Financial Performance and Working Capital Management Practices of

Nigeria’s Consumer Goods Manufacturing Firms

OPRITE MALVAN IYALLA, UMAR ABBAS IBRAHIM

Department of Business Administration,

Nile University of Nigeria,

Cadastral Zone C-OO, Research & Institution Area, Airport Rd, Jabi 900001, Abuja,

NIGERIA

Abstract: - This study examines the impact of working capital management practices on the financial

performance of consumer goods manufacturing firms listed on the Nigeria Stock Exchange. The analysis is

based on a sample of 20 firms over a ten-year period from 2011 to 2020, utilizing a generalized method of

moment (GMM) model. Four indicators of working capital management, including the cash conversion cycle

(CCC), inventory turnover period (IVP), accounts payable period (APP), and accounts receivable period

(ARP), are assessed, while return on assets (ROA) is used as the measure of financial performance. The

findings reveal that a shorter cash conversion cycle and a higher inventory turnover period positively influence

the firm's financial performance. Conversely, a longer accounts payable period has a negative impact, while a

longer accounts receivable period positively affects financial performance. These results highlight the

importance of adopting effective working capital management practices for enhancing the financial

performance of consumer goods manufacturing firms. The study's conclusions provide valuable insights for

firms, investors, and policymakers, emphasizing the significance of optimizing working capital management to

drive financial success.

Key-Words: - Quoted consumer goods manufacturing, Financial Performance, Working Capital Management,

Nigeria; Financial Sustainability.

Received: November 10, 2022. Revised: August 19, 2023. Accepted: October 6, 2023. Published: October 20, 2023.

1 Introduction

In recent times, particularly since the emergence of

the Covid-19 pandemic in 2019 in Wuhan City,

China, followed by its rapid global spread, [1], [2],

the world has been significantly affected. To control

the spread of the Covid-19 virus, nations

implemented lockdown measures, resulting in

reduced productivity and a negative supply shock.

This led to disruptions in global supply chains and

the closure of factories, impacting consumer goods

manufacturing activities in both developed and

developing countries, including Nigeria.

Consequently, businesses faced severe constraints

on their cash and working capital, [3], [4]. As

countries gradually recover from the effects of the

Covid-19 pandemic, the responsibility falls on

financial managers to address capital raising and

utilization challenges, both in the private and public

sectors. Working Capital Management (WCM) is

recognized as a crucial aspect of managerial finance,

[5]. Efficient WCM can have a lasting impact on a

firm's financial performance.

2 Problem Formulation

In recent times, numerous research studies have

focused on exploring the connection between

working capital management and corporate

profitability, [6], [7], [8], [9], [10], [11]. These

studies have primarily concentrated on

understanding how efficient working capital

management practices influence a firm's

profitability and, in turn, its impact on shareholder

value. However, most of these investigations have

primarily examined large firms in developed

economies, overlooking the variations in working

capital requirements across industries and firms.

Factors such as business nature, scale of operation,

production cycle, credit policy, and raw material

availability can significantly influence the necessary

amount of working capital.

Despite the extensive literature available on this

topic, many firms, particularly those in Nigeria's

consumer goods manufacturing sector, have

encountered financial challenges and even faced

bankruptcy due to inadequate working capital

management, [12], [13], [14]. Additionally,

investments with promising returns have failed due

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to insufficient working capital, leading to the

closure of numerous factories and subsequent job

losses, [12], [13], [14], [15], [16], [17], [18].

Unfortunately, Nigeria's capital and money markets

have not provided much assistance in alleviating

this issue, often imposing stringent conditions that

struggling companies are unable to meet.

Hence, this study aims to address the need for

consumer goods manufacturing organizations'

managers to prioritize working capital management

to enhance their firms' financial performance.

Specifically, the research examines the impact of the

cash collection cycle, inventory turnover period,

accounts payable period, and accounts receivable

period on return on assets (ROA), which serves as a

metric for financial performance, within listed

consumer goods manufacturing firms in Nigeria.

