CaPLIM: The Next Generation of Product Lifecycle Information
Management?
Sylvain Kubler and Kary Fr¨amling
Aalto University, School of Science and Technology
P.O. Box 15500, FI-00076 Aalto, Finland
Keywords:
Product Lifecycle Information Management, Context-awareness, Internet of Things, Enterprise Information
Systems, Quantum Lifecycle Management.
Abstract:
Product Lifecycle Information Management (PLIM) aims to enable all participants and decision-makers to
have a clear, shared understanding of the product lifecycle, and to get feedback on product use conditions.
Each product, whether as a physical or virtual product is designed to provide a range of services aimed at
supporting daily activities of each product stakeholder (e.g., designers, manufacturers, distributors, users,
repairers, or still recyclers). Such services are usually considered once, where parameters are fine-tuned once
and for all. A future generation of services could attempt to self-adapt to the product context by discovering
and exchanging helpful information with other devices and systems within its direct or indirect surrounding.
The so-called Internet of Things (IoT) is a tremendous opportunity to support the development of such a
new generation of services by taking advantage of powerful concepts such as context-awareness. Embedding
context-awareness into the product is a possible solution to learn about the product’s context and to make
appropriate decisions. However, today, this is not enough because of the large number of objects, systems,
networks, and users comprising the IoT that require, more than ever before, standardized ways and interfaces
to exchange all kinds of information between all kinds of devices. In an IoT context, this paper opens up new
research directions for providing a new generation of PLIM services by investigating context-awareness. The
combination of these two visions is referred to as CaPLIM (Context-awareness & PLIM), whose originality
lies in the fact that it takes maximum advantage of IoT standards, and particularly of the recent Quantum
Lifecycle Management (QLM) standard proposal.
1 INTRODUCTION
Since 1960’s, the concept of Product Life Cycle
(PLC) was used in different areas such as in product
management, marketing mix, linking production pro-
cesses and pricing (Utterback and Abernathy, 1975).
Today, the study of the PLC is an integral part of
the company strategy to plan, design and manage the
whole life of their products more effectively (Asiedu
and Gu, 1998). From the 1990
′
s onwards, many new
information systems have been brought to market,
giving the opportunity to work more efficiently in-
ternally and externally, for instance by getting closer
to customers, suppliers and partners (Rockart and
Short, 2012). With the arrival of these new systems
and applications, the concept of Product Lifecycle
Management (PLM) was born to manage the entire
product’s life, from Beginning of Life (BoL) includ-
ing design, production and distribution of the prod-
uct, through Middle of Life (MoL) including use and
maintenance, up to End of Life (EoL) including recy-
cling and disposal. Lee et al. (Lee et al., 2008) ex-
plain that PLM originated from two types of manage-
ment: enterprise management and product informa-
tion management. Enterprise management involves
material and enterprise resource planning (MRP &
ERP), customer relationship management (CRM) and
supply chain management (SCM). Product infor-
mation management involves Computer-Aided De-
sign/Manufacturing (CAD/CAM), Computer Aided
Process Planning (CAPP) and Product Data Manage-
ment (PDM). PLM evolved rapidly and now aims to
integrate people, data, products, processes, organiza-
tions, equipments, and methods throughout the PLC
(Stark, 2011).
To date, many solutions, concepts, standards have
emerged and have been integrated into PLM systems
in order to achieve, among others, Product Lifecy-
cle Information Management (PLIM). PLIM can be
defined as a subpart of PLM since it essentially fo-
539
Kubler S. and Främling K..
CaPLIM: The Next Generation of Product Lifecycle Information Management?.
DOI: 10.5220/0004861705390547
In Proceedings of the 16th International Conference on Enterprise Information Systems (ICEIS-2014), pages 539-547
