Research on Strategies to Improve Operational Efficiency of Catering

Enterprises from the Perspective of Data Empowerment

Yanchen Qin

School of Sociology and Social Policy, University of Leeds, Leeds, LS2 9JT, U.K.

Keywords: Data Empowerment, Restaurant Industry, Digital Transformation, Inventory Management, Intelligent

Scheduling.

Abstract: In the context of increasing market fluctuation and high operational pressures, the traditional management

models in the restaurant industry are facing serious limitations in terms of responsiveness and efficiency. This

study explores how digital technologies, particularly big data analytics, artificial intelligence and intelligent

systems can empower restaurant enterprises to strengthen operational performance. By analyzing industry

issues across inventory management, employee scheduling and customer marketing, this paper proposes three

data-driven strategies: dynamic inventory management, intelligent scheduling systems, and precision

marketing. The paper will use cases and data to illustrate the effectiveness of data empowerment in reducing

waste, optimizing labor resources and improving customer engagement. Furthermore, the paper discusses the

institutional safeguards and technological prerequisites necessary for successful digital transformation. The

findings offer both theoretical insights and practical frameworks to support restaurant enterprises in their

transition toward a data-enabled operational paradigm.

1 INTRODUCTION

In today's rapidly changing market environment, the

restaurant industry is facing unprecedented

competitive pressure. On the one hand, consumers

need more personalized services. It promotes

restaurant businesses to continuously optimize their

services. On the other hand, rising labor costs and raw

material prices are squeezing profit margins. In this

context, traditional restaurant management models

are gradually exposing their problems such as

information lag and slow response (He et al., 2019),

making it difficult to meet the demands of modern

operations.

With the development of technologies such as big

data, artificial intelligence (AI), and the Internet of

Things (IoT), data empowerment has become a key

force driving industrial transformation. In the

restaurant sector, leveraging data technologies to

guide operational decisions has evolved from an

“optional” approach to a “mandatory” one. For

example, restaurants can optimizing their

procurement plans based on historical sales data,

conducting precision marketing using customer

https://orcid.org/0009-0007-7405-8976

behavior data (Nygaard et al., 2007), and using data

models to predict foot traffic for scheduling purposes

are all practical examples of data empowerment.

This study aims to explore how restaurant

enterprises can enhance operational efficiency

through data technologies from the perspective of

data empowerment. By analyzing practices from

representative domestic and international companies,

the research examines the underlying driving

mechanisms and paths for strategy implementation.

While existing literature widely acknowledges the

positive impact of data technologies on operational

performance, there is still a lack of systematic studies

with universal applicability and practical operability

at the implementation level. Therefore, this paper

focuses on data empowerment and scene innovation,

focusing on the main operational problems faced by

catering businesses and how to solve these problems

with data empowerment, and further exploring what

feasible strategies can be applied to improve

operational efficiency.

Methodologically, this study combines literature

review and case analysis, focusing on three key

dimensions-inventory management, employee

554

Qin, Y.

Research on Strategies to Improve Operational Efﬁciency of Catering Enterprises from the Perspective of Data Empowerment.

DOI: 10.5220/0013849500004719

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 554-559

ISBN: 978-989-758-775-7

scheduling, and customer marketing-to propose a

systematic, data-driven strategy for optimizing

operations.

2 OPERATIONAL

SHORTCOMINGS OF

CURRENT RESTAURANT

ENTERPRISES AND THE

DRIVING FORCES OF DATA

EMPOWERMENT

Although the restaurant industry has experienced

rapid growth in recent years, it still faces multiple

challenges in actual operations (Hanaysha, 2016; He

et al., 2019), particularly in areas such as refined

management, resource allocation, and responsiveness

to market changes (Rumore et al., 1999; Al-Abbadi et

al., 2020). This section analyzes these issues from

four key dimensions-inventory, workforce,

technology, and application-and interprets them in

relation to the driving forces behind data

empowerment (He et al., 2016; Nygaard et al., 2007).

2.1 Prominent Issues of Inventory

Waste

Traditional restaurant enterprises often rely on chefs’

personal experience or historical sales data to make

decisions regarding ingredient procurement. This

method lacks in-depth analysis of real-time market

fluctuations, climate impacts, and other influencing

factors. Such an experience-oriented approach often

leads to a disconnect between procurement forecasts

and actual demand, resulting in imbalanced inventory

structures. Some ingredients may expire and will be

discarded due to overstocking. Others may run short

due to underestimation, directly affecting the stability

of menu offerings and the overall dining experience.

