An Assessment of Liquidity, Profitability and Working Capital Management Strategy in Polish Manufacturing Companies in the Pressure-Casting Industry During the Crisis

Abstract

This study assesses liquidity, profitability, and working capital management (WCM) strategy in Polish manufacturing companies in the pressure-casting industry, drawing on evidence from the pre-COVID-19, COVID-19, and Russia–Ukraine war periods. Using panel data from 19 companies representing 90% of the Polish aluminum diecasting industry, we employ non-parametric tests (Mann–Whitney U and Kruskal–Wallis) to analyze the data. The period after the COVID-19 crisis coincides with the Russian–Ukrainian war. These countries are Poland’s neighbors. This period of uncertainty for Poland has led to supply chain disruptions and reduced investments. For manufacturing companies, this is dangerous because they have limited development opportunities. The results indicate the adoption of a conservative WCM strategy in Polish aluminum foundries during the pre-COVID-19, COVID-19, and Russia–Ukraine war periods, characterized by increased inventory levels, extended operating cycles in large firms. Additionally, the results showed reduced the level of receivables in large companies and visible decrease in the level of financial liquidity and profitability—however, these differences are not statistically significant. Polish aluminum foundries are adapting their WCM strategies toward an optimal, conservative approach that incorporates both safe and risky elements to ensure continued operations and profits. In addition, larger Polish aluminum foundries exhibit distinct liquidity patterns relative to smaller foundries, particularly in indicators of inventory, receivables, and fixed assets. In addition, the Russia–Ukraine war period exhibits distinct liquidity characteristics in Polish aluminum foundries compared with the COVID-19 and pre-COVID-19 periods, particularly in inventory turnover and operating cycle. The results of this study offer several novel contributions to the existing literature on financial security indicators by examining unexplored factors related to size and period. The results of this study have several practical implications for business leaders seeking to adopt an optimal liquidity, profitability, and WCM strategy.

1. Introduction

The COVID-19 crisis has demonstrated that existing financial security management strategies for companies are ineffective. The pandemic has deeply affected not only the healthcare system but also the global socioeconomic structure (Mourad et al. 2021; Nuta et al. 2025).

Since early 2020, supply–demand imbalances, supply chain disruptions, and sudden recessions have caused serious problems for the operational networks and financial structures of businesses (Baldwin and di Mauro 2020). As a result of the physical lockdowns imposed across nations, production and consumption phases were disrupted, causing businesses to experience liquidity problems (OECD 2020). Although large firms have been affected by this process, it has revealed the financial pressure and vulnerability of small and medium-sized enterprises (SMEs), which have been the most affected (Juergensen et al. 2020). Companies found that their existing financial strategies were ineffective during the crisis, and the need for new, more flexible financial management approaches emerged after the extraordinary period (Fernandes 2020; Habib 2022).

Tarkom (2022) confirms this in his research the severe conditions presented by the COVID-19 pandemic have considerably rendered firms inefficient in managing the WC.

In fact, many businesses have gone beyond the usual methods to sustain production and sales volume at a high, stable level, revealing the need for companies to update their strategic planning (Breier et al. 2021; Mazanec 2022a). Companies around the world have been seeking original solutions to maintain production and sales continuity. The policies of individual countries have both supported companies and made it difficult for them to operate by closing them for the duration of COVID-19. Additionally, after the COVID-19 crisis, the war in Ukraine, border blockades along the Poland–Belarus line, and pirate attacks on sea routes have caused further problems with the timeliness of deliveries. All these factors force company managers to optimize their financial security management strategies, which are directly related to working capital.

In addition, working capital is a measure of operational efficiency and directly affects a company’s financial security (Habib and Mourad 2022). Effective WCM is fundamental to a company’s financial health (Mazanec 2022b; Kayani et al. 2025). The right amount is a key factor in maintaining a company’s liquidity, solvency, and profitability, and it also determines its survival (Zimon et al. 2024). Working capital levels should be optimal, as excessive levels can incur additional costs associated with debt or the opportunity cost of idle funds. Insufficient funds, by contrast, can disrupt production and sales (Habib and Dalwai 2024). Maintaining appropriate levels of working capital components, such as cash, receivables, inventory, and payables, is crucial. An optimal level of working capital also demonstrates a balance between risk and efficiency (Afza and Nazir 2008; Tarkom 2022). Higher-risk investments typically yield higher returns; therefore, maintaining high working capital and liquidity is associated with lower risk but also lower profitability. Conversely, a low level of working capital and the associated lower liquidity translate into higher risk but also potentially higher profitability. The essence of WCM, therefore, lies in striving to achieve a high rate of return at an acceptable level of risk.

Additionally, the COVID-19 pandemic undoubtedly contributed to this trend. The financial disruptions experienced by many global economies at the time, and the resulting payment problems, led the net working capital days ratio to reach its highest level in five years in 2020 (Ahmad et al. 2022). The pandemic created an abrupt cash-flow and liquidity shock and triggered unusually large, system-wide financing responses—firms drew heavily on credit lines and relied on banks as “lenders of first resort” (Chen et al. 2026; Almeida 2021).

In response to this situation, steps were taken to change the approach to WCM. This study assesses this aspect by examining manufacturing companies in Poland’s die-casting industry. Manufacturing firms exhibit distinct WCM patterns compared to service or retail firms due to higher inventory requirements and longer production cycles (Deloof 2003; García-Teruel and Martínez-Solano 2007). Pressure foundry industries face additional complexity from: supply chain sensitivity, caused by raw material price volatility and supplier concentration (Walicka 2024); customer concentration, caused by dependence on automotive and industrial equipment manufacturers; capital intensity, caused by high fixed asset requirements that affect working capital optimization (Mielcarz et al. 2018); and the regulatory environment, caused by environmental and safety regulations that affect operational flexibility.

Further, the crisis has shown that traditional WCM strategies are often insufficient to ensure survival. That is why companies have tried to modify their strategy. In the literature, there are statements that “survival strategies” were created. Now, after the COVID-19 crisis, managers are slowly changing their approach to WCM. Conservative management is characterized by low profitability, which is why management strategies are currently being modified toward more profitable ones. However, the current political situation in the world and the US policy of withdrawing from its promises to defend Europe are creating further unrest in European countries. In this regard, this study addresses several critical gaps in the literature. First, existing research primarily focuses on the impact of single crises on WCM, while firms today face overlapping crises that require adaptive strategies (Baños-Caballero et al. 2016; Ramadan et al. 2024). Second, most studies examine broad industry samples, potentially masking sector-specific dynamics crucial for understanding WCM evolution in manufacturing contexts (García-Teruel and Martínez-Solano 2007; Altaf and Shah 2018). Third, there is limited understanding of how WCM strategies persist or change following crisis periods, particularly in European manufacturing contexts affected by both pandemic and geopolitical tensions.

