The impact of working capital management on f&b and retail industries in vietnam

The impact of working capital management on f&b and retail industries in vietnam The impact of working capital management on f&b and retail industries in vietnam

VIETNAM NATIONAL UNIVERSITY, HANOI

INTERNATIONAL SCHOOL

GRADUATION PROJECT

THE IMPACT OF WORKING CAPITAL MANAGEMENT ON

F&B AND RETAIL INDUSTRIES IN VIETNAM

Nguyen Le Trung Kien

VIETNAM NATIONAL UNIVERSITY, HANOI

INTERNATIONAL SCHOOL

GRADUATION PROJECT

THE IMPACT OF WORKING CAPITAL MANAGEMENT ON

F&B AND RETAIL INDUSTRIES IN VIETNAM

SUPERVISOR: Dr Ha Manh Hung STUDENT: Nguyen Le Trung Kien

ACKNOWLEDGMENTS

I would like to begin by extending my profound appreciation to my supervisor, Dr Ha Manh Hung, for his exceptional guidance, unwavering encouragement, and indispensable support throughout this thesis process His expertise and thoughtful insights have significantly shaped the direction and caliber of my research

I am also profoundly grateful to the esteemed lecturers of the teaching council at Vietnam National University, Hanoi, International School, for providing me with a strong foundation in Informatics and Computer Engineering Although my academic background

is in this technical field, my growing passion for economics and business has inspired me

to undertake this research The interdisciplinary nature of my studies has allowed me to explore new perspectives and contribute unique insights to this topic

Finally, I want to express my sincere appreciation to my family, friends, and colleagues for their steadfast support and encouragement throughout this journey Their confidence in

my abilities has consistently fueled my motivation

This thesis is a reflection of my enthusiasm for bridging the gap between technology and business, and I hope it contributes meaningfully to the understanding of working capital management in Vietnam’s F&B and retail industries

2.2 Impacts of Working Capital on the Financial Performance in a corporation 7

LIST OF TABLES

Table 3-1: Summary table of how to measure variables 13

Table 4-1: Descriptive Statistics of Sample 20

Table 4-2: Correlation Matrix among Variables 23

Table 4-3: KMO and Bartlett's Test of Sample 25

Table 4-4: Total Variance Explained 27

Table 4-5: Rotated Component Matrix 29

Table 4-6: Regression for Model 31

LIST OF GRAPHS Graph 4-5: Data Analysis workflow 17

Graph 5-7: Random forest model in SPSS 33

Graph 5-8: F&B Correlation Matrix heatmap 40

Graph 5-9: Retail Correlation Matrix heatmap 42

LIST OF PICTURES Picture 4-1: IBM SPSS Statistic 15

Picture 4-2: IBM SPSS Modeler 16

Picture 4-3: Python programing language 17

Picture 4-4: Jupyter Notebook 18

Picture 4-5: Dataset window in SPSS Statistic 19

LIST OF FIGURES Figure 3-0: F&B Industry Dataset 10

Figure 3-1: Retail Industry Dataset 11

Figure 3-2: Variables of the research 12

CHAPTER 1: INTRODUCTION

1.1 Rationale of the study

Financial performance is a critical element in corporate managerial activities It helps firms secure the necessary capital, implement strategies that boost operational efficiency, and maintain oversight of their business processes Lamberson (1995) noted that managing working capital is extremely important within companies, driving some financial managers to explore methods of controlling working capital and determining appropriate adequacy levels The sufficiency of working capital not only affects day-to-day corporate functions but also extends its influence to broader business operations With ample working capital, management has greater latitude to develop products aligned with market demands

Working capital management represents a vital component of corporate finance, exerting a direct influence on a firm’s financial well-being By effectively administering current assets—such as inventory, receivables, and cash—and overseeing current liabilities (e.g., accounts payable), companies can shape their liquidity, profitability, and overall financial outcomes Working capital reflects both a firm’s operational efficiency and the pool of liquid resources at its disposal It further indicates the organization’s short-term financial viability and its ability

to cover day-to-day operating expenses Consequently, sound working capital management significantly impacts a company’s performance

