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CaPLIM: the next generation of Product Lifecycle Information Management?

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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.
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CaPLIM: the next generation of Product Lifecycle Information
Management?
Sylvain Kubler, Kary Fr¨
amling
Aalto University, School of Science and Technology
P.O. Box 15500, FI-00076 Aalto, Finland
firstname.lastname@aalto.fi
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 man-
age the whole life of their products more effec-
tively (Asiedu and Gu, 1998). From the 1990son-
wards, many new information systems have been
brought to market, giving the opportunity to work
more efficiently internally and externally, for in-
stance by getting closer to customers, suppliers and
partners (Rockart and Short, 2012). With the arrival
of these new systems and applications, the con-
cept of Product Lifecycle Management (PLM) was
born to manage the entire product’s life, from Be-
ginning of Life (BoL) including design, production
and distribution of the product, through Middle of
Life (MoL) including use and maintenance, up to
End of Life (EoL) including recycling and disposal.
Lee et al. (Lee et al., 2008) explain that PLM orig-
inated from two types of management: enterprise
management and product information management.
Enterprise management involves material and enter-
prise resource planning (MRP & ERP), customer
relationship management (CRM) and supply chain
management (SCM). Product information manage-
ment involves Computer-Aided Design/Manufactur-
ing (CAD/CAM), Computer Aided Process Planning
(CAPP) and Product Data Management (PDM). PLM
evolved rapidly and now aims to integrate people,
data, products, processes, organizations, 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-
cuses on the product data aspect, while PLM deals
with all elements involved in a PLC (not only prod-
uct data but also people, facilities, workflows. . . ).
PLIM is commonly understood as a strategic ap-
proach that incorporates the management of data as-
sociated with products of a particular type, and per-
haps the versions 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 it-
self via RFID technologies. Later, the same authors
(Harrison, 2011) explained that PLIM could be in-
terpreted as a certain extent of the so-called Inter-
net of Things (IoT) since the IoT also relies on au-
tomatic capture of observations of physical objects
at various locations and times, their movements be-
tween locations, data collected from sensors attached
to the objects or within their immediate surround-
ings. The advent of the IoT and related concepts such
as context-awareness provides tremendous opportu-
nities to propose more advanced services to all prod-
uct stakeholders (e.g., services able to self-adapt to
the product and user contexts). Embedding context-
awareness into the product is, indeed, a possible so-
lution 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 project1, in which
the paradigm of closed-loop PLM R
, recently re-
named CL2M (Closed-Loop Lifecycle Management),
was introduced (Kiritsis et al., 2003). The break-
through challenge of CL2M is to enable the informa-
tion flow to include the customer and to enable the
seamless transformation of information to knowledge.
CL2M 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 throughout the
1http://promise-innovation.com
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 CL2M
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 capa-
ble of outliving systems, while being consistent
(McFarlane 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 Group2. 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 infrastruc-
tures.
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-awareness was first introduced by
(Schilit and Theimer, 1994) but a definition that is
widely accepted by the research community today
was proposed 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
2http://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 in-
teroperability in the IoT and, as a consequence,
hinders the development of more advanced, stan-
dardized and pervasive services. Numerous schol-
ars provide evidence and arguments in this respect
(Dey et al., 2001), one of which being the recent sur-
vey made by (Perera et al., 2013) on context-aware
computing for ubiquitous systems. The authors state
that: “sharing context information between distinct
organizations is one of the toughest challenges be-
cause systems are designed in isolated factions, thus
limiting their openness and collaboration”. Various
types of middleware supporting context-awareness
based on CORBA, CARISMA, Gaia, MoCA, Jini,
etc., have been developed and enable communica-
tion between different entities. However, they al-
ways fail to answer one or more requirements for data
exchange interoperability in the IoT3. For instance,
some of these solutions rely on centralized architec-
tures like CORBA or Jini (somehow prevents “real”
peer-to-peer communications), are limited to a unique
message payload (e.g., CARISMA, Moca only sup-
port XML), do not include strategies to deal prop-
erly with products and systems that are mobile or lo-
cated 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 propos-
als 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 participants is done by
3See (Fr¨
amling and Maharjan, 2013) for such require-
ments.
passing messages between nodes using the set of
interfaces defined in QLM-MI. Whereas the Inter-
net uses the HTTP protocol for transmitting HTML-
coded information mainly intended for human users,
QLM is used for conveying lifecycle-related informa-
tion mainly intended for automated processing by in-
formation systems. In the same way that HTTP can be
used for transporting payloads in formats other than
HTML, QLM can be used for transporting payloads
in nearly any format. The accompanying standard
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 requirements as discussed in
(Fr¨
amling and 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 interventions are concrete and
easy-to-use algorithms, software, and methodolo-
gies that users and system managers can safely
implement and adapt to their own application.
Table 1: CaPLIM contributions
Framework n ˚Contributions
Problem in 1aCompare with traditional PLIM, what are the fundamental issues underlying CaPLIM
context 1bDefine what is called “context” in CaPLIM and the respective working assumptions
Interventions
of interest
2aProvide context-aware and personalized dynamic product services (and information) using generic
IoT interfaces
2bEffectively 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 appropriate decisions. 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 context
representation, context reasoning and knowledge
sharing, context classification, context depen-
dency and quality of context (Chen et al., 2003).
