A semantic-based decision support platform to assist products’ eco-labeling process
| Published date | 14 August 2017 |
| Date | 14 August 2017 |
| Pages | 1340-1361 |
| DOI | https://doi.org/10.1108/IMDS-09-2016-0405 |
| Author | Da Xu,Mohamed Hedi Karray,Bernard Archimède |
| Subject Matter | Information & knowledge management,Information systems,Data management systems,Knowledge management,Knowledge sharing,Management science & operations,Supply chain management,Supply chain information systems,Logistics,Quality management/systems |
A semantic-based decision
support platform to assist
products’eco-labeling process
Da Xu, Mohamed Hedi Karray and Bernard Archimède
Ecole Nationale d'Ingénieurs de Tarbes, Tarbes, France
Abstract
Purpose –With the rising concern of safety, health and environmental performance, eco-labeled product and
service are becoming more and more popular. However, the long and complex process of eco-labeling
sometimes demotivates manufacturers and service providers to be certificated. Thepurpose of this paper is to
propose a decision support platform aiming at further improvement and acceleration of the eco-labeling
process in order to democratize a broader application and certification of eco-labels, also to consolidate the
credibility and validity of eco-labels.
Design/methodology/approach –This decision support platform is based on a comprehensive
knowledge base composed of various domain ontologies that are constructed according to an official eco-label
criteria documentation.
Findings –Through standard Resource Description Framework and Web Ontology Language ontology
query interface, the assets of the decision support platform will stimulate domain knowledge sharing and can
be applied into other applications. A case study of laundry detergent eco-labeling process is also presented in
this paper.
Originality/value –The authors present a reasoning methodology based on inference with Semantic Web
Rule Language (SWRL) rules which allows decision making with explanation.
Keywords Ontology engineering, OWL, Decision support, Eco-labeling, Knowledge base, SWRL
Paper type Research paper
1. Introduction
Since recent decades, there has been a growing demand from consumers, especially in
developed economies, that products must be safe to use and are encouraged to do less
harm to the environment. From a global point of view, promote of environment-friendly
produce-consume-recycle progress will contribute not only to the life quality but also to the
economy itself. The need of evaluating a product’s safety, effectiveness and environmental
performance has led to the establishment of eco-labels in order to certificate a product or
service that meets certain environmental criteria.
On the other hand, for an eco-label applicant, usually a manufacturer or a service
provider, it is easy to provide the required information in whatever formats. However, the
difficulties encountered in the evaluating process are representative in decision-making
process. To efficiently assess product or service, we need to manipulate different types of
voluminous data; take into account different criteria and conduct a multi-criteria analysis;
consider different phases of product or service life cycle. Usually, a group of human experts
coming from various domains will work together and the evaluating process will take a long
time, and errors and conflicts may exist. In addition, the evaluation result is actually a good
resource that could have been made better use of. In order to better solve problems above,
we propose to design and develop a decision support platform to assist the eco-labeling
decision-making process.
According to the survey made in Golden et al. (2010), among single-standard eco-labels,
the most common labels for time required to certification was three to six months, with
37 percent of respondents falling into this category. The average time to certification across
single-standard labels is 4.33 months, the standard deviation is 4.37 months, which indicates
that the time to get certificated is quite long, especially for some small- and medium-sized
Industrial Management & Data
Systems
Vol. 117 No. 7, 2017
pp. 1340-1361
© Emerald PublishingLimited
0263-5577
DOI 10.1108/IMDS-09-2016-0405
Received 30 September 2016
Revised 20 January 2017
29 March 2017
5 April 2017
Accepted 5 April 2017
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0263-5577.htm
1340
IMDS
117,7
enterprises and there is still a significant lack of uniformity in the market. Some digitalized
management software tools are currently used, however, the evaluation process of
eco-labeling certification is still mainly accomplished by the manual inspection or checking
of domain experts. If some decision support tools are used in the heavy-duty and
knowledge-intensive evaluation process, we believe that the response time will be
drastically shortened and the certification cost will be reduced. This is the main gap we try
to fill in this paper. We believe that faster certification with quality and credibility means
more opportunities for enterprises and more efficient market. This will also be helpful when
eco-labeling is introduced to a fast developing and much bigger market, i.e. China and India.
Our research is to develop a decision support platform in the scope of eco-label
certification, i.e. eco-labeling process. The decision support process will take target product’s
profile as input, compare it with criteria which are stored in the knowledge base composed
of ontologies, then generate the reasoning result and argumentation or explanation that tells
whether the target product can be eco-labeled or not. A significant improvement of our
approach compared to traditional decision support systems (DSSs) as mentioned and
discussed in Whyte (1986) and Eom (1999) lies in the way the knowledge and data are
stored. The knowledge cross-covered in such a decision support process will be represented
in modularized ontologies and stored in a knowledge base. Synthetic engineering methods
are proposed to organize these modularized ontologies in configurable context by
combining them with infrastructure based on E-connection (Grau et al., 2006). Another
advantage of our approach is the argumentation or explanation that accompanies the
labeling decision. In the light of the argumentation, decision makers can have clearer
understanding on how the decision result is made and why. As is illustrated on the left side
of Figure 1, the objective of our research is to assist and accelerate the evaluation process of
eco-labeling to help domain experts make wiser decisions on behalf of the administration
and management of eco-labeling. The proposed knowledge base of this system will
contribute the reuse of eco-label products knowledge and improve its interoperability with
other systems, such as environment management systems (EMS), product lifecycle
management (PLM) systems, and enterprise resource planning systems. Simultaneously,
from the point of view of a producer as an eco-label applicant, such a decision support tool
can serve as a simulation tool that will assist the design and validation phases of new
product’s development, as shown on the right side of Figure 1.
Eco-labeling administration
Domain experts’ view
Eco-labeling Decision
Support Platform
Manufacturer and Producer
Managers’ view
Product profile from
eco-labeling application
Labeling decision with
argumentation and
explanations
Product details from
local system
Validation and
assessment result which
could help the product’s
design and development
Figure 1.
The objective and
function of the
decision support
platform from
both eco-labeling
administration
and producer’s
point of view
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Semantic-based
decision support
platform
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