ISBN: 978-989-758-028-4
Copyright
c
2014 SCITEPRESS (Science and Technology Publications, Lda.)
cuses on the product data aspect, while PLM deals
with all elements involved in a PLC (not only product
data but also people, facilities, workflows...). PLIM
is commonly understood as a strategic approach that
incorporates the management of data associated with
products of a particular type, and perhaps the ver-
sions and variants of that product type. PLIM was
first mentioned by Harrison et al. in a manufacturing
context (Harrison et al., 2004), where product-related
data was linked to the product itself via RFID tech-
nologies. Later, the same authors(Harrison, 2011) ex-
plained that PLIM could be interpreted as a certain ex-
tent of the so-called Internet of Things (IoT) since the
IoT also relies on automaticcapture of observationsof
physical objects at various locations and times, their
movements between locations, data collected from
sensors attached to the objects or within their imme-
diate surroundings. The advent of the IoT and related
concepts such as context-awareness provides tremen-
dous opportunitiesto proposemore advanced services
to all product stakeholders (e.g., services able to self-
adapt to the product and user contexts). Embedding
context-awareness into the product is, indeed, a pos-
sible solution to learn about the product’s context and
to make appropriate decisions. However, today, this
is not enough because of the large number of objects,
technologies, and users comprising the IoT, which re-
quire standardized ways and interfaces to exchange
all kinds of information between all kinds of devices.
In lack of standardized approaches and protocols, it
is difficult to access the right information, whenever
needed, wherever needed, by whoever needs it, which
is a major hurdle to efficient context-aware systems
(Perera et al., 2013). This research initiative aims
at investigating a new generation of PLIM services
that takes advantage of both context-aware systems
and standardized communication interfaces defined
by IoT standards. These new types of systems will
play an accelerating role to help companies to deal
with complex, changing product environments and to
meet the new organizational and customer needs.
Section 2 provides the necessary background on
PLIM and context-awareness to better understand the
ongoing relevance of the CaPLIM research initiative.
The IoT standard used to support CaPLIM devel-
opments are briefly introduced in section 3. Sec-
tion 4 opens up new research directions consider-
ing CaPLIM and provides preliminary thinking about
the research objectives and contributions. Section 5
presents a few examples of IoT applications with var-
ious actors, within which it could be benefit to use
CaPLIM services to improve various aspects of prod-
uct information management.
2 BACKGROUND
PLIM deals with various information aspects and
challenges that are introduced in section 2.1. Sec-
tion 2.2 provides the necessary research background
on context-awareness in order to better understand the
ongoing relevance of the CaPLIM research initiative.
2.1 PLIM Background
PLIM aims to enable all product stakeholders and
decision-makers to have a clear, shared understand-
ing of the product’s life. As mentioned, PLIM is
understood to be a strategic approach that incorpo-
rates the management of data associated with prod-
ucts (Fr¨amling et al., 2013). These product definition
data are generated when the product is first conceived,
and it then continues to evolve with the addition of
detailed specifications, user manuals, computer-aided
design drawings, manufacturing instructions, service
manuals, disposal and recycling instructions. In tra-
ditional PLIM, the product information generation
process seems to end after BoL. When the prod-
uct enters actual use (i.e., MoL), PLIM mainly sig-
nifies providing access to the existing information
but hardly any new information is generated about
the products. Within this context, there has been
only slight interest in how the customer uses each
individual product, or in how that product has be-
haved. Concepts such as “product agents” and “intel-
ligent products” (Meyer et al., 2009) have been pro-
posed as solutions for enabling such item- or instance-
enabled PLIM. Such concepts were the cornerstones
of the product instance-enabled PLIM solutions de-
veloped in the PROMISE EU FP6 project
1
, in which
the paradigm of closed-loop PLM
R
, recently re-
named CL
2
M (Closed-Loop Lifecycle Management),
was introduced (Kiritsis et al., 2003). The break-
through challenge of CL
2
M is to enable the informa-
tion flow to include the customer and to enable the
seamless transformation of informationto knowledge.
CL
2
M and similar paradigms like “Closed-Loop Sup-
ply Chains” (Van Wassenhove and Guide, 2003) con-
tribute to enhance various aspects of PLIM, five of
which being of the utmost importance:
1. Information Security: to maintain the level of se-
curity and confidentiality required by organiza-
tions (Dynes et al., 2007);
2. Information Manageability: to efficiently process
large amounts of raw data (Perera et al., 2013);
3. Information Interoperability: to manage the many
changes in data media and formats throughoutthe
1
http://promise-innovation.com
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540
product lifecycle and to ensure information ex-
changes between any kinds of products, users and
systems (Panetto and Molina, 2008);
4. Information Visibility: to make data available for
any system, anywhere and at anytime. The CL
2
M
consortium defines the visibility of the informa-
tion as the possibility to gather, process and ex-
change the desired information throughout the
whole life of a “thing” (Fr¨amling et al., 2013);
5. information sustainability: to make data capable
of outliving systems, while being consistent (Mc-
Farlane et al., 2013).
