Design of Efficient Logistics Management System Based on Cloud
Database
Lin Chen and Ruolin Song
Weifang Engineering Vocational College, 262500, China
Keywords: Cloud Database, Efficient Logistics Management, Logistics, System Design.
Abstract: This paper studies a logistics management system based on cloud database, which aims to solve the problems
of inventory forecasting and low transportation efficiency in logistics management of manufacturing
enterprises. In the research process, based on the design of data collection, storage, analysis and decision
support subsystem, a variety of machine learning algorithms are integrated and applied in practical scenarios.
After the implementation of the system, the experimental data showed that the inventory turnover rate of the
study subjects increased by 83.3%, the transportation cost decreased by 26.9%, and the order delay rate
decreased by 60%. The results show that the system significantly improves the logistics management
efficiency of the enterprise, reduces the operating cost, and the application effect is very significant. It can be
seen that the design is very successful and effective, and can be invested in practical applications to better
demonstrate the application effect of cloud database-related technologies in it.
1 INTRODUCTION
The logistics management system is a key part of
enterprise supply chain management, and its core
problems are inaccurate inventory management and
low transportation efficiency. Many researchers have
suggested that such problems could be better
addressed by optimizing transportation routes and
improving the accuracy of their inventory forecasts.
However, these methods generally ignore the
dynamic changes of real-time data, which makes the
optimization effect limited. Some researchers also
propose to use static data analysis to optimize the
system, but it cannot effectively cope with the
fluctuation of demand during peak periods, and it is
easy to cause inventory shortages and backlogs. It has
been proposed to use a simple linear operating
mechanism for forecasting, but the prediction effect
is not satisfactory because it ignores the complexity
in the supply chain. In order to solve these problems,
this paper adopts the method of intelligent algorithm
combined with the real-time data processing
capability of cloud database, and dynamically adjusts
the decision-making strategy based on the seamless
connection of multiple operating mechanisms, so as
to improve the adaptability and efficiency of the
system, with the aim of optimizing inventory
management and transportation scheduling based on
intelligent means. This round focuses on the analysis
of the importance of logistics management in
enterprise supply chain management, and analyzes
the problems therein, and at the same time, analyzes
the shortcomings of the commonly used logistics
management methods, and proposes a logistics
management method based on cloud database.
2 RELATED WORKS
2.1 Cloud Data Development
With the continuous development of cloud computing
technology, cloud data storage and processing
technology has become a key to modern information
technology. Cloud databases have many
characteristics (Adeleke, 2022), such as high
efficiency, elasticity, and scalability. As a result, this
allows enterprises to process massive amounts of data
and store and compute in real time without increasing
hardware investment. In logistics and logistics
management, the application of cloud database can
significantly improve the efficiency of data access
(Bhargava, Bhargava et al. 2022), especially when
dealing with complex supply chain management
Chen, L. and Song, R.
Design of Efficient Logistics Management System Based on Cloud Database.
DOI: 10.5220/0013539800004664
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 3rd International Conference on Futuristic Technology (INCOFT 2025) - Volume 1, pages 269-276
ISBN: 978-989-758-763-4
Proceedings Copyright © 2025 by SCITEPRESS – Science and Technology Publications, Lda.
269