Moreover, the crisis has shown that traditional WCM strategies are often insufficient to ensure survival. That is why companies have tried to modify their strategies. In the literature, there are statements that “survival strategies” were created. Now, after the COVID-19 crisis, managers are slowly changing their approach to WCM. Conservative management is characterized by low profitability, which is why management strategies are currently being modified toward more profitable ones. However, the current political situation in the world and the US policy of withdrawing from its promises to defend Europe are creating further unrest in European countries.

In this regard, this study aims to assess liquidity, profitability, and WCM strategy in Polish manufacturing companies operating in the pressure-casting industry, drawing on evidence from the pre-COVID-19, COVID-19, and Russia–Ukraine war periods. This study also addresses several critical gaps in the literature. First, existing research primarily focuses on the impact of single crises on WCM, while firms today face overlapping crises that require adaptive strategies (Baños-Caballero et al. 2016; Ramadan et al. 2024). Second, most studies examine broad industry samples, which may obscure sector-specific dynamics crucial to understanding WCM evolution in manufacturing contexts (García-Teruel and Martínez-Solano 2007; Altaf and Shah 2018). Third, there is limited understanding of how WCM strategies persist or change following crisis periods, particularly in European manufacturing contexts affected by both the pandemic and geopolitical tensions. Our study contributes to the literature by performing the following: (1) examining the status of liquidity, profitability, and WCM strategy across three distinct crisis periods using longitudinal data, (2) focusing on a homogeneous manufacturing sector (aluminum pressure foundries) to control for industry-specific effects, (3) analyzing firms in Poland—a country uniquely positioned between Western Europe and the Russia–Ukraine conflict zone, and (4) challenging traditional risk–return trade-off assumptions in liquidity, profitability, and WCM during crisis periods. Finally, this study aims to answer the following questions:

Q1. 

Following the COVID-19 pandemic and the Russia–Ukraine war, do Polish aluminum foundries shift from conservative to moderate or aggressive WCM strategies to achieve financial security?

Q2. 

Do larger Polish aluminum foundries exhibit different liquidity, profitability, and WCM strategy patterns than smaller foundries?

Q3. 

Does the Russia–Ukraine war period exhibit distinct liquidity, profitability, and WCM characteristics in Polish aluminum foundries compared with the COVID-19 and pre-COVID-19 periods?

The rest of this article is organized as follows. The next section reviews the literature. Section 3 outlines the research methodology. Section 4 presents the results, and Section 5 presents the discussion and conclusions.

2. Literature Review

WCM is a critical financial issue that emerged in the early 20th century and encompasses the strategic oversight of a company’s current assets and liabilities to ensure optimal operational efficiency and liquidity (Knauer and Wöhrmann 2013; Tarighi et al. 2024). The significance of WCM gained prominence during the industrial revolution, when businesses needed to manage short-term resources effectively to support growing manufacturing operations (Pass and Pike 1984). WCM became increasingly important as companies realized that efficient management of inventory, accounts receivable, and accounts payable could significantly affect their profitability and survival (Ukaegbu 2014; Salehi et al. 2019). In the modern business landscape, WCM serves several critical functions. It helps organizations maintain optimal cash flow, reduces the risk of operational disruptions, and enhances overall financial performance (Tauringana and Adjapong Afrifa 2013). Effective WCM is particularly crucial across business cycles, helping firms maintain stability during economic downturns and capitalize on growth opportunities during expansionary periods (Enqvist et al. 2014). The contemporary significance of WCM has been further amplified by globalization and increased market volatility, with studies showing that companies with efficient working capital practices consistently outperform their peers in terms of profitability and market value (Padachi 2006). By finding the right balance between maintaining sufficient cash on hand and investing in growth, they can keep costs down while remaining sufficiently flexible to capitalize on new opportunities (Zimon and Tarighi 2021; Tarighi et al. 2024).

From a theoretical perspective, WCM theory evolved from early trade-off models emphasizing the balance between liquidity and profitability (Smith 1980) to more sophisticated frameworks that incorporate behavioral finance and crisis management perspectives (Fazzari and Petersen 1993; Duchin et al. 2010). There are three WCM theories. Trade-off theory suggests that firms balance the costs and benefits of holding working capital, with optimal levels determined when the marginal cost of financing additional working capital equals the marginal benefit of increased liquidity (Baños-Caballero et al. 2016). During crises, this balance shifts as firms prioritize survival over optimization. Pecking Order Theory (Myers and Majluf 1984) posits that firms prefer internal financing to external financing, leading to increased cash holdings and conservative WCM during periods of uncertainty. Recent studies by Aktas et al. (2015) and Hill et al. (2010) demonstrate the relevance of this theory for understanding crisis-period WCM behavior. The Resource-Based View (RBV) framework posits that working capital is a strategic resource that enables firms to respond to market opportunities and threats (Kieschnick et al. 2013).

From a practical perspective, WCM has three distinct strategies: aggressive, conservative, and moderate, each aligned with different financial theories and organizational contexts (Nazir and Afza 2009; Demiraj et al. 2022; Ahmad et al. 2022; Zimon et al. 2024). Aggressive strategies involve keeping current assets low relative to fixed assets and relying heavily on short-term financing, aligning with the risk–return trade-off theory but exposing firms to higher liquidity risk (Weinraub and Visscher 1998; Akgün and Memiş Karataş 2021; Ahmad et al. 2022; Khan et al. 2024; Habib and Dalwai 2024; Tarighi et al. 2024). In contrast, conservative strategies prioritize higher levels of current assets and long-term financing, following the pecking order theory and appealing to firms operating in volatile industries or during economic uncertainty (Maswadeh 2015; Tarighi et al. 2024; Khan et al. 2024). The moderate approach strikes a balance between aggressive and conservative strategies, typically adopted by firms in stable industries during normal economic conditions (Enqvist et al. 2014; Tarighi et al. 2024). High-growth companies in competitive markets often prefer aggressive strategies to maximize returns, while established firms in regulated industries tend toward conservative approaches (Laghari and Chengang 2019). During economic downturns, firms typically shift from aggressive to more conservative strategies to maintain financial stability and buffer against market uncertainties (Simon et al. 2021; Zimon and Tarighi 2021; Yousaf and Bris 2021; Mazanec 2022b). In short, economic conditions are a fundamental determinant of organizational working capital policy formulation (Dash et al. 2023; Tarighi et al. 2024). The efficacy of WCM policies assumes greater significance during periods of economic contraction than during phases of economic expansion (Enqvist et al. 2014; Tarighi et al. 2024). This differential importance stems from the fact that economic downturns present elevated risks of operational inefficiencies, whereby suboptimal management practices may precipitate deterioration in financial liquidity positions (Salehi et al. 2019; Zimon and Tarighi 2021).