According to the 2023 China Catering Industry

Report, approximately 23% of raw material loss are

due to poor inventory management. This not only

increases operating costs but also raises food safety

concerns, potentially harming brand reputation and

customer satisfaction.

2.2 Lack of Scientific Basis in

Workforce Scheduling

In daily operations, restaurants often struggle with a

mismatch in labor allocation-experiencing staff

shortages during peak hours and idle labor during off-

peak times. Most restaurants still rely on store

managers’ experience to make scheduling decisions.

This situation is especially pronounced in fast-food

chains where customer flow fluctuates frequently.

This approach makes it difficult to match staffing

with service demand accurately. It also increases

employee workload and labor costs. When sudden

events or holiday traffic surges occur, the lack of

intelligent tools for real-time analysis makes it more

complicated to response. As a result, service quality

declines, customer waiting times increase,

satisfaction declines and return rates increase.

Lacking a scientifically and dynamically adjustable

scheduling system has become a major obstacle in

improving operational efficiency and customer

experience.

2.3 Weak Technological Infrastructure

Although some restaurant enterprises have gradually

adopted systems such as POS (Point of Sale), CRM

(Customer Relationship Management), and supply

chain management platforms in recent years, there are

still significant data silos persist among these

systems. Due to the lack of unified technical

architecture and data hub, these systems remain

disconnected, making data integration more difficult.

Operators often must manually export and compare

data across multiple platforms for analysis, which is

time-consuming and error prone. Furthermore, the

lack of cross-module data synergy hampers the ability

to implement truly data-driven strategies in key areas

such as marketing, inventory replenishment, and

workforce management, undermining the overall

efficiency and responsiveness of the enterprise.

2.4 Immature Application of Data

Scenarios

With the advancement of digitalization, more small

and medium-sized restaurant businesses are

beginning to recognize the value of data. They

attempt to collect customer data through membership

systems, online ordering platforms, and other

channels. However, the depth and breadth of data

utilization remain at a basic level. On the one hand,

there is a lack of internal capabilities for data analysis

and technological application. On the other, most

enterprises only use data for generating simple reports

and sales summaries, without establishing effective

customer segmentation, predictive models, or

personalized marketing systems. While many

companies possess large volumes of customer

transaction data, they fail to extract underlying

Research on Strategies to Improve Operational Efﬁciency of Catering Enterprises from the Perspective of Data Empowerment

555

behavioral patterns or preferences, nor can they

conduct targeted marketing or service innovations

based on data. As a result, data remains “asleep,”

unable to be truly converted into business value.

3 MAIN STRATEGIES

To address the current operational challenges faced

by restaurant enterprises-such as inventory waste,

inefficient workforce scheduling, and lack of

precision in marketing-this study proposes three

operational optimization strategies. These strategies

are developed in line with trends in data

empowerment and the application of technological

tools. Focusing on inventory management, employee

scheduling, and marketing management. The aim is

to leverage data-driven approaches to enhance both

operational efficiency and customer satisfaction,

thereby helping enterprises gain competitiveness in

the market.

3.1 Dynamic Inventory Management

Traditional inventory management in the restaurant

industry often relies on chefs’ experience or past sales

data for forecasting. This approach typically lacks

real-time responsiveness and can result in both over-

purchasing and frequent stockouts. To solve this type

of problems, this study proposes a data-empowered

strategy of “Dynamic Inventory Management” (He et

al., 2019). This strategy encourages enterprises to

build intelligent procurement forecasting models

based on multidimensional data. These models

integrate key variables such as historical sales data,

holiday data, weather trends, and promotional

activities to forecast future sales scientifically. This

would optimize inventory allocation.

In practice, enterprises can utilize Business

Intelligence (BI) systems or professional supply chain

SaaS platforms to adjust procurement plans and

coordinate across multiple locations, thereby

improving the efficiency of using raw material. For

example, after KFC introduced a sales forecast-based

inventory management system in some of its stores, it

successfully reduced food waste and minimized

customer complaints due to stockouts, enhancing

overall stability and satisfaction.

3.2 Intelligent Scheduling Systems

Traditional employee scheduling is based on

subjective judgments by store managers, lacking

analysis of customer flow data and employees’

working habits. To improve efficiency and resource

utilization, this paper proposes building an

“Intelligent Scheduling System” (Hanaysha, 2016).