Since PLIM is a wide-ranging concept intended
to manage the entire PLC in all possible domains,
one can understand that it is important to develop and
propose sufficiently generic and portable services and
systems to efficiently address each of these aspects.
In this regard, the PROMISE consortium proposed
a set of specifications aimed primarily at improving
information interoperability and visibility throughout
the PLC. Two main specifications were proposed: the
PROMISE Messaging Interface (PMI) that defines
what kinds of interactions between objects are possi-
ble, and the PROMISE System Object Model (SOM)
that provides specifications for representing PLIM in-
formation. At the end of the PROMISE project, the
work on these standards proposals was moved to the
Quantum Lifecycle Messaging (QLM) workgroup of
The Open Group
2
. QLM messaging standards are
derived from PMI and are intended to provide suffi-
ciently generic and standardized application-level in-
terfaces for exchanging the kind of information re-
quired by an IoT (Fr¨amling and Maharjan, 2013)
and, accordingly, to properly support PLIM infras-
tructures.
2.2 Context-awareness in the IoT
Context-awareness. Since the 1990’s, research on
context-awareness also gained a great success in the
IoT community (Perera et al., 2013). The term
context-awarenesswas first introduced by (Schilit and
Theimer, 1994) but a definition that is widely ac-
cepted by the research community today was pro-
posed by (Abowd et al., 1999):
“A system is context-aware if it uses context
to provide relevant information and services,
where relevancy depends on the user’s task”
Although the product context plays a significant role
when dealing with the reality of product and infor-
mation management (i.e., PLIM), there is still too lit-
tle research on context-aware systems/products that
2
http://www.opengroup.org/qlm/
considers the entire product’s life and experience.
This often leads to context-aware systems designed
vendor-, domain- or application-specific, and that use
communication interfaces and data formats barely
compatible with each other (Baldauf et al., 2007).
Such a design strongly limits data exchange interop-
erability in the IoT and, as a consequence, hinders
the development of more advanced, standardized and
pervasive services. Numerous scholars provide evi-
dence and argumentsin this respect (Dey et al., 2001),
one of which being the recent survey made by (Perera
et al., 2013) on context-aware computing for ubiqui-
tous systems. The authors state that: “sharing context
information between distinct organizations is one of
the toughest challenges because systems are designed
in isolated factions, thus limiting their openness and
collaboration”. Various types of middleware support-
ing context-awarenessbased on CORBA, CARISMA,
Gaia, MoCA, Jini, etc., have been developed and en-
able communication between different entities. How-
ever, they always fail to answer one or more require-
ments for data exchange interoperability in the IoT
3
.
For instance, some of these solutions rely on central-
ized architectures like CORBA or Jini (somehow pre-
vents “real” peer-to-peer communications), are lim-
ited to a unique message payload (e.g., CARISMA,
Moca only support XML), do not include strategies
to deal properly with products and systems that are
mobile or located behind firewalls (e.g., the support
of the “piggy-backing” property), and so on.
In this regard, QLM messaging standards are a
tremendous opportunity to investigate new ways to
design and use context-aware systems by taking max-
imum advantage of the standardized IoT interfaces,
which should leverage traditional context-aware ap-
proaches and support the development of portable
product management services. In order to better un-
derstand the interest of using QLM messaging stan-
dards as foundation of CaPLIM, section 4 introduces
the main properties of that standards.
3 QLM MESSAGING STANDARDS
In this section, the two standards proposals derived
from PMI are briefly introduced, namely the QLM
Messaging Interface (QLM-MI) and the QLM Data
Format (QLM-DF). These standards are described in
greater detail in (Fr¨amling and Maharjan, 2013). In
the QLM world, communication between the partici-
pants is done by passing messages between nodes us-
ing the set of interfaces defined in QLM-MI. Whereas
3
See (Fr¨amling and Maharjan, 2013) for such require-
ments.
CaPLIM:TheNextGenerationofProductLifecycleInformationManagement?