2.1 Literature Review

2.1.1 Conceptual Review

This study seeks to establish the impact of Working

Capital Management on the performance of Listed

consumer goods manufacturing Firms in Nigeria

and the conceptual framework is presented in Figure

1.

Fig. 1: Author’s Conceptual Model, 2022

2.1.2 Organizational Performance

The selection of these variables has been influenced

by previous studies on working capital management

conducted by other researchers. These variables are

essential for testing the hypotheses of this study as

they are the estimated key components of working

capital that require management to improve

efficiency and effectiveness. The efficiency of these

variables impacts the dependent variable, which in

this study is represented by the firm's return on

assets (ROA). The relationship between working

capital management and organizational performance

is influenced by various factors, such as firm size,

firm leverage, and firm age, which are considered

control variables in this study, [7], [9], [13].

Inventory management involves how a company

manages its inventory, which is measured by the

inventory level at a given period, the number of

days required to convert inventory into cash, and the

frequency of inventory turnover. Accounts

receivable days represent the number of days

required by a company to collect its outstanding

debts. This is calculated by dividing the average

accounts receivable by the daily revenue. Accounts

payable days represent the number of days required

by a company to pay its creditors. It is estimated by

dividing the average accounts payables by the daily

cost of goods sold. The cash conversion cycle is

calculated by subtracting the number of days

required by a company to pay its creditors from the

sum of the number of days required to convert

inventory into cash and the number of days required

by a company to collect its receivables, [7], [9],

[13]. On the other hand, the working capital

financing policy deals with the sources and the

amount of working capital that a company should

maintain. Working capital investment refers to the

amount of money required by an organization to

expand its business, meet short-term business

obligations, and cover business expenses. The

dependent variable, return on assets (ROA), is

calculated by dividing a company's net income by

its total average assets and then expressing it as a

percentage.

2.1.3 Theoretical Framework

In this study, the literature review identified several

working capital theories that are relevant, including

the operating cycle theory, cash conversion cycle

theory, pecking order theory, agency theory, and the

risk-return trade-off theory. While each of these

theories has its significance, the Cash Conversion

Cycle theory is considered the most relevant for this

research based on the identified variables and

formulated objectives. The study primarily focuses

on the cash conversion cycle as an indicator of

working capital management, along with the

variables of inventory turnover period, accounts

payable period, and accounts receivable period. The

Cash Conversion Cycle theory examines the time

interval between expenditure on raw materials and

the receipt of revenue from the sales of finished

goods, [19], [20]. It emphasizes that effective

management of working capital components,

particularly by extending the credit period with

suppliers compared to that granted to customers, can

reduce borrowing costs and improve financial

performance. Previous studies, [21], [22], [23], have

also utilized the cash conversion cycle theory to

support their investigations into the impact of

working capital management on financial

performance.

Working Capital Management Firm Performance

Control Variables

- Accounts Payable Period

- Accounts Receivable Period

- Inventory Turnover Period

- Cash Collections Cycle

- Return on Asset (ROA)

-Firms’ Size

-Firm’s Leverage

-Firm’s Age

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2.1.4 Empirical Review

Previous empirical studies examining the correlation

between working capital management and financial

performance have yielded inconsistent findings.

While some studies have discovered a positive

association between WCM and firms' financial

performance, others, such as, [24], [25], [26], [27],

have identified a negative statistical relationship

between profitability/return on assets and variables

such as inventory (IN), average receivable (AR),

average payable (AP), and cash conversion cycle

(CCC). Hence, the literature demonstrates that

working capital management components can

exhibit both positive and negative relationships with

profitability. However, it remains unclear whether

these findings can be replicated within the

manufacturing sector, particularly in light of the

direct impact of the COVID-19-induced global

economic recession, [16], [28], [29]. As a result, this

study seeks to explore the connection between

working capital management and financial

performance specifically in the manufacturing

sector.

