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© Springer-Verlag Berlin Heidelberg 2011
Information Quality in PLM:
A production process perspective
Thorsten Wuest
1
, Stefan Wellsandt
2
, Klaus-Dieter Thoben
2,3
1
Industrial and Management Systems Engineering, Benjamin M. Statler College of
Engineering and Mineral Resources, West Virginia University, Morgantown, USA
thwuest@mail.wvu.edu
2
BIBA - Bremer Institut für Produktion und Logistik GmbH, Bremen, Germany
3
Faculty of Production Engineering, University of Bremen, Germany
{wel, tho}@biba.uni-bremen.de
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 com-
plexity of products, and concurrent, collaborative processes along the lifecycle.
Additional information flows are continuously explored by industry and aca-
demia – a recent example is the backflow of information from the usage phase.
The large amount of information, that has to be handled by companies nowa-
days and even more in the future, makes it important to separate the “fitting”
from the “unfitting” information. A way to distinguish both is to explore the
quality 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
lifecycle process, in this case production. An existing approach, describing in-
formation quality by 15 dimensions, is applied to the selected production pro-
cess.
Keywords: Product Lifecycle Management; quality management; manufactur-
ing; production; production planning and control; data quality
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 activities like design, production, sales and
maintenance will be able to realize products and services with a better ratio between
expected and delivered characteristics (i.e. higher quality). As product quality is di-
Post-Print Version
The final publication is available at Springer via http://link.springer.com/chapter/10.1007/978-3-319-33111-9_75
rectly influenced by the quality of the production processes [3, 4], an increased avail-
ability of information will benefit production planning and control activities. Addi-
tional information can help to understand complex problems and take the most suita-
ble decisions to address them in a timely manner – common examples for these bene-
fits are Concurrent Engineering [5] and agile software development [6].
Technical capabilities for the collection and analysis of information, as well as a
sound business case are important prerequisites to increase the availability of infor-
mation in decision-making. However, the growing amount and heterogeneity of in-
formation caused by, e.g. the industrial Internet and the Internet of Things, foster the
need to identify information that is fit-for-purpose (i.e. focus on information quality).
Recent literature about PLM puts little emphasis on this aspect.
This paper will discuss the importance of information quality in PLM from the per-
spective of production. The same issue but from a product design perspective is de-
scribed in a sister paper (see [7]). Section two of this paper outlines information flows
in PLM and an exemplary approach to describe information quality.
2 Related work
2.1 Information flows in PLM
Handling product data and information along the complete product lifecycle is
stated as PLM [8]. A product’s lifecycle can be structured into three subsequent phas-
es stated as ‘beginning of life’ (BOL), ‘middle of life’ (MOL) and ‘end of life’ (EOL).
The initial concept of PLM was extended during the EU-funded large-scale research
project PROMISE – it demonstrated the possibilities of closing information loops
among different processes of the lifecycle [9]. The recent concept of PLM is illustrat-
ed 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 [10]
Among the three lifecycle phases, at least two types of information flows can be
established. The forward-directed flows are the ones that are typically mandatory to
design, produce, service and dismiss the product. Backwards-directed flows are typi-
cally optional and allow optimization of processes/activities.
2.2 Information Quality (IQ)
Seamless decision-making processes are largely based on high-quality information.
Decision-makers realize issues with information quality if their expectations about the
information are not met. Examples for potential problems caused by low information
quality are summarized in Table 1.
Table 1. Examples of problems related to flawed information [11]
not based on fact
consists of inconsistent meanings
is irrelevant to the work
of doubtful credibility
is incomplete
is hard to manipulate
presents an impartial view
is hard to understand
-
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 infor-
mation user (derived from ISO 9000:2005 [12]). Since the topic is intensely discussed
for at least two decades, several sophisticated 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. Rohweder et al. propose a
framework for information quality that is an extension of the work conducted by
Wang and Strong [13] – the framework contains 15 information quality dimensions
that are assigned to four categories as summarized in Table 2.
Table 2. Dimensions of information quality [14]
Quality category
Scope
Quality dimensions
Inherent
Content
Reputation; free of error; objectivity; believability
Representation
Appearance
Understandability; interpretability; concise representation;
consistent 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 contains four categories of dimen-
sions that are related to a specific scope. Each category has dimensions that character-
ize information by two to five dimensions (15 in total). A brief description of some
dimensions is provided in Table 3.
Table 3. Excerpt of quality dimensions and their description (based on [14])
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 judgment
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 charac-
teristics (preferably using all of the quality dimensions). The better the matching is,
the higher the information quality is considered.