541
the Internet uses the HTTP protocol for transmitting
HTML-codedinformation mainly intended for human
users, QLM is used for conveyinglifecycle-related in-
formation mainly intended for automated processing
by information systems. In the same way that HTTP
can be used for transporting payloads in formats other
than HTML, QLM can be used for transporting pay-
loads in nearly any format. The accompanying stan-
dard QLM-DF partly fulfills the same role in the IoT
as HTML does for the Internet, meaning that QLM-
DF is a generic content description model for things
in the IoT.
3.1 QLM Data Format
QLM-DF is defined as a simple ontology that is
generic enough for representing “any” object and in-
formation that is needed for information exchange
in the IoT. It is intentionally defined in a similar
manner as data structures in object-oriented program-
ming. QLM-DF is structured as a hierarchy with an
“Object” element as its top element. The “Object”
element can contain any number of “Object” sub-
elements, which can have any number of properties,
referred to as InfoItems. The resulting Object tree
can contain any number of levels. Every Object has
a compulsory sub-element called “id” that identifies
the Object. The “id” should preferably be globally
unique or at least unique for the specific application,
domain, or network. XML Schema might currently
be the most common text-based payload format due
to its flexibility but others such as JSON, CSV can
also be used.
3.2 QLM Messaging Interface
A defining characteristic of QLM-MI is that QLM
nodes may act both as “servers” and as “clients”, and
thus communicate directly with each other or with
back-end servers in a peer-to-peer manner. Typical
examples of exchanged data are sensor readings, life-
cycle events, requests for historical data, notifications,
etc. One of the fundamental properties of QLM-MI is
that QLM messages are protocol agnostic so they can
be exchanged using HTTP, SOAP, SMTP or similar
protocols. Three QLM operations are possible:
1. Write: used to send information updates to QLM
nodes;
2. Read: used for immediate retrieval of informa-
tion and for placing subscriptions for deferred re-
trieval of information from a node;
3. Cancel: used to cancel a subscription.
The subscription mechanism is a cornerstone of
that standard. Two types of subscriptions can be
performed: i) subscription with callback address:
the subscribed data is sent to the callback address
at the requested interval (two types of intervals can
be defined: interval-based or event-based), and ii)
subscription without callback address: the data is
memorized on the subscribed QLM node as long as
the subscription is valid. Historical data can therefore
be retrieved by issuing a new QLM read query.
It must be noted that other relevant interfaces and
properties (not detailed in this paper) are proposed by
the QLM-MI standard, which cover most of the IoT
requirementsas discussed in (Fr¨amlingand Maharjan,
2013).
4 CaPLIM: RESEARCH
OBJECTIVES
CaPLIM is primarily intended to reliably and dy-
namically manage context-aware product data and
services, and to efficiently support product context
acquisition, discovery, and reasoning. Given this
consideration, the research hypothesis of CaPLIM is
twofold. First, the development of CaPLIM services
should consider the whole product’s life and experi-
ence, and thereby should include life cycle assess-
ment. Second, because of changing product envi-
ronmental factors, technological solutions cannot be
developed in isolation from product lifecycle actors
and systems; rather, all solutions must take into ac-
count changing behaviors of actors using context-
aware techniques.
Using the framework of (Denyer et al., 2008) for
evidence-based management research, the research
contributions can be articulated around four pillars:
• the problem in context is introducing context-
awareness to product information management;
• the interventions of interest are the development
of CaPLIM services using generic and standard-
ized interfaces for data exchange in the IoT so as
to reach our objectives in terms of service porta-
bility and interoperability;
• the generative mechanisms studied are the ways
through which the interventions affect the overall
adaptability, portability and security of services
provided to users;
• the outcomes of the interventionsare concrete and
easy-to-use algorithms, software, and methodolo-
gies that users and system managers can safely
implement and adapt to their own application.
ICEIS2014-16thInternationalConferenceonEnterpriseInformationSystems
542
Table 1: CaPLIM contributions.