3 Methodology

The study will encompass all twenty (20) consumer

goods manufacturing companies that are listed on

the Nigerian Stock Exchange as of December 31,

2020. The entire population of these companies will

be included in the study. The research will span a

period of ten years, from 2011 to 2020, and will

analyze data related to working capital management

(WCM) variables, including accounts payable

period, accounts receivable period, inventory

turnover period, and cash conversion cycle. These

variables will serve as the independent variables in

the study. The dependent variable will be the

financial performance of the consumer goods

manufacturing sector in Nigeria, measured by the

return on assets (ROA) accounting-based

performance variable, for the same ten-year period

from 2011 to 2020. To analyze the panel (cross-

sectional and time-series) secondary data and test

the formulated hypotheses, the study will employ

panel regression analysis. The population of interest

consists of the 20 consumer goods manufacturing

firms listed on the Nigerian Stock Exchange, with a

focus on a ten-year dataset encompassing both

working capital management and the financial

performance of the manufacturing sector in Nigeria.

Model Specification: The study's model

specification draws inspiration from the work of

[11], which explored the relationship between

working capital management (WCM) and financial

performance in non-financial firms in Nigeria.

However, the model has been adapted to include

additional WCM indicators and will be used to test

the research hypotheses. The statistical model

employed in this study is based on, [30], with

certain modifications. This model will be utilized to

examine the impact of working capital management

(WCM) on the financial performance of the

consumer goods manufacturing sector. To test the

hypotheses, the study will employ panel regression

analysis, specifically utilizing the Generalized Least

Square (GLS) technique.

The explicit forms of the models for the six

hypotheses are stated thus:

ROAt = λ0 + λ1APPt + λ2ARPt + λ3ITPt + λ4CCCt

+Ut (1)

Where: ROAt: Return on an asset at time t; APPt:

accounts payable period at time t; ARPt: accounts

receivable period at time t; ITPt: inventory turnover

period at time t, and CCCt: cash conversion cycle at

time t; U, = Error Term.

4 Analysis and Results

Descriptive Statistics: In this section, we provide an

overview of the descriptive statistics for the working

capital management indicators of the Nigerian

quoted manufacturing firms that affect their

financial performance. The descriptive statistics

include mean, median, maximum/minimum values,

standard deviation, and the Jarque-Bera normality

test, which tests the skewness and kurtosis of the

sample data to determine if it conforms to a normal

distribution. This is a necessary condition for

applying the system GMM regression model. The

results of the descriptive statistics for all variables

are presented in Table 1.

Table 1. Summary Statistics

Source: Author's Computation, 2022

The summary statistics for the variables

measuring the working capital management and

performance of the quoted manufacturing firms are

presented in Table 1. Table 1 also includes a test for

ROA

TQ

CCC

IVP

APP

ARP

FS

LEV

Mean

22.71439

17.90525

1.457664

17435.92

215.8666

16.88797

60.35332

12.05888

Std. Dev.

1.51114

4.671451

1.873523

4638.873

67.59312

0.42802

3.314248

2.967075

Skewness

-0.376399

-0.259361

8.323218

0.000923

0.438223

-0.034751

0.773362

0.198454

Kurtosis

2.274876

1.932815

102.2671

2.376484

1.385354

3.007388

2.53663

1.807502

Jarque-Bera

19.48309

25.10854

180670.5

6.933172

60.19173

0.087119

46.49266

28.16933

Probability

0.000059

0.000004

0.0000

0.031223

0.0000

0.957376

0.0000

0.000001

Sum

9721.76

7663.445

623.88

7462575

92390.92

7228.05

25831.22

5161.2

Sum Sq. Dev.

975.0737

9318.189

1498.808

9.19E+09

1950890

78.22693

4690.27

3759.11

Observations

200

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normality, which shows that all variables, except for

ARP, have a p-value greater than the 5% level of

significance, indicating non-normal distribution. To

address this non-normality, a natural logarithm

transformation is applied to the variables before

fitting the model.

Pre-model Diagnostic Test: This step is essential to

check for potential conditions and biases that could

affect the accuracy of the results. The tests were

conducted to verify that the data meets the

fundamental assumptions of the dynamic panel

model.

Unit Root Test: The purpose of this test is to

determine if a time series data is stationary or not.

Stationarity in a time series occurs when a shift in

time does not change the distribution's shape,

whereas non-stationarity occurs when a shift in time

does change the shape of the distribution. The unit

root is the cause of non-stationarity. The results and

interpretation of the unit root test are presented in

Table 3.

