Research on Collaborative Management and Optimization Strategy

of Enterprise Digital Supply Chain

Yubai Li

Haidian Foreign Language Academy, Beijing, China

Keywords: Digital, Supply Chain, Management.

Abstract: Against the backdrop of global industrial chain restructuring and digital technology revolution, collaborative

management of enterprise digital supply chains has emerged as a core pathway to enhance industrial resilience.

This paper constructs a three-dimensional theoretical framework of "Technology-Organization-Ecology",

integrating 12 Chinese core literature sources (2022-2025) and manufacturing industry cases to reveal how

digital technologies such as digital twin, blockchain traceability, and federated learning break down

information silos, while optimizing node coordination mechanisms through dynamic game models. Empirical

analysis shows that a 10% increase in supply chain collaboration maturity correlates with a 15.2% average

improvement in inventory turnover ratio (based on listed company data from Reference. However, challenges

persist, including system heterogeneity (interface standard conflicts in 43% of enterprises) and data privacy

concerns (62% of enterprises worry about cross-chain data leakage). The paper concludes by proposing future

research directions, emphasizing the deep integration of edge computing and digital twin technology to build

resilient supply chain ecosystems.

1 INTRODUCTION

1.1 Research Background and

Significance

In the contemporary global business landscape, the

trend of supply chain digital transformation has

gained significant momentum. The acceleration of

economic globalization and the rapid advancement of

emerging technologies, such as the Internet of Things,

big data, and artificial intelligence, have propelled

enterprises towards digitalizing their supply chains

(Wu & Yao, 2024).

However, enterprises are confronted with

numerous uncertainties. The COVID - 19 pandemic

has severely disrupted global supply chains, leading

to shortages of raw materials, production delays, and

transportation bottlenecks (not explicitly in given refs,

but common knowledge). Geopolitical tensions,

including trade wars and sanctions, have further

complicated the international business environment,

making it challenging for enterprises to manage their

supply chains effectively. Additionally, market

demand has become increasingly volatile, with

consumers' preferences changing rapidly, which

requires enterprises to be more agile and responsive

in their supply chain operations.

Digital supply chains offer a solution to these

challenges. They play a crucial role in enhancing

efficiency, reducing costs, and increasing the

resilience of enterprises. By leveraging digital

technologies, enterprises can achieve real - time

information sharing and automation of processes,

which improves the visibility and transparency of the

supply chain. For example, IoT devices can track the

movement of goods in real - time, while big data

analytics can help enterprises predict market demand

more accurately and optimize their inventory

management (Ding & Zhu, 2025).

This paper aims to explore the impact of

collaborative management on the overall

performance of the supply chain and the application

value of optimized supply chain strategies in data -

driven decision - making. Understanding the

influence of collaborative management on supply

chain performance is essential for enterprises to

improve their competitiveness in the market.

Moreover, optimizing supply chain strategies in the

context of data - driven decision - making can help

enterprises better adapt to the dynamic business

environment.

Li, Y.

Research on Collaborative Management and Optimization Strategy of Enterprise Digital Supply Chain.

DOI: 10.5220/0013853100004719

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 2nd International Conference on E-commerce and Modern Logistics (ICEML 2025), pages 723-729

ISBN: 978-989-758-775-7

723

Through a review of relevant literature and

analysis of practical application cases, this research

provides theoretical guidance and reference for

enterprises to implement digital transformation. It

also analyzes the application scenarios, advantages,

and disadvantages of different optimization methods

in typical enterprises, offering corresponding

implementation path suggestions for enterprises to

carry out digital supply chain collaborative

management. Ultimately, this study strives to help

enterprises improve their supply chain management

level in the new era and gain a competitive edge in

the market.

1.2 Purpose and Structure of

Literature Review

This literature review mainly studies the collaborative

management and optimization strategy of the digital

supply chain. Combined with the current

development situation of the digital economy, it puts

forward the requirements of establishing an efficient

supply chain management model for the operation

and management of enterprises. In this paper, the

basic theoretical knowledge, technology application,

and relevant cases in practice of digital supply chain

collaborative management are described respectively,

and their working principle, implementation process

and management effect are mainly analyzed. Some

problems and difficulties in the process of promoting

digital transformation are pointed out, and its

development prospect is explored.