In addition, such circumstances can trigger a cascade of adverse financial outcomes, potentially culminating in severe liquidity constraints that imperil organizational sustainability (Enqvist et al. 2014; Tarighi et al. 2024; Ramadan et al. 2024; Habib et al. 2024). Managerial personality traits and overconfidence may also affect working capital decisions during crises (Zheng et al. 2022). Systemic risks arising from financial crises have significantly challenged WCM practices across both developed and emerging markets, with distinct impacts observed in different economic contexts (Akbar et al. 2021; Hamshari et al. 2022; Akgün and Memiş Karataş 2023). For instance, the 2008 financial crisis exposed fundamental vulnerabilities in WCM structures, particularly affecting cash conversion cycles and credit accessibility in EU markets (Akgün and Memiş Karataş 2021). In emerging markets, companies face more severe challenges due to weaker institutional frameworks and limited access to alternative financing sources during crises (Ahmad et al. 2022; Tarighi et al. 2024). While developed market firms typically have better access to alternative financing sources during crises (Casey and O’Toole 2014; Zogning 2023), emerging market companies often face more severe liquidity constraints and higher working capital costs during periods of crisis (Raykov 2017; Salehi et al. 2019; Akbar et al. 2021; Tarighi et al. 2024). Ahmad et al. (2022) conducted a comparative analysis of 577 firms across three Asian developing countries, finding that the impact of COVID-19 on WCM was more severe than that of the 2008 financial crisis. Their study revealed that developing markets experienced greater volatility in working capital cycles due to supply chain disruptions and reduced access to international markets. In the context of emerging markets, Struwig and Watson (2023) also examined South African firms, highlighting how developing countries faced unique challenges in managing working capital during the pandemic. Their research showed that import-dependent businesses particularly struggled with inventory management and cash conversion cycles. In summary, robust WCM is vital during economic downturns to mitigate liquidity risks and operational inefficiencies. Challenges are especially pronounced in emerging markets with limited financing and weaker institutional support. Accordingly, we now turn our focus to the Polish market, presenting an in-depth analysis of how COVID-19 and the Russia–Ukraine war have reshaped working capital practices and enhanced financial resilience.

Additionally, Zimon et al. (2021) add a crucial layer to our understanding of Polish firms during the COVID-19 crisis. The study highlights how SMEs in group purchasing organizations (GPOs) adapted their inventory strategies. Initially, firms stockpiled inventory to counter supply chain disruptions, prioritizing security over efficiency (Zimon et al. 2021). This shift underscores a key lesson: resilience comes not from rigid adherence to standards but from strategic adaptability. Polish SMEs must balance financial discipline with operational flexibility to navigate future crises effectively. In an earlier study, Zimon and Tarighi (2021) found that firms maintained a moderate–conservative strategy, with the pandemic having little impact on their overall approach. High liquidity and favorable cash conversion cycles enabled firms to extend receivables and improve sales performance, while those with a greater proportion of receivables and short-term investments achieved higher sales returns. Notably, SMEs in large cities experienced lower returns, suggesting that centralized support in GPOs can boost performance regardless of location. Moreover, a complementary study by Zimon and Dankiewicz (2020) specifically examined trade credit management strategies in Polish SMEs during the pandemic. Their research found that group purchasing organizations played a crucial role in helping Polish SMEs maintain efficient WCM during the crisis. The 2008 financial crisis literature established a foundational understanding of the crisis’s impacts on WCM. Duchin et al. (2010) demonstrated that financially constrained firms reduced investment more than unconstrained firms during the crisis. Akbar et al. (2021) found similar patterns in Islamic markets, while Akgün and Memiş Karataş (2021) documented WCM deterioration across EU-28 countries during 2008–2012. Kayani et al. (2025) find that the relationship between working capital and firm performance is stronger at non-SC firms.

Recent COVID-19 studies reveal both similarities and differences compared with financial crises. Hamshari et al. (2022) analyzed Jordanian firms and found more severe WCM disruption than in 2008, while Struwig and Watson (2023) documented unique challenges in South African markets. Importantly, Ahmad et al. (2022) conducted a comparative analysis of 577 Asian firms and found that COVID-19 impacts exceeded those of the 2008 financial crisis in terms of WCM volatility and cash cycle disruption. Liu et al. (2024) analyzed China’s agri-food sector (2006–2021) and compared the impact of WCM during two crises. The analysis shows that the WCM-performance relationship was more affected during COVID-19 than during the 2008 crisis. Studies in the literature show a positive impact of WCM on sales growth during periods of economic uncertainty (Tarighi et al. 2024; Rodeiro-Pazos et al. 2023; Zimon et al. 2024). Tarighi et al. (2024) analyzed Iranian firms during both COVID-19 and economic sanctions and found that firms adopted increasingly conservative WCM strategies. However, their study focused on a single country with unique sanctions-related constraints, limiting generalizability to other contexts. Recent studies by Ramadan et al. (2024), examining the UK furniture industry, and Sokołowska et al. (2024), analyzing Polish retail firms during the COVID-19 war in Ukraine, provide initial insights into overlapping crisis effects but lack a systematic analysis of the evolution of WCM across distinct crisis phases.

Anton and Afloarei Nucu (2020) examined a sample of 719 Polish listed firms from 2007 to 2016 and found that working capital positively affects profitability up to an optimal level, after which it negatively affects performance. A key takeaway is that efficient, balanced WCM is essential. Mielcarz et al. (2018) investigated the relationship between working capital investments and profitability across economic cycles in Polish firms. They found that profitable companies adopt conservative strategies during downturns by building cash reserves, while underperforming firms reduce working capital amid falling cash flows. This highlights the role of liquidity in navigating economic turbulence. The concurrent impact of the COVID-19 pandemic and the Russia–Ukraine war has created unprecedented challenges for WCM, particularly in manufacturing sectors. Zimon et al. (2024) have documented that Polish SMEs have had to significantly adapt their WCM strategies in response to these dual crises. Their research demonstrates that the implementation of quality management systems has become increasingly crucial for WCM efficiency during these challenging periods. Sokołowska et al. (2024) also provide essential insights into how Polish manufacturing companies have managed their financial stability during these overlapping crises. Their research reveals that companies have had to adapt to disruptions in both demand and supply chains, making WCM a critical factor for survival and success. The dual impact of COVID-19 and the Russia–Ukraine war has created unique challenges for financial performance and risk management. Recent research by Pilch (2024), examining Polish entities, shows that these crises have affected the value relevance of accounting information, suggesting that traditional approaches to WCM may need to be reconsidered in light of new market realities. An essential dimension of WCM during this period has been the impact of energy costs and supply chain disruptions. Walicka (2024) has documented how Polish companies have had to adapt their net WCM strategies in response to rising energy costs and market uncertainties stemming from the war in Ukraine. This research particularly highlights how companies have had to balance WCM efficiency with the need for increased liquidity buffers.

