A model-driven decision approach to collaborative planning and obsolescence for manufacturing operations
| DOI | https://doi.org/10.1108/IMDS-05-2019-0264 |
| Pages | 1926-1946 |
| Date | 21 October 2019 |
| Published date | 21 October 2019 |
| Author | Swee Kuik,Li Diong |
| Subject Matter | Information & knowledge management |
A model-driven decision
approach to collaborative
planning and obsolescence for
manufacturing operations
Swee Kuik
School of Business and Law,
Central Queensland University, Brisbane, Australia, and
Li Diong
School of Commerce, Faculty of Business,
University of Southern Queensland, Springfield, Australia
Abstract
Purpose –The purpose of this paper is to present the model-driven decision support system (DSS)
for small and medium ma nufacturing enterpri ses (SMMEs) that act ively participates i n collaborative
activities and manages the planned obsolescence in production. In dealing with the complexity of such
demand and supply scena rio, the optimisation mo dels are also developed t o evaluate the performanc e of
operations practices.
Design/methodology/approach –The model-driven DSS for SMMEs, which uses the optimisation models
for managing and coordinating planned obsolescence, is developed to determine the optimal manufacturing
plan and minimise operating costs. A case application with the planned obsolescence and production scenario
is also provided to demonstrate the approach and practical insights of DSS.
Findings –Assessing planned obsolescence in production is a challenge for manufacturing managers.
A DSS for SMMEs can enable the computerised support in decision making and understand the planned
obsolescence scenarios. The causal relationship of different time-varying component obsolescence and
availability in production are also examined, which may have an impact on the overall operating costs for
producing manufactured products.
Research limitations/implications –DSS can resolve and handle the complexity of production and
planned obsolescence scenarios in manufacturing industry. The optimisation models used in the DSS
excludes the variability in component wear-out life and technology cycle. In the future study, the optimisation
models in DSS will be extended by taking into the uncertainty of different component wear-out life and
technology cycle considerations.
Originality/value –This paper demonstrates the flexibility of DSS that facilitates the optimisation models
for collaborative manufacturing in planned obsolescence and achieves cost effectiveness.
Keywords Decision support system, Supply chain, Industry 4.0, Manufacturing system
Paper type Research paper
Nomenclature
Parameters
Nnumber of remanufactured product i,
where i¼1, 2, 3, …,N
Jnumber of component j, where j¼1, 2,
3, …,J
Tplanning horizon comprising of time
period t, where t¼1, 2, 3, …,T
ftime period f, upon receiving
component j
R
i
unit cost of remanufactured product i
C
j
unit cost of component j
U
i
inventory holding unit cost for
remanufactured product i
H
j
inventory holding unit cost for
component j
A
i
fixed set-up cost for remanufactured
product i
G
j
fixed ordering cost for component j
V
i
production capacity for
remanufactured product i
Industrial Management & Data
Systems
Vol. 119 No. 9, 2019
pp. 1926-1946
© Emerald PublishingLimited
0263-5577
DOI 10.1108/IMDS-05-2019-0264
Received 3 May 2019
Revised 28 July 2019
Accepted 27 August 2019
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0263-5577.htm
1926
IMDS
119,9
M
j
ordering capacity for component j
α
j
disposal treatment unit cost for
component j
S
j
order lot size ordering for component j
O
j
order lead-time for component j
Variables
X
it
quantity of produced iin tperiod
I
it
quantity of inventory iin tperiod
y
jt
quantity of jordered in tperiod
Y
jt
component jscheduled order receipt in t
period
L
jt
quantity of inventory jin tperiod
L
jtf
quantity of inventory jin tperiod upon
receiving at the end of fperiod
β
j
quantity of disposal treatment j
γ
jtf
quantity of usage in tperiod upon
receiving jat the end of fperiod
D
it
demand iin tperiod
B
ji
bill of material (BOM) with jof i
e
j
obsolescence with jlifecycle
a
it
fixed setup-up of iin tperiod (binary)
P
jt
fixed (scheduled) ordering of jin t
period (binary)
p
jt
fixed ordering of jin tperiod (binary)
1. Introduction
Industry 4.0 in big data environment has become a buzzword in manufacturing and service
industries (Xu et al., 2018; Vaidya et al., 2018; Lee et al., 2018; Xu and Duan, 2018). It also refers
to extracting real-time data and developing new and innovative information systems (IS)
through the use of computer and/or sensing devices as well as information communication
tools (Kokuryo et al., 2017; Kaihara et al., 2017). Its ultimate goal is to adopt ground-breaking
and advanced technologies that may rapidly improve the effectiveness and efficiency of a
firm’s productivity and gain significantly financial benefits (Vaidya et al., 2018; Xu and Duan,
2018). The implementation design of Industry 4.0 is based on four principles of system
interoperability and data analysis, information transparency with real-time data, technical
assistance for human decision making and decentralised decision with direction from humans
and response to new information (Xu and Duan, 2018; Lin et al., 2018; Yang et al., 2018;
Kokuryo et al., 2017; Kaihara et al., 2017). However, the emergence of the Internet of Things
(IoT) and the model-driven decision support systems (DSS) can promote computerised support
in collaborative manufacturing and decide upon optimal technologies and production plan to
adopt. The ability for a firmto make changes in production planningand adapt to technology
can give them the significant advantage over the competition (Yu et al., 2018; Leng and Jiang,
2018). To remain competitive, numerousfirms have been consideredto possibly adopt reliable
and latest technologiesand approaches to everyday businesstasks for collaborative activities,
such as procurement, manufacturing, quality assurance, warranty services, marketing, returns
and recovery and logistics (Seuring, 2004; Kain and Verma, 2018; Kuik et al., 2016, 2017). An
emerging technology is often utilising big data analytics to improve the effectiveness and
efficiency of manufacturing industry and minimise used resources of producing different
manufactured products (Kain and Verma, 2018; Seuring, 2004; Kokuryo et al., 2017; Kuik et al.,
2017). In dealing with planned obsolescence and production, there is a need to develop a DSS
and promote computerised support for collaborative manufacturing. However, it has become
more challenging in terms of managing different time-varying component availability and
obsolescence in unpredictable market trends and change in consumer behaviours.
Big data can provide clear understanding of recent market trends and consumer buying
and usage patterns (Kokuryoet al., 2017 ; Vaidya et al.,2018). By consolidating and usingthese
data correctly, it may minimiseoperating associatedcosts by maintaining lessstocks on hand
and initiate data-driven business decisions (Vaidya et al., 2018; Lee et al., 2018; Xu and Duan,
2018). Hopkins and Hawking (2018) also showed that th e IoT-related applications can be
utilised by supply chain practitioners to manage manufactured products to reduce an overall
of the production-related costsand wastes. Integrated logisticsservices that are supported by
various latest technologies including smart tags, radio frequency identification (RFID) and
1927
Amodel-driven
decision
approach
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