the other hand, are more ‘objective’, since they do not have a re-
sponse-bias [16]. Due to influences like the response-bias, REL decision-makers may
not be able to derive requirements that fulfill the ‘complete’-dimension – the available
usage information (e.g. from weblogs or forums) is limited/scoped by the perceptions
of its author.
Believability (bel). Information that is used to elicit requirements can be extracted from
product reviews. These reviews may be authored by renowned professionals that are
familiar with a product and terminology to describe it (higher bel) or by common users
with unknown identity, knowledge and skills (lower bel). In addition, the reviews can
be based on a structured, transparent testing process (higher bel) or an unstructured/un-
known process (lower bel). The dimension might influence the “correct”-dimension of
the REL process, since a higher believability of usage information might be associated
with less errors effectively reducing potential corruption of requirements. Further the
dimension is related to the “objectivity”-dimension.
4.2 Appearance scope
Understandability (und). Human authored usage information, e.g. user feedback in
discussion forums, is created by users with different backgrounds (e.g. writing skills,

language and expertise on topic). The language, for instance, is an important factor in
REL as it limits the understandability of usage information. In a similar way, raw meas-
urement data from sensors (e.g. without graph plotting) are barely understandable by
decision-makers. Not being able to take these kinds of usage information into account
for REL may lead to an incomplete requirements list (e.g. missing key requirements).
Interpretability (int). Extracting the meaning of usage information can be difficult in
case that important context information is lost or originally not provided. Missing con-
text may cause ambiguity of usage information. Measurement protocols, for instance,
require context information about the sensor that was used to collect the data. In case
this context is not provided, tolerances of measurement remain unclear. This IQ-
dimension affects the RQ-dimension for “correctness”, as ambiguous usage infor-
mation may lead to erroneous assumptions and finally to flawed requirements.
Concise representation (ccr). Usage information that is based on human-authored
contents is not necessarily uniform. Product reviews may contain a mixture of media,
like text, pictures and videos, or different languages. When dealing with these infor-
mation in the REL process, an in concise representation makes analysis more time con-
suming.
Consistent representation (csr). Contents generated in the Internet (e.g. weblogs, dis-
cussion forums) do not follow standardized procedures. Text can be created freely fol-
lowing limited formal structures, such as templates in ‘WordPress’ and form fields of
forum posts. Content can further contain media formats like pictures and videos. Mul-
timedia formats of usage information require more elaborate tools and in general more
effort for analysis. Therefore, consistent representation benefits cost-efficient collec-
tion and use of information during the REL process.
4.3 Use scope
The five IQ dimensions of the ‘use’ scope are not covered further in this paper, since at
this stage, no specific use case has been chosen. Without a use case, the range of pos-
sible requirements from REL is too large to provide value to this paper.
4.4 System scope
Accessibility (acc). Getting access to usage information (in a technical sense) is chal-
lenging for several reasons. Usage information is, for instance:
• distributed across different sources (e.g. weblogs and databases);
• heterogeneous concerning its format, i.e. representation;
• potentially copyrighted or otherwise restricted (e.g. forum with registration).
Furthermore, the collection of usage information may require special skills and/or
knowledge (e.g. data or text mining). Barriers for easy accessibility affect the ‘com-
plete’-dimension of requirements, since restricted or too costly access to information
might result in missing requirements (that could be derived from the information).
Ease of manipulation (eas). Usage information, like product reviews and posts in dis-
cussion forums, may contain pictures and videos. These contents are provided in for-
mats that can be difficult to manipulate (e.g. video stream). The ‘eas’-dimension is also
ambiguous in relation to REL, since manipulation might not be desired by decision
makers. The requirements should be framed in a way that they reflect the user’s expec-
tations and needs. Ease of manipulation might affect the ‘correctness’-dimension of
requirements when manipulation of usage information leads to wrong conclusions. An
example concerns losing context information during a copy and paste procedure – in

consequence, decision-makers might take wrong assumptions about product or user be-
havior.
5 Conclusion and Outlook
While the availability of new information sources, such as usage information in de-
sign, provides new opportunities to improve products (see [3], [7]), newly created in-
formation flows and new kinds of information can introduce problems along the lifecy-
cle. Feeding usage information into the BOL phase, for instance, can cause issues in
related decision-making processes. These issues can affect product quality in a negative
way, e.g. when incorrect or incomplete requirements are elicited based on flawed usage
information. The impact of flawed information may further affect later stages of the
lifecycle such as maintenance, disassembly and disposal of products. Therefore, the
example provide in this paper helps to justify why IQ has to be considered more thor-
oughly in PLM. In future research, the following aspects should be considered, in order
to extend the understanding of IQ in PLM:
- Collection of additional cases from all lifecycle phases (e.g. production, sales,
maintenance and EOL scenarios).
- The adequacy of IQ dimensions for each case has to be argued. This requires an
analysis of decision-making processes.
- Interdependencies of IQ dimensions need to be detailed. This should be sub-
stantiated by practical examples from use cases.
Acknowledgement
This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement no. 636951 (Manutelligence) and
grant agreement no.
636868 (Falcon).
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