Enterprise Information System Enabling Digital Transformation of

Retail Industry in the Context of AI: Take Multi-Point Dmall and Fat

Donglai Cooperation as an Examples

Lindi Zhang

School of Management, Beijing Institute of Technology

Haidian District, South Street, China

Keywords: AI Technology, Enterprise Information Systems, Retail, Digital Transformation.

Abstract: Against the backdrop of the deep integration of digital economy and artificial intelligence technology, the

retail industry is undergoing a digital transformation from traditional mode to the whole chain. Enterprise

information system, as the core carrier of technology empowerment, has become a key force to promote the

change of retail industry through data integration, intelligent decision-making and ecological synergy. This

paper takes the cooperation between Multipoint Dmall and Fat Donglai as a case study to explore how AI

technology can reconfigure the operation mode and value chain of the retail industry through the enterprise

information system. The study analyses the technological applications of multi-point Dmall's intelligent

replenishment, dynamic pricing, and omni-channel operation tools, revealing the significant role of AI

technology in improving the operational efficiency and user experience of retail enterprises. The results of

the study show that Dmall's practice provides a replicable model for the digital transformation of the retail

industry and promotes the transformation of the industry from an "efficiency revolution" to an "ecological

revolution", which is of great theoretical and practical significance.

1 INTRODUCTION

In today's era of deep integration of digital economy

and technology, the retail industry is experiencing an

unprecedented change. With the continuous

development of artificial intelligence, big data, cloud

computing and other cutting-edge technologies, the

role of enterprise information systems as an important

bridge connecting the internal operations of

enterprises and the external market is becoming more

and more prominent. By introducing advanced

enterprise information systems, retail enterprises can

achieve efficient integration and deep mining of

massive data, provide powerful support for intelligent

decision-making, and at the same time promote

ecological synergy between upstream and

downstream of the industrial chain, so as to stand out

in the fierce market competition (Xie et al., 2023).

The local retail digitisation rate is 3.1% in China and

4.5% in Asia in 2023, much lower than the 13.3% in

the US. The market size of the local retail industry in

Asia grew from RMB30.0 trillion in 2018 to

https://orcid.org/0009-0004-6343-1024

RMB31.1 trillion in 2023, at a CAGR of 0.8%. The

market size of the local retail industry in Asia is

projected to grow at a CAGR of 1.4% from 2024 to

2028, reaching RMB33.5 trillion by 2028. Against

this backdrop, digital transformation of retail

companies has become the key to enhancing

competitiveness (Dekimpe and van Heerde, 2023).

Multi-Dot Dmall, a leader in retail technology, has

successfully expanded its business to several

countries and regions in Asia. in 2023, Multi-Dot

Dmall is the largest retail digital solutions provider in

China by revenue, with a market share of 6.5% (He,

2023). Multipoint Dmall's Retail Core Service Cloud

is based on the proprietary Dmall SaaS operating

system, which helps retailers manage their entire

operational processes. The system integrates a range

of functionalities across service components such as

product sourcing process management, supply chain

management, product management, shop

management, consumer membership management

and head office management, enabling real-time

tracking of inventory levels, reducing out-of-stock

Zhang, L.

Enterprise Information System Enabling Digital Transformation of Retail Industry in the Context of AI: Take Multi-Point Dmall and Fat Donglai Cooperation as an Examples.

DOI: 10.5220/0013848300004719

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

In Proceedings of the 2nd Inter national Conference on E-commerce and Modern Logistics (ICEML 2025), pages 501-506

ISBN: 978-989-758-775-7

501

rates and increasing sales revenue. As of 2023, the

number of customers for the Retail Core Service

Cloud is approximately 430, and the company's

revenue grows from $848 million in 2021 to $1.585

billion in 2023 (He and Ran, 2023).

Multi-point Dmall's technical support has built a

competitive barrier of "technology + data" for Fat

Donglai and promoted the digital transformation of

the retail industry.