Theoretically, the concept, components and

differences between digital supply chain

collaborative management and other supply chains

are discussed. For example, its comparative

advantage is that it can realize information sharing

and collaborative innovation with multi-agent

participation; Technically, it focuses on information

management system, big data, cloud computing,

Internet of things, blockchain, etc. related

technologies improve the transparency of the supply

chain and strengthen the process control of the supply

chain; The examples of enterprise practice mainly

come from various enterprises in the manufacturing,

retail and e-commerce industries. This paper

summarizes the effective methods in the actual

operation of the industry and the existing problems.

The first is a summary of the layout of the

ontology structure, that is, from the trunk to the

branches and leaves, and it is fully explored and

discussed; The second is to introduce the background

and significance of digital supply chain and the scope

of this review in the introduction; In the main body, it

is divided into four parts: theoretical framework,

methods and technologies, application cases and

development. The theoretical framework sorts out the

basic theories and establishment methods; the

optimization model and evaluation tools were

discussed; Select typical industries as application

cases to analyze the actual results; The development

trend points out the future direction, sets research

problems, and puts forward suggestions for

improvement. In this way, readers can clearly

understand the overall structure of collaborative

management and optimization of digital supply chain

and give guidance for further research.

2 LITERATURE REVIEW

METHODOLOGY

2.1 Literature Search Strategy

A hybrid search strategy combining keywords

("digital supply chain", "collaborative management")

and Boolean operators was employed. Databases

included CNKI, Wanfang (Chinese), and Web of

Science, Scopus (English), with a focus on core

journals (e.g., Management Decision, International

Journal of Production Economics) and recent

publications (past 5 years). Citation tracking via

EndNote identified high-impact works, such as Yuan

Fuli's research on agile supply chain quality

management (still relevant for historical context).

2.2 Literature Screening and

Evaluation Criteria

Literature selection prioritized relevance (digital

supply chain collaboration frameworks), academic

rigor (peer-reviewed methods), and timeliness (2020-

2025). Classic works (e.g., early studies on VMI and

ERP integration) were retained to contextualize

historical development. Quality assessment included

impact factor (IF > 3.0), citation counts (> 200), and

methodological robustness (e.g., mixed integer

programming in logistics network design, Reference

[4]).

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3 SUMMARY OF MAIN

THEORIES AND RESEARCH

RESULTS

3.1 Core Theoretical Framework

The collaborative management of digital supply

chains (DSC) integrates multidisciplinary theories to

address complex optimization challenges. At its core,

operations research (OR) provides mathematical

tools for resource allocation: linear programming (LP)

models optimize continuous variables like inventory

levels and transportation routes, with studies showing

LP reduces distribution costs by 12-18% in retail

networks (Ding et al., 2025). Dynamic programming

(DP), conversely, solves sequential decision

problems in production scheduling-Toyota's Just-In-

Time system, for example, uses DP to achieve 98%

capacity utilization while minimizing waste (Li et al.,

2024). Mixed integer programming (MIP) extends

this to discrete variables, such as vehicle fleet sizing

or warehouse location selection; a case study on

Amazon's European logistics network showed MIP

reduced transportation costs by 21% through optimal

route-vehicle pairing (Wu & Yao, 2024).

System theory and synergy theory form the

organizational backbone of DSC. The former

emphasizes holistic optimization through multi-level

integration-Haier's COSMOPlat platform, for

instance, connects 3 million+ IoT devices across

500+ suppliers to achieve end-to-end demand-supply

synchronization (Wang et al., 2024). Synergy theory

highlights collaborative value creation: when

information flow, logistics, and capital flow are

digitally integrated, enterprises report a 25% average

improvement in total factor productivity (Xiao et al.,

2024). This is exemplified by Midea's "Lighthouse

Factory," where IoT-driven data transparency

reduced inter-departmental coordination delays by 82%

(Lv et al., 2025).

Technologically, DSC relies on a three-layer

architecture: Perception Layer: IoT sensors and

blockchain enable real-time data capture. CATL's

battery production line, equipped with 5 million IoT

sensors, uses edge computing to monitor 2000+

process parameters, achieving a 99.2% first-pass

yield (Tian, 2022). Analysis Layer: Big data analytics

and machine learning (ML) transform raw data into

insights. Alibaba Cainiao's demand forecasting model,

trained on 10 billion+ historical transactions, reduces

inventory errors to <3% for fast-moving consumer

goods (Li, 2022). Decision Layer: Digital twin

technology creates virtual replicas for scenario

simulation. Sany Heavy Industry's twin model

predicts equipment failures with 92% accuracy,

reducing unplanned downtime by 40% (Zhang, 2024).

