Inventory Turnover and Firm Profitability: A Saudi Arabian Investigation

Abstract

The purpose of this paper is to explore the impact of inventory turnover on the profitability level of Saudi manufacturers. The data comprises 78 manufacturers listed on the Saudi Stock Exchange and was used to test the research hypothesis. The related data over the 2017–2021 period were collected from annual reports and the Datastream database. After running a multiple regression analysis with a fixed effects model, findings showed that the higher the inventory turnover ratio, the higher the cost which could be suppressed, and the greater the profitability of a company. The outcomes of this study have significant implications for managerial accounting issues in the setting of Saudi Arabia. Further, they provide policy recommendations to decision makers and assist managers in enhancing sustainability in the manufacturing sector. This research is the first to investigate this relationship including the impact of COVID-19 among Saudi companies in several industries, thus filling a gap in comparable research.

1. Introduction

The International Accounting Standards Board’s (IASB) Conceptual Framework for Financial Reporting, states that “the financial statements are normally prepared on the assumption that the reporting entity is a going concern and will continue in operation for the foreseeable future. Hence, it is assumed that the entity has neither the intention nor the need to enter liquidation or to cease trading. If such an intention or need exists, the financial statements may have to be prepared on a different basis.” Therefore, corporate stakeholders are aware of the concerned obligations in the International Accounting Standard (IAS) to disclose material fears concerning an entity’s capability to carry on as a going concern. Going concern is a fundamental accounting principle that guarantees long term sustainability. The ability to remain in operation as a viable entity is influenced by many factors, notably firm profitability. Hence, businesses struggle to reach their main goal which is profit maximization. Profit is crucial for firms’ survival. It is a driving force and an important factor that influences the corporate reputation.

Inventories are important assets of a company’s manufacturing process. The inventory management is a major component of any supply chain [1]. Therefore, supervision of inventories is essential to avoid losses resulting from stock deficits and surpluses. Inventory is a commonly used assessment of manufacturing processes and performance [2]. In fact, the number of inventories held has a considerable influence on the revenues and eventually the profitability [3,4]. One of the important and commonly used means for assessing the inventory management is the inventory turnover ratio [5,6]. The manufacturing industry relies heavily on inventory turnover, which is a measure of operational effectiveness and an indicator of how quickly products are sold. It is an indication of how efficiently items are moving along the supply chain [7]. Inventories consist of an important percentage of the entire investment and represent the greatest cost for producers. These companies fulfill the production process from purchasing direct materials, handling these materials to finished goods to maximize profit. Therefore, there is a direct association between efficient inventories management and profit maximization [8]. This perfect management permits the company to retain an optimal amount of inventory to deal with the orders in time and align with corporate goals. Minimizing the order fulfillment cycle time helps reduce the cost of inventory which leads to realized earnings and positively affects purchaser satisfaction. A business with a good inventory control deals with extended-term and robust growth opportunities and has a better survival situation.

Despite the importance of inventory management for the operating efficiency of the business and its ability to generate earnings (profitability level), few studies [9,10,11] have been done on the influence of inventory control on the level of firm profitability in an emergent context such as the Kingdom of Saudi Arabia (KSA). We find the KSA to be a good country of choice because of the possible shift from an oil-based to a manufacturing-based economy in the last few years due to the Vision of 2030. To address this gap in the existing literature, we extend the current studies by utilizing a bigger and newer sample of 78 manufacturing companies indexed on the Saudi Stock Exchange from 2017 to 2021.

The research questions that this work is based on are the following: is inventory turnover a determinant of firm profitability for Saudi manufacturers? Has the coronavirus pandemic affected Saudi firms’ profitability levels?

To address these questions, this study adopted quantitative research methodology based on multiple regression analysis, to assess the research hypothesis. We employ a fixed effects model to control for the heterogeneity in the dataset by involving firm-specific fixed effects. Other than testing the developed hypothesis, we control for a set of variables that may influence firm profitability. Data are collected from annual reports and the DataStream database. Empirical findings show that inventory turnover is strongly correlated to firm profitability level of manufacturers in the KSA. This study is the first to explore whether inventory management is a determinant of firm profitability for Saudi manufacturing companies, taking into consideration the coronavirus pandemic period. It is assumed that the results obtained enhance the understanding of the influence of inventory turnover on the profitability of Saudi manufacturing companies. These findings could be useful to the management team to identify their responsibility in handling inventories.

The rest of the paper is structured as follows: Section 2 presents the literature review and the hypothesis development. Section 3 presents the research design. Section 4 describes the empirical outcomes. Section 5 provides conclusions.

