Working Capital Management and Cash Conversion Cycle – Thinking Outside the Box with New Insights

1. Introduction

Working capital is a critical component of financial management, reflecting a company's efficiency and short-term financial health. It involves managing the balance between a firm's currents assets, primarily cash, inventory, and receivables, against its current liabilities such as payables and accruals (Khan & Jain, 2010). The significance of effective working capital management is underscored by its correlation with organizational performance and profitability (Deloof, 2003). By understanding the working capital life cycle (the period from cash outflow to cash inflows), organizations can improve their liquidity and increase their operational efficiency. This view aligns with the positions of Nobanee (2009) and Shin and Soenen 1998, that a good knowledge and management of a firm’s cash conversion cycle can greatly improve working capital management and positively impact profitability.   

The current method of estimating the cash conversion cycle using articulated period of completion though makes analytical sense, but its efficiency and effectiveness in guiding proper working capital management decisions appears suspicious and could result in exercises in futility.  This study aims to scrutinize the concept of the working capital life cycle, its method of estimation, and its effects on working capital management with its overall implications for organizational performance.

1.1 Literature Review

1.1.1 Working Capital and Working Capital Management (WCM)

Various studies on the effect of working capital and its management on the performance of firms were almost of the same opinion. Boisjolya, et al. (2020) examine how different financing decisions affect the financial performance and stability of hotels. The research provided empirical evidence on the positive relationship between financing decisions, working capital management, and overall firm performance in the hotel sector.  The study conducted by Baker, et al. (2017) also provides valuable insights into the WCM practices of Indian firms, helping managers understand common practices and areas for improvement. It suggested that Indian firms tend to use centralized cash management and rely heavily on material requirement planning (MRP) and enterprise resource planning (ERP) for proper inventory management but mainly consider the cash conversion cycle and net working capital for WCM monitoring. Abbadi and Abbadi (2013) identified the key determinants that influence working capital requirements in Palestinian industrial firms to include the cash conversion cycle (CCC), operating cash flow, leverage and firm size. The study revealed that the management of working capital impacts significantly on the firms’ return on assets (ROA).

Akinlo (2012) examined the determinants of working capital requirements in selected quoted companies in Nigeria. The research identified several factors that significantly influence working capital requirements, such as sales growth, profitability, liquidity, and leverage. The study highlighted how these determinants affect the Cash Conversion Cycle, providing insights into managing liquidity more effectively. Nobanee (2009) introduces the concept of an optimal cash conversion cycle. The study suggests that simply shortening the cash conversion cycle (CCC) isn't always beneficial. Instead, identifying optimal levels of inventory, receivables, and payables where total holding and opportunity costs are minimized provides a more accurate measure of working capital management. It emphasizes a balanced approach to managing the CCC, considering the potential negative impacts of overly aggressive reductions in inventory, receivables, or payables. The optimal CCC offers a more comprehensive measure of working capital efficiency by considering both the timing and amount of funds committed at each stage of the cycle.

González, et al. (2021) explore the relationship between Working Capital Management, Cash Conversion Cycle (CCC), and firm performance. The study provides empirical evidence on how effective working capital management impacts firm performance. It reveals that while a shorter inventory conversion period (ICP) and a shorter debtor collection period (DCP) positively affect performance, a shorter cash conversion cycle (CCC) and a shorter creditor payment period (CPP) negatively impact performance.

Alvarez and Vazquez (2021) investigated the relationship between Working Capital Management and Profitability in an emergent economy. The study provides empirical evidence on how effective working capital management impacts firm profitability in an emergent economy. It identifies key determinants such as Days Sales Outstanding (DSO), Days Sales Inventory (DSI), and Days Payable Outstanding (DPO) that significantly influence profitability. The findings suggest that longer DSO and DSI negatively impact profitability, while longer DPO has a positive effect.

1.1.2 The Components of Working Capital

A fundamental aspect of working capital is its composition, which can be delineated into several core components which include cash, inventory, accounts receivable, and accounts payable. Each component plays a vital role in the working capital life cycle.

1. Cash: Often regarded as the lifeblood of any business, cash is necessary for meeting short-term obligations (Brigham & Ehrhardt, 2013). Ensuring optimal cash levels assists organizations in avoiding financial distress.