Table 2. Unit-Root

Source: Stata 15 Output, 2022

Table 2 presents the results of the Levin-Lin-

Chu unit-root test conducted for the panel variables.

The null hypothesis (Ho) states that the panels

contain unit roots, indicating non-stationarity, while

the alternative hypothesis (Ha) suggests that the

panels are stationary. The test was performed with

ADF regressions using one lag and Bartlett kernel

with an average of 6.00 lags, as chosen by the

Levin-Lin-Chu (LLC) method.

For the variable "ROA," the adjusted t-statistic

is -16.5243, and the p-value is 0.000. This provides

strong evidence to reject the null hypothesis and

conclude that the variable is stationary, indicating it

does not contain a unit root. Similarly, for the

variables "CCC," "IVP," "APP," and "LEV," the

adjusted t-statistics are -4.7764, -3.8154, -4.4424,

and -10.6677, respectively, all with p-values of

0.000. This suggests that these variables are also

stationary and do not contain unit roots.

However, for the variable "ARP," the adjusted t-

statistic is 3.1671, and the p-value is 0.999. In this

case, the high p-value indicates that the null

hypothesis cannot be rejected, suggesting that the

variable may contain a unit root and is not

stationary.

Lastly, for the variable "FS," the adjusted t-

statistic is -0.798, and the p-value is 0.212. Since the

p-value is greater than the significance level of 0.05,

there is insufficient evidence to reject the null

hypothesis. This implies that the variable may

contain a unit root and is not stationary. Thus, based

on the Levin-Lin-Chu unit-root test, the variables

"ROA," "CCC," "IVP," "APP," and "LEV" are

found to be stationary, while the variables "ARP"

and "FS" may contain unit roots and are not

stationary.

Cointegration Test: Cointegration tests are used to

analyze non-stationary time series, which are

processes that have variances and means that change

over time. This method allows us to estimate the

long-run parameters or equilibrium in systems with

variables that have unit roots, [31].

Table 3. Cointegration Test

Source: Stata 15 Output, 2022

Table 3 presents the results of the cointegration

test. The modified Dickey-Fuller test statistic has a

p-value of 0.000, which is lower than the 5% level

of significance. This indicates that the panel is

cointegrated, and it is suitable for long-run

parameter estimation. The implication is that the

variables move together, suggesting a long-run

equilibrium relationship among the variables. The

Variance Inflation Factor for Multicollinearity Test

is presented in Table 4.

Table 4. Variance Inflation Factor for

Multicollinearity Test

Levin-Lin-Chu unit-root test for Panel Variables

Ho: Panels contain unit roots

Number of panels = 20

Ha: Panels are stationary

Number of periods = 10

ADF regressions: 1 lag

LR variance: Bartlett kernel, 6.00 lags average (chosen by LLC)

Variable

Test

Statistic

p-value

xtunitroot llc ROA

Adjusted t*

-16.5243

0.000

xtunitroot llc CCC

Adjusted t*

-4.7764

0.000

xtunitroot llc IVP

Adjusted t*

-3.8154

0.000

xtunitroot llc APP

Adjusted t*

-4.4424

0.000

xtunitroot llc ARP

Adjusted t*

3.1671

0.999

xtunitroot llc FS

Adjusted t*

-0.798

0.212

xtunitroot llc LEV

Adjusted t*

-10.6677

0.000

Unadjusted Dickey-Fuller t -12.6479 0.0000

Unadjusted modified Dickey-Fuller t -8.8129 0.0000

Augmented Dickey-Fuller t -14.0015 0.0000

Dickey-Fuller t -12.6276 0.0000

Modified Dickey-Fuller t -8.7283 0.0000

Statistic p-value

AR parameter: Same Augmented lags: 1

Time trend: Not included Lags: 0.20 (Newey-West)

Panel means: Included Kernel: Bartlett

Cointegrating vector: Same

Ha: All panels are cointegrated Avg. number of periods = 6.7273

Ho: No cointegration Number of panels = 44

Kao test for cointegration

Model

Collinearity Statistics

Tolerance

VIF

1

(Constant)