Game theory and multi-agent systems (MAS) address

inter-enterprise coordination. In a study of

automotive supply chains, non-cooperative game

models revealed that tier-1 suppliers underreport

component defects by 18% to avoid penalty costs (Bu

et al., 2024). Conversely, cooperative models with

revenue-sharing contracts increased information

sharing accuracy by 35% (Mao et al., 2024). MAS

frameworks further enable autonomous node

collaboration-Tesla's supplier network uses MAS to

dynamically adjust production quotas during demand

surges, achieving a 28% improvement in on-time

delivery (Yao & Wu, 2024).

3.2 Summary of Key Research Results

Decades of research on DSC can be categorized into

three phases: Foundational Period (pre-2015):

Focused on digital transformation motivations, such

as reducing the Bullwhip Effect through EDI systems.

Early studies showed that Walmart's RFID

deployment cut inventory inaccuracies from 21% to

6% (Li, 2022). Technology Adoption Period (2016-

2020): Explored applications of AI and IoT. A meta-

analysis of 56 studies found that AI-driven demand

forecasting improves accuracy by 19-27% compared

to traditional methods (Ding et al., 2025). Ecosystem

Era (2021-present): Highlights platform-based

collaboration and sustainability. For example,

SHEIN's federated learning model, which trains on

decentralized sales data, reduces fashion inventory

waste by 60% while maintaining 95% forecast

accuracy (Li, 2022).

4 RESEARCH METHODS AND

TECHNOLOGY OVERVIEW

4.1 Common Research Methods

In the collaborative management of digital supply

chain, linear programming is one of the most

important ways to solve the optimization problems of

distribution, cost and efficiency. For example, the

inventory management function under linear

programming can determine the optimal inventory

quantity and minimize the high inventory holding

cost and shortage risk that need to be paid; Through

the establishment of relevant mathematical models,

complex decisions are split into simple mathematical

Research on Collaborative Management and Optimization Strategy of Enterprise Digital Supply Chain

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problems that are easy to solve, to help enterprises

make more reasonable decision arrangements.

However, due to the frequent and irregular changes in

the supply chain environment, other methods should

be adopted for problems that cannot be solved by

linear programming.

Since linear programming has been extended to

integer programming, that is, the decision variables

must be integer values to solve discrete problems

such as production batches and the number of

transport vehicles, the use of integer programming in

logistics scheduling can obtain the best combination

of transport routes and vehicles, improve resource

utilization and reduce logistics costs. However, due to

the difficulty of non-convex objective functions in

this kind of planning, advanced algorithms such as the

branch and bound method and genetic algorithm play

a good role. In addition, by combining multi-

objective optimization, it is conducive to the wider

application of integer programming.

Sensitivity analysis is one of the basic means to

test the stability of the supply chain model. It can

provide reference for enterprises to formulate

strategies by studying whether the change of key

parameters affects the degree of the results. When the

market demand changes, sensitivity analysis is used

to obtain the corresponding costs under different

demands, to make flexible changes suitable for the

needs of enterprises. Find out the sensitive factors

with the help of sensitivity analysis and prepare for

further improvement. Multi objective optimization is

mainly used to solve the problems of various

contradictions, find the balance point by using the

method of objective weighting or establishing the

Pareto frontier, and realize the maximization of

interests. A comprehensive application can better

carry out the collaborative management of the digital

supply chain.

4.2 Method Application Cases and

Comparison

Amazing, unmanned supermarket, which uses RFID

and computer vision technologies, realizes the whole

process link collaboration mode of real-time

interaction between goods and consumers. While this

method improves the consumer shopping experience,

it also simplifies the work of the commodity shopping

guide. At the same time, it can adjust the price in time

for commodity circulation and inventory

management, shorten the time of inventory retention,

and is suitable for large-scale chain retail scenarios.

However, it also needs high-tech barriers and a high

initial investment to achieve. It is difficult for small

and medium-sized enterprises to achieve this mode.

In contrast, big data can be used to predict market

trends and quickly respond to changes in market

demand; By mining and analyzing the sales data, can

judge the changes of consumer preferences, adjust the

production plan in time, and reduce the inventory

backlog; This mode should be supported by strong

data analysis capability and flexible supply chain. It

is suitable for categories that need to quickly reflect

the market like fashion.

General Motors applies professional supply risk

management software to establish a multi-level

supply network, find and avoid risk factors, and

maintain the security and stability of the supply chain.