2. Inventory: Inventory management involves maintaining sufficient stock to meet customer demand while minimizing holding costs. An excess inventory can tie up critical cash resources, affecting the overall cash cycle negatively (Gaur & Goyal, 2009).

3. Accounts Receivable: This refers to the money owed to a firm by its customers. Efficient accounts receivable management involves expediting the collection process, thus reducing the cash conversion period (Lind, 2012).

4. Accounts Payable: Accounts payable management entails negotiating optimal payment terms with suppliers and managing cash outflows judiciously. Efficiently leveraging accounts payable can extend the cash cycle, allowing firms to utilize cash effectively (Baños-Caballero et al., 2014).

1.1.3 The Working Capital Life Cycle 

The working capital life cycle, commonly referred to as the cash conversion cycle, is a crucial metric that quantifies the time taken for a company to convert its investments in inventory and receivables back into cash. It provides insights into how efficiently a company manages its operational cycle, which encompasses the acquisition of raw materials, production of goods, sales, and collection of cash from customers. The cash cycle can also be defined as the time taken for a firm to convert its investments in inventory and other resources into cash flows from sales (Gitman, 2009). It comprises three main phases:

1. Inventory Period: The duration it takes for a company to sell its inventory. This phase can be optimized through just-in-time (JIT) inventory systems, reducing holding costs and increasing turnover rates. However, the ability of a firm to apply this strategy effectively depends on how well the organization can coordinate all aspects of its activities to achieve a homeostatic balance.

2. Accounts Receivable Period: This is the time taken to collect cash from customers. Effective credit policies and collection strategies can shorten this period, thereby improving cash flow.

3. Accounts Payable Period: This period signifies how long a company takes to pay its creditors. A longer accounts payable period can enhance working capital, provided it does not damage supplier relationships.

In all, the cumulative effect of optimizing each of these phases leads to a shorter overall cash cycle, which significantly enhances working capital efficiency (Richards & Laughlin, 1980).

1.1.4 Formula for the Cash Conversion Cycle (CCC)

The cash conversion cycle can be calculated using the following formula:

CCC = DIO + DSO – DPO                                                                 (1)

Where:

DIO = Days Inventory Outstanding - which measures the average number of days it takes to sell all the inventory. It is calculated as:

DIO = Average Inventory / Cost of Goods Sold * 365                                         (2)

DSO = Days Sales Outstanding - which indicates the average collection period for accounts receivable. It can be computed using the formula:

DSO = Average Accounts Receivable / Net Credit Sales * 365                             (3)

DPO = Days Payables Outstanding - which reflects the average number of days a company takes to pay its suppliers. It is formulated as:

DPO = Average Accounts Payable / Cost of Goods Sold * 365                             (4)

1.1.5 Justifications for the study

Analyzing and understanding the components of the cash conversion cycle (CCC) is important to the management of a firm especially as it is the major guiding focus in working capital management. A spurious metric has the dire consequence implications of misleading the judgment of management in the efficient management of scarce organizational resources. Particularly, a good knowledge of a firm’s cash conversion cycle will shed light on the following:

- A longer DIO is an indication that inventory remains unsold for a more extended period thereby potentially tying up capital.

- Higher DSO is a suggestion that a firm takes longer periods to collect cash from customers, and this can adversely affect liquidity.

- A longer DPO can improve immediate cash flow, as it implies extended credit terms with suppliers but there could be penalties for delayed payments to creditors which might negate gains from improved cash flow.

The goal of effective working capital management is to minimize the CCC, reflecting a shorter cycle, happier creditors, and improved liquidity (Gitman, 2009; Richards & Laughlin, 1980). However, another important justification for this study is that the period-based metric of CCC enunciated above never considers the operational efficiency of the firm which is critical to every operational decision it must take.