CCC

0.983

1.017

IVP

0.111

8.979

APP

0.075

3.335

ARP

0.500

2.001

FS

0.995

1.005

LEV

0.146

6.866

a. Dependent Variables: ROA

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Determination of GMM technique: Before

proceeding with the analysis of the impact of

prudential regulation on the financial performance

of quoted manufacturing firms in Nigeria, the

appropriate GMM technique for estimation must be

determined. Bond's rule of thumb for choosing

between difference and system GMM, as used in

previous studies, [11], [32], [33], will be followed.

First, the autoregressive model will be estimated

using Pooled OLS to obtain an upper-bound

estimate for the coefficient of the lagged dependent

variable (φ), while the corresponding fixed effects

estimate will provide a lower-bound estimate. The

results of this estimation are presented in the

appendix, including a summary of the estimated

coefficients of the lagged dependent variable.

Table 5. Summary: Difference or System GMM

Candidate Models

lnROA L1.

*Pooled OLS model

0.26416

*Fixed-Effect Model

0.26342

*One-step difference GMM

0.26342

*Two-step difference GMM

0.26336

*One-step system GMM

0.26340

*Two-step system GMM

0.26340

Source: Author's estimation (2022)

To validate the efficiency of the internal

instruments that are included in the SGMM

technique, and to ensure that such instruments are

not over-identified, the test for autocorrelation (AR

(1) and AR (2)) and the Sargan test is performed

respectively for the absence of autocorrelation and

validity of instruments. The instrument ratio for the

different estimations is expected to be greater than 1

to satisfy the condition that the instruments are not

proliferated, [22], [23], [34]. Thus, the result shows

that the one-step difference GMM satisfies most of

all the necessary conditions to return a robust

estimate as presented in Table 5.

4.1 Hypothesis Testing

The plausibility of the hypotheses was assessed by

conducting regression analysis using the available

data through the GMM panel model. The one-step

difference GMM method was chosen for its

consistency and adherence to the required

assumptions. The study employed return on assets

(ROA) as the performance measure for Nigerian

manufacturing firms, with the working capital

management variables serving as explanatory

factors. The regression analysis applied a

significance level of 5% to determine the statistical

significance of the relationships between the

variables.

The analysis presented in Table 6 provides

insights into the relationships between working

capital management variables and the financial

performance of quoted consumer goods

manufacturing firms on the Nigeria Stock

Exchange, aligning with the hypotheses stated.

H01: The hypothesis states that there is no

relationship between the cash collection cycle

(CCC) and the financial performance of quoted

consumer goods manufacturing firms. The analysis

supports this hypothesis, as the coefficient for

lnCCC is found to be statistically insignificant

(coefficient = 0.000, p-value = 0.933). Therefore, it

can be concluded that the cash collection cycle does

not have a significant impact on the financial

performance of these firms as corroborated by, [35],

[36].

H02: The hypothesis suggests that there is no

relationship between the inventory turnover period

(ITP) and the financial performance of quoted

consumer goods manufacturing firms. However, the

analysis reveals a significant positive relationship

between lnIVP (lnITP) and financial performance

(coefficient = 0.189, p-value = 0.000).

Consequently, the hypothesis is rejected, indicating

that a longer inventory turnover period is associated

with better financial performance in line with the

result of, [37].

H03: The hypothesis posits that there is a

relationship between the accounts payable period

(APP) and the financial performance of quoted

consumer goods manufacturing firms. The analysis

supports this hypothesis, as the coefficient for

lnAPP is found to be statistically significant and

negative (coefficient = -0.133, p-value = 0.000).

This implies that a longer accounts payable period

negatively affects the financial performance of these

firms as described in, [38].

H04: The hypothesis states that the accounts

receivable period (ARP) does not influence the

financial performance of quoted consumer goods

manufacturing firms. However, the analysis reveals

a significant positive relationship between lnARP

and financial performance (coefficient = 0.669, p-

value = 0.000). Thus, the hypothesis is rejected,

indicating that a longer accounts receivable period is

associated with improved financial performance.