3 Approach
Production is the process of realizing products according to the specifications orig-
inating from product development. In this paper, production includes production
planning, manufacturing and assembly processes. During production, several charac-
teristics of the later product and its behavior during usage are defined, e.g. by the
chosen materials, machines and machine parameters. The decision of which materials,
machines and parameters are going to be used is taken during the production planning
phase and the previous product development phase. In this paper, the product devel-
opment phase is not in the focus.
During production, information between different manufacturing processes is ex-
changed. The exchanged information is highly important to ensure the final product
quality [15]. In manufacturing, especially in the area of process monitoring and con-
trol, information quality can play a decisive role in whether an analysis and the subse-
quent action is successful or not. In order to apply the selected quality dimensions to
production, relevant information flows are divided into three categories as illustrated
in Fig. 2.
Fig. 2. Types of information flows in production
Information flows within production (internal). In production, information qual-
ity is generally of very high relevance as it often has a direct impact on key figures of
a company or a production network. Information used in manufacturing is often used
as input for machines with a low level of robustness against, e.g., missing values.
Today, production involves multiple processes exchanging not only physical goods
but also information. Those process chains can become rather complex and can be
considered dynamic. Looking at manufacturing at a more granular level, each process
and product has to be considered individually due to, e.g., deviations in its materials.
Fig. 3. Internal information flows of a production process chain
Through the backflow of information about the individual product earlier in the
same process or from previous processes individual adjustment of process parameters
becomes possible. These adaptions of the process may lead to significantly improved
performance and/or avoidance of significant problems. Today, many decisions regard-
ing value adding production processes are taken based on available information. Con-
trol loops, scheduling decisions and program planning are just some examples which
strongly depend on information quality. Information in this context can include real-
time sensor information, e.g., for monitoring and control purposes, as well as quality
measures for subsequent process adjustments. A practical example can be information
about the individual chemical composition of the steel, used during heat treatment.
This information is vital for reaching the quality goal.
Information flows towards production (inbound). Extending this towards the
potential use of information from lifecycle phases other than production to support
production processes in a similar way, certain differences come to mind and present
specific requirements towards the information quality. The two main inbound infor-
mation sources are depicted in Fig. 4.: 1) information from the product design phase
and 2) information from the usage and maintenance phase.
Fig. 4. Inbound information flows towards production
The product design phase is essential for production. In design the main properties
of the later product are set and the processes and process parameters are chosen ac-
cording to the information received from design. For information from usage, there
are two possibilities. First, the information is directly transferred and utilized or the
usage information is indirectly utilized via the design phase. An example for relevant
information from usage/maintenance is the surface quality of a product that depends
on environmental factors during usage. A product’s surface characteristics can be
influenced to some extend during the production process (e.g., heat treatment).
Information flows from production (outbound). In production, information is
not only utilized but also produced in large quantities – machinery and tools are
equipped with sensors continuously producing information. Also process monitoring
and advanced systems, like Manufacturing Execution Systems, contribute to the in-
creased information generation. This information may be a valuable source for stake-
holders outside of production. In Fig. 5., outbound information flows to three other
lifecycle phases are depicted: 1) recycling and disposal, 2) usage and maintenance and
3) product design and development. Examples cases for these three information flows
are:
1. Information about potentially hazardous materials of the product introduced during
production (e.g. heavy metals).
2. Information about lubricants used during production which could influence the ar-
eas of application of the product (e.g. regulation in food processing industry).
3. Information can be directly utilized for future design improvements that lead to a
variety of improvements, e.g. quality, efficiency or safety.
From the perspective of production, the example number three can be considered
as the most important outbound information flow. Popular approaches like ‘Design
for Manufacturing’ actually rely on such outbound information from production.
Fig. 5. Outbound information flows from production
Within all these different possible information flows, an important aspect to con-
sider is the information quality. Whereas the right information in the right quality may
lead to significant improvements, flawed information may even have a worse impact
than no information at all. In the next section, this is discussed according to the previ-
ously introduced IQ dimensions.
4 Discussion
In this section, the feasibility of the different IQ dimensions during application in
manufacturing and production planning is discussed. The overall structure follows the
one depicted in table 2, with sub-sections resembling the four ‘scope’ categories. As
mentioned, production is very diverse in the applied processes and also individual for
each product type. Therefore, the given examples used to emphasize certain quality
aspects are not meant to be comprehensive – there are, most likely, multiple other
influences and aspects that are not covered in this paper. For each scope of the select-
ed IQ framework, the three different information flows introduced in the previous
section (i.e. internal, inbound and outbound) are briefly discussed.