While existing studies have provided valuable insights into individual crisis periods, there remains a substantial research gap in understanding how WCM strategies evolve across multiple concurrent crises. This study aims to address this gap by examining the WCM strategies of Polish pressure-casting companies across three distinct periods: pre-COVID-19, during COVID-19, and post-COVID-19 (coinciding with the Russia–Ukraine war). Poland’s unique geographical and economic position provides an ideal setting for examining the effects of multiple crises. By systematically analyzing these periods, we seek to contribute to a more nuanced understanding of how manufacturing firms can develop robust, adaptive WCM strategies in an increasingly volatile global economic environment. Accordingly, this study assesses liquidity, profitability, and WCM strategy in Polish manufacturing companies in the pressure-casting industry, drawing on evidence from the pre-COVID-19, COVID-19, and Russia–Ukraine war periods. Based on the literature review, we propose a theoretical model in which WCM strategy choice depends on crisis type, firm characteristics, and environmental uncertainty. During the initial crisis phase, the pecking order theory predicts that strategies are conservative, dominated by liquidity preservation. As crises evolve or multiply, trade-off theory suggests firms should optimize WCM to restore profitability while maintaining adequate liquidity buffers. The following hypotheses are presented in the article:

H1: 

Following the COVID-19 pandemic and the Russia–Ukraine war, Polish aluminum foundries shifted from conservative to moderate-aggressive WCM strategies to achieve financial security.

H2: 

Larger Polish aluminum foundries exhibit different liquidity and WCM strategy patterns than smaller foundries.

H3: 

The Russia–Ukraine war period exhibits distinct liquidity and WCM characteristics in Polish aluminum foundries compared with the COVID-19 and pre-COVID-19 periods.

3. Methodology

3.1. Research Context

This study examines 19 Polish companies operating in aluminum-pressure foundries. The 19 companies represent approximately 90% of the industry’s total turnover, ensuring high representativeness despite the limited sample size. This sector-specific focus enables control for industry-specific factors while capturing meaningful variation in firm size and WCM strategies. The aluminum-pressure foundry industry provides an ideal setting for WCM analysis because of the following: (1) it has high working capital intensity, (2) it is sensitive to economic cycles, (3) it is exposed to both domestic and international market shocks, and (4) it uses standardized production processes that enable meaningful cross-firm comparisons. Companies in the analyses were classified as either large or small. The 2026 SME classification, based on accounting regulations, divides companies into micro, small, and medium-sized enterprises based on employment, balance sheet total, and net revenue. Micro enterprises employ up to 10 people (up to EUR 2 million in assets/EUR 2 million in revenue), small enterprises employ up to 50 people (up to EUR 10 million in assets/EUR 10 million in revenue), and medium-sized enterprises employ up to 250 people, with a turnover of up to EUR 50 million and a balance sheet total of up to EUR 43 million. Large enterprises employ 250 or more people. Turnover and balance sheet: Annual net turnover exceeds EUR 50 million and the annual balance sheet total exceeds EUR 43 million.

In addition, the period from 2018 to 2022 was divided into three groups: pre-COVID-19 (2018–2019), COVID-19 (2020–2021), and the Russian–Ukrainian War (2022). During the COVID-19 pandemic, many companies faced financial constraints and went bankrupt (Habib et al. 2024). Global economic indicators were also affected by events and economic fluctuations stemming from the Russian–Ukrainian War, which reduced companies’ financial security, particularly those operating in Europe (Dash et al. 2023). It is important to note that Poland borders Ukraine and Russia, which are both at war. Poland is occasionally attacked by Russian drones, airports are temporarily closed, and borders are attacked by refugees from Russia and Ukraine. The borders are temporarily closed. Supply chains are disrupted, which has a serious impact on the functioning of businesses. Therefore, wartime is considered a separate period.

For the current study, companies operating in aluminum-pressure foundries were selected because of their sensitivity to economic fluctuations and macroeconomic environmental changes. In addition, the activities of companies operating in aluminum-pressure foundries in conditions of high competitiveness and crises that have occurred in recent years are exposed to several risks that make it difficult to maintain high financial liquidity. Studying and analyzing the differences in companies’ liquidity, profitability, and WCM strategy across different sizes and periods of economic crises may help decision-makers improve their financial security and avoid any financial failures.

3.2. Research Design

Several statistical tests were used to achieve the research objectives. Initially, the normality of the data was tested using the Kolmogorov–Smirnov and Shapiro–Wilk tests, which indicated that the data were not normally distributed, so nonparametric tests were used. Therefore, parametric procedures were not considered appropriate for the present dataset. In line with the distributional characteristics of the data and the assumptions underlying the selected statistical techniques, all subsequent analyses were conducted using non-parametric tests. This approach was adopted to ensure methodological consistency and to obtain robust findings without relying on the normality assumption.

The Mann–Whitney U test was used to assess differences in financial liquidity management strategy factors between the small and large groups, including financial liquidity, quick ratio, receivables turnover, liabilities turnover, inventory turnover, operation cycle, return on sales, leverage, productivity index, inventory ratio, receivables ratio, return on equity, cash conversion cycle, share of fixed assets, net working capital cycle, and surplus of working capital. Mathematically, the Mann–Whitney U statistics are defined for each group as follows:

$$U_a=n_a{n}_b+\left(\right(n_a(n_a+1))/2)-R_a$$

(1)

$$U_b=n_a{n}_b+\left(\right(n_b(n_b+1))/2)-R_b$$

(2)

$$U=min\left(U_a,U_b\right)$$

(3)

where $n_a$ is the number of observations in the first group, $n_b$ is the number of observations in the second group, $R_a$ is the sum of the ranks assigned to the first group, $R_b$ is the sum of the ranks assigned to the second group, and U is the Mann–Whitney statistic.

In addition, the Kruskal–Wallis test was used to assess differences in financial liquidity management strategy factors across the following period groups: pre-COVID-19 (2018–2019), COVID-19 (2020–2021), and the Russo-Ukrainian War (2022). Mathematically, the Kruskal–Wallis test statistic is defined as follows for each group:

$$H=({\displaystyle \frac{12}{N\left(N+1\right)}}{\sum }_{j=1}^k({R_j}^2/n_j))-3\left(N+1\right)$$

(4)

where $N$ is the total observations in all groups, $k$ is the number of groups, $n_j$ is the sample size for the jth group, and $R_j$ is the sum of ranks of the jth group.