Amid various market fluctuations, the 2022 Socio-Economic Situation Report published by the General Statistics Office estimates an 8.02% rise in Gross Domestic Product (GDP) for 2022 compared to 2021, driven by economic recovery—marking the highest annual GDP increase from 2011 to 2022 The Retail sector notably expanded by 10.15%, making a substantial contribution to the economy’s total value At the same time, the F&B industry achieved the highest regional growth rate at 40.61%

Additionally, the November 2023 Report on Industrial Production and Commercial Activities from the Ministry of Industry and Trade indicates that Vietnam’s retail market surpassed USD

140 billion in value and is expected to reach around USD 350 billion by 2025 This figure will account for roughly 59% of the nation’s GDP The retail sector is projected to experience strong growth, making a substantial contribution to GDP expansion and driving economic restructuring toward a higher share of industry and services Yet, as Vietnam’s retail market

Besides, Additionally, the F&B sector is regarded as showing more positive shifts than the Retail industry, even though Vietnam’s F&B market has been subdued in the early months of

2024, with consumer spending dropping significantly Economic headwinds directly affect household incomes, leading to tighter spending patterns in both retail and consumer markets overall, as well as in the F&B segment specifically

However, the broader outlook of Vietnam’s F&B industry is not entirely bleak In spite of the global economic slowdown, the local F&B market is witnessing rapid expansion of major chains Projections indicate the F&B sector will grow by 18% this year and could reach a valuation of VND 1 million billion by 2026 Trends in 2024 suggest fierce competition among large chains vying for market share, while smaller chains may act more cautiously Moreover, Vietnam’s F&B market poses numerous challenges for business owners to address

First is the pressure on capital and the ability of businesses to mobilize finance In particular, with small corporations, cash flow management is still unprofessional, which causes losses A significant amount of money has been and continues to be lost because businesses have not optimized the effective use of this capital source

Effective working capital management brings numerous advantages to F&B and Retail companies First and foremost is enhancing liquidity—this involves measuring a firm’s ability

to meet short-term commitments and manage cash flow, ensuring that loans, liabilities, and bills are paid on time, maintaining strong credit, and avoiding late-payment penalties Equally important is improving leverage, which gauges how a company manages its financial obligations by balancing equity and debt to fund operations, including the total debt level and the firm’s capacity to repay when liabilities are due

Another aspect concerns the structure of current assets, which include cash, receivables from customers, and inventories—assets that can be converted into cash within a year How current assets are allocated directly influences working capital: if a company holds more short-term assets (like receivables and inventory) than short-term liabilities, its working capital increases; conversely, fewer current assets than short-term liabilities cause working capital to decline

Moreover, sound working capital management grants businesses a competitive edge over other players in their sector Consequently, this study identifies a direct influence on financial performance, which is particularly vital in the F&B and Retail sectors characterized by high capital turnover Additionally, it is crucial to compare the F&B and Retail industries in terms

of business characteristics and working capital management practices to assess the impact accurately and devise fitting solutions

1.2 Research objectives

The main goal is to deeply understand the impact of working capital management on the financial performance of businesses in the Food & Beverage (F&B) industry and the Retail industry in Vietnam

By analyzing and comparing the working capital practices of these corporations, valuable insights can be gained into their efficiency and financial health

The research brings scientific significance: Adding more knowledge about the impact of working capital management on financial performance Provides comparative information on the effectiveness of working capital management between the F&B and Retail industries in Vietnam This research aims to provide concrete evidence and data-driven conclusions that can guide strategic decision-making for businesses operating in these industries

Understanding how working capital management affects financial performance is critical for corporationsin F&B, Retail as well as other industries looking to optimize operations, enhance liquidity, and maximize profitability The research hopes to uncover key findings and implications from this insightful study that can revolutionize financial strategies in the F&B and Retail industries in Vietnam

1.3 Research questions

This research aims to evaluate the impact of working capital management on financial performance and compare management efficiency between the food beverage and retail industries in Vietnam So I have posed the following research questions:

1 How does working capital management specifically affect the financial performance of

1.4 Research method

To serve the research objectives, the topic uses the research methods below:

 Data collection method: T h e data is collected on the Vietstock Stock Exchange in the form of excel (.xlsx) and audited financial reports and annual reports of businesses The data is quantitative The study is based on the results of analyzing statistical data information to compare and contrast, then synthesize into tables to

CHAPTER 2: LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT 2.1 Working Capital concept

Working capital is the capital required for a firm to manage its day-to-day expenses In general, the definition of working capital is current assets minus current liabilities (Knauer & Wohrmann, 2013) Current assets generate a majority of cash inflows for companies while current liabilities constitute the largest source of short-term cash outflows for companies (Knauer & Wohrmann, 2013; Rani, 2013; Sharma & Kumar, 2011)

Knauer, Wohrmann, and Rani suggest that, in order to understand a firm’s cash flow requirements, managers focus on the largest components of current assets-specifically inventory and accounts receivable-minus the most significant element of current liabilities, which is accounts payable By examining previous research on effective working capital management strategies within Vietnamese food and beverage (F&B) and retail companies, one can gain considerable insights into how these strategies influence financial performance

The term "corporate financial efficiency" refers to the effectiveness of capital mobilization and capital use strategies that aim to optimize company value

To the extent of violating the group's research topic, the author only analyzes the financial efficiency at the edge of the effectiveness of the enterprise's use of working capital management

to maximize corporate value through evaluating several relevant standards and indicators documents The efficiency of using working capital is also the effective result of using assets

2.2 Impacts of Working Capital on the Financial Performance in a corporation

Any company hoping for long-term growth must comprehend the relationship between working capital and financial success This field's research serves as a reference, influencing corporate value and assisting administrators in making informed judgments

Assessing the effectiveness of working capital use is a crucial objective in the investigation of this connection In order to increase ROA, or return on assets, managers may proactively develop plans and provide targeted solutions that will increase revenue and provide value to their company

Because of its precision and capacity to accurately represent future performance using a company's assets, return on assets (ROA) continues to be the most often used metric

In several recent studies, a novel approach to measuring financial performance has emerged—specifically, the use of regression methods employing fixed and random effects models based

on balanced panel data (Tran Thi Thu Trang, 2020) Drawing on data from 28 plastics companies listed on the Vietnamese stock market from 2014 to 2019, these findings indicate that the random effects model better explains the link between independent variables and firms’ financial performance The study also identifies a positive association between the number of days of inventory turnover, the current liquidity ratio, and return on total assets (ROA), alongside a negative relationship between the number of days of accounts receivable turnover and ROA

Furthermore, in their investigation of the extent and direction of working capital components’ effects on the financial efficiency of construction enterprises listed on Vietnam’s stock market from 2016 to 2020, MSc Ngo Thi Ngoc and MSc Vu Thi Nhung (2022) found that the average inventory period, average collection period, and the cash conversion cycle (CCC) all exert negative impacts on corporate financial performance The correlation between the CCC and both ROA and ROE is negative Additionally, the debt ratio shows a negative correlation with financial efficiency, and the fixed assets ratio has a negative correlation with both ROA and ROE By contrast, the revenue growth rate displays a positive relationship with financial performance indicators Similarly, Tanveer et al (2016), in an empirical examination of working capital management in manufacturing firms, found that the CCC has a negative and statistically significant influence on ROA

Based on the liquidity ratios, leverage ratios, cash flow ratios, and short-term asset structure

of firms, the study identified gaps in existing knowledge and future research topics linked to working capital management through the literature review Rarely have these metrics been employed to assess working capital management in the majority of earlier research

2.3 Hypothesis development

An overview of prior research reveals a connection between financial performance and expenses tied to working capital management Drawing on empirical findings and anticipated relationships between the dependent and independent variables, the author proposes hypotheses for each variable in the subsequent study:

H1.1: There is a significant relationship between the Liquidity ratio and Return on Assets(ROA) of the corporations in the Retail industry

H1.2: There is a significant relationship between the Liquidity ratio and Return on Assets(ROA) of the corporations in the F&B industry

H2.1: There is a significant relationship between the Leverage ratio and Return on Assets(ROA) of the corporations in the Retail industry