This paper focuses on how AI technology has

improved the operational efficiency and user

experience of retail enterprises through enterprise

information systems, reshaped the value chain and

competitive landscape, and built technical barriers

and ecological synergies. Specifically, Fat Donglai,

as a well-known regional retail brand in China, has

been limited in brand expansion due to insufficient

digitalisation capabilities. In order to break through

the bottleneck of the traditional operation mode, Fat

Donglai introduced the enterprise information system

of Multi-point Dmall. This decision not only reflects

Fat Donglai's deep insight into the trend of industry

development, but also its urgent need for enterprise

digital transformation.

2 INFORMATION ENABLED

RETAIL TRANSFORMATION

2.1 AI Technology to Enhance

Operational Efficiency and User

Experience

2.1.1 Intelligent Replenishment and

Dynamic Pricing

The Dmall OS system independently developed by

Multi-point Dmall, as the core of its technological

innovation, builds a powerful technological base for

Fat Donglai. The system covers 15 modules,

including product management, supply chain

optimisation and member operation, and supports

modular combination and plug-and-play functions.

Through the deep integration of AI and big data, the

Dmall OS system plays a significant role in many

business processes.

In terms of intelligent replenishment, the system

predicts inventory demand based on average daily

sales, helping Fat Dong Lai reduce the out-of-stock

rate of fresh food categories from 18% to 3%,

effectively reducing sales losses due to out-of-stock.

After the introduction of intelligent replenishment

system, the inventory turnover days of fresh food

category in Fat Dong Lai shops was shortened from

the original 7 days to 4 days, which significantly

reduced the cost of capital occupancy and

significantly improved the operational efficiency.

Dynamic pricing function shows its unique

advantage in the situation of slow-selling goods, AI

algorithm analyses the shelf life and sales trend in real

time, and dynamically adjusts the promotion strategy,

so that the loss rate of expired food in Fat Tung Lai is

reduced from 15% to 3%, which significantly reduces

the wastage of commodities and improves the profit

margins.

Multi-point Dmall's intelligent replenishment and

dynamic pricing features are typical applications of

its AI technology in the retail industry. Through

intelligent replenishment, the system is able to

monitor inventory levels in real time and

automatically forecast replenishment demand based

on historical sales data and market trends, thereby

reducing out-of-stock rates and minimising inventory

backlogs. For example, after Fat Donglai introduced

the system, the out-of-stock rate of the fresh food

category was significantly reduced, and the capital

turnover efficiency was improved. At the same time,

the dynamic pricing function automatically adjusts

the price by analysing the shelf life of the goods, the

sales rate and the market demand, in order to

maximise profits. In actual application, Fat Donglai

has effectively reduced the loss of expiring goods and

improved overall profitability through dynamic

pricing strategy.

2.1.2 Omni-Channel Operation Tools

Multi-point Dmall integrates third-party platforms

such as Meituan and HungryMall through a unified

central platform to achieve cross-channel synergy of

inventory and supply chain and creates an omni-

channel operation tool for Fat Donglai. The number

of Fat Donglai's members increased from 1 million to

6 million, and online GMV grew by 300%.

Specifically, after the launch of Fat Donglai's online

mall, its number of monthly active users grew from

an initial 100,000 to 700,000, and its order volume

increased by more than 50% per month, greatly

expanding its market coverage.

Multi-point Dmall's omni-channel operation tool

is another highlight of its retail empowerment. By

integrating online and offline resources and channels,

multi-Dot Dmall helps Fat Donglai realise the

opening of its membership system and the seamless

connection between online and offline. For example,

Fat Donglai's members can place orders online

through Multi-Dot Dmall's platform and enjoy the

ICEML 2025 - International Conference on E-commerce and Modern Logistics

502

same membership rights and services as offline

shops. This omni-channel operation mode not only

improves user experience but also brings significant

business growth for Fat Donglai. According to the

data on the company's official website, after Fat

Donglai cooperated with Multi-point Dmall, the

number of members grew from 1 million to 6 million,

online GMV increased by 300%, the number of

monthly active users of the online mall grew from

100,000 to 700,000, and the number of orders

increased by more than 50% per month. As shown in

Figure 1, Fat Donglai's annual sales were

approximately 7 billion yuan in 2022, grew to

approximately 10 billion yuan in 2023, with a year-

on-year growth rate of approximately 0.53 (i.e.,

53%), and further grew to approximately 17 billion

yuan in 2024, with a year-on-year growth rate of

approximately 0.59 (i.e., 59%).