CaPLIM services should support such ontologies
to provide knowledge sharing in an open and dy-
namic distributed systems, and means for intelli-
gent devices not expressly designed to work to-
gether to interoperate, thus achieving “serendipi-
tous 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 out-
dated 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. According to (Bellavista et al., 2013),
developing 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
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 (automati c 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 assess CaPLIM services. Such demonstrators
have the particularity to be defined in different PLC
phases and contexts such as for monitoring EoL vehi-
cles, for heavy load vehicle decommissioning (EoL),
for predictive maintenance for trucks (MoL), for pre-
dictive 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 apart-
ment and some of the actors/devices/systems involved in
its lifecycle. This figure also provides a view of the QLM
“cloud” that interconnects all phases and organizations/ac-
tors from 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 significant role in de-
cisions making. In our scenario, smart appliances and users
are able to exchange specific information with each other
using the generic QLM interfaces (see communications de-
noted by “1” in Figure 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
energy consumed over a certain period of time. . . ), but also
to learn about the user context (at home, at work, in vaca-
tion) or to be notified about unusual event occurrences (e.g.,
the resident no longer move in the apartment). Such “real-
time” data are more than necessary to provide the types of
information required by context-aware systems.
To succeed in this task, fundamental interfaces are re-
quired such as the automatic discovery of information about
the product or about its (direct or indirect) surrounding.
The RESTful QLM “discovery” mechanism is an unde-
niable asset for developing such data discovery services.
An example of this mechanism using the Unix
wget
util-
ity is shown in Figure 2.
wget 1
requests for receiving
the set of devices in the smart apartment (devices that
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
implement QLM messaging standards to be more accu-
rate). Three appliances implement such standards, namely
Fridge123
,
AirConditioner321
, and
Watch448
(see Fig-
ure 2). Algorithms developed in CaPLIM could eventu-
ally refine their research (if needed) by retrieving the set
of InfoItems related to one or several of those devices, and
so on.
wget 2
(cf. Figure 2) requests for such informa-
tion regarding
Fridge123
, whose result 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 un-
derstand 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 even-
tually detect product discrepancies. This scenario is de-
picted in Figure 1 with communications denoted by “2”,
through which the manufacturer subscribes to particular
InfoItems to the smart fridge (namely InfoItem named
PowerConsumption
). Such a subscription request is pro-
vided in Figure 3 including:
the type of operation: the operation is of type “read”
(see row 2) since it is a subscription request;
the callback address: the callback address corresponds
to the manufacturer’s database system (see rows 2-3);
the interval parameter: set to “3600 s” (see row 2),
which means that at the requested interval the sub-
scribed 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 InfoItem
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 accord-
ing to the product context, the manufacturer needs, etc. This
CaPLIM service
Parameters to be automatically discover and set up according to the product’s context
1<ql m En v e lo p e xm l ns = ”QL M mi . x sd ” ve r s i o n = 1. 0 t t l =1”>
2<r e a d m sg f o rm a t = ”QLM mf . x s d ” i n t e r v a l = ”3 60 0 ” c a l l b a c k
3=” h t t p : / / 2 0 7 . 4 6 .1 3 0 . 1 / D B ma nu f ac t ur er ”>
4<msg>
5<O b j e c t s x ml n s = ”QLM mf . xs d ”>
6<O b j e c t t y p e = ” F r i d g e U s e r X >
7<i d>Fridge123</ i d>
8<I n f o I t e m c l a ss = Te m p e r a t u r e ></ I n f o I t e m>
9</ Ob j e c t>
10 </ O b j e c t s>
11 </ msg>
12 </ re a d>
13 </ ql mE nve lo pe>
New
Figure 3: Automatic self-setting of the QLM parameters
contribution is emphasized in Figure 3 (see CaPLIM ser-
vice), and will help make the tasks of the engineer easier,
even transparent for such configuring settings. Once sub-
scriptions 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 prod-
uct behave, and to enhance their design for generations to
come. CaPLIM should provide algorithms and methodolo-
gies that could automatically retrieve such information, at
the right time, in the right format and from the appropriate
information system (e.g., it could be retrieved either from
the manufacturer’s database system or from the fridge itself
depending 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 ex-
act), such information could be used as inputs to machine
learning algorithms, neural networks, statistical algorithms,
and so on. Ultimately, CaPLIM should make use of ap-
propriate tools according to the designer needs, the product
environment under in-use conditions, and other factors.
6 Conclusion
To a certain extent, the IoT relies on automatic capture
of observations of 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. Each of these objects or products is de-
signed to provide a range of services aimed at supporting
daily activities of each product user (e.g., designers, man-
ufacturers, users, repairers. . . ) . Such services are usu-
ally considered once and 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 ex-
changing helpful information with other devices and sys-
tems within its direct or indirect surrounding. The IoT and
related concepts like context-awareness are key ingredients
for supporting the development of such a new generation
of services. Embedding context-awareness into the prod-
uct is a possible solution but is not enough because more
advanced and standardized interfaces are required to ex-
change the kind of information required by an IoT, which
has a direct impact on Product Lifecycle Information Man-
agement (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 combina-
tion 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 particu-
larly of the recent QLM standard proposal. This new gener-
ation of services will play an accelerating role to provide
new generations of services that help companies to deal
with complex and changing product environments. 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 Luxembourg, as well
as Prof. Andr´
e THOMAS and Dr. William DERIGENT
from the University of Lorraine for their contribution and
support regarding this research initiative.
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... Internet of Things (IoT) concept refers to the idea that product information should be easily available everywhere. This underlying principle allows it to behave as a fundamental information system which can be used to access the information of smart products [1]. This capability of IoT can be extended by employing IoT technology in the entire product lifecycle (PLC), consisting of Beginning of Life (BoL), Middle of Life (MoL), and End of Life (EoL). ...
... Although the terms and approaches are different, all the definitions integrate the idea of using a DT for managing the lifecycle of physical objects, which can be considered groundbreaking for PLM [7]. A subcategory of PLM incorporating the management of data associated with physical objects during their lifecycle is known as Product Lifecycle Information Management (PLIM) [1,14]. PLIM mostly enables accessing the existing information; however, it processes poorly any new information generated about the products [1]. ...
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