2. Literature Review and Hypothesis Development

2.1. The Shift from Oil-Based to Manufacturing-Based Economy in Saudi Arabia

Drawing on a range of export-oriented industries (e.g., oil and gas, metals and mining, logistics, agrochemicals and food and beverages), the KSA has the highest industrial output in the Middle East and North Africa (MENA) region. This industrial output has largely been driven by oil and gas related sectors, such as oil production and petrochemicals, due to the country’s abundant hydrocarbon resources and minimal extraction costs. However, the recent slump in oil prices has encouraged the KSA government to re-evaluate the contribution of its relatively under-performing non-oil and gas related industries to the economy of the country. Therefore, the global oil price collapse has moved the attention to other sectors and the manufacturing sector tops the list, both in terms of potential and financial backing. Based on the most recent update from Statista (https://www.statista.com/topics/1630/saudi-arabia/ (accessed on 15 November 2022) accessed on October 2022 (General Authority for Statistics in the KSA) (https://www.stats.gov.sa/en (accessed 15 November 2022), a revenue enhancement from the Saudi manufacturing market has been noticed since the COVID-19 pandemic (from 163.60 billion USD in 2020, to a forecasted 182.40 billion USD by the end of 2022 and it is projected to reach 205.26 billion USD by the end of 2023). Further, in 2020, the expected gross domestic product (GDP) from the manufacturing sector in KSA was 340.4 billion SAR while the total expected GDP for that year was approximately 2.62 trillion SAR. Hence, this sector contributed roughly 13% to the nation’s GDP in 2020. This could turn out to be blessing for Saudi Arabia, as it will give their largely oil-dependent economy an alternative for the future. In fact, the non-oil and gas focused industries have been recognized in the KSA’s Vision of 2030 (https://www.vision2030.gov.sa/ (accessed on 20 November 2020) as a major emphasis for additional development to help drop the dependency on hydrocarbon returns and produce private sector employment opportunities for people. Governmental backing, huge investments to boost the manufacturing sector, and welcoming external direct investments bring opportunities for this sector in the KSA. The government, based on the 2030 Vision requirements, is offering huge financial and administrative support to the manufacturing sector. This support includes implementation of the essential infrastructure, construction of new industrial zones like Jubail and Yanbu (Royal Commission for Jubail and Yanbu “RCJY”), the establishment of the Saudi Industrial Development Fund (SIDF) and other incentives and programs which help industrialization such as the National Industrial Cluster Development Program (NICDP), a governmental agency recognized for encouraging industrial investment in the KSA.

2.2. Inventory Turnover and Corporate Profitability

The classical control theory concentrates on capturing the input/output representation of systems [12]. It is a means to control manufacturing systems including the in-process inventories. The rise of the circular economy pattern and the development of a green supply chain, force businesses to adjust and take a stronger role in the value chain [13,14]. Within the new value chain, the manufacturer plays an important role re-designing products for various uses and suggesting recent consumption patterns across innovative services encompassing both present and upcoming life cycles of the product. This new level of complication pressurizes the inventory management system. A subsequent stream of research made a considerable development in this classical control theory [15,16,17,18,19,20,21,22]. This stream investigated the dynamic processes resulting in typical control systems for production. This modern control approach focuses on developing models and techniques to represent the internal dynamics of a system through state space methods. These techniques were developed to deal with the manufacturing control issues (e.g., linear programming and dynamic programming, strong and adaptive management models, genetic algorithms).

Strategic inventory management ensures the efficiency of the whole organization. However, it remains an ongoing challenge for most companies. Inventory efficiency indicates how the management effectively uses the inventory to stabilize the demand of clients and warehouse expenditures. This efficiency is about having the right items, with the right volume, at the proper time, considering cost–benefit rules. Hence, an effective inventory management system allows a company to react quickly to market demands and avoid running short of inventory. Corporate management ought to be conscious of the importance of supply chain integration to accomplish the operational and financial objectives. In this sense, [23] examined the effect of a vertically integrated organization structure on inventory turnover and operating performance. They selected a sample of 2193 manufacturing firms during the period 1986–2010. Results from causal model and structural equation modelling analysis show that vertical integration positively impacts raw materials and finished goods inventories turnover. However, it has no important effect on work in progress inventory turnover.

The relationship of inventory management to corporate performance has been exposed to various examinations. Empirical evidence on this link generated mixed results. The effect of Just-In-Time (JIT) manufacturing adoption on the reported return on assets (ROA) was explored by [24,25]. The evidence presented by the authors suggested mixed findings. Researchers [26], extended the study of [25] and examined the link between financial performance and JIT. The authors found that JIT adoption was linked with improved ROA. In fact, the empirical analysis showed that JIT adopters experience an inventory turnover growth six to eight times greater than that of non-adopters. Drawing inferences from a sample of US manufacturing businesses over the 1981–2000 period, [27] examined the changes in inventories. Empirical findings show that the median of inventory carrying periods were decreased from 96 to 81 days. Furthermore, the average rate of inventory dropped by 2% per year. In a similar vein, [28] explored the influence of inventory management on the performance level. Using a sample of Greek industrial firms from 2000 to 2002, the author found that the greater the level of inventories maintained by a business, the lower the rate of returns. The inventory management policies used by small and medium manufacturing businesses in Zimbabwe was measured by [29]. Findings established that when the management uses the JIT method, it faces encounters in the supply chain as they must always verify communication with the suppliers and reduce the time span of receiving materials. Researchers [30], explored sustainable inventory management under controllable carbon emissions from a greenhouse farm to achieve green and sustainable supply chains. The findings demonstrated a reasonable level of profit matched with other backordering cases.