H2.2: There is a significant relationship between the Leverage ratio and Return on Assets(ROA) of the corporations in F&B industry

H3.1: There is a significant relationship between Accrual ratio CF and Return on Assets

CHAPTER 3: DATA & METHODOLOGY

3.1 Data collection

The data for this study is secondary data taken from VietStock Stock Exchange (Vietstock.vn) and the financial statements of the respective corporations Financial ratios including ROA and ratios belonging to liquidity ratio, leverage ratio, cash flow ratio and short-term asset structure are taken directly from Vietstock.vn The research data has 264 observations, of which 81 observations are compiled from 15 corporations in the Retail industry and 183 observations are compiled from 31 businesses in the Food & Beverage industry listed on the Vietstock Stock Exchange from 2015 to 2023 shown in Figure 3-0 and Figure 3-1 Data are taken from audited and annual financial reports of corporations

Users can collect company financial statement datasets through Vietstock's DataFeed service, which integrates economic and financial data into applications and digital platforms via APIs

or Sync Data Vietstock provides access to financial data from over 3,000 enterprises listed on HOSE, HNX, and UPCoM1 The available financial statement data includes balance sheets, income statements, and cash flow statements, along with related information such as FS date, disclosure date, audit date, auditor, and audit opinion This comprehensive data coverage allows users to retrieve specific financial information for in-depth analysis

Alternatively, users can utilize VietstockXLS, a service that delivers macroeconomic, financial, and securities data in Excel files based on customer-defined forms This service offers access to all items in financial statements, including balance sheets, income statements, cash flow statements, and even detailed notes to financial statements With a data history spanning at least 20 years, updated in real-time, daily, quarterly, or annually, VietstockXLS is suitable for extensive data analysis and statistical modeling, catering to researchers, economists, and students needing data for quantitative analysis Financial reports can also be downloaded directly in PDF, Word, and Excel formats

Present raw data:

3.2 Variables measurement scales

The study uses data to measure working capital management activities to achieve financialefficiency, in other words, finds the relationship between ROA and the 12 factors below:

Table 3-1: Summary table of measurement variables

%

Cash Ratio Cash + Cash Equivalents /

Debt To Assets Total Debt/Total Assets %

Quick Ratio (Current assets - Inventory)

Receivable Turnover Net Credit Sales/Average Accounts

Equity to asset

Equity turnover Net Revenue/Average Shareholder's

Short-term liability to total liabilities Short-term liabilities/Total liabilities Times

3.3 Methodology

To examine the influence of working capital management on the financial performance of corporations in Vietnam’s F&B and Retail sectors, this study adopts the following approach: First, Descriptive Statistics and Correlation Analysis are utilized to characterize the fundamental quantitative features of the data This process involves:

Step 1: Determining the mean, median, maximum, minimum, and standard deviation to draw initial conclusions and assess the sample

Step 2: Computing the correlation among variables to validate the regression analysis and uncover relationships between the independent and dependent variables

Second, devised a plan to run KMO, Variance Explained, and Rotated Component Matrix on

SPSS software to build a regression model In particular, use the average method to select

the variables with the most optimal values Create a Random Forest Model (basic machine

learning) using SPSS modeler And then I apply visual Correlation Model using Jypyter

Notebook

CHAPTER 4: SOFTWARE & DATA ANALYSIS PROCESSES

4.1 Software

IBM SPSS Statistics

Picture 4-1: IBM SPSS Statistic software

SPSS Statistics (Statistical Package for the Social Sciences) is a statistical analysis software developed by IBM, widely used in research fields like business and social sciences It offers tools for data analysis and visualization with a user-friendly interface, making it suitable for both beginners and experts

For this research, SPSS was chosen due to its ease of use, reliability, and flexibility in implementing algorithms like regression analysis and hypothesis testing These features make

it ideal for studying the relationship between working capital management and the performance of Vietnam’s F&B and retail industries

By leveraging SPSS, the study efficiently analyzes large datasets, revealing patterns and trends in working capital usage This ensures accurate, actionable insights to support businesses in these dynamic sectors