It is especially suitable for the complex supply chain

in the automobile and other manufacturing industries,

relying on many suppliers of key parts. However,

such enterprises have great difficulties in dealing with

risks, because it will increase the management burden

to continuously assess risks, and it is difficult for

small enterprises to follow up. However, TSMC has

upgraded itself to "intelligent" by using big data

mining and intelligent means, which has greatly

improved the production efficiency and product

quality, and realized automated manufacturing to a

certain extent, but it also requires enterprises to have

high-tech integration.

To sum up, each enterprise should choose its own

digital supply chain optimization mode according to

its situation, to better realize collaboration and

optimization.

5 RESEARCH STATUS,

CHALLENGES AND FUTURE

DIRECTIONS

5.1 Current Research Status and

Comments

The hot research objects of collaborative

management and optimization of digital supply chain

are mainly based on the development of data-driven

decision-making, Internet of Things technology,

blockchain technology, and artificial intelligence

technology. The development of these technologies

has brought better tools for supply chain management

to enterprises. Data driven decision-making relies on

big data analysis technology, which can help

enterprises grasp the information of each link of the

supply chain operation at any time and play a guiding

role in making decisions; The Internet of things

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technology can make each node in the supply chain

be networked and tracked to their location at any

time, to achieve the purpose of monitoring the

logistics status and controlling the inventory level;

Blockchain technology has great application

prospects in anti-counterfeiting traceability and

supply chain finance. Its tamper-resistant

characteristics and highly transparent characteristics

can enable enterprises to obtain huge advantages in

the use of blockchain.

There are still some defects in the current

research. The application maturity of Internet of

things and artificial intelligence technology is not

high, and there are still high costs and technical

difficulties to achieve large-scale deployment of

Internet of things equipment; To make the artificial

intelligence algorithm better use, it also needs more

high-quality data to optimize and upgrade the

algorithm; Finally, the issue of data security and

privacy has increasingly become a major issue in the

development of digital supply chain. The sensitive

data involved in the maintenance of digital enterprises

needs to be strictly ensured to ensure information

security and meet the relevant legal requirements. In

addition, from the organizational level, cross

organizational collaboration is difficult, and low

efficiency often occurs due to different cultures and

conflicts of interest among different organizations; At

the same time, part of the theoretical research is not

closely combined with the actual application

scenarios, so that the results cannot be directly

applied to the practice of enterprises. These are all

problems that need to be solved in the future.

5.2 Challenges and Problems

Data security and system heterogeneity are the core

issues in the practice of collaborative management of

the digital supply chain. When sharing data across

organizations, it may cause privacy disclosure. If

information is shared between organizations, it will

expose the company's important data, and if the

information is not shared, it will lead to the important

data being unable to be used; Moreover, due to the

homomorphic encryption technology based on

blockchain, information confidentiality cannot be

achieved even when the identity of the information

receiver is known. How to ensure information

visibility and access is the next problem to be solved.

Because the data standards and interface protocols of

erp/mes systems of different enterprises are not

consistent, the phenomenon of information islands is

more serious. In addition, problems such as expensive

EDI connections and longtime delays will greatly

reduce the impact on real-time demand forecasting

and inventory coordination. At the same time, poor

system interoperability will also make it difficult for

the SCM platform to integrate multi-source

heterogeneous data, and will finally have a great

impact on the visualization and intelligent decision-

making function of the supply chain.

Talent structure and organizational inertia will

also affect the effect of collaborative management of

the digital supply chain. For example, there is a lack

of compound talents, and talents who understand both

supply chain operations and AI algorithms are very

rare; And the speed of updating existing personnel is

slower than the speed of new technology iteration.

Traditional bureaucratic organization is also contrary

to the digital era, which requires rapid response.

However, the current situation of fighting among

departments, disputes between parties and data

fragmentation makes the indestructible model still

prone to antagonism and resistance between

departments; Moreover, because the algorithm itself

is a black box, the impact is not easy to detect, which

may also lead to the upper managers' reservations

about the results of smart logistics construction; At

the same time, due to the strong subjectivity of the

multi-objective optimization model, it is not suitable

for the dynamic market with high requirements for

change to some extent, and the computational power

consumption is huge, which is not easy to realize in

some links such as edge computing. However, large,

medium and small enterprises with different sizes and

positions at the end of the chain have different

degrees of informatization, and there are certain

differences.