Inventory turnover refers to the liquidity and how efficient the company holds and manages its inventory [31]. Therefore, a low inventory turnover indicates a big number of unused inventories, while high inventory turnover indicates that inventory is rapidly sold and that the firm handles its inventories efficiently. When inventory is rapidly sold, corporate profit earned is higher. The effect of inventory management (i.e., inventory conversion time and inventory turnover) on financial performance of 29 listed manufacturing companies in Sri Lanka for a period from 2014 to 2018 was analyzed by [32]. Results of this study showed that inventory turnover is not linked to the performance level of manufacturers in Sri Lanka.

In the Saudi Arabian context, the growth and development of some manufacturing industries, urged scholars to analyze the relationship between firm profitability and some chosen financial ratios. Researchers [9,10] attempted to determine the profitability of indexed Saudi companies (i.e., petrochemical and cement industries) for the period from 2008 to 2012. The authors selected a set of financial ratios; inventory turnover ratio is one of them. Empirical findings showed a significant correlation between this ratio and corporate profitability measured by the net profit margin. In a recent study, [11] explored the association between the efficiency of inventory management and financial performance in the Saudi market for the period 2016–2020. Findings revealed two important points: (i) a positive and significant relationship between inventory management and firms’ financial growth proxied by return on assets and (ii) a positive and significant link between the inventory conversion period and the inventory turnover. The authors’ conclusions support the idea that the inventory management in Saudi firms is efficient. This reveals that handling inventory effectively, positively influences performance.

Based on the aforementioned, the review of the literature regarding the association between inventory turnover and firms’ profitability level revealed mixed findings, especially in the Western market. The emergent KSA market combines distinct features compared to the global markets [33,34,35]. We look for such an association in Saudi Arabia because of the shift of the Saudi economy from oil-based to manufacturing-based over the last years due to the Vision of 2030 and the coronavirus pandemic impacts. In fact, the recent slump in oil prices during the COVID-19 period has encouraged the Saudi government to re-evaluate the involvement of the non-oil and gas industries in the economy of the country. Such a global oil price collapse has moved attention to the manufacturing sector. Consequently, it becomes significant and interesting to assess the effect of inventory management on firms’ profitability for Saudi manufacturers. Therefore, the research hypothesis is as follows:

Hypothesis. 

There is a significant nexus between inventory turnover and profitability level of the indexed Saudi manufacturing companies.

3. Research Design and Methodology

We realize that inventory turnover could be linked with features not considered in the regression due to some unavailable data (e.g., economic conditions, firm’s cycles, monetary policies, industry specific conditions, growth opportunities). In fact, the profitability related to the inventory policies can vary according to the industry specific and economic conditions at the country level [32]. To adjust for possible bias expected from the omitted firm-specific features that may influence inventory turnover and firm profitability, we used a linear regression model with fixed effects. The fixed-effect model allows the existence of time-invariant unobservables among firms over the period of the time series. In addition, the result of the Hausman test confirm that a fixed-effect model is favored over a random effect model. Furthermore, we execute the modified Wald test for heteroskedasticity and Wooldridge checks for first order correlation and prove that the two OLS assumptions are violated in our panel dataset. Hence, our statistical inferences are based on robust standard errors corrected for heteroskedasticity and autocorrelation issues.

3.1. Sample Selection and Model Specifications

This study investigates the association among inventory turnover, a set of control variables, and Saudi firms’ profitability. Figure 1 displays the relationship tested in this study. Therefore, we select manufacturing Saudi firms for sampling determinations since these firms are with inbound and outbound supply chain processes and hence can adequately measure the variations in the inventory turnover performance in their internal supply chain [23]. The preliminary sample consisted of 217 Saudi companies indexed on the Saudi Exchange, as accessed from the Thomson Reuters Database in July 2022 (Index Code: TDWTASI). From this initial sample, we eliminate the bank sector (10 banks), financial service sector (9 institutions), life insurance sector (2 firms), non-life insurance sector (26 firms), real estate investment service sector (12 firms), real estate investment trust sector (16 firms), other non-manufacturing firms, and firms with missing data (64 firms).

Table 1. Sample distribution by industry group.

	Sector	Industry Group	Firms	Obs.	%
1	Energy	Energy	2	10	3
2	Materials	Materials Industry Group	41	205	53
3	Industrials	Capital Goods	10	50	13
4	Consumer Discretionary	Consumer Durables and Apparel	6	30	8
5	Consumer Staples	Food and Beverages	13	65	17
6	Health Care	Pharma, Biotech and Life Science	1	5	1
7	Utilities	Utilities	5	25	6
	Total		78	390	100

presents the number of the studied firms (first column), the number of observations per industry group (second column), and the frequency of observations per industry (third column). The study ends up with a sample of 78 firms that operated in the Saudi manufacturing sector during the period 2017–2021 (390 firm year observations). It is made up of seven industry groups based on the global industry classification standard (GICS). The greatest number of companies comes from the materials industry group (41 companies). All necessary data for this research are derived from the Thomson Reuters Datastream.