Framework n ˚ Contributions
Problem in 1a Compare with traditional PLIM, what are the fundamental issues underlying CaPLIM
context 1b Define what is called “context” in CaPLIM and the respective working assumptions
Interventions
of interest
2a Provide context-aware and personalized dynamic product services (and information) using generic
IoT interfaces
2b Effectively communicate with users through an easy-to-use context-aware query language
Generative
mechanisms
3 Qualitatively and quantitatively evaluate the benefits to use CaPLIM strategies in real and diversified
applications, systems and projects
Outcomes 4 Provide self-adapting services, techniques and algorithms to be used in any information manage-
ment project/system
The major contributions related to these four pillars
are presented in Table 1. As mentioned, all CaPLIM
originality comes from the use of generic and stan-
dardized IoT interfaces to support the development
of portable and self-adapting context-aware prod-
uct services. Appropriate QLM interfaces must be
solicited according to the product context, user re-
quirements, and system constraints, and should lead
to make appropriatedecisions. These decisions could,
in turn, eventually use specific QLM interfaces to ac-
complish their tasks (e.g., by subscribing new infor-
mation or by controlling particular devices). Ulti-
mate, the goal is to propose product services to ad-
dress each of the five PLIM aspects introduced in sec-
tion 2.1. Examples of such services include:
1. Information Security Services: to decide what in-
formation must be hide or shared with product
stakeholders throughout the PLC. The benefits of
taking into account the product context is that it
provides more meaningful information that helps
understanding a situation or data. However, at the
same time, it increases the security threats due to
possible misuse of the context (e.g., identity, loca-
tion, activity, and behavior) (Perera et al., 2013).
New services able to handle the challenging con-
flict between data “security” and “usability” must
be proposed in CaPLIM;
2. Information Manageability Services: to automat-
ically understand the raw data (e.g., generated
by sensors) and related context. In this regard,
CaPLIM services should integrate, among others,
tools for data analysis, reasoning, and machine
learning, but also strategies for refining as much
as possible the context modeling within which the
product operates in order to draw correct conclu-
sions (e.g., an unusual value collected on a prod-
uct can be due to external events and does not nec-
essary imply a product malfunction). Such a re-
finement is made possible using particular QLM
messaging interfaces to discover, read or sub-
scribe in “real-time” any new information about
the product and its surrounding;
3. Information Interoperability Services: to support
a wide variety of ontologies for semantic con-
text representation, context reasoning and knowl-
edge sharing, context classification, context de-
pendency and quality of context (Chen et al.,
2003). CaPLIM services should support such on-
tologies to provide knowledge sharing in an open
and dynamic distributed systems, and means for
intelligent devices not expressly designed to work
together to interoperate, thus achieving “serendip-
itous interoperability” (McIlraith et al., 2001);
4. Information Visibility Services: to assess and rank
product-related information as well as sensors
and other information systems generating this in-
formation to help deciding what information, or
piece of this information, is relevant to be used
and shared between product stakeholders. Assess-
ment models developed in CaPLIM should pro-
pose dynamic combinations of information qual-
ity factors such as data accuracy, accessibility,
completeness, interoperability, intelligibility, and
privacy (Maurino and Batini, 2009);
5. Information Sustainability Services: to handle
outdated or wrong product-related data, which is
a frequent and significant problem in PLIM envi-
ronments. Indeed, product data is often accessed
and modified by different actors, stored in differ-
ent systems and organizations, which leads to nu-
merous replicas of the same data (Stark, 2011).
To address this issue, CaPLIM should provide
suitable peer-to-peer data synchronization mech-
anisms able to self-adapt according to the product
context. Accordingto (Bellavista et al., 2013), de-
veloping context data distribution strategies (in-
cluding data synchronization) able to self-adapt
autonomously depending on current management
conditions is still an unexplored research field.
A key challenge in CaPLIM is to be able to ex-
trapolate the key features of traditional context-aware
models and to combine, or enrich them, using the
generic interfaces defined in the QLM standards (or
similar IoT standards) in order to benefit from their
CaPLIM:TheNextGenerationofProductLifecycleInformationManagement?
543
Real-time appliance monitoring
Study of future
product generations
Designer
Manufacturer
Warehouser
Dealer
M
b
I
CaPLIM
QLM
CaPLIM
QLM
CaPLIM
QLM
CaPLIM
QLM
B
o
L
E
o
L
M
o
L
x
A
Distributor
Users
Cardiologist
Recycler
)
)
)
)
)
)
2
x
U
HRV
sensor
+
Monitoring of user body features by subscribing
appropriate information to the smart watch
e.g., heart rate variability (HRV), muscle activity. . .