systems, it can quickly respond to business changes,
and then achieve real-time data processing and
decision support (Cherchata, Popovychenko et al.
2022). With the advent of the era of big data, cloud
data has become an important technical foundation in
an efficient logistics management system, providing
an efficient data support environment for various
intelligent algorithms (Gomes, de Lima 2023).
2.2 Efficient Logistics Management
Covers Inventory Management,
Transportation Scheduling and
Other Parts
An efficient logistics management system relies on
accurate inventory management and transportation
scheduling, and theoretically covers supply chain
optimization, inventory control theory and
transportation management theory. In inventory
management, the classic economic order quantity
(EOQ) operation mechanism and material
requirement planning (MRP) operation mechanism
have been widely used (Kozhamkulova, Kuntunova
et al. 2024), but these traditional operation
mechanisms will basically show shortcomings when
dealing with complex demand fluctuations and multi-
dimensional inventory problems. Therefore, modern
efficient logistics management theory has begun to
introduce intelligent technologies, such as machine
learning and big data analysis, to enhance prediction
and scheduling capabilities. Transportation
management theories rely more on route optimization
and vehicle scheduling operation mechanisms, such
as the use of linear programming and integer
programming algorithms to optimize transportation
costs and time (Kundu, Sheu et al. 2022). However,
these theories rely on static data in traditional
applications, while modern enterprises need real-time
dynamic scheduling capabilities, which provides a
new development direction for the combination of
cloud computing and intelligent algorithms
(Verbivska, Zhygalkevych et al. 2023).
2.3 Advantages of Combining Random
Forest Algorithms with Cloud Data
As a seamless connection learning method, random
forest algorithm is based on the seamless connection
of multiple decision trees to improve the accuracy and
stability of prediction, which is especially suitable for
processing complex nonlinear data. When random
forests are combined with cloud data (Wang, 2023),
they can be of great advantage. Cloud databases
provide random forests with powerful data storage
and processing capabilities, can quickly process
large-scale datasets, and supports parallelized
computing, which will effectively improve the
efficiency of training and prediction (Wang, Luo et
al. 2024). In addition, the real-time nature of the cloud
database also allows the random forest operation
mechanism to be continuously updated and
dynamically adjusted based on the latest inventory
and transportation data, which is especially critical in
peak periods and complex supply chain
environments. Based on the elastic scalability of
cloud computing, enterprises can adjust computing
resources according to their needs, and then achieve
rapid response and optimized decision-making (Zhai,
Han et al. 2022). This combination greatly improves
the forecasting accuracy of the logistics management
system, effectively reducing the risk of inventory
backlog and transportation delays.
3 METHODS
3.1 Functions of Each Part of an
efficient Logistics Management
System Based on a Cloud Database
In this system, it mainly includes 6 major subsystems.
Specifically, the function of the data acquisition
subsystem is to collect warehousing data,
transportation status, order information based on
sensors, RFID, GPS and other devices, and then
upload it to the cloud database in real time. The
system ensures timely aggregation of data from
different sources and supports the flow of data in
large-scale logistics networks. The function of the
data storage subsystem is to provide distributed
storage, support scalable and efficient data access,
and ensure the security management of historical data
and real-time data. The task of the data analysis and
operation mechanism subsystem is to intelligently
predict inventory and transportation based on a
variety of machine learning algorithms, such as
random forests and time series operation
mechanisms. Seamlessly connect with automated
reporting to generate inventory and logistics trend
analysis. The decision support subsystem will be
responsible for providing managers with inventory
replenishment recommendations, transportation
scheduling optimization, and manual and automated
decision-making processes based on the analysis
results. The user interface subsystem needs to provide
a graphical monitoring panel that allows the user to
view real-time data, forecast results, and make
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necessary adjustments to the settings of the logistics
system. The system security and monitoring
subsystem ensures the security of the system, based
on user rights management, data encryption, and real-
time monitoring of system resources and running
status.
3.2 Design of Various Subsystems of an
Efficient Logistics Management
System
In terms of design, the data acquisition subsystem
provides a standardized API interface to ensure that
all kinds of equipment can be connected to the
system, and the data is unified based on the
preprocessing process and ensures the accuracy of the
data. The data storage subsystem uses partitioning
and index optimization to improve the query speed of
its data, and implements a backup and recovery
mechanism to prevent data loss. The data analysis and
operation mechanism subsystem is based on the
parallel processing of cloud computing to further
accelerate the operation speed of the operation
mechanism, and automatically optimize the operation
mechanism parameters to ensure the accuracy of
prediction. The subsystem also automates the report
generator, which provides users with detailed
periodic summaries based on real-time data and
analysis results. The decision support subsystem
dynamically adjusts the decision parameters based on
the built-in rule engine, and users can manually
modify the rules and let the system automatically
adjust. In addition, the user interface subsystem is
designed as an intuitive dashboard, which allows for
real-time display of key indicators such as inventory
levels, shipping status, and support for custom queries
and data exports. The system security and monitoring
subsystem is designed to integrate permission
management, logging functions, and use encryption
technology to ensure data security and real-time
monitoring of system resource usage. In addition, the
system needs to respond to potential threats based on
an alarm mechanism.
3.3 Efficient Logistics Management
Mechanism Based on Cloud
Database
In order to select an optimal logistics management
system in the cloud database, it is necessary to select
the optimal solution, for which the random forest
algorithm operation mechanism is selected. This is
because the operating mechanism can handle
complex data relationships, especially in logistical
scenarios such as fluctuations in inventory demand
and optimization of transportation routes. Random
forests are based on seamlessly connecting multiple
decision trees to predict nonlinear data, which can
show its ability when processing large-scale datasets.
See Eq. (1) for details.
Provides strong predictive capabilities and excels
when working with large-scale data sets.
𝑀
rf
=𝑎𝑟𝑔𝑚𝑖𝑛