This is also supported by, [39], and, [40].

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Table 6. Model Parameter Estimate

The F-statistic and its associated p-value are not

provided (NA). The R-squared value is also not

available (NA). The Wald chi-square test shows a

significant overall model fit (chi2(7) = 3060000000,

p-value = 0.000). Furthermore, various tests are

conducted to assess the validity of the model. The

Arellano-Bond tests for autoregressive structure in

first differences indicate significant values for

AR(1) (z = -6.62, p-value = 0.000) and AR(2) (z = -

6.57, p-value = 0.430), suggesting the no presence

of serial correlation. The Sargan test and Hansen

test of overidentifying restrictions both yield

significant results, indicating that the instruments

used in the GMM estimation are valid.

5 Conclusion

In conclusion, this study utilized a generalized

method of moment (GMM) model analysis to

investigate the impact of working capital

management on the financial performance of quoted

consumer goods manufacturing firms on the Nigeria

Stock Exchange. The panel data collected from 20

firms from 2011 to 2020 provided comprehensive

coverage, including all quoted consumer goods

manufacturing firms in Nigeria and a long

observation period of 10 years post-release of

annual financial statements. This extensive dataset

allowed for robust and conclusive results.

One significant contribution of this study is the

inclusion of both accounting-based and market-

based measurements for financial performance,

which adds value and novelty compared to many

previous studies that solely relied on accounting-

based measures. The expanded set of dependent

variables, particularly return on assets (ROA),

provided a comprehensive evaluation of financial

performance.

The findings of this study revealed important

insights regarding the relationship between working

capital management and financial performance. The

results demonstrated that a shorter cash conversion

cycle (CCC) and higher inventory turnover period

(IVP) positively influenced the firms' ROA. On the

other hand, a longer accounts payable period (APP)

had a negative effect, while a longer accounts

receivable period (ARP) had a positive impact on

ROA.

These findings highlight the significance of

effective working capital management practices in

enhancing the financial performance of consumer

goods manufacturing firms. It is recommended that

these firms adopt optimal working capital

management strategies to improve their financial

performance.

Conclusively, this study provides valuable

evidence and conclusive results regarding the

impact of working capital management on financial

performance. The comprehensive dataset,

encompassing a wide range of firms and a long

observation period, adds credibility to the findings.

The incorporation of both accounting-based and

market-based measures of financial performance

further enhances the study's contributions to the

existing literature. These findings have important

implications for managers, investors, and

policymakers in the consumer goods manufacturing

industry in Nigeria.

Table 4.7a Estimated Results Two-Step System GMM – ROA

ROA

*Pooled OLS

model

*Fixed-Effect

Model

*One-step difference

GMM

*Two-step difference

GMM

*One-step system

GMM

*Two-step system

GMM

Coef.

P>|z|

Coef.

P>|z|

Coef.

P>|z|

Coef.

P>|z|

Coef.

P>|z|

Coef.

P>|z|

lnROA L1.

0.264

0.000

0.263

0.000

0.263

0.000

0.263

0.000

0.263

0.000

0.263

0.000

lnCCC

0.000

0.997

0.000

0.981

0.00283

0.981

0.000

0.980

0.000

0.933

0.000

0.934

lnIVP

0.189

0.000

0.190

0.000

0.190

0.000

0.190

0.000

0.189

0.000

0.189

0.000

lnAPP

-0.133

0.000

-0.133

0.000

-0.133

0.000

-0.133

0.000

-0.133

0.000

-0.133

0.000

lnARP

0.659

0.000

0.653

0.000

0.653

0.000

0.652

0.000

0.669

0.000

0.669

0.000

FS

0.000

0.748

0.002

0.001

0.002

0.000

0.002

0.037

0.000

0.384

0.000

0.454

LEV

0.011

0.000

0.011

0.000

0.011

0.000

0.011

0.000

0.011

0.000

0.011

0.000

_cons

-0.832

0.000

-0.899

0.000

NA

-0.849

0.000

-0.849

0.000

Model Summary

F(7, 386)[p-value]