To test the developed hypothesis of this study, we analyzed three econometric regressions with three dependent variables to proxy for firm profitability, namely, return on assets (ROA), earnings per share (EPS), and gross profit margin (GPM), and main independent variable, inventory turnover. Furthermore, the models control for the accounts receivable collection period, days payable outstanding, firm size, leverage ratio, market to book ratio, sales growth, board size, coronavirus pandemic, big4 auditor, firms, and years dummies effect. To prevent the influence of outliers, we winsorize all continuous variables at the 1st and 99th percentiles. Please refer to Appendix A for variables measurement.

$$\begin{gathered} {\mathrm{ROA}}_{\mathrm{it}}={\mathrm{a}}_0+{\mathrm{a}}_1{\mathrm{ITR}}_{\mathrm{it}}+{\mathrm{a}}_2\mathrm{ARCP}+{\mathrm{a}}_3{\mathrm{DPO}}_{\mathrm{it}}+{\mathrm{a}}_4{\mathrm{FSIZE}}_{\mathrm{it}}+{\mathrm{a}}_5{\mathrm{LEV}}_{\mathrm{it}}+{\mathrm{a}}_6{\mathrm{MTB}}_{\mathrm{it}}+{\mathrm{a}}_7\mathrm{SGROW}+{\mathrm{a}}_8\mathrm{BSIZE} \\ +{\mathrm{a}}_9{\mathrm{COVID}}_{\mathrm{it}}+{\mathrm{a}}_{10}{\mathrm{BIG}}_{\mathrm{it}}+{{\displaystyle \sum }}^{}\mathrm{FIRMS}+{{\displaystyle \sum }}^{}\mathrm{YEARS} \end{gathered}$$

(1)

$$\begin{gathered} {\mathrm{EPS}}_{\mathrm{it}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1{\mathrm{ITR}}_{\mathrm{it}}+{\mathsf{\beta }}_2\mathrm{ARCP}+{\mathsf{\beta }}_3{\mathrm{DPO}}_{\mathrm{it}}+{\mathsf{\beta }}_4{\mathrm{FSIZE}}_{\mathrm{it}}+{\mathsf{\beta }}_5{\mathrm{LEV}}_{\mathrm{it}}+{\mathsf{\beta }}_6{\mathrm{MTB}}_{\mathrm{it}}+{\mathsf{\beta }}_7\mathrm{SGROW}+{\mathsf{\beta }}_8\mathrm{BSIZE} \\ +{\mathsf{\beta }}_9{\mathrm{COVID}}_{\mathrm{it}}+{\mathsf{\beta }}_{10}{\mathrm{BIG}}_{\mathrm{it}}+{{\displaystyle \sum }}^{}\mathrm{FIRMS}+{{\displaystyle \sum }}^{}\mathrm{YEARS} \end{gathered}$$

(2)

$$\begin{gathered} {\mathrm{GPM}}_{\mathrm{it}}={\mathsf{\gamma }}_0+{\mathsf{\gamma }}_1{\mathrm{ITR}}_{\mathrm{it}}+{\mathsf{\gamma }}_2\mathrm{ARCP}+{\mathsf{\gamma }}_3{\mathrm{DPO}}_{\mathrm{it}}+{\mathsf{\gamma }}_4{\mathrm{FSIZE}}_{\mathrm{it}}+{\mathsf{\gamma }}_5{\mathrm{LEV}}_{\mathrm{it}}+{\mathsf{\gamma }}_6{\mathrm{MTB}}_{\mathrm{it}}+{\mathsf{\gamma }}_7\mathrm{SGROW}+{\mathsf{\gamma }}_8\mathrm{BSIZE} \\ +{\mathsf{\gamma }}_9{\mathrm{COVID}}_{\mathrm{it}}+{\mathsf{\gamma }}_{10}{\mathrm{BIG}}_{\mathrm{it}}+{{\displaystyle \sum }}^{}\mathrm{FIRMS}+{{\displaystyle \sum }}^{}\mathrm{YEARS} \end{gathered}$$

(3)

ROA is return on assets, EPS is earnings per share, GPM is gross profit margin, ITR is inventory turnover, ARCP is accounts receivable collection period, DPO is days payable outstanding, FSIZE is firm size, LEV is leverage ratio, MTB is market to book ratio, SGROW is sales growth, BSIZE is board size, COVID is coronavirus pandemic, BIG is big4 auditor. FIRMS and YEARS are firms’ and years’ indicators. Please refer to Appendix A for variables’ measurement.

3.2. Variables Measurement

3.2.1. Sustainable Firm Profitability Measure

Sustainable firm profitability is the operating efficiency of the business and its ability to generate earnings. Common examples of profitability ratios used in the literature include return on sales, return on investment, return on equity, return on capital employed, economic value added, cash return on capital invested, gross profit margin, and net profit margin [36]. These ratios inform about the firm’s performance in generating profits relative to a selected standard of measurement.

To proxy for firm profitability in this work, we use three indicators based on the available data, namely, ROA, EPS, and GPM. ROA assesses how efficient a business’ management is in earning profits from their economic resources or assets on their balance sheet. ROA is calculated as the book value of net profit after tax divided by total assets. EPS signifies the profitability of a company. It is computed by dividing the net income with the total number of outstanding shares. A higher EPS denotes greater firm value and price that investors will pay more for a company’s shares. GPM reveals how effective an executive management team is in creating revenue, considering the costs engaged in manufacturing products or delivering services [31,37]. This measurement is based on a firm’s net sales since sales can generate profit [38]. Hence, the higher the GPM value is, the more efficient the management is in engendering earnings for every Saudi riyal of cost involved.

Overall, higher values for these three ratios express that the company is delivering adequate balance in generating earnings, revenues, and cash flows.