Data source: Debonair Institute

Figure 1: Fat Dong Lai's annual sales

2.2 Reshaping the Value Chain and

Competitive Landscape

2.2.1 “SaaS+VAS” Model

Multi-dot Dmall adopts the "SaaS + value-added

services" model, with 58.1% (1.1 billion yuan) of

retail core service cloud revenue in the first nine

months of 2022, and value-added services covering

precision marketing and supply chain finance. Nestle

achieved 300% growth in online GMV through

precision marketing. Nestle's online sales grew from

5 million yuan to 20 million yuan per month after

cooperating with Multi-point Dmall, and the

promotion cycle of new products was shortened by 30

per cent, with significantly faster market response.

Multi-Dot Dmall's "SaaS+Value-added Services"

model is an important innovation in the digital

transformation of the retail industry. Through this

model, DotDmall not only provides basic software

services for retailers, but also helps retailers improve

operational efficiency and market competitiveness

through value-added services such as precision

marketing and supply chain finance. For example,

after cooperating with Multipoint Dmall, Nestle has

achieved a 300% increase in online GMV through

precision marketing, an increase in online sales from

5 million yuan to 20 million yuan per month, a 30%

shortening of the new product promotion cycle, and a

significant acceleration of market response.

2.2.2 Ecological Synergy and Data

Realisation

Multi-point Dmall has developed more than 10 AI

application scenarios by building a technology

ecosystem with partners such as Microsoft and

Flybook. Its user profile data provides advertising

services for brands, with marketing and advertising

service cloud revenue accounting for 11.4% in 2022.

For example, a cosmetic brand accurately positioned

its target customer groups through Multi-Point

Dmall's user profile data, which increased its

advertising effectiveness by 40% and reduced new

customer acquisition costs by 25%, effectively

improving its marketing ROI.

Multi-point Dmall's ecological synergy and data

realisation capability is another major advantage in its

digital transformation in the retail industry. Through

cooperation with Microsoft, Flybook and other

technology partners, Multipoint Dmall has developed

a number of AI application scenarios, such as

intelligent customer service and unmanned guarding,

which not only improves its own service capability,

but also provides more diversified solutions for retail

enterprises (Yi, 2024).

2.3 Construction of Technical Barriers,

Closed-Loop Data Operation and

Ecological Synergies

2.3.1 Customer Growth and Data

Accumulation

Multi-point Dmall serves 677 retailers globally (as of

2023), covering 15,000 shops. Data from the

expanded customer base feeds AI model

optimisation. With the increase in the number of

customers, Multi-Point Dmall's data processing

capacity has been improved, and the accuracy of its

AI model has increased from the initial 85% to 95%,

enabling it to more accurately predict market demand

and consumer behaviour.

50,00%

55,00%

60,00%

100

200

2022 2023 2024

Annual Sales of

Pang Donglai

Annual Sales yoy

Enterprise Information System Enabling Digital Transformation of Retail Industry in the Context of AI: Take Multi-Point Dmall and Fat

Donglai Cooperation as an Examples

503

Multi-point Dmall's customer growth and data

accumulation are the key foundations on which it

builds its technological barriers. By serving 677 retail

companies worldwide and covering 15,000 shops,

Multi-Point Dmall has accumulated a huge amount of

retail data. These data not only provide rich training

materials for the optimisation of AI models but also

form a virtuous cycle in which customer growth

promotes data accumulation, which further promotes

technological optimisation and thus attracts more

customers. With the increase in the number of

customers, Multi-Point Dmall's data processing

capacity has been continuously improved, and the

accuracy of the AI model has increased from the

initial 85% to 95%, enabling it to more accurately

predict the market demand and consumer behaviour

(Sagar, 2024).