8017.34(0.000)

1250000(0.000)

NA

R-squared

0.859

0.854

NA

Wald chi2(7)

NA

30600(0.000)

81000(0.000)

Arellano-Bond test for AR(1)

in first differences: z

NA

-6.61(0.000)

-6.58(0.000)

-6.62(0.000)

-6.6(0.000)

Arellano-Bond test for AR(2)

in first differences: z

NA

-6.58(0.000)

-6.57(0.000)

-6.59(0.000)

Sargan test of overid.

restrictions: chi2(7)

NA

350(0.000)

352.77(0.000)

Hansen test of overid.

restrictions: chi2(7)

NA

43.98(0.000)

44(0.000)

Source: STATA 15 Output

WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2023.20.201

Oprite Malvan Iyalla, Umar Abbas Ibrahim

E-ISSN: 2224-2899

2349

Volume 20, 2023

5.1 Recommendations

Based on the findings and analysis conducted in this

study, the following recommendations are put

forward to consumer goods manufacturing firms

listed on the Nigeria Stock Exchange:

1. Optimize Cash Conversion Cycle (CCC): The

results indicate that a shorter cash conversion cycle

positively influences the financial performance of

the firms. Therefore, it is recommended that

companies focus on efficiently managing their cash

flows by reducing the time it takes to convert

inventory into cash. This can be achieved by

streamlining the production and distribution

processes, negotiating favorable payment terms with

suppliers, and implementing effective inventory

management systems.

2. Enhance Inventory Turnover Period (IVP): The

study demonstrates that a higher inventory turnover

period is associated with improved financial

performance. To achieve this, companies should

adopt inventory management strategies aimed at

reducing excess inventory levels, improving demand

forecasting accuracy, and optimizing supply chain

processes. By effectively managing their inventory,

firms can free up capital and enhance their overall

financial performance.

3. Optimize Accounts Payable Period (APP): The

findings reveal that a longer accounts payable

period has a negative impact on the financial

performance of the firms. It is recommended that

companies implement efficient accounts payable

practices, such as negotiating extended payment

terms with suppliers while maintaining good

relationships, monitoring invoice processing times,

and taking advantage of early payment discounts.

By effectively managing their accounts payable,

firms can optimize cash flows and improve their

financial performance.

4. Manage Accounts Receivable Period (ARP): The

study indicates that a longer accounts receivable

period positively influences the financial

performance of the firms. To strike a balance

between maximizing sales and minimizing the time

it takes to collect receivables, companies should

implement effective credit policies, monitor

customer payment behavior, promptly follow up on

overdue payments, and establish strong customer

relationships. By optimizing their accounts

receivable management, firms can improve cash

flow and enhance their financial performance as

illustrated in, [41].

5. Consider Firm Size and Leverage: The study

suggests that firm size and leverage have significant

effects on the financial performance of consumer

goods manufacturing firms. It is recommended that

companies carefully manage their size and leverage

ratios, taking into account the optimal levels that

align with their business strategies and risk

tolerance. Companies should regularly assess their

capital structure, debt repayment capabilities, and

potential risks associated with high leverage to

ensure sustainable financial performance.

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Contribution of Individual Authors to the

Creation of a Scientific Article (Ghostwriting

Policy)

- Oprite Malvan Iyalla carried out the

conceptualization and manuscript drafting.

- May Ifeoma Nwoye was responsible for the

Statistics.

- Umar Abbas Ibrahim has implemented the

statistical analysis and supervision.

Sources of Funding for Research Presented in a

Scientific Article or Scientific Article Itself

This research received no specific grant from any

funding agency in the public, commercial, or not-

for-profit sectors.

Conflict of Interest

The Authors have no conflict of interest to declare.

Creative Commons Attribution License 4.0

(Attribution 4.0 International, CC BY 4.0)

This article is published under the terms of the

Creative Commons Attribution License 4.0

https://creativecommons.org/licenses/by/4.0/deed.en

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WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2023.20.201

Oprite Malvan Iyalla, Umar Abbas Ibrahim

E-ISSN: 2224-2899

2352

Volume 20, 2023