ℒ
(𝑀

,𝑋
train
,𝑦
train
,
𝑛
trees
,depth,compute

ost
)
(1
)
In this formula, 𝑀
rf
is the random forest operation
mechanism is represented and used to handle tasks
such as inventory demand forecasting and transit time
estimation in logistics management.
ℒ(𝑀

,𝑋
train
,𝑦
train
,𝑛
trees
,depth,compute

ost
) is a
function that evaluates the operating mechanism, and
its main purpose is to calculate the cost by combining
the operating mechanism error. 𝑋
train
Characteristics
that represent the training dataset, such as inventory
records as well as shipping routes and order
processing times. 𝑦
train
Represents target variables,
such as future inventory requirements, optimal time
for transportation routes. 𝑛
trees
represents the number
of decision trees, which affects the complexity and
prediction ability of the operating mechanism, and
improves the robustness of the operating mechanism.
depthRepresents the maximum depth of the tree,
controls the complexity of each tree, and ensures that
the operating mechanism does not overfit to a specific
transportation and inventory pattern.
compute

ost
Represents the computing cost, which
measures the resource consumption of the running
mechanism in the cloud database.
Each tree in a random forest can run in parallel in
a cloud database, and the advantage of cloud
computing is the ability to support efficient parallel
computing and quickly process large-scale datasets.
In the logistics scenario of large data sets, such as
warehousing and transportation data, parallel
computing will be used to significantly reduce the
training time of the operating mechanism and
improve the response speed of the system. See Eq. (2)
for details.
𝑇
parallel
=
𝑇
sequential
𝑛
nodes
(2
)
In this formula, 𝑇
parallel
represents the parallel
running time, and the training speed of the running
mechanism is improved based on parallelized
computing. 𝑇
sequential
Represents the sequential run
Design of Efficient Logistics Management System Based on Cloud Database
271

time, that is, the training time when parallel is not
used. 𝑛
nodes
Represents the number of nodes for
parallel computing, i.e., distributed cloud computing
resources, which can be used to increase the
computing power of the system.
Once the runtime is in place, it will be deployed
in the cloud and used to process new data in logistics
in real time. When real-time inventory information
and transportation data are constantly updated, the
operating mechanism can automatically make
forecast adjustments based on its new inputs, which
in turn provides important support for logistics
decisions. See Eq. (3) for details.
𝑦