2.3.2 Synergistic Value of the Technology

Ecosystem

Jointly developed with Microsoft, Flybook and other

partners, AI inspection, intelligent clearing and other

scenarios have enhanced the technological diversity

of the platform. For example, Wellcome Hong Kong's

"yuu" membership platform has a user base of 5

million, driving cashless transformation. With the

technical support of Multi-point Dmall, Wellcome's

member repurchase rate increased by 20 per cent, and

the proportion of mobile payment increased from 60

per cent to 85 per cent, greatly improving operational

efficiency and customer experience.

Multi-point Dmall's technology ecosystem

synergy is an important support for its digital

transformation in the retail industry. Through

cooperation with Microsoft, Flying Book and other

technology partners, DotDmall has developed a

number of AI application scenarios, such as AI

inspection and intelligent clearing, which not only

improves the technological diversity of the platform,

but also provides retailers with richer solutions. For

example, Hong Kong Wellcome's "yuu" membership

platform has reached a user base of 5 million with the

technical support of multipoint Dmall, promoting

cashless transformation. Through the synergy of these

technology ecosystems, multipoint Dmall not only

enhances its own competitiveness, but also brings real

business growth and operational efficiency

improvement to retailers (Kao et al., 2024).

3 CHALLENGES

3.1 Difficulties in Getting the

Technology off the Ground

3.1.1 The Problem of Data Silos

In retail enterprises, the information systems of

various departments are independent of each other,

making it difficult to circulate and share data. For

example, the sales department has detailed customer

purchase records, the inventory management

department knows the real-time inventory of goods,

and the marketing department has accumulated rich

data on promotional activities, but this key

information is often confined to their own

independent systems. This fragmented data situation

greatly limits the ability of enterprises to grasp market

dynamics and consumer demand from a holistic

perspective. The lack of effective data interaction

mechanisms between different systems,

inconsistencies in data formats, untimely updates and

other problems are common, bringing huge obstacles

to data integration. For example, when enterprises try

to introduce advanced intelligent forecasting systems,

the reliability of the forecasting results is greatly

reduced due to the lack of access to complete and

accurate sales and inventory data, making it difficult

to effectively assist decision-making (ISG, 2022).

3.1.2 High Cost of Staff Training

The widespread adoption of AI technology is placing

new demands on the skill level of retail employees.

Employees need to master data analysis tools and

even understand complex machine learning

algorithms. However, training employees in AI

technologies is a time-consuming and resource-

intensive task. Not only do companies have to invest

heavily in developing or purchasing training courses,

but they also need to arrange for professionals to

teach and mentor them. In practice, employees may

resist due to unfamiliarity and fear of the new

technology, thus affecting the effectiveness of the

training. Especially for large retail chains with a large

and widely distributed workforce, how to ensure that

every employee receives effective training and is

skilled in the use of AI technology has become a

serious challenge for the enterprise. For example,

when a large supermarket chain promoted the smart

checkout system, the efficiency of the checkout was

reduced and the queuing time of customers was

prolonged due to the unskilled operation of the

ICEML 2025 - International Conference on E-commerce and Modern Logistics

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employees, which led to a lot of dissatisfaction and

complaints (Noble et al., 2022).

3.2 Difficulties in Getting the

Technology off the Ground

3.2.1 Insufficient Transparency in the

Collection and Use of Data

Retail businesses often suffer from a lack of

transparency in the collection and use of consumer

data. Some enterprises do not adequately and clearly

inform users of the specific purpose of data collection,

the manner of use and the scope of sharing when they

knowledge, personal information may be used for

secondary marketing or other commercial purposes.