1
4
4
4
1
2
3
2
33
)
)
))
)
Legend
)
QLM messages
Smart appliances
or devices
2
3
4
1
Design of future product generations (automatic retrieval of historical values in the manufacturer database)
Maintenance appliance scheduling (automatic self-diagnosis about appliances based on “real-time” data)
Healthcare assistance (automatic self-diagnosis about residents based on “real-time” data)
Home automation (automatic house control services)
Figure 1: Possible scenarios using CaPLIM for various product information management purposes.
high portability and interoperability. Another major
challenge in CaPLIM is to propose strategies to mea-
sure both ab initio and in fine the benefits of using
CaPLIM solutions over traditional ones.
5 REAL-LIFE
IMPLEMENTATIONS
Several demonstrators developed in PROMISE ought
to be re-used in this research (i.e., updated with QLM
messaging standards) to investigate, deploy, and as-
sess CaPLIM services. Such demonstrators have the
particularity to be defined in different PLC phases
and contexts such as for monitoring EoL vehicles,
for heavy load vehicle decommissioning (EoL), for
predictive maintenance for trucks (MoL), for predic-
tive maintenance for machine tools (MoL), or still for
adaptive production (BoL).
The CaPLIM research initiative makes a point of
using real-life implementations for deploying and as-
sessing services offered to users, which will enable
to refine as much as possible the CaPLIM’s theoreti-
cal body. The following sections present several sce-
narios considering a unique platform (a smart apart-
ment), whose objective is to show how CaPLIM ser-
vices could contribute to enhance product informa-
tion management from different user perspectives. In
these different scenarios, first insights into concrete
actions to be fulfilled/undertaken by the CaPLIM al-
gorithms are provided. Figure 1 depicts the smart
apartment and some of the actors/devices/systems in-
volved in its lifecycle. This figure also provides a
viewof the QLM “cloud” that interconnectsall phases
and organizations/actorsfrom the apartment lifecycle.
5.1 Home Automation
Numerous services for automatic house control could
be developed and proposed by the CaPLIM initiative,
whose product and user contexts will play a signifi-
cant role in decisions making. In our scenario, smart
appliances and users are able to exchange specific in-
formation with each other using the generic QLM in-
terfaces (see communications denoted by “1” in Fig-
ure 1), which is a good opportunity to learn in “real-
time” about their respective features (e.g., about the
appliance mode “On mode”, “Sleep mode”; the en-
ergy consumed over a certain period of time...), but
also to learn about the user context (at home, at work,
in vacation) or to be notified about unusual event oc-
currences (e.g., the resident no longer move in the
apartment). Such “real-time” data are more than nec-
essary to provide the types of information required by
context-aware systems.
ICEIS2014-16thInternationalConferenceonEnterpriseInformationSystems
544
CaPLIM service
Internet
t t
wget
1
wget http://dialog.hut.fi/qlm/Objects/
<Objects>
<Object>
<id>Fridge123</id>
</Object>
<Object>
<id>AirConditioner321</id>
</Object>
<Object>
<id>Watch448</id>
</Object>
</Objects>
wget
2
wget http://dialog.hut.fi/qlm/Objects/Fridge123
<infoItemList>
<infoItem>
<id>Temperature</id>
</infoItem>
<infoItem>
<id>PowerConsumption</id>
</infoItem>
</infoItemList>
Figure 2: RESTful QLM “discovery” mechanism.
To succeed in this task, fundamental interfaces are
required such as the automatic discovery of informa-
tion about the product or about its (direct or indirect)
surrounding. The RESTful QLM “discovery” mech-
anism is an undeniable asset for developing such data
discovery services. An example of this mechanism
using the Unix
wget
utility is shown in Figure 2.
wget 1
requests for receiving the set of devices in the
smart apartment (devices that implement QLM mes-
saging standards to be more accurate). Three appli-
ances implement such standards, namely
Fridge123
,
AirConditioner321
, and
Watch448
(see Figure 2).
Algorithms developed in CaPLIM could eventually
refine their research (if needed) by retrieving the set
of InfoItems related to one or several of those de-
vices, and so on.
wget 2
(cf. Figure 2) requests
for such information regarding
Fridge123
, whose re-
sult highlights that two InfoItems are reachable on
that appliance (e.g., for read, write, or subscription
operations), namely InfoItems named
Temperature
and
PowerConsumption
. One can then understand
how such a mechanism will help to built dynamic and
portable algorithms to discover and monitor, at any
time, aspects required by context-aware algorithms.