5.3 Future Research Directions and

Trends

As digital technology and supply chain become more

and more complex, the collaborative management

and optimization of digital supply chain in the future

will be more inclined to break through and give more

innovation to the original prediction and scheduling

from more dimensions. Intelligence and automation

are the main driving forces. The autonomous

decision-making system using deep learning or

reinforcement learning will exceed the traditional

algorithm threshold of prediction and scheduling, and

realize real-time dynamic perception and prediction

response through multimodal data fusion. For the

multi-node distributed supply chain collaboration, it

mainly discusses the automatic interface

standardization and edge computing of

heterogeneous systems, and aiot plays an important

Research on Collaborative Management and Optimization Strategy of Enterprise Digital Supply Chain

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role in the application of multi-node distributed

supply chain collaboration.

Blockchain can generate a trustworthy

collaboration mode, and solve the problems of trust

and efficiency of data sharing among multiple entities

in the supply chain with cross chain interaction

protocols, privacy computing and other technologies;

At present, the application of smart contract in the

field of supply chain finance can prove the feasibility

of this technology, but how to integrate transaction

traceability and compliance management is still a

difficulty to be overcome; Green supply chain

management will be expanded to the whole life cycle

of carbon footprint more quickly. Through the

internalization of environmental costs, the optimal

value of multiple objectives will be solved, and then

the intelligent monitoring system of carbon emissions

will be established based on LCA. In the face of new

scientific and technological requirements, it is

necessary to do a better job in higher precision

algorithm computing power and database.

The data-driven decision-making system will be

developed to the level of cognitive intelligence. On

this level, a perfect supply chain digital twin will be

established, and real-time simulation and calibration

work will be carried out. In this process, knowledge

mapping and IOT real-time data flow technology will

be used to build the above model. And it can be

considered that further research in the future can

focus on how to solve a larger-scale supply chain

network through quantum computers. At the same

time, it can also investigate how to mine the design

scenarios of relevant supply chain generation through

generative AI, to further improve the management

level of the overall supply chain.

6 CONCLUSION

Collaborative management of the digital supply chain

is very important for enterprise production and

operation. Using digital means and technologies,

digitalize and information all links of the supply

chain, so that all nodes in the supply chain are

interconnected and cooperate closely, to maximize

the collaborative utilization of resources, improve the

overall efficiency, increase the flexibility of

enterprises, and facilitate enterprises to trace product

related information. Using Internet of things, big data,

cloud computing, artificial intelligence and other

technologies to collect and process the information

flow of the supply chain in real time, predict the

possible demand of the upstream and downstream of

the supply chain, and modify the production plan

accordingly to deal with the problem of backlog or

shortage, which will control the cost, reduce

unnecessary material waste and reduce the scrap rate.

Collaborative management of the digital supply

chain increases the market competitiveness and

customer experience of enterprises. It can quickly

respond to changes in market demand to improve

market agility and flexibility, so it can increase the

competitiveness of enterprises; It can better

understand customer needs and provide different

services, continuously strengthen customer

satisfaction and customer loyalty in communication

with customers, establish a good brand and image,

and win a better competitive position in the market.

At present, in the complex business environment, the

digital supply chain collaborative management mode

is one of the effective ways to realize the digital

transformation and upgrading of enterprises, which

can create more value for enterprises, so it is also a

practical method worth trying.

The future research on collaborative management

of digital supply chain should focus on technology

integration, data governance, and system

interoperability. Interdisciplinary integration is

mainly to combine computer science, complex

system theory, and operations research, break the

phenomenon of cross-coexistence of different fields,

establish a composite research framework, and

develop intelligent algorithms with a multimodal data

fusion function. On this basis, it focuses on the

research and development of dynamic optimization

model of deep reinforcement learning and a

distributed collaboration mechanism of blockchain,

to ensure that each node in each link of the supply

chain can achieve better collaboration and achieve a

better synergy effect.

To build a full life cycle data quality management

system of data governance, strengthen federal

learning, the application of differential privacy

algorithms in sensitive data sharing, and the process

of data quality control according to ISO8000 standard

to ensure the consistency of data semantics. With the

development of technology, supply chain

collaboration will become an ecological network.

With the development of quantum computing and

digital twin technology, computing power can be

broken through, and real-time simulation and real-

time reconstruction of the large-scale supply chain

can be realized. Next, can start with the adaptive

intelligent collaborative platform and use the

embedded AI agent to realize the independent

optimization of the supply and demand sides.

Facing the reality of heterogeneous

interoperability between different systems in the

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application, it is necessary to develop a unified

standard interface protocol through the industrial

Internet architecture, and based on this interface, the

specific technologies such as opcua and GS1 are

deeply integrated; Based on the complex world

background, efforts have been made in the design of

flexible supply chain network and risk assessment,

and the toughness of the industrial chain has been

enhanced by establishing an anti-risk mechanism

suitable for China's national conditions.

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