This information asymmetry not only violates

consumers' right to know but may also trigger

consumers' distrust of the company. For example,

some e-commerce platforms allow data sharing to

third-party partners to be ticked by default during user

registration, and consumers' personal information is

passed on to other companies without their awareness,

resulting in frequent receipt of harassing

advertisements (Ratchford et al., 2022).

3.2.2 High Risk of Data Breaches

Retail businesses store vast amounts of sensitive

consumer data that is highly attractive to cyber

attackers. Hackers try to steal information from

businesses' databases through malware attacks,

phishing and other means. Once the data is leaked,

sensitive data such as consumers' names, addresses,

contact details, payment information, etc. will be at

risk of being misused, which may lead to serious

consequences such as fraud and identity theft. In

recent years, data breaches in the retail industry have

been commonplace, bringing huge losses to

businesses and consumers. For example, the official

website of a well-known sports brand had a security

breach that led to the leakage of hundreds of

thousands of users' personal information, triggering

serious public questions about the company's data

security and a sharp drop in its share price (Neslin,

2022).

To sum up, retail enterprises are facing challenges

in the process of digital transformation under the

background of AI, such as the difficulty of technology

implementation and the risk of consumer privacy and

data security. Enterprises need to consider and plan

comprehensively in terms of technology, talent, and

management to effectively address these challenges

and achieve sustainable digital transformation

(Dekimpe and van Heerde, 2023).

4 CONCLUSION

This paper adopts the case study method to analyse

the cooperation between Multipoint Dmall and Fat

Donglai and summarises the core value of enterprise

information system in the digital transformation of

the retail industry. Through the application of

intelligent replenishment, dynamic pricing and omni-

channel operation tools, Multi-point Dmall has built

a competitive barrier of "technology+data" for Fat

Donglai, and promoted the transformation of the

retail industry from an "efficiency revolution" to an

"ecological revolution". revolution" in the retail

industry.

Specifically, Multipoint Dmall's intelligent

replenishment system helped Fat Donglai reduce the

out-of-stock rate of fresh food from 18% to 3% and

shorten the inventory turnover days from 7 days to 4

days, significantly reducing the cost of capital

consumption. The dynamic pricing function reduces

the loss rate of expired food from 15% to 3% through

real-time analysis of shelf life and sales trends,

reducing waste and increasing profit margins. Omni-

channel operation tools helped increase Fat Donglai's

membership from 1 million to 6 million, online GMV

by 300%, monthly active users on the online mall

from 100,000 to 700,000, and order volume by more

than 50% per month (He and Ran,2023).

Further analyses show that Multi-Dot Dmall's

"SaaS+value-added services" model and eco-synergy

is the key to its success. Nestle achieved a 300%

increase in online GMV and a 30% reduction in new

product promotion cycle through precision

marketing. A cosmetic brand used user profile data to

increase advertising effectiveness by 40% and reduce

new customer acquisition costs by 25%. Multi-point

Dmall has developed scenarios such as AI inspection

and intelligent clearing by cooperating with

Microsoft, Flybook and other technology partners to

enhance the technological diversity of the platform.

In terms of future outlook, it is recommended that

Multi-Point Dmall deepen the application of AI

scenarios and expand predictive analytics and

automated decision-making, such as real-time early

warning of the fresh food supply chain. Meanwhile, it

should balance R&D and profitability, accelerate

market penetration in Southeast Asia, and learn from

SHEIN's globalisation path. In addition, it should

strengthen ESG practices, combine green retail

solutions with carbon trading, and enhance social

Enterprise Information System Enabling Digital Transformation of Retail Industry in the Context of AI: Take Multi-Point Dmall and Fat

Donglai Cooperation as an Examples

505

value. The integration of AI and enterprise

information systems will drive the retail industry

from an "efficiency revolution" to an "ecological

revolution", and the practice of Dot Dmall provides a

replicable digital model for the industry. The practice

of multipoint Dmall provides a replicable digital

model for industry.

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