IBM SPSS Moduler

SPSS Modeler is an advanced data mining and predictive analytics tool developed by IBM Designed to help researchers uncover hidden patterns and trends, SPSS Modeler is widely used in business analytics, customer segmentation, and financial forecasting Its drag-and-drop interface simplifies complex analysis, making it accessible to both technical and non-technical users

For this research, SPSS Modeler was selected due to its ability to handle large datasets and deploy sophisticated predictive models like regression, classification, and clustering These

capabilities are instrumental in exploring the relationship between working capital management and the performance of Vietnam’s F&B and retail industries

Picture 4-2: IBM SPSS Modeler software

By utilizing SPSS Modeler, the study benefits from advanced analytics and clear visualizations, enabling actionable insights that support strategic decision-making This ensures the research provides meaningful contributions to understanding working capital dynamics in these industries

Python

Picture 4-3: Python programing language

Picuture 4-4: Jupyter notebook

This pairing is ideal for researchers and analysts, as Anaconda handles environment management and package dependencies, while Jupyter Notebook enables an interactive and iterative coding experience By combining coding, visualization, and documentation in a single environment, these tools streamline workflows and enhance collaboration across project

4.2 Data Analysis Processes

Graph 4-2: Integrated workflow

Clean Data

Picture 4-5: Dataset window in SPSS Statistic

Data cleaning was performed using SPSS Statistics to ensure the dataset’s completeness,

consistency, and accuracy Key steps in this process included:

 Removing duplicate entries and handling missing values using appropriate imputation

methods

 Standardizing data formats for key variables such as liquidity ratios, accrual ratios,

and short-term liabilities

 Verifying data integrity by cross-referencing with the original dataset

Statistical Analysis and Model Execution

Statistical analyses were conducted in SPSS Statistics to explore and understand relationships between variables The following techniques were applied:

5 Total Variance Explained: The total variance explained method was used to determine

the proportion of variance in the dataset accounted for by the extracted factors Factors with eigenvalues greater than 1 were retained, as they significantly contribute to explaining variance

6 Rotated Component Matrix: A rotated component matrix was employed to enhance

interpretability by minimizing the number of variables that load highly onto a single factor Varimax rotation was applied to simplify factor structures while preserving total variance explained

Random Forest Modeling

Using SPSS Modeler, a Random Forest model was executed to identify key variables that significantly impact the dependent variable, ROA The steps included:

 Preparing the dataset by selecting relevant features identified during the regression analysis

 Training the Random Forest model using a combination of decision trees for improved prediction accuracy

 Analyzing feature importance to understand which variables contributed most to predicting ROA

Jupyter Notebook Analysis

Further analysis was conducted on Jupyter Notebook to complement the findings from SPSS tools Python libraries such as pandas, numpy, and scikit-learn were utilized for:

 Data visualization to provide a graphical representation of relationships and trends

 Advanced statistical testing to verify the robustness of findings

 Analyze the relationships between the dependent variable, ROA (Return on Assets), and various independent variables, such as liquidity ratio, leverage ratio, and accrual ratio cash flow

CHAPTER 5: RESULTS & DISCUSSIONS 5.1 Descriptive Statistics

Table 4-1: Descriptive Statistics of Sample

The descriptive statistics results show that the variables in the estimated model all collect enough data with 264 observations:

ROA dependent variable represents the ability to generate profits from total assets of listed

businesses in the sample with an average value of 0.082 (8.2%), the level of fluctuation is relatively large for the lowest ROA was -0.190 (- 19%) belonging to Long An Export Processing Joint Stock Company in 2018, compared to the highest ROA of 0.722 (72.2%) in 2015 achieved

by KIDO Group The standard deviation of ROA is 0.085

Accrual ratio CF with a standard deviation of 0.140 shows that operating cash flow to net

revenue of the two industries is quite similar

Cash ratio - involved in measuring a company’s ability to pay short-term debts with its capital

cash resources - with the mean is 0.507 and the maximum and minimum values are 8.240 and 0.000, respectively The standard deviation is 0.920

Debt to Assets ratio indicates the company’s financial stability It has the highest value of 0.766

(76.6%) while the lowest value is 0.000 (0%), meaning there is a significant difference between businesses in the level of debt usage

