An outcome-based process optimization model using fuzzy-based association rules
| Pages | 1138-1152 |
| DOI | https://doi.org/10.1108/IMDS-08-2017-0347 |
| Published date | 09 July 2018 |
| Date | 09 July 2018 |
| Author | Henry Lau,C.K.M. Lee,Dilupa Nakandala,Paul Shum |
| 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 |
An outcome-based process
optimization model using
fuzzy-based association rules
Henry Lau
School of Management, The University of Western Sydney, Penrith, Australia
C.K.M. Lee
Department of Industrial and Systems Engineering,
The Hong Kong Polytechnic University, Hunghom, Hong Kong
Dilupa Nakandala
University of Western Sydney, Penrith, Australia, and
Paul Shum
Human Resources and Management, Western Sydney University,
Parramatta, Australia
Abstract
Purpose –The purpose of this paper is to propose an outcome-based process optimization model which can
be deployed in companies to enhance their business operations, strengthening their competitiveness in the
current industrial environment. To validate the approach, a case example has been included to assess the
practicality and validity of this approach to be applied in actual environment.
Design/methodology/approach –This model embraces two approaches including: fuzzy logic for
mimicking the human thinking and decision making mechanism; and data mining association rules approach
for optimizing the analyzed knowledge for future decision-making as well as providing a mechanism to apply
the obtained knowledge to support the improvement of different types of processes.
Findings –The new methodology of the proposed algorithm has been evaluated in a case study and the
algorithm shows its potential to determine the primary factors that have a great effect upon the final result of
the entire operation comprising a number of processes. In this case example, relevant process parameters
have been identified as the important factors causing significant impact on the result of final outcome.
Research limitations/implications –The proposed methodol ogy requires the depen dence on human
knowledge and personal experience to determine the various fuzzy regions of theprocesses. This can be
fairly subjective and e ven biased. As such, it is adv isable that the develo pment of artificial int elligence
techniques to support automatic machine lea rning to derive the fuzzy sets should be p romoted to provide
more reliable results.
Originality/value –Recent study on the relevant topics indicates that an intelligent process optimization
approach, which is able to interact seamlessly with the knowledge-based system and extract useful
information for process improvement, is still seen as an area that requires more study and investigation. In
this research, the process optimization system with an effective process mining algorithm embedded for
supporting knowledge discovery is proposed for use to achieve better quality control.
Keywords Fuzzy logic, Data mining, Optimization model, Algorithms, Association rules
Paper type Research paper
1. Introduction
The issue of “brain drain”in today’s competitive industrial environment is always a concern
that needs to be noticed. To address this challenge, companies have been trying to keep and
store useful experience and knowledge using various approaches and techniques. This is
essential to ensure that the continual growth of company business can be sustained.
As such, knowledge management has become an important area of study to capture tacit
and explicit knowledge. It is obvious that sudden departure of key technical staff will cause
significant disruption of operations in companies no matter they are big or small.
In particular, small and medium enterprises are those companies which will suffer most of
Industrial Management & Data
Systems
Vol. 118 No. 6, 2018
pp. 1138-1152
© Emerald PublishingLimited
0263-5577
DOI 10.1108/IMDS-08-2017-0347
Received 5 August 2017
Revised 9 November 2017
Accepted 16 December 2017
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0263-5577.htm
1138
IMDS
118,6
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