4. Results

The findings in

Table 1. Descriptive statistics summary.

Variables	Obs.	Mean	Std. Dev.	Min	Max
Financial liquidity	95	2.069	1.567	0.30	12.1
Quick ratio	95	1.323	1.341	0.20	11.5
Receivables turnover	95	61.16	39.21	9	257
Liabilities turnover	95	89.05	53.18	17	343
Inventory turnover	95	50.44	25.16	1	118
Operation cycle	95	111.6	49.77	22	341
Return on sales	95	0.073	0.164	−0.12	0.90
Leverage	95	1.357	1.481	0.042	6.10
productivity index	95	1.430	0.683	0.50	4.31
Inventory ratio	95	0.358	0.147	0.01	0.65
Receivables ratio	95	0.424	0.145	0.08	0.77
Return on equity	95	0.105	0.174	−0.33	0.82
Cash conversion cycle	95	22.55	52.14	−202	195
Share of fixed assets	95	46.00	21.24	3.00	91
Net working capital cycle	95	56.87	75.57	−186	375
Surplus of working capital	95	34.33	53.09	−127	347

indicate significant financial differences among firms. Although the average financial liquidity ratio of 2.07 (SD = 1.57) suggests that short-term liabilities are generally met, the range from 0.30 to 12.1 indicates that some firms exhibit weak or robust liquidity. The acid-test ratio (1.32; SD = 1.34) also shows a heterogeneous distribution. Differences are evident in working capital indicators. The receivables turnover is 61.16 days, the inventory turnover is 50.44 days, and the operating cycle (111.6 days) makes cash flow management critical. The cash conversion cycle (mean = 22.55 days; −202 to 195 days) indicates that some firms benefit from credit, whereas others face significant liquidity pressures. Variation is also observed in profitability and capital structure. While return on sales (ROS) is moderate, averaging 7.3%, some companies report negative values. Return on equity (ROE) (avg. = 10.5%) ranges from −33% to 82%. While leverage is generally moderate, extreme values indicate heightened financial risk. Consequently, companies exhibit substantial heterogeneity in liquidity, profitability, and cash-cycle efficiency. Therefore, it is essential to account for extreme values and departures from normality in subsequent analyses.

The analysis results in

Table 2. Descriptive statistics summary concerning size.

Variables	Size	Obs.	Mean	Std. Dev.	Min	Max
Financial liquidity	Small	45	2.053	1.768	0.30	12.1
	Large	50	2.082	1.380	0.60	6.30
Quick ratio	Small	45	1.467	1.701	0.20	11.5
	Large	50	1.194	0.902	0.32	4
Receivables turnover	Small	45	69.62	51.73	9	257
	Large	50	53.54	20.53	22	118
Liabilities turnover	Small	45	90.60	66.95	18	343
	Large	50	87.66	37.38	17	153
Inventory turnover	Small	45	42.38	25.91	1	109
	Large	50	57.70	22.32	21	118
Operation cycle	Small	45	112	66.38	22	341
	Large	50	111.2	28.19	62	169
Return on sales	Small	45	0.068	0.151	−0.12	0.80
	Large	50	0.078	0.177	−0.05	0.90
Leverage	Small	45	1.108	0.983	0.10	5
	Large	50	1.581	1.797	0.04	6.10
productivity index	Small	45	1.533	0.822	0.50	4.31
	Large	50	1.337	0.519	0.50	2.80
Inventory ratio	Small	45	0.285	0.152	0.01	0.57
	Large	50	0.423	0.108	0.23	0.65
Receivables ratio	Small	45	0.457	0.164	0.08	0.77
	Large	50	0.394	0.120	0.09	0.70
Return on equity	Small	45	0.118	0.219	−0.30	0.82
	Large	50	0.093	0.122	−0.33	0.40
Cash conversion cycle	Small	45	21.40	67.28	−202	195
	Large	50	23.58	33.88	−54	96
Share of fixed assets	Small	45	41.20	25.90	3	91
	Large	50	50.32	14.92	24	77
Net working capital cycle	Small	45	55.33	98.31	−186	375
	Large	50	58.26	47.66	−50	132
Surplus of working capital	Small	45	33.93	71.38	−127	347
	Large	50	34.68	28.85	3	112

reveal significant differences in financial structure and performance indicators by firm size. Financial liquidity is similar across groups (Small: 2.05; Large: 2.08), but the acid-test ratio is higher in smaller firms (1.47 vs. 1.19), suggesting that small businesses may tend to have more prudent cash management. Working capital turnover rates clearly reflect the size difference. Small firms have a higher average receivables turnover (69.6 days), while large firms stand out in inventory turnover (57.7 days). This suggests that small firms are more efficient in inventory management, while large firms are more active in collections. Profitability indicators are similar across sizes; return on sales is 6.8% in small firms and 7.8% in large firms. Return on equity is higher in small firms (11.8% vs. 9.3%), but due to higher standard deviations, the risk of volatility is higher. The leverage ratio is significantly higher in large firms (1.58) than in small firms (1.10), indicating that debt is a significant component of the financial structure of large firms. Asset structure and current asset composition also differ by size. Large firms allocate more resources to fixed assets (50.3% of assets), while small firms allocate 41.2%. The inventory ratio is higher in large firms (42.3% vs. 28.5%), while the receivables ratio is higher in small firms (45.7% vs. 39.4%). Consequently, small firms stand out with more prudent liquidity policies and higher returns on equity, while large firms are less effective in inventory management, more effective in receivables turnover, and more active in fixed asset investments, but have a riskier structure in terms of the debt ratio. These differences suggest that firm size should be considered as a control variable in future comparative regression or panel data analyses.

The data show significant differences in firms’ financial and operational indicators across the three periods examined (

Table 3. Descriptive statistics summary concerning period.