Quick ratio reflects a company’s capacity to meet its immediate financial obligations using

cash and cash equivalents Within the sample, the quick ratio reaches a maximum of 14.010 times and a minimum of 0.080 times, indicating substantial variability and illustrating a significant capacity among firms to settle their short-term debts

Receivables turnover reflect the effectiveness of a business in collecting debts from customers

The higher index shows that the business is effectively recovering cash from customers, helping the business have enough money to pay short-term debts, business investments and development this sample, receivables turnover ranges from 0 days to 3,577.110 days, indicating a wide variation among the firms On average, receivables turnover exceeds 100 days (roughly three months), accompanied by a standard deviation of 306.599 days

Short-term assets/Total assets account for an average of 0.617 of total assets, suggesting that

most listed firms in the sample may not possess strong short-term liquidity

Short-term liabilities to equity: the average value is 1.089, meaning that the firms in the sample

have a high ratio of short-term debt, relying heavily on short-term funding to maintain operations

Short-term ratio with an average of 2.255 times, it suggests a decent level of short-term liquidity

for the firms in the sample

Short-term receivables/Short-term assets with the largest being 93.6%, which indicates the

structure of short-term assets is also at a high level

Equity to assets mean ratio 0.534, with a maximum observed value of 0.951 and a minimum

of 0.056 The standard deviation is 0.193

Short-term liability to total liability ranges from 0.057 to 1.000 The mean is 0.841 and the

standard deviation is quite small (0.223)

Equity turnover w it hin ranges from 0 days to 51.970 days, indicating a relatively wide equity

cycle among the firms The average equity turnover is 6.301 days, with a standard deviation of 7.672 days

5.2 Correlation Analysis

Table 5 -2 indicates that ROA is negatively correlated with Debt to Assets, Short-Term

Liabilities to Equity, Short-Term Receivables/Short-Term Assets, and Equity Turnover Conversely, it shows positive correlations with liquidity ratios, cash flow ratios, and the short-term asset structure

Additionally, the correlation matrix reveals that most correlation coefficients among the variables remain below 0.7, suggesting only moderate impacts on ROA In particular, Receivables Turnover, Short-Term Assets/Total Assets, Short-Term Ratio, Short-Term Receivables/Short-Term Assets, and Equity Turnover exhibit no correlation

Consequently, it can be concluded that the variables in the model do not exhibit a high degree

of intercorrelation, indicating that multicollinearity is unlikely to be an issue

The correlation matrix presented here, generated using SPSS Statistics, serves as an essential starting point for analyzing the relationships between financial ratios and ROA While SPSS provides a clear and structured visualization of these correlations, later sections of the analysis will include correlation matrices generated using Jupyter Notebook This complementary approach allows for greater flexibility in customization, deeper exploration of the data, and alignment with other analytical methods performed in the notebook By utilizing both tools, the analysis ensures a comprehensive and multidimensional perspective, ultimately strengthening the basis for drawing conclusions about the impact of working capital management on financial performance in the F&B and Retail industries

Table 5-2: Correlation Matrix among Variables

Accru

al ratio

CF

Cash ratio

Debt

to assets

Quick ratio

Receivabl

es turnover

Short- term assets/Tot

al assets

Short- term liabilities

to equity

Short- term ratio

Short- term receivable s/Short- term assets

Equity

to assets

Short-term liabilities to total liabilities

6 1.0000

9 -0.1689 -0.0121 1.0000

6 0.3780 -0.3673 0.0501 0.1595 1.0000

4 0.2751 0.0060 -0.2169 -0.1430 0.1859 -0.1362 1.0000

0 -0.3781 -0.1064 0.1142 0.6022 0.0717 -0.0048 -0.1656 0.2463 1.0000

Equity

- 0.172

8 0.0128 -0.2185 0.2149 0.3231 0.7048 -0.2280 0.0698 -0.4214 0.2357 1.0000

ROA

0.2965 0.209

7 -0.3935 0.2965 0.0200 0.1625 -0.2886 0.1991 -0.0248 0.4310 0.3108 -0.1544 1.0000