As seen from various studies, there is a demonstrable link between effective working capital management and organizational performance. Efficient management of the working capital life cycle can lead to - Enhanced Liquidity which results from shortening the cash cycle thereby increasing sufficient liquidity to meet short-term obligations (Deloof, 2003). Increased Profitability which results from lower costs through effective management of inventory and receivables (Shin & Soenen, 1998). Improved Operational Efficiency from streamlined processes in managing inventory and collections which enable firms to respond to market changes promptly (Brealey et al., 2011). It can also lead to Sustainable Competitive Advantage for entities that master the working capital life cycle as they can navigate market fluctuations better than their counterparts (Filbeck & Krueger, 2005).

1.1.6 Evaluating the Effectiveness of the Cash Conversion Cycle in Working Capital Management

As posited earlier, the cash conversion cycle (CCC) is a critical metric in evaluating a firm’s efficiency in managing working capital. By assessing the time it takes for a company to convert its investments into cash flows, stakeholders can glean insights into operational efficiency and financial health. The effectiveness of the CCC hinges upon the delicate balance between these components. Ideally, a firm aims for a short CCC relative to its industry peers, indicating an efficient conversion of investments into cash (Filbeck & Krueger, 2005).

However, recognizing that every firm has its own distinctive intrinsic value, it might not be totally a good idea to always benchmark against peers, rather this study posits that industrial or peer benchmarks should be employed as an addition to the use of the firm’s own capabilities in measuring the cash conversion cycle. This is the void which this paper shall attempt to fill by introducing the operational breakeven point CCC measurement metric.

1.1.7 Determining the Effectiveness of the Cash Conversion Cycle 

To assess the effectiveness of the CCC in working capital management, several methodologies can be employed, and these include - benchmarking against industry norms by comparing the firm’s CCC with industry averages to determine relative performance. A significantly higher CCC indicates inefficiencies that necessitate remedial action (Shin & Soenen, 1998). Trend Analysis reveals positive or negative trends in working capital performance which may indicate effective or deteriorating management strategies, which could signal potential liquidity issues (Gaur & Goyal, 2009).

Conducting regression analyses can help establish correlations between the CCC and overall financial performance indicators, such as profitability and return on assets. This quantitative approach can offer insights into how the CCC impacts financial outcomes (Deloof, 2003). Also, examining the relationship between CCC and cash flow metrics can provide a qualitative measure of effectiveness. An efficient CCC typically correlates with positive cash flow from operations, enhancing a firm’s ability to invest in growth opportunities (Brealey et al., 2011). Companies can, in addition, perform scenario analyses by evaluating the effects of potential changes in inventory turnover, receivables collection, and payables management on their CCC. This helps in planning and identifying areas for improvement (Baker et al., 2013).

1.1.8 Thinking Outside the Box – The Operational Breakeven Point approach to measuring Cash Conversion Cycle

A recent development in the metric for the determination of working capital adequacy and its associated cash conversion cycle using the Operational Break-Even Point (OBEP) theory and its inclusive relative solvency metric tends to suggest that the working capital life cycle might significantly differ from the metric produced or suggested using the traditional inventory, payables and receivables formula.

The operational breakeven metric hinges its idea on the working capital required (WCR) when a firm has reached its operational break-even point. The operational breakeven point is defined in Enyi (2021) as the point or stage of activity where cumulative contribution margin on recovered production outputs equal the total cumulative production costs and losses of the learning periods (Enyi, 2021). The formula for determining OBEP, WCR, and the relative solvency metric (RSR) are outlined as follows:

OBEP = t/(2p)                                                                                   (5)

WCR = tc/104p                                                                                 (6)

RSR = 104p(a – l) / tc                                                                         (7)

Where,

t = Overall Turnover or Total Sales

p = Profit Before Tax

c = t-p = Total Operating Cost

a = Current Asset

l = Current Liabilities

Using these metrics, it is easy to deduce that the cash conversion cycle can rightly be measured by multiplying the operational breakeven point with the number of days in a production stock-up period which this study assumes to be an average of 30 days. Therefore, a more realistic cash conversion cycle formula is given as:

         CCC = OBEP x 30 days                                                             (8)

Which is same as:

         CCC = 30t/(2p)                                                                        (9)

For instance, if the normal stock-up period for a firm is 30 days and assuming its OBEP is 2.214, the OBEP-based cash conversion cycle will be 2.214 x 30 = 66.42 days.

The choice of OBