Enhancing Quality 4.0 adoption: integrative analysis using Fuzzy-TOPSIS and Fuzzy-DEMATEL for strategic dimension prioritization
The TQM Journal,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 7, 2025
Purpose
The
study
aims
to
explore
the
dimensions
of
Quality
4.0
adoption,
prioritization
these
and
influential
their
causal
relationships
that
can
guide
smooth
adoption
boost
organizational
performance.
Design/methodology/approach
are
explored
from
extant
literature.
qualitative
data
were
captured
12
highly
experienced
experts
diverse
industries
academia
through
structured
interview
questions
group
discussions
in
multiple
phases.
inputs
obtained
analyzed
using
Fuzzy-Technique
for
Order
Preference
by
Similarity
Ideal
Solution
dimension
priority,
Fuzzy-Decision-Making
Trial
Evaluation
Laboratory
was
employed
reveal
relationship
between
them.
Findings
analysis
reveals
quality
scalability,
culture
conformance
investigated
as
primary
drivers
adoption.
Data-driven
analytical
thinking
customer
centricity
emerge
dynamic
act
deliverable
ends.
Integrating
methodologies
provides
a
robust
framework
understanding
managing
complexities,
offering
actionable
insights
prioritizing
initiatives
addressing
interdependencies
ensure
successful
implementation.
Practical
implications
practical
creating
strategic
action
plans
tailored
needs
fostering
quality-focused
culture.
also
offers
valuable
into
government
policies,
promoting
sustainability,
efficiency
circular
economy.
Originality/value
study’s
novelty
lies
its
examination
most
causes
effects
within
dimensions.
This
approach
highlights
core
critical
factors,
providing
comprehensive
Язык: Английский
Breaking down barriers: strategic approaches and prioritization for renewable energy adoption in MSMEs sector
Kybernetes,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 25, 2025
Purpose
The
adoption
of
renewable
energy
sources
(RES)
into
the
Indian
micro-,
small-
and
medium-sized
enterprises
(MSMEs)
sector
opens
up
various
avenues
advantages
such
as
better
security
lesser
carbon
emissions.
However,
despite
significant
potential,
numerous
barriers
limit
RES
among
MSMEs;
therefore,
research
is
needed
regarding
strategies
to
counter
them.
Design/methodology/approach
This
study
reviews
extensive
literature
identify
connected
affecting
MSMEs,
technological
obstacles,
market
dynamics,
infrastructure
challenges,
environmental
concerns,
technical
limitations,
socio-cultural
factors,
institutional
financial
constraints.
In
this
study,
these
have
been
prioritized
using
AHP
TOPSIS,
indicating
constraint
most
important,
followed
by
concerns.
Additionally,
employs
interpretive
structural
modeling
(ISM)
alongside
Matrix
Impact
Cross-Reference
Multiplication
Applied
a
Classification
(MICMAC)
analysis
systematically
classify
according
their
driving
dependency
power,
thereby
offering
an
in-depth
perspective
MSME
environment.
Findings
According
TOPSIS
results,
constraints
are
ranked
at
top,
implying
they
critical
in
adopting
MSMEs.
findings
emphasize
need
offer
incentives
create
innovative
financing
mechanisms
tailored
specifically
for
overcome
barriers.
Research
limitations/implications
These
insights
can
guide
industry
stakeholders
policymakers
on
how
could
navigate
many
complexities
involved
that
supports
future
with
sustainability.
Originality/value
uniquely
addresses
sectors
proposes
model
mitigate
Язык: Английский
Deep Learning Forecasting Model for Market Demand of Electric Vehicles
Applied Sciences,
Год журнала:
2024,
Номер
14(23), С. 10974 - 10974
Опубликована: Ноя. 26, 2024
The
increasing
demand
for
electric
vehicles
(EVs)
requires
accurate
forecasting
to
support
strategic
decisions
by
manufacturers,
policymakers,
investors,
and
infrastructure
developers.
As
EV
adoption
accelerates
due
environmental
concerns
technological
advances,
understanding
predicting
this
becomes
critical.
In
light
of
these
considerations,
study
presents
an
innovative
methodology
demand.
This
model,
called
EVs-PredNet,
is
developed
using
deep
learning
methods
such
as
LSTM
(Long
Short-Term
Memory)
CNNs
(Convolutional
Neural
Networks).
model
comprises
convolutional,
activation
function,
max
pooling,
LSTM,
dense
layers.
Experimental
research
has
investigated
four
different
categories
vehicles:
battery
(BEV),
hybrid
(HEV),
plug-in
(PHEV),
all
(ALL).
Performance
measures
were
calculated
after
conducting
experimental
studies
assess
the
model’s
ability
predict
vehicle
When
performance
(mean
absolute
error,
root
mean
square
squared
R-Squared)
EVs-PredNet
machine
regression
are
compared,
proposed
more
effective
than
other
methods.
results
demonstrate
effectiveness
approach
in
considered
have
significant
application
potential
assessing
vehicles.
aims
improve
reliability
future
market
develop
relevant
approaches.
Язык: Английский