3.2.2. Inventory Turnover Measure

The selection of variables in inventory measurement is influenced by the review of the operations management literature [3,31,39,40,41,42,43]. Inventory turnover refers to the liquidity and how efficient the company holds and manages its inventory [40]. It is a benchmark for evaluating firms’ competitiveness and operational efficiency [7]. Following [7,27,31], we use a standardized measure, inventory turnover ratio (ITR), to proxy for inventory turnover. ITR is the cost of Sales (cost of goods sold) over the average value of total inventory (beginning inventory + ending inventory/2). A low ITR is a sign of weak sales or excessive inventory, known as overstocking, while a high ITR means that the firm efficiently manages its inventory and can sell goods quickly. When the inventory is quickly sold the profit earned is higher. Furthermore, the choice of inventory turnover ratio is because data related to its measurement are objective and publicly available from the financial reports of Saudi manufacturing companies.

3.2.3. Control Variables

To decrease the potential bias that may occur due to some omitted variables, we control for some firm attributes. We incorporate the average collection period, days payable outstanding, firm size, debt ratio, market to book ratio, sales growth rate, board of directors’ size, coronavirus pandemic effect, and Big4 audit service. The symbol and variables measurements are provided in Appendix A. First, the average collection period of accounts receivable (ARCP) is a useful measurement to evaluate credit and collection policies [31]. It is one of the most important calculations used to verify short-term liquidity since it relies on the average number of days a business takes to gather and convert the accounts receivable into cash. ARCP is calculated by dividing the average accounts receivable balance by total sales and multiplying the product by 365 [44]. An extended collection period reveals the accounts receivable amount may become uncollectible and can make a company unprofitable. To determine whether the average collection period for a given company is good, it is important to compare it against the credit terms the company offers its clients (unreachable data). Second days payable outstanding (DPO) is the average accounts payable balance over cost of goods sold sales and multiplying the product by 365 [45]. It designates the average time (in days) that a business takes to reimburse its liabilities to the trade creditors (e.g., suppliers, sellers, bankers, etc.). A low DPO indicates a short average payment period and that the company is making quick payments of its bills and obligations without any delay. However, a very low DPO indicates that the firm is not taking full advantage of credit terms allowed by the supplier. A higher value of DPO may indicate a cash deficit and incapacity to pay. An ideal average payment period is 90 days by many companies. Third, we control for firm size (FSIZE) using the natural logarithm of total assets [46]. Larger firms can achieve economies of scale by increasing production and lowering costs [47]. Fourth, we use debt ratio (LEV) measured by total liabilities to total assets to proxy for firms’ stability. A lower debt ratio reveals improved financial firm stability. Fifth, we use the market to book ratio (MTB) to control for corporate investment and growth opportunities that are bound to affect firm profitability. MTB measures the relative firm value matched to its stock price. A lower MTB implies investment opportunities. Sixth, we use sales growth rate (SGROW) to control for a firm’s growth. It shows how much sales have increased. Seventh, we control for board of directors’ size (BSIZE). It is the total number of members on the board of directors [48,49]. Eighth, we test for the influence of the coronavirus pandemic crisis (COVID) on the corporate profitability. Given the present COVID-19 pandemic emergencies and the revenue improvement in industrial companies, it becomes a necessity to assess how effective the old systems of inventory management and control work [50,51]. COVID is an indicator that takes a value of 1 for the firm-year observations that falls in the Coronavirus pandemic period (2020–2021), and 0 otherwise. Last, we use big4 audit firms (BIG) to control for the impact of external audit quality. The Wald checks of simple and composite linear assumptions of yearly dummy variables also indicate that the time-specific fixed effects should be controlled in the model (YEARS).

4. Analysis Results and Discussion

4.1. Summary Statistics

Table 2. Descriptive summary.

	Mean	Median	St. Dev.	Kurtosis	Skewness	Minimum	Maximum
Panel A. Summary statistics
Total Assets	61,184,715	4,767,888	120,358,734	3	2	542,858	406,391,118
Total Inventories	3,075,648	394,955	7,409,813	7	3	3502	28,274,921
Total Cost of Goods Sold	11,529,290	1,916,849	25,559,565	6	3	9930	97,751,803
Total Sales	13,921,728	3,580,205	20,406,841	6	2	67,210	102,589,000
Average Accounts Receivable	3,051,529	290,678	12,412,566	57	7	3810	138,657,500
Average Accounts Payable	8,221,192	655,377	16,964,661	4	2	0	62,837,131
ROA	9.4745	9.1658	6.4163	2.9393	1.3718	0.0500	33.7124
EPS	2.5510	1.2950	2.4602	37.4209	5.4057	0.09	19.8741
GPM	34.1705	32.200	18.9046	−0.6023	0.4641	3.6300	84.2000
ITR	7.9907	6.4900	3.3171	1.6577	0.3361	3.9718	15.7100
ARCP	81.0069	35.1963	21.0878	82.9970	8.4962	29.0526	150.9347
DPO	59.1641	60	26.8383	1.9355	−0.0524	10	104
FSIZE	16.009	15.3774	1.9687	−0.7058	0.6488	13.2046	19.8228
LEV	1.1722	0.3906	2.8225	18.0328	4.1928	0.0052	18.7467
MTB	2.7930	2.2950	1.8160	9.6643	2.0843	0.7092	13.0860
SGROW	10.5288	4.5027	63.1338	159.3817	11.8110	−81.1050	945.1838
BSIZE	9.7769	9	2.1074	11.8833	2.0473	3	25
Panel B. Frequencies statistics	Obs.		Freq (1)	%		Freq (0)	%
COVID	390		156	40		234	60
BIG	390		285	73		105	27