By collecting and analyzing historical customer flow

data, trends during holidays and unique events, along

with employee attendance preferences and

performance records, enterprises can create data-

driven scheduling models to optimally allocate

human resources by day and time slot.

Taking Haidilao as an example, by deploying a

data analysis platform it can access customer

reservation information and on-site customer flow

data in real time and intelligently allocate personnel

positions and shift times based on preset algorithms

(Biao & Rojniruttikul, 2023). With predefined

algorithms, it intelligently assigns staff roles and shift

times. This approach significantly reduces customer

wait times during peak hours, improves the dining

experience, and increases employee efficiency and

satisfaction. This type of systematized scheduling is

especially suitable for chain restaurant operations,

helping maintain service quality as businesses scale.

3.3 Precision Marketing

In the context of increasingly fierce competition in

the catering industry, marketing activities have

become the key to improving customer stickiness and

conversion rates. Different from the traditional "one-

size-fits-all" marketing approach, data-driven

precision marketing strategies emphasize customer

behavior data as the basis , and through in-depth

mining of consumption records, taste preferences,

purchase frequency, social media tags and other

dimensions, customers are refined AS profiled and

classified, and personalized marketing content and

push strategies are formulated (Gupta et al., 2024).

Using membership systems and customer

management platforms, businesses can categorize

users into segments such as weekday high-frequency

lunch customers, weekend family diners, or holiday

travelers, and push differentiated promotions,

personalized coupons, and dish recommendations

based on real-time weather, seasonal holidays, and

store locations. Furthermore, some leading

enterprises have adopted A/B testing mechanisms to

experiment with different marketing messages and

timing strategies to further improve conversion rates

and customer satisfaction. For example, by pushing

highly targeted text messages or app notifications,

chain brands have significantly increased the

response rate of membership activities (Hanaysha,

2022).

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Overall, these three strategies offer high

practicality and strong potential for implementation,

providing a clear pathway for digitalized operations

in the restaurant industry. However, successful

strategy execution is not instantaneous. It requires a

comprehensive support system and robust internal

management mechanisms. Particularly during the

process of digital transformation, enterprises must

also overcome multiple challenges such as

organizational restructuring, employee training,

technology adaptation, and data security. The next

section will further explore the safeguards and

potential risks involved in implementing these

strategies, offering actionable solutions to ensure that

the concept of “data empowerment” can be

effectively transformed into sustainable operational

performance.

4 IMPLEMENTATION

SAFEGUARDS,

OPPORTUNITIES, AND

CHALLENGES

Although data empowerment offers a new path for

restaurant enterprises to improve operational

efficiency and service quality, its practical

implementation requires comprehensive safeguards

and supporting measures. Only by aligning efforts in

key areas such as technical capabilities and talent

deployment can data strategies be effectively

transformed from concept to practice. At the same

time, the opportunities and challenges brought about

by digital transformation must be properly addressed

to ensure steady and sustainable development

throughout the process.

4.1 Establishing a Data Management

Mechanism

Data management is the foundation of data-driven

decision-making. To ensure systematic and

standardized data usage, enterprises should set up

dedicated data management positions or a centralized

data team for data collection, cleansing, modelling,

analysis, and output. This team should not only have

technical expertise but also be able to work closely

with different departments to ensure data accuracy.

It is recommended to adopt a “headquarters-led +

store-level execution” governance framework.

Headquarters should define data collection standards,

build platform infrastructure, and develop analysis

models, while stores focus on front-end data

collection and preliminary application. This setup

improves management efficiency while maintaining

consistency across stores. Additionally, enterprises

should develop a “data asset map” to define the use

cases for various types of data (e.g., sales, inventory,

customer feedback), providing a clear pathway for

future data applications and business innovation.

4.2 Clarifying Interdepartmental

Collaboration Mechanisms

Data empowerment is not the exclusive domain of the

IT department-it is a company-wide initiative

requiring cross-functional collaboration. Many

restaurant enterprises currently suffer from

departmental silos and severe data fragmentation,

limiting the effectiveness of data empowerment. To

address this bottleneck, companies must promote

interdepartmental cooperation and break down the

barriers between IT, finance, supply chain, HR, and

marketing functions.

Practical steps include forming cross-functional

data project teams, where each department has clear

responsibilities and objectives. Establishing KPIs and

incorporating data usage performance into

evaluations for each department can also improve

efficiency. Regular holding data meetings will further

enhance data literacy and team engagement. Only

through the alignment of organizational structure and

data strategy can the full value of data be realized.