real-time
=𝑀rf(𝑋
real-time
)
(3)
In this formula, 𝑦
real-time
is the result of real-time
forecasting, such as real-time inventory demand and
transit time estimation, is represented. 𝑀
rf
represents
the trained random forest operation mechanism,
which is executed in real time in the cloud.
𝑋
real-time
Represents the logistics data obtained by the
cloud database in real time, such as the latest
warehouse inventory and transportation status.
3.4 Further Operation and
Optimization of the Operating
Mechanism
After the operation mechanism is selected and the
data is prepared, the operation mechanism is trained
based on cloud computing resources. The large-scale
storage and parallel computing capabilities of the
cloud database enable the random forest operation
mechanism to quickly process massive logistics data,
such as warehouse inventory records and daily
transportation route information. The operation
process uses distributed computing, and the
parameters of the operation mechanism are optimized
to improve the accuracy and generalization ability of
prediction. After the training is completed, the
operating mechanism can accurately predict future
inventory demand and transit time.
In order to improve the prediction performance of
the operating mechanism, it is necessary to optimize
the features based on the importance of logistics data
based on the automatic feature selection method. For
example, based on the feature importance assessment
of random forests, the most influential variables for
inventory forecasting and transit time, such as
inventory turnover rate and weather impact, are
selected. For this, see Eq. (4).
Features
opt
= SelectTopK(𝜙(𝑋
train
), 𝐾)
(4
)
In this formula, Features
opt
refers to an optimized
set of features that select the features that contribute
the most to logistics forecasting.
SelectTopK(𝜙(𝑋
train
), 𝐾) refers to the feature
selection function, which automatically screens out
the most important K features, such as inventory
change rate, transportation timeliness, etc. 𝜙(𝑋
train
)
refers to the characteristics of all operating sets,
covering various dimensions of inventory,
transportation, and equipment. 𝐾is the number of
features retained after optimization is mainly used to
ensure that the operating mechanism can focus on the
most critical prediction indicators.
To accommodate the scale of different logistical
tasks, the system dynamically adjusts the architecture
of the random forest based on real-time workloads.
For example, when processing a large number of
orders, increase the number of decision trees; At low
loads, you need to reduce the number of trees to
optimize resource usage. See Eq. (5) for this.
𝑛
trees adjusted
=
𝑓
(order

olume
,response

ime
)
(5
)
In this formula, the 𝑛
trees adjusted
is number of
decision trees adjusted according to the real-time
order volume is used to ensure the efficiency of the
operating mechanism under different loads.
order

olume
Refers to the current order volume, which
determines the calculation requirements of the
system. response

ime
Refers to:
Response time requirements to ensure fast
response during peak hours.
In order to improve the forecasting accuracy of its
logistics management system, it is necessary to
seamlessly connect the methods of multiple operating
mechanisms. For example, the random forest is
combined with the time series operation mechanism
and the linear regression operation mechanism, and
the advantages of different operation mechanisms are
used to make comprehensive predictions to improve
the overall performance of the system. See Eq. (6) for
details.
𝑦

ensemble
=𝑤

⋅𝑀rf(𝑋) + 𝑤

⋅𝑀
ts
(𝑋) +
𝑤

⋅𝑀
l
r
(𝑋)
(6
)
In this formula, the 𝑦
ensemble
is final prediction
result of the seamlessly connected operating
mechanism is depresented, and the prediction based
on the different operating mechanisms can be
weighted averaged. 𝑀
rf
refers to the prediction results
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of the random forest operation mechanism, which
deals with nonlinear complex data. 𝑀
ts
refers to the
prediction results of the time series operation
mechanism, which is suitable for the prediction of
short-term data fluctuations. 𝑀
lr
Refers to the
prediction results of the linear regression operating
mechanism, which can be used to deal with long-term
linear trends. 𝑤

