Assessing Sustainability of Green Supply Chain Performance: The Roles of Agile Innovative Products, Business Intelligence Readiness, Innovative Supply Chain Process Integration, and Lean Supply Chain Capability as a Mediating Factor
Journal of Open Innovation Technology Market and Complexity,
Год журнала:
2025,
Номер
unknown, С. 100476 - 100476
Опубликована: Янв. 1, 2025
Язык: Английский
Trends and Applications of Artificial Intelligence in Project Management
Electronics,
Год журнала:
2025,
Номер
14(4), С. 800 - 800
Опубликована: Фев. 19, 2025
The
integration
of
artificial
intelligence
(AI)
into
project
management
(PM)
transforms
how
projects
are
planned,
executed,
and
monitored.
main
objective
this
study
is
to
provide
a
comprehensive
bibliometric
analysis
exploring
trends,
thematic
areas,
future
directions
in
AI
applications
by
examining
publications
from
the
last
decade.
This
research
uncovers
dominant
themes
such
as
machine
learning,
decision
making,
information
management,
resource
optimization.
findings
highlight
growing
use
enhance
efficiency,
accuracy,
innovation
PM
processes,
with
recent
trends
favoring
data-driven
approaches
emerging
technologies
like
generative
AI.
Geographically,
China,
India,
United
States
lead
publications,
while
Kingdom
Australia
show
high
citation
impact.
landscape,
including
AI-enhanced
decision-making
frameworks
cost
analysis,
demonstrates
diversity
PM.
An
increased
interest
its
impact
on
managers
was
observed.
contributes
field
offering
structured
overview
defining
challenges
opportunities
for
integrating
practices
perspectives
technologies.
Язык: Английский
Leveraging Artificial Intelligence in Project Management: A Systematic Review of Applications, Challenges, and Future Directions
Computers,
Год журнала:
2025,
Номер
14(2), С. 66 - 66
Опубликована: Фев. 13, 2025
This
article
presents
a
systematic
literature
review
exploring
the
integration
of
Artificial
Intelligence
(AI)
methodologies
in
project
management
(PM).
Key
applications
include
cost
estimation,
duration
forecasting,
and
risk
assessment,
which
are
critical
factors
for
success.
synthesizes
findings
from
97
peer-reviewed
studies
published
between
2011
2024,
using
PRISMA
methodology
to
ensure
rigor
transparency.
AI
techniques
such
as
machine
learning,
deep
hybrid
models
have
exhibited
their
potential
enhance
PM
across
projects’
phases,
including
planning,
execution,
monitoring.
Decision
trees
created
represent
application
various
stages
tasks
facilitate
understanding
real-world
implementation.
Among
these
that
well
categorization
based
on
phases
optimize
integration.
Despite
advancements,
there
still
gaps
addressing
dynamic
environments,
validating
with
data,
expanding
research
into
underexplored
like
closure.
Язык: Английский