Variables	Period	Obs.	Mean	Std. Dev.	Min	Max
Financial liquidity	Pre COVID-19	38	2.195	2.069	0.50	12.1
	During COVID-19	38	2.158	1.263	0.40	6.30
	During R-U war	19	1.637	0.708	0.30	2.70
Quick ratio	Pre COVID-19	38	1.451	1.858	0.20	11.5
	During COVID-19	38	1.368	0.959	0.20	4
	During R-U war	19	0.979	0.494	0.20	1.80
Receivables turnover	Pre COVID-19	38	54.26	26.65	9	135
	During COVID-19	38	63.87	42.05	20	254
	During R-U war	19	69.53	52.40	29	257
Liabilities turnover	Pre COVID-19	38	78.26	34.83	18	134
	During COVID-19	38	90.63	57.30	17	316
	During R-U war	19	107.5	70.14	34	343
Inventory turnover	Pre COVID-19	38	42.95	21.12	8	109
	During COVID-19	38	55.05	27.61	1	118
	During R-U war	19	56.21	24.97	6	117
Operation cycle	Pre COVID-19	38	97.21	36.24	22	186
	During COVID-19	38	118.9	51.55	34	341
	During R-U war	19	125.7	63.34	35	341
Return on sales	Pre COVID-19	38	0.107	0.202	−0.10	0.90
	During COVID-19	38	0.064	0.151	−0.06	0.90
	During R-U war	19	0.024	0.068	−0.12	0.18
Leverage	Pre COVID-19	38	1.243	1.399	0.042	5
	During COVID-19	38	1.329	1.503	0.10	6.10
	During R-U war	19	1.639	1.635	0.30	6
productivity index	Pre COVID-19	38	1.507	0.716	0.60	4.31
	During COVID-19	38	1.357	0.692	0.50	3.14
	During R-U war	19	1.421	0.611	0.60	2.80
Inventory ratio	Pre COVID-19	38	0.350	0.141	0.06	0.57
	During COVID-19	38	0.353	0.154	0.01	0.629
	During R-U war	19	0.381	0.151	0.158	0.651
Receivables ratio	Pre COVID-19	38	0.433	0.145	0.08	0.75
	During COVID-19	38	0.405	0.148	0.095	0.757
	During R-U war	19	0.444	0.144	0.25	0.766
Return on equity	Pre COVID-19	38	0.123	0.196	−0.30	0.82
	During COVID-19	38	0.090	0.163	−0.33	0.64
	During R-U war	19	0.099	0.153	−0.11	0.44
Cash conversion cycle	Pre COVID-19	38	18.95	35.76	−65	112
	During COVID-19	38	28.29	57.28	−189	195
	During R-U war	19	18.26	68.61	−202	167
Share of fixed assets	Pre COVID-19	38	48.92	20.87	3	89
	During COVID-19	38	44.87	21.15	12	87
	During R-U war	19	42.42	22.49	5	91
Net working capital cycle	Pre COVID-19	38	53.87	72.98	−74	375
	During COVID-19	38	66.71	77.12	−166	232
	During R-U war	19	43.21	78.93	−186	190
Surplus of working capital	Pre COVID-19	38	34.92	59.29	−49	347
	During COVID-19	38	38.42	54.08	−127	217
	During R-U war	19	24.95	36.82	−91	85

). Liquidity ratios were high before COVID-19 (2.20) and during the COVID-19 period (2.16), but declined to 1.64 during the Russia–Ukraine war. Similarly, the acid-test ratio fell to 0.98 during the war. Working capital indicators were more volatile during crisis periods. While the receivables turnover ratio increased to 69.5 days during the COVID-19 and war periods, the inventory turnover ratio increased significantly during the pandemic (55.1–56.2 days), suggesting that inventory management was emphasized, particularly during periods of significant uncertainty. The operating cycle lengthened during the pandemic (118.9 days) and the war (125.7 days), suggesting that the cash conversion process became more difficult. Profitability indicators are sensitive to these shocks. Return on sales (ROE), which was 10.7% before COVID-19, fell to 6.4% during the pandemic and to 2.4% during the war. Similarly, return on equity (ROE) declined from 12.3% before COVID-19 to 9–10% during the crisis. Conversely, the leverage ratio reached its highest level (1.64) during the war, indicating that companies increasingly resorted to borrowing. Consequently, periods of crisis have a particularly negative impact on profitability, liquidity, and the operating cycle, while companies appear to seek balance in inventory and receivables management. These differences necessitate controlling for seasonal effects in subsequent regression and panel data analyses.

Table 4. Size differences analysis.

Null Hypothesis	Size	Mean Rank	Z-Value	Sig.	Decision
The distribution of financial liquidity is the same across categories of size.	Small	48.34	0.116	0.908	Retain the null hypothesis.
	Large	47.69
The distribution of quick ratio is the same across categories of size.	Small	50.77	0.929	0.353	Retain the null hypothesis.
	Large	45.51
The distribution of receivables turnover is the same across categories of size.	Small	51.81	1.279	0.201	Retain the null hypothesis.
	Large	44.57
The distribution of liabilities turnover is the same across categories of size.	Small	45.13	0.962	0.336	Retain the null hypothesis.
	Large	50.58
The distribution of inventory turnover is the same across categories of size.	Small	38.63	3.142	<0.01	Reject the null hypothesis.
	Large	56.43
The distribution of operation cycle is the same across categories of size.	Small	44.50	1.174	0.240	Retain the null hypothesis.
	Large	51.15
The distribution of return on sales is the same across categories of size.	Small	47.12	0.295	0.768	Retain the null hypothesis.
	Large	48.79
The distribution of leverage is the same across categories of size.	Small	48.11	0.037	0.970	Retain the null hypothesis.
	Large	47.90
The distribution of productivity index is the same across categories of size.	Small	50.30	0.773	0.440	Retain the null hypothesis.
	Large	45.93
The distribution of inventory ratio is the same across categories of size.	Small	35.09	4.331	<0.01	Reject the null hypothesis.
	Large	59.62
The distribution of receivables ratio is the same across categories of size.	Small	53.93	1.990	<0.05	Reject the null hypothesis.
	Large	42.66
The distribution of return on equity is the same across categories of size.	Small	47.38	0.209	0.835	Retain the null hypothesis.
	Large	48.56
The distribution of cash conversion cycle is the same across categories of size.	Small	49.43	0.481	0.631	Retain the null hypothesis.
	Large	46.71
The distribution of share of fixed assets is the same across categories of size.	Small	40.49	2.520	<0.05	Reject the null hypothesis.
	Large	54.76
The distribution of net working capital cycle is the same across categories of size.	Small	47.63	0.123	0.902	Retain the null hypothesis.
	Large	48.33
The distribution of surplus of working capital is the same across categories of size.	Small	45.72	0.764	0.445	Retain the null hypothesis.
	Large	50.05

reveals significant differences between small and large firms in several critical financial indicators. Specifically, inventory turnover (Z = 3.142, p < 0.01), inventory ratio (Z = 4.331, p < 0.01), and the share of receivables in current assets (Z = 1.990, p < 0.05) vary by firm size. According to these results, large firms have significantly higher values for inventory turnover (mean rank: 56.43) and inventory ratio (mean rank: 59.62). This suggests that large firms allocate more capital to inventory management and turn inventory over more quickly. This may be explained by more efficient supply chain management, driven by economies of scale, or by the ability to maintain high inventory levels to meet demand fluctuations. Conversely, the receivables ratio was higher in small firms (mean rank: 53.93). This may be attributed to smaller firms’ customer-focused sales policies, their tendency to prioritize longer-term collections, and their assumption of greater cash-flow risk. While this makes them more flexible, it also increases their liquidity risks. In contrast, no statistically significant differences by firm size were observed in key indicators, including financial liquidity, acid-test ratio, debt turnover, profitability (ROS and ROE), leverage, cash conversion cycle, and efficiency index (all p > 0.05). This result suggests that size differences are concentrated primarily in working capital components rather than in firms’ overall financial strength. In particular, the most striking differences by size are observed in inventory and receivables management strategies, suggesting that firm size should be included as a control variable in future panel-data or regression analyses. Furthermore, these results indicate that larger firms have an advantage in managing their supply chains during crises, while smaller firms are more dependent on customer relationships and collection flexibility.