,𝑤

,𝑤

Refers to the weight of each
operating mechanism. Adjusted based on its historical
performance, seamless connectivity results will be
optimized.
3.5 Seamless Connection of Efficient
Logistics Management System
Design
The purpose of the seamless connection of the system
is to effectively integrate data acquisition and storage,
analysis, decision support, and user interface
subsystems to form an efficient logistics management
system. Among them, the data acquisition subsystem
is responsible for docking with the warehouse system,
transportation management system, and various
sensing devices based on standardized API interfaces,
obtaining inventory, order, and transportation data in
real time, and uploading them to the cloud database.
The data storage subsystem is responsible for using
the distributed storage of cloud databases to ensure
fast access and elastic expansion of data, and support
real-time access and backup of large-scale data. The
data analysis subsystem is responsible for seamlessly
connecting multiple machine learning algorithms,
such as random forests and time series operation
mechanisms, to intelligently predict key indicators
such as inventory demand and transit time. The
decision support subsystem is responsible for
dynamically providing inventory replenishment
suggestions, transportation route optimization, and
vehicle scheduling strategies based on the analysis
results, and automatically adjusting parameters based
on the rule engine to improve the adaptability and
response speed of the system. The user interface
subsystem is responsible for displaying inventory
levels, shipping status, and order progress in real time
based on interactive dashboards, and supporting
users' custom queries and operations. The system
security and monitoring subsystem is responsible for
comprehensive data encryption, user authentication,
and permission management, real-time monitoring of
system operation status and capturing abnormal
behaviors, and timely generation of alarms to ensure
the stability and security of the system. Based on the
deep and seamless connection of the above
subsystems, the system can provide efficient and
accurate intelligent services in large-scale and
changeable logistics management scenarios.
4 RESULTS AND DISCUSSION
4.1 Background of the Case
Company C is a large-scale manufacturing enterprise,
mainly engaged in the production and sales of
household appliances. The company has multiple
factories and distributed warehouses, and its products
are transported to the major points of sale based on a
complex supply chain network,The introduction of
the data situation of the school logistics management
system is shown in Table 1..
Table1: Logistical management issues prior to the
implementation of the system by company
Project Data
Inventory turnover 6 times/year
Average delivery time 70 hours
Demand forecast accuracy 60%
Shipping costs 13 million yuan/year
Inventory overstock rate 35%
Peak order delay rate 25%
Table 1 shows the logistical management issues
of Company C prior to the implementation of the
system. The inventory turnover rate is only 6
times/year, the inventory backlog rate is as high as
35%, the average delivery time is 70 hours, the
demand forecast accuracy is 60%, and the
transportation cost and order delay problems are more
serious.
4.2 The Overall Structural Changes of
the Logistics Management System
In recent years, because the order volume of
Company C has increased year by year, the
company's logistics management system has exposed
many problems, such as low inventory management
efficiency, unoptimized transportation routes, and
inaccurate demand forecasts, resulting in inventory
backlogs and shortages, and high warehousing and
transportation costs. Especially during the
promotional season, the number of orders surges,
because of inaccurate forecasts, resulting in untimely
inventory scheduling, delayed deliveries, and
Design of Efficient Logistics Management System Based on Cloud Database
273