5.2 Maintenance Appliance Scheduling
Manufacturers or a service providers could use
CaPLIM services to monitor in “real-time” appliances
and to eventually detect product discrepancies. This
scenario is depicted in Figure 1 with communica-
tions denoted by “2”, through which the manufacturer
subscribes to particular InfoItems to the smart fridge
(namely InfoItem named
PowerConsumption
). Such
a subscription request is provided in Figure 3 includ-
ing:
• the type of operation: the operation is of type
CaPLIM service
Parameters to be automatically discover and set up according to the product’s context
Figure 3: Automatic self-setting of the QLM parameters.
“read” (see row 2) since it is a subscription re-
quest;
• the callback address: the callback address corre-
sponds to the manufacturer’sdatabase system (see
rows 2-3);
• the interval parameter: set to “3600 s” (see
row 2), which means that at the requested inter-
val the subscribed value is pushed to the callback
address;
• the TTL parameter: the TTL is set to “-1” (see
row 1), which indicates that the subscription is
“forever”;
• InfoItem(s) to be subscribed: the subscribed In-
foItem is
Temperature
(see row 8).
Currently, such parameters must be specified by the
user/engineer. CaPLIM should provide algorithms
able to automatically set the appropriate parameter
values according to the product context, the manufac-
turer needs, etc. This contribution is emphasized in
Figure 3 (see CaPLIM service), and will help make
the tasks of the engineer easier, even transparent for
such configuring settings. Once subscriptions have
been set up, CaPLIM algorithms should be able to
process values received at the requested interval, to
identify unusual behaviors, and to react accordingly.
5.3 Future Product Generations
Product designers are increasingly looking for full-
services that make it possible to retrieve information
about their products under in-use conditions, to learn
how the product behave, and to enhance their design
for generations to come. CaPLIM should provide al-
gorithms and methodologies that could automatically
retrievesuch information, at the right time, in the right
format and from the appropriate information system
(e.g., it could be retrieved either from the manufac-
turer’s database system or from the fridge itself de-
CaPLIM:TheNextGenerationofProductLifecycleInformationManagement?
545
pending on privacy rules). Figure 1 illustrates the
first situation where historical information related to
the smart fridge of ID Fridge123 is retrieved from the
manufacturer’s database (see communication denoted
by “3” in Figure 1). Considering a wide panel of users
(fridges to be more exact), such information could be
used as inputs to machine learning algorithms, neu-
ral networks, statistical algorithms, and so on. Ulti-
mately, CaPLIM should make use of appropriate tools
according to the designer needs, the product environ-
ment under in-use conditions, and other factors.
6 CONCLUSION
To a certain extent, the IoT relies on automatic cap-
ture of observations of physical objects at various
locations and times, their movements between loca-
tions, data collected from sensors attached to the ob-
jects or within their immediate surroundings. Each
of these objects or products is designed to provide
a range of services aimed at supporting daily activi-
ties of each product user (e.g., designers, manufactur-
ers, users, repairers...) . Such services are usually
considered once and parameters are fine-tuned once
and for all. A future generation of services could at-
tempt to self-adapt to the product context by discov-
ering and exchanging helpful information with other
devices and systems within its direct or indirect sur-
rounding. The IoT and related concepts like context-
awareness are key ingredients for supporting the de-
velopment of such a new generation of services. Em-
bedding context-awareness into the product is a pos-
sible solution but is not enough because more ad-
vanced and standardized interfaces are required to ex-
change the kind of information required by an IoT,
which has a direct impact on Product Lifecycle Infor-
mation Management (PLIM). In an IoT context, this
paper opens up new research directions for providing
a new generation of PLIM services by investigating
context-awareness. The combination of these two vi-
sions is referred to as CaPLIM (Context-awareness&
PLIM), whose originality lies in the fact that it takes
maximum advantage of IoT standards, and particu-
larly of the recent QLM standard proposal. This new
generation of services will play an accelerating role to
provide new generations of services that help compa-
nies to deal with complex and changing product en-
vironments. This should lead to propose ideas for
new environment-friendly products, and to improve
the customer experience.
ACKNOWLEDGEMENTS
We would like to thank Prof. Yves LE TRAON and
Dr. Patrice CAIRE from the University of Luxem-
bourg, as well as Prof. Andr´e THOMAS and Dr.
William DERIGENT from the University of Lorraine
for their contribution and support regarding this re-
search initiative.
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