According to

Table 5. Period differences analysis.

Null Hypothesis	Size	Mean Rank	Z-Value	Sig.	Decision
The distribution of financial liquidity is the same across categories of period.	Pre COVID-19	46.37	1.866	0.393	Retain the null hypothesis.
	During COVID-19	52.39
	During R-U war	42.47
The distribution of quick ratio is the same across categories of period.	Pre COVID-19	48.05	1.521	0.467	Retain the null hypothesis.
	During COVID-19	51.14
	During R-U war	41.61
The distribution of receivables turnover is the same across categories of period.	Pre COVID-19	43.28	1.861	0.394	Retain the null hypothesis.
	During COVID-19	51.18
	During R-U war	51.08
The distribution of liabilities turnover is the same across categories of period.	Pre COVID-19	43.71	2.445	0.294	Retain the null hypothesis.
	During COVID-19	48.39
	During R-U war	55.79
The distribution of inventory turnover is the same across categories of period.	Pre COVID-19	38.75	7.262	<0.05	Reject the null hypothesis.
	During COVID-19	53.24
	During R-U war	56.03
The distribution of operation cycle is the same across categories of period.	Pre COVID-19	39.26	6.365	<0.05	Reject the null hypothesis.
	During COVID-19	53.68
	During R-U war	54.11
The distribution of return on sales is the same across categories of period.	Pre COVID-19	54.08	3.775	0.151	Retain the null hypothesis.
	During COVID-19	46.09
	During R-U war	39.66
The distribution of leverage is the same across categories of period.	Pre COVID-19	43.96	3.268	0.195	Retain the null hypothesis.
	During COVID-19	47.13
	During R-U war	57.82
The distribution of productivity index is the same across categories of period.	Pre COVID-19	51.57	1.386	0.500	Retain the null hypothesis.
	During COVID-19	44.16
	During R-U war	48.55
The distribution of inventory ratio is the same across categories of period.	Pre COVID-19	47.46	0.275	0.871	Retain the null hypothesis.
	During COVID-19	47.07
	During R-U war	50.95
The distribution of receivables ratio is the same across categories of period.	Pre COVID-19	50.28	0.924	0.630	Retain the null hypothesis.
	During COVID-19	44.67
	During R-U war	50.11
The distribution of return on equity is the same across categories of period.	Pre COVID-19	52.12	1.520	0.468	Retain the null hypothesis.
	During COVID-19	44.42
	During R-U war	46.92
The distribution of cash conversion cycle is the same across categories of period.	Pre COVID-19	44.32	1.553	0.460	Retain the null hypothesis.
	During COVID-19	52.13
	During R-U war	47.11
The distribution of share of fixed assets is the same across categories of period.	Pre COVID-19	52.54	1.880	0.391	Retain the null hypothesis.
	During COVID-19	46.01
	During R-U war	42.89
The distribution of net working capital cycle is the same across categories of period.	Pre COVID-19	42.93	4.165	0.125	Retain the null hypothesis.
	During COVID-19	55.05
	During R-U war	44.03
The distribution of surplus of working capital is the same across categories of period.	Pre COVID-19	45.55	1.061	0.588	Retain the null hypothesis.
	During COVID-19	51.57
	During R-U war	45.76

, no statistically significant differences were found (p > 0.05) in most financial indicators across the three periods (pre-COVID-19, during the COVID-19 period, and during the Russia–Ukraine war period). However, two key variables, inventory turnover (χ² ≈ = 7.262, p < 0.05) and operating cycle (χ² ≈ = 6.365, p < 0.05), differed significantly across periods. The results show that firms had higher mean rankings for inventory management during both the pandemic and war periods (COVID-19: 53.24; war: 56.03; pre-COVID-19: 38.75) and longer operating cycles (COVID-19: 53.68; war: 54.11; pre-COVID-19: 39.26). This suggests that businesses increase inventory levels during crisis periods due to supply chain uncertainty, but their cash conversion cycles lengthen. Conversely, no significant differences were found between periods in indicators such as liquidity, profitability (ROS, ROE), leverage, cash conversion cycle, fixed asset ratio, and working capital cycle. However, considering the rank means, profitability indicators fell to relatively low levels, particularly during the war period (ROS: 39.66, ROE: 46.92), while the leverage ratio reached its highest level (57.82) during the war period. This suggests an increased tendency to borrow during the war and a suppression of profitability; however, the differences are not statistically significant. In general, crisis periods affect the financial structure most significantly through inventory and operational cycle management, whereas differences in liquidity and profitability do not reach statistical significance. Therefore, in subsequent panel-data or regression analyses, it will be critical to examine period effects, particularly with respect to inventory management and cash-cycle dynamics.

5. Discussion and Conclusions

In Poland, following the COVID-19 crisis, companies are seeking to return to pre-COVID-19 strategies, with an emphasis on strengthening financial security. This results in higher inventory turnover, and the share of inventories in current assets also increases. The operating cycle is long, and profitability, liquidity, and working capital levels decrease. However, this has not been statistically confirmed. Some changes in liquidity and profitability levels are visible, but companies continue to pursue conservative strategies, as evidenced by their inventory and receivables management strategies—statistically significant differences were observed. The first hypothesis was not verified positively. This study assessed liquidity, profitability, and WCM strategy in Polish manufacturing companies in the pressure-casting industry, drawing on evidence from the pre-COVID-19, COVID-19, and Russia–Ukraine war periods. The period after the COVID-19 crisis coincides with the Russian–Ukrainian war. These countries are Poland’s neighbors. This period of uncertainty for Poland has led to supply chain disruptions and reduced investments. For manufacturing companies, this is dangerous because they have limited development opportunities. The results indicated the adoption of a conservative WCM strategy in Polish aluminum foundries during the pre-COVID-19, COVID-19, and Russia–Ukraine war periods, characterized by increased inventory levels, extended operating cycles, and reduced profitability. Polish aluminum foundries are adapting their WCM strategies toward an optimal, conservative approach that incorporates both safe and risky elements to ensure continued operations and profits. In addition, larger Polish aluminum foundries exhibit distinct liquidity patterns relative to smaller foundries, particularly in indicators of inventory, receivables, and fixed assets. Large companies have typically adopted a conservative strategy, increasing inventories and reducing receivables. In crisis situations, such actions protect companies against working capital shortages and should generate very high profits. The second hypothesis can be positively verified by looking at the structure of current assets that shape the level of liquidity (high inventories and low receivables in large companies).