decreased customer satisfaction, which in turn leads
to rising operating costs.
Table 2: Logistics management improvements following
the implementation of the system
Project Data
Inventory turnover 11 times/year
Average delivery time 45 hours
Demand forecast accuracy 85%
Shipping costs 9.5 million yuan/year
Inventory overstock rate 18%
Peak order delay rate 10%
Table 2 shows the improvements made after the
implementation of the system. Company C's
inventory turnover rate in terms of logistics
management. Specifically, its inventory turnover rate
has increased from 6 times per year to 11 times per
year, the inventory backlog rate has decreased
significantly, the transportation cost has been
significantly reduced, the demand forecast accuracy
has been greatly improved, and the order delay rate
has been significantly reduced. The process of data
changes in the management system is shown in
Figure 1.
Figure 1: Changes in system data
The specific data shows that before the
introduction of the system, Company C's inventory
turnover rate was 6 times per year, the average
delivery time reached 70 hours, and the accuracy of
demand forecasting was only 60%. In addition, the
company's transportation cost is as high as 13 million
yuan/year, and the inventory backlog rate is 35%.
During peak order periods, 25% of orders are
delayed, which seriously affects customer satisfaction
and operational efficiency. To this end, Company C
decided to introduce an efficient logistics
management system based on cloud database to
improve management efficiency and optimize
various operational indicators.
4.3 The Management Results of the
Logistics Efficient Management
System
From the tabular data, it can be seen that Company C
has significantly improved all indicators of its
logistics management after the implementation of the
system. The inventory turnover rate has increased
from 6 times per year to 11 times per year, indicating
that the inventory management efficiency has been
improved, the inventory backlog rate has also been
greatly reduced from 35% to 18%, and the inventory
scheduling is more reasonable. In terms of
transportation efficiency, the average delivery time
has been shortened from 70 hours to 45 hours, the
transportation cost has been reduced by 26.9%, and
the system has optimized transportation routes and
scheduling, The overall design result of the logistics
system is shown in table.3.
Table 3: Analysis of the combined benefits of the system
after implementation
Project Before
optimization
After
optimization
Degree of
improvement
Inventory
turnove
r
6 times/year 11
times/yea
r
83.3% higher
Average
delivery
time
70 hours 45 hours 35.7% reduction
Demand
forecasting
accurac
y
60% 85% Increase it by
25%
Shipping
costs
13 million
yuan/yea
r
9.5 million
yuan/yea
r
26.9% reduction
Inventory
overstock
rate
35% 18% 48.6% reduction
Peak order
dela
y
rate
25% 10% 60% reduction
Table III summarizes the changes in the key data
in the logistics management of Company C after the
application of the system. Among them, its inventory
turnover rate has been greatly improved, the
inventory backlog rate has been basically halved, the
transportation cost has been significantly reduced, the
average delivery time has been significantly
shortened, the accuracy of demand forecasting has
been significantly improved, and the order delay rate
during peak periods has also been greatly improved.
Better conduct systematic analysis of management
and continuously monitor its processes, as shown in
Table 2.
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274

Figure 2: Results of data and demand changes
Thus, it can be seen that the process of changes in
the logistics management system presents a dramatic
change, and the content is relatively ideal, which can
meet the practical analysis needs.The accuracy rate of
demand forecasting has been increased from 60% to
85%, significantly reducing the risk of inventory
shortages and surpluses. The order delay rate has been
reduced from 25% to 10%, and customer satisfaction
has been greatly improved. It can be seen that after
applying the system built this time, Company C has
significantly improved the accuracy of inventory
management and transportation optimization and
demand forecasting, effectively reducing operating
costs and improving the efficiency of its overall
logistics management. The system has been
successfully designed and the application effect is
remarkable.
5 CONCLUSIONS
The efficient logistics management system based on
the cloud database proposed in this study has shown
that it can successfully solve the problems of
inventory management and transportation efficiency
of manufacturing enterprises. Based on the function
of the application cloud database and combined with
the random forest algorithm, the efficient logistics
management system significantly improves the
accuracy of inventory forecasting, the efficiency of
transportation scheduling, and effectively reduces the
risk of operating costs and supply chain uncertainty.
The experimental results show that the system plays
a key role in optimizing the logistics management
process and improving the overall operational
efficiency of the enterprise. It can be verified that the
efficient post-management system design based on
cloud database has practical application value. At the
same time, the system can flexibly respond to demand
fluctuations and peak pressures, significantly
improving its enterprise resource scheduling
capabilities. In short, the system built this time has a
relatively broad application prospect, and then
provides a reliable solution for the modernization of
logistics management. Although this paper has some
achievements in many aspects, there are inevitably
errors and omissions in the paper, and I hope that this
can be further optimized in the future.
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