ROA: return on assets, EPS: earnings per share, GPM: gross profit margin, ITR: inventory turnover, ARCP: accounts receivable collection period, DPO: days payable outstanding, FSIZE: firm size, LEV: leverage ratio, MTB: market to book ratio, SGROW: sales growth, BSIZE: board size, COVID: coronavirus pandemic, BIG: big4 auditor.

shows the descriptive statistics. Panel A depicts sample mean, median, standard deviation, kurtosis, skewness, minimum and maximum for continuous variables. Panel B provides the frequency of dichotomous variables. All the used variables are winsorized at the 1% and 99% levels to remove extreme observations. Please refer to Appendix A for variables measurement. The descriptive summary shows that the mean value of the ROA is 9.47%. Therefore, the Saudi manufacturing companies can generate earnings of about 9.5 percent from the assets held. The mean (median) of EPS is 2.5510 (1.2950). The minimum and maximum values of this variable are 0.09 and 19.8741, respectively. These values indicate how much money the Saudi companies make for each share of their stock. The average percentage of GPM ratio is 34.17%. It refers to the percentage of revenue that surpasses the cost of goods sold [31]. Therefore, 34.17% of the revenue can be paid out for operation expenditures and still reach satisfactory bottom line profitability [37]. The minimum and maximum of the GPM ratio are successively 3.63% and 84.20%. Therefore, the selected manufacturing companies are financially stable and healthy. The inventory turnover ratio (ITR) is between 4 and 16, which indicates that the manufacturing companies sell and restock their inventory every 1–4 months. On average, the inventory is turned into sales eight times a year. High levels of inventories decreases production and trading breaks that further promote corporate profit maximization [4,52]. Stock-outs not only harm the reputation of the company but also drives the clients away to other competitors [53]. The average collection period (ARCP) for the sampled Saudi manufacturing companies is 81. Hence, the number of days sales persist with debtors is around 81 days. The average DPO is around 60 days. This can be considered as a short average payment period which indicates that the selected manufacturing companies are making quick payments. The average relative firm value compared to its stock price (MTB) is 2.7. This value (greater than 1) could mean that the stock is overvalued and suggests a possibility of investment opportunities [23]. Panel B from Table 2 shows frequencies of observations. Forty percent of firms-observations fall in the coronavirus pandemic and 73 percent of the sampled observations are from manufacturing companies audited by at least one of the big4 audit firms.

4.2. Multicollinearity Tests

Table 3. Pearson and Spearman correlations matrices and variance inflation factors (VIFs) coefficients.

	ROA	EPS	GPM	ITR	ARCP	DPO	FSIZE	LEV	MTB	SGROW	BSIZE	VIF
ROA	1.0000	0.1946 ***	0.0636	0.0984 **	0.0687	0.0709	0.0980 *	−0.0132 *	0.3806 ***	0.0376 *	0.0542	-
EPS	0.0951 *	1.0000	0.0781	0.1002 **	0.1271 **	0.0438	0.4021 ***	−0.1351 ***	−0.0940 *	0.0596	0.3000 ***	-
GPM	0.0009	0.0134	1.0000	0.0327 ***	0.2835 ***	0.0120	0.0917 *	−0.0251 *	−0.0111	0.0249 **	0.1061 **	-
ITR	0.0131 **	0.0818 *	0.0327 *	1.0000	−0.0314	−0.0929 *	−0.1512 ***	−0.0973 *	−0.1603 ***	0.1100 **	−0.0614	1.25
ARCP	0.0084	0.0307	0.2835 ***	−0.0314	1.0000	0.0267	0.3050 ***	−0.0427	0.0182	−0.0249	0.0913 *	1.20
DPO	0.0813	0.0242	0.0120	−0.0929 *	0.0267	1.0000	−0.0761	0.2778 ***	0.1140 **	−0.0251	−0.0435	1.13
FSIZE	0.0727 *	0.2926 **	0.0917 *	−0.1512 ***	0.3050 ***	−0.0761	1.0000	−0.2196 ***	0.0226	−0.0130	0.3901 ***	1.43
LEV	−0.0194 *	−0.0934 *	−0.0251 *	−0.0973 *	−0.0427	0.2778 ***	−0.2196 ***	1.0000	0.1701 ***	−0.0129	−0.2637 ***	1.23
MTB	0.3691 ***	−0.0829	−0.0111	−0.1603 ***	0.0182	0.1140 **	0.0226	0.1701 ***	1.0000	0.0003	−0.0101	1.07
SGROW	0.0063 *	0.0231 *	0.0249 *	0.1100 **	−0.0249	−0.0251	−0.0130	−0.0129	0.0003	1.0000	−0.0278	1.02
BSIZE	0.0281 *	0.3002 ***	0.1061 **	0.061	0.0913 *	−0.0435	0.3901 ***	−0.2637 ***	−0.0101	0.0278	1.0000	1.34