In addition, the Russia–Ukraine war period exhibits distinct liquidity characteristics in Polish aluminum foundries compared with the COVID-19 and pre-COVID-19 periods, particularly in inventory turnover and operating cycle (but there are no statistically significant differences). Hypothesis three was not verified positively. However, when analyzing individual results, valuable differences can be seen that indicate conservative management. Careful management of working capital costs money. There is a decrease in financial liquidity, net working capital, and sales profitability.

The analysis conducted results in the following conclusions:

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Reduction in Fixed Asset Investments: One of the primary reasons for the observed decline in fixed asset investments is heightened economic uncertainty caused by the geopolitical crisis (Russian–Ukrainian war) and the lingering effects of the COVID-19 pandemic. Manufacturing companies have adopted a cautious approach, prioritizing liquidity over long-term investments to mitigate risks in an unpredictable environment. The Pecking Order Theory, as proposed by Myers and Majluf (1984), explains that in times of uncertainty, firms prefer to use internal funds rather than external financing. This cautious behavior often results in reduced investments in fixed assets, as companies prioritize maintaining cash reserves.

-

Decrease in Financial Liquidity and Net Working Capital: The increase in the liabilities turnover ratio (in days) has a significant impact on this indicator. Companies are trying to adopt less aggressive liability management policies following the COVID pandemic. More favorable lending offers are driving the increase in short-term liabilities, which reduces liquidity despite the increase in inventory levels in the current asset structure. The decline in financial liquidity and net working capital can be attributed to increased operational costs. These factors have intensified pressure on companies’ WCM. The Risk–Return Tradeoff Theory suggests that in uncertain times, firms adopt conservative policies to minimize risk (Zimon and Dankiewicz 2020).

-

Drop in Sales Profitability (but there are no statistically significant differences).

-

Increase in Inventory Turnover Cycle and Operating Cycle: The prolonged inventory turnover cycle and operating cycle can be explained by stagnant market demand and supply chain disruptions. To manage these uncertainties, firms have increased their inventory levels, leading to longer operating cycles. The WCM Model highlights that maintaining excessive inventory can reduce operational efficiency by increasing holding costs and delaying cash inflows. Furthermore, the Inventory Management Theory suggests that during uncertain times, firms stockpile inventories as a strategic response to anticipated supply chain disruptions. In the case of inventory management and operating cycle, statistical differences are visible in the analysis of large and small companies.

-

Large companies take advantage of economies of scale and achieve higher profitability ratios. The structure of current assets differs from that of small companies. Large companies benefit from economies of scale and make larger purchases, securing better prices. Higher purchase levels lead to higher inventory levels. Large companies have lower receivable levels than small companies and manage their receivables more effectively.

In addition, across selected diecasting foundries, decreases in financial liquidity, net working capital, and sales profitability are evident. Following the COVID-19 crisis (the period of the Russian–Ukrainian war), increases in inventory and operating cycles are evident. The share of inventories in current assets is rising. During periods of uncertainty, companies are reluctant to invest in fixed assets, leading to declines in fixed assets. A lack of long-term investment in fixed assets can be a mistake. For manufacturing companies to be competitive, it is necessary to take risks and invest in fixed assets. In our opinion, companies should require continuous investments in fixed assets. Post-COVID-19, there seems to be a return to the earlier pre-COVID-19 strategy, with a more cautious approach to WCM—inventory, receivables, and liabilities turnover is extended. These types of activities result in a decrease in profitability. It can be concluded that maintaining inventory incurs costs. After a crisis, increased competition causes companies to adjust prices to market levels in everyday situations, not in times of crisis, and this leads many companies to reduce their sales margins. Our findings partially align with international studies but reveal important differences. While Ahmad et al. (2022) found that Asian firms reduced working capital levels during COVID-19, Polish foundries maintained or increased working capital, suggesting different strategic priorities or market conditions.

The results of this study offer several novel contributions to the existing literature on financial security indicators by examining unexplored factors related to size and period. The results of this study have several practical implications for business leaders seeking to adopt optimal liquidity, profitability, and WCM strategy. Additionally, our findings contribute to WCM theory in several important ways. First, the absence of the traditional inverse relationship between liquidity and profitability during crisis periods challenges conventional trade-off theory assumptions. This suggests that during overlapping crises, firms may face simultaneous pressure on both liquidity and profitability, necessitating modifications to existing theoretical frameworks. The persistence of conservative WCM strategies post-COVID-19 supports predictions from pecking-order theory but contradicts trade-off theory’s expectations of post-crisis optimization. This suggests that behavioral factors and uncertainty aversion may dominate rational optimization during extended crisis periods, consistent with recent contributions in behavioral finance to WCM theory (Afrifa 2016; Baños-Caballero et al. 2016).

This research contributes to WCM theory by performing the following: (1) documenting the breakdown of traditional trade-off relationships during extended crises, (2) demonstrating the persistence of pecking-order behavior beyond immediate crisis periods, and (3) revealing the importance of crisis type in determining optimal WCM strategies. In addition, this research has practical implications for manufacturing firms:

-

Reconsider inventory strategy, as the observed increase in inventory levels suggests firms prioritized supply security over efficiency. While costly, this strategy may provide necessary operational flexibility during supply chain disruptions.

-

Adopt integrated crisis management, as traditional WCM strategies developed for single-crisis periods may be inadequate for overlapping crises. Firms should develop adaptive frameworks capable of responding to multiple simultaneous shocks.

-

Consider size-specific strategies, as larger firms’ superior inventory management efficiency suggests economies of scale in WCM. Smaller firms might benefit from collaborative approaches or technology investments to improve WCM efficiency.

-

Develop flexible strategies, as manufacturing firms should develop flexible WCM frameworks that can adapt to different crisis types while maintaining operational continuity.

Future research should examine broader industrial samples to test generalizability, employ longitudinal panel data methods to capture dynamic relationships, investigate behavioral factors influencing WCM decisions during crises, and develop theoretical frameworks specifically designed for multiple crisis contexts.