4.3 Introducing and Integrating

Technological Tools

The effective selection and integration of

technological tools is critical for achieving data

empowerment. For small- and medium-sized

restaurant businesses with limited technical

foundations, building an in-house data platform may

be costly and risky. It is therefore advisable to first

adopt comprehensive systems such as Meituan's

catering ERP, Keruyun’s digital store management

platform, or 2DFire’s restaurant system. These

platforms typically offer integrated modules for sales

analytics, procurement and inventory management,

employee scheduling, and membership marketing—

helping to automate operational data collection and

enable closed-loop management. This reduces the

technical threshold and cost of implementing data

empowerment tools.

During system selection and deployment, special

attention should be paid to platform compatibility and

integration feasibility to avoid high secondary

development costs and disconnected data flows. Open

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platforms that support API interfaces and cloud

deployment are preferred to ensure seamless data

connectivity between different business systems and

create a unified data infrastructure within the

enterprise.

4.4 Opportunities and Challenges

Coexist

As intelligence gradually becomes the mainstream of

the industry, data empowerment has undoubtedly

brought unprecedented development opportunities to

catering companies (Pantano et al., 2020). It supports

the integrated goal of “cost reduction, efficiency

improvement, and customer acquisition”-by

optimizing inventory management to reduce food

waste, improving labor efficiency through smart

scheduling, and enhancing customer retention and

frequency via precision marketing. As chain and

large-scale operations become more prevalent, digital

capability is increasingly becoming a core

competitive advantage.

However, the transformation process also

presents several real-world challenges.

First, the initial investment of digital

transformation is high. Digital transformation

requires substantial funding for system procurement,

hardware/software upgrades, data platform

construction, and long-term maintenance, which may

place financial pressure on small and medium-sized

enterprises.

Second, employees resist change. Some frontline

employees and middle managers may distrust or lack

proficiency in digital systems, exhibiting resistance

that hinders system adoption and project progress.

Third, digital transformation faces data security

and privacy risks. With large-scale data collection

and sharing, safeguarding customer privacy and IT

system security is critical to preventing data leaks or

cyberattacks.

Therefore, as transformation progresses,

enterprises must implement risk mitigation

mechanisms, staff training programs, and incentive

systems. For example, offering data literacy

workshops, creating awards like Star of Data

Application, and strengthening cybersecurity

infrastructure can improve organizational resilience.

These measures ensure the sustainability of the

strategy and help enterprises truly harness the

competitive advantages brought by data

empowerment.

5 CONCLUSION

This paper takes data empowerment as the entry point

of research, focusing on how restaurant enterprises

can improve operational efficiency and service

quality through digital means in the context of

technological transformation. Based on a systematic

analysis of the industry's current state, it is found that

most enterprises face common issues in areas such as

inventory, workforce scheduling, and marketing

outreach. This study draws on the practices and

technological applications of leading companies and

proposes three pragmatic and forward-looking core

strategies: dynamic inventory management,

intelligent scheduling system, and precision

marketing operations.

The study demonstrates that if restaurant

enterprises systematically introduce data analysis

tools, establish complicated data management

mechanisms, and tailor their strategies based on

localized business scenarios, they can effectively

overcome the limitations of traditional operational

models. On one hand, data-driven approaches help

reduce operational costs such as raw material waste

and labor redundancy, thereby improving overall

efficiency. On the other hand, they also enhance

customer experience, strengthen brand loyalty, and

improve responsiveness to market changes. Together,

these benefits support the dual goals of “cost

optimization” and “value creation,” providing an

intrinsic driving force for sustainable business

growth.

In the future, with the popularization of

technologies such as artificial intelligence, algorithm

optimization, and digital twins, data empowerment in

the catering industry will move from simple

applications to deep intelligence. Data will not only

improve logistics management, supply chain

collaboration, and operational forecasting, but will

also go deeper into areas such as menu innovation,

customer emotion perception, and customized

experiences, continuously expanding the digital

capabilities of enterprises.

At the same time, enterprises must continue to

strengthen the cultivation of a “data culture” applying

transformation across multiple dimensions, including

company concepts, organizational mechanisms, and

employee skill development. This is essential to

transit from “passive response” to “proactive

innovation.”

In conclusion, data empowerment is not a short-

term tactic, but a profound transformation of the

operational paradigm. Only by seizing the chance of

digital age, restaurant companies can achieve higher

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development and become technology-driven industry

leaders through continuous investment.

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