ROA: return on assets, EPS: earnings per share, GPM: gross profit margin, ITR: inventory turnover, ARCP: accounts receivable collection period, DPO: days payable outstanding, FSIZE: firm size, LEV: leverage ratio, MTB: market to book ratio, SGROW: sales growth, BSIZE: board size, COVID: coronavirus pandemic, BIG: big4 auditor. *, **, *** next to a coefficient indicate significance level of 10%, 5%, 1%, respectively. To avoid the influence of outliers, we winsorize all continuous variables at the 1% and 99% levels. Please refer to Appendix A for variables’ definition and measurement.

presents Pearson correlation (upper triangle) and Spearman correlation (lower triangle) matrices. These tests are used to identify the correlation association between independent variables and control for possible multicollinearity issues. Based on the correlation shape of the data, we observe that the higher correlation coefficient is observed between BSIZE and FSIZE with a coefficient of 0.39. This coefficient is significant at the 1 percent level. Inventory turnover ratio (ITR) appears to be positively and significantly correlated to the firm profitability measures (ROA, EPS, and GPM). Results from the Spearman matrix show that EPS and GPM are positively and significantly affected by the average ARCP (r = 0.1271 and r = 2835). This means that the ARCP contributes by 12.71% in increasing EPS and by 28.35% in increasing GPM. Further, ROA, EPS, and GPM are found to be negatively correlated with LEV suggesting that better performing manufacturing companies tend to have a lower level of indebtedness. Overall, the two matrices show that all coefficients are less than 0.5 and higher than −0.5 signifying that no multicollinearity issue is detected. Additionally, the VIF test is carried out. By referring to [54], the VIF value of less than ten is tolerable. The VIF figures are all lower than two. Hence, no serious correlation problems among independent variables are reported.

4.3. Hypothesis Testing Results

The empirical investigation is based on multiple regression analysis techniques to assess the effect of the selected financial ratio (i.e., inventory turnover ratio) on the level of ROA, EPS, and GPM of manufacturing Saudi companies.

The analysis outcomes are presented in

Table 4. Empirical findings.

	Panel A Y = ROA		Panel B Y = EPS		Panel C Y = GPM
	Coef.	t	Coef.	t	Coef.	t
Constant	3.6050 **	1.97	3.4416 *	1.69	6.3031 ***	2.69
ITR	0.3426 *	1.66	0.4328 **	2.32	1.0416 **	2.04
ARCP	0.0000	1.50	0.0000	1.41	0.0000 **	1.95
DPO	−0.0032	−0.24	−0.0012	−033	0.0326	0.73
FSIZE	0.5395	1.41	0.2764 **	2.44	1.8386 *	1.95
LEV	−0.3800 *	−1.67	−0.0200 *	−1.70	−0.274 *	−1.69
MTB	0.5969 ***	3.81	−0.0188	−0.40	−0.0804	−0.21
SGROW	0.0045	1.31	0.0002	0.27	−0.0026	−0.30
BSIZE	0.0491	0.30	0.0274	0.57	0.7936 **	1.98
COVID	0.1769	0.28	0.3793 **	2.04	0.7029	0.45
BIG	0.1652	1.61	0.1307	0.91	0.1162	1.15
Adjusted R-sq	8.6		8.1		12.9
F-stat	7.88 ***		15.37 ***		13.88 ***

ROA: return on assets, EPS: earnings per share, GPM: gross profit margin, ITR: inventory turnover, ARCP: accounts receivable collection period, DPO: days payable outstanding, FSIZE: firm size, LEV: leverage ratio, MTB: market to book ratio, SGROW: sales growth, BSIZE: board size, COVID: coronavirus pandemic, BIG: big4 auditor. *, **, *** next to a coefficient indicate significance level of 10%, 5%, 1%, respectively. To avoid the influence of outliers, we winsorize all continuous variables at the 1% and 99% levels. Please refer to Appendix A for variables’ definition and measurement.

. Panel A reports results from model (1), Panel B presents findings from model (2), and Panel C reports results from model (3). The adjusted R² values are 8.6%, 8.1%, and 12.9%, respectively for the three regressions. This is consistent with prior literature in the Saudi Arabian context (e.g., [11]). According to the different Panels, we conclude a positive association between ITR and profitability level of the sampled Saudi indexed manufacturing companies. In fact, the coefficients of ITR for the three models are all positive and statistically significant (${\mathrm{a}}_1=0.3426,{\mathsf{\beta }}_1=0.4328,$ ${\mathsf{\gamma }}_1=1.0416)$. Therefore, the higher the ITR is, the better is the firm’s profitability. These findings suggest that the Saudi manufacturing firms efficiently manage their inventories and can sell their products quickly which positively impact the profit earned. This provides support to the research hypothesis which suggests the presence of a significant nexus between inventory turnover and profitability level of the Saudi manufacturing companies. Our findings shows that efficient inventory management has a positive influence on firms’ financial growth. This designates that there is efficient inventory management in Saudi indexed manufacturers.