4.1 Content scope
The content scope, resembling the IQ dimensions ‘reputation’, ‘free of error’, ‘ob-
jectivity’ and ‘believability’, is very relevant in production. For information flows
within production (internal), ‘free of error’ is very important as the information is
often directly utilized by technical systems. Given that process chains are often dis-
tributed between different locations and companies, ‘reputation’ and ‘believability’
may also be relevant. However, ‘objectivity’ may be considered less relevant in this
area as measuring and sensor data can be considered rather objective by nature. For
outbound and inbound information however, all four IQ dimensions are highly rele-
vant. From a production perspective, these IQ dimensions matter most for inbound
information. However, for other stakeholders within the product lifecycle, the im-
portance of information quality of outbound information can be considered equally
high. Here ‘objectivity’ is also relevant as these information items may contain hu-
man-authored feedback information including its characteristic of subjectivity – also
stated as response-bias [15].
4.2 Appearance scope
The relevant IQ dimensions of this scope are ‘understandability’, ‘interpretability’,
‘concise representation’ and ‘consistent representation’. For information flows within
production (internal), all four IQ dimensions discussed here are important. In highly
automated production environments, the appearance of information is mostly defined,
due to standardization or design of the production system itself. If standards are not
met nor the communication rules of the automated system are not followed, the sys-
tem will fail in most cases. Thus, these IQ dimensions are hard requirements, which
have to be fulfilled. In production processes with more manual work and thus more
human-based decision-making, the appearance of information is less regulated and,
therefore, must be controlled more. For inbound and outbound information flows,
these IQ dimensions cannot be assumed fulfilled due to standardization. There, the
information is more diverse and the possibility of different systems and/or require-
ments is rather high. Thus, these IQ dimensions have to be carefully considered prior
to establishing collaboration along the product lifecycle.
4.3 Use scope
Regarding the use scope, the IQ dimensions ‘timeliness’, ‘value-added’, ‘com-
pleteness’, ‘appropriate amount of data’ and ‘relevancy’ are in the focus. Within pro-
duction (internal) it can be observed, that timeliness, completeness, appropriate
amount of data, and relevancy are highly important. The often-automated use of in-
formation by machinery and monitoring tools relies on information fulfilling these
requirements. For instance, even though today’s computing power and algorithms can
handle large amounts of data rather well, it is still important to evaluate what data is
really relevant with the goal in mind. For inbound and outbound information flows
in production these factors are also of relevance, however, there the potential use is
broader and thus the variety of quality requirements acceptable may be higher. For all
information flows in production the IQ dimensions ‘value-added’ is very important,
as it is after-all a business operation.
4.4 System scope
From a system perspective, ‘accessibility’ and ‘ease of manipulation’ are the de-
sired IQ dimensions. Within production (internal), accessibility is critical, especially
in distributed production environments. Assuming that in production information is
mostly based on sensor or other non-human-authored data, the access is mostly de-
pending on a) available communication means (technical) and b) the access rights.
Ease of manipulation is on the other hand not considered critical within production.
Regarding inbound and outbound information flows, accessibility is again highly
critical, with access rights being rather complicated to manage. Ease of manipulation
is more important here, as it might be necessary to reformat or pre-process infor-
mation for different purposes.
5 Conclusion and Outlook
This paper discusses the importance of information quality in PLM from a produc-
tion process perspective. From literature, a framework with 15 IQ dimensions is se-
lected. Then three different categories of information flows are defined to structure
the discussion. These flows concern the usage of information within production (in-
ternal), coming from production used elsewhere (outbound) and coming towards pro-
duction form different phases (inbound). In the following discussion, the importance
of information quality in production is discussed by mapping the IQ dimensions with
the three types of information flows identified before.
While the depth of the investigation conducted in this paper remains rather low
(e.g. few examples and no consistent use case), it aims to substantiate a debate about
the importance of information quality in PLM. This topic is of major importance, as
the amount, heterogeneity and velocity of available information is growing and the
selection of relevant information becomes more difficult. The definition of three types
of information flows (i.e. internal, inbound and outbound) can be applied to other
processes along the product lifecycle, in order to receive examples for all major
lifecycle phases. In future work, a combined paper is envisaged for that purpose.
Acknowledgement
This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement no. 636951 (Manutelligence). The
authors wish to acknowledge the Commission and all the project partners for a fruitful
collaboration. Finally, the authors would like to thank the reviewers for their com-
ments that helped to improve the manuscript.
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