The coefficient on the average accounts receivable collection period (ARCP) is found to be positive (γ₁ = 0.0000) and statically significant at the one percent level for the third regression. Therefore, the average contributes to increasing the profitability of manufacturing companies as measured by GPM. Shorter collection period indicates accounts receivable amounts are collectible and can make a company profitable. Firm size (FSIZE) is positively (${\mathsf{\beta }}_4=$ 0.2764) and significantly (at five percent level) correlated to EPS. This result agrees with the argument that larger firms can achieve economies of scale by increasing production and lowering costs [47], and hence, increasing the profitability level. Further, ROA, EPS, and GPM are found to be negatively affected by the leverage ratio (LEV) suggesting that better performing manufacturing companies tend to have a lower level of indebtedness.

The effect of the coronavirus period (COVID) on EPS is positive (${\mathsf{\beta }}_9=$ 0.3793) and significant at the five percent level. This finding suggests that the profitability level of Saudi manufacturing companies was significantly improved during the coronavirus pandemic period (2020 and 2021). Our result is consistent with the result of [51]. They find that inventory efficiency is augmented during the COVID-19 pandemic. This may be the reason for an improved manufacturing firm profitability.

4.4. Further Robustness Checks

To check for the robustness of our estimation, outcomes are additionally tested by dividing the overall sample into two sub-samples: (i) before the coronavirus pandemic period and (ii) during the pandemic period. For the sake of brevity, we tested only the following regression (using ROA as firm profitability measure):

$$\begin{gathered} {\mathrm{ROA}}_{\mathrm{it}}={\mathrm{a}}_0+{\mathrm{a}}_1{\mathrm{ITR}}_{\mathrm{it}}+{\mathrm{a}}_2\mathrm{ARCP}+{\mathrm{a}}_3{\mathrm{DPO}}_{\mathrm{it}}+{\mathrm{a}}_4{\mathrm{FSIZE}}_{\mathrm{it}}+{\mathrm{a}}_5{\mathrm{LEV}}_{\mathrm{it}}+{\mathrm{a}}_6{\mathrm{MTB}}_{\mathrm{it}}+{\mathrm{a}}_7\mathrm{SGROW}+{\mathrm{a}}_8\mathrm{BSIZE} \\ +{\mathrm{a}}_9{\mathrm{BIG}}_{\mathrm{it}}+{{\displaystyle \sum }}^{}\mathrm{FIRMS}+{{\displaystyle \sum }}^{}\mathrm{YEARS} \end{gathered}$$

(4)

The first group includes 156 firm-year-observations, while the second group includes 234 firm-year-observations. We find the same results as in the main analysis.

5. Conclusions and Contributions

Despite the rising awareness of the consequence on inventory management in the manufacturing sector, relatively little study has been performed on how inventory turnover may or may not enhance the firm profitability, and even less in the emergent markets such as the KSA. The present paper is motivated by the gap in literature regarding this relationship in the Saudi manufacturing sector especially during the coronavirus pandemic period (COVID19).

Due to the contradictory findings from the previous investigations on the concerns of inventories, this investigation focuses more on this issue by using statistical tests applied to a larger and undated sample of Saudi manufacturers across different industry groups. Therefore, using a sample of 78 manufacturing firms listed on the Saudi Stock Exchange, our statistical inferences were based on robust standard errors corrected for heteroskedasticity and autocorrelation. We proxy for firm profitability using three measures to validate the robustness of the linearity of the link. Results of this study provide answers to the research questions and show that inventory turnover is significantly related to the profitability level of manufacturing companies in KSA. Therefore, inventory management as proxied by inventory turnover ratio is a determinant of firm profitability. Furthermore, findings provide a strong argument on the improvement of the profitability level of Saudi manufacturing companies during the COVID-19 pandemic period.

The outcomes of this work contribute to theoretical awareness and the present literature as follows: First, in an analysis of the literature dealing with the concerns of working capital management, [11] identified the effects of inventory management on Saudi firms’ performance but did not provide evidence of the impact of the COVID-19 pandemic. For the first time the effects of the coronavirus pandemic are examined in relation to the inventory management–firm profitability. Hence, this study contributes by investigating the inventory turnover ratios as a performance proxy among the manufacturing industry after the global oil price collapse and the shifted focus to the manufacturing sector. Second, we add to this literature by also examining the effects of two variables related to the cash conversion cycle (average accounts receivable collection period and days payable outstanding).

6. Managerial Implications and Research Limitations

The findings of this research have significant implications for the managerial accounting issues in the setting of Saudi Arabia in two ways. First, the outcomes of this work boost the knowledge on the link between inventory turnover–profitability levels of Saudi manufacturers. Therefore, they could be useful to management teams to identify their responsibility in handling inventories. Second, they offer policy directives to decision makers and assist managers to enhance subsequent sustainability practices in the manufacturing industry.

This work encountered a main limitation related to the investigation time span. Research of this kind would require enough time to cover many areas of activity effectively. However, the analysis is restricted to post 2016 since that at the end of this fiscal year, Thomson Reuters Datastream provides access to larger data on Saudi equities. This leads to a representative sample used in statistical analysis reflecting the characteristics of the entire population of the Saudi market.

The outcomes of this study imply that inventory turnover is a reliable indicator for the profitability level of the manufacturing sector. Therefore, we recommend reporting the effect of inventory turnover on firm profitability for each manufacturing industry group separately to examine which industry is most affected. This finding is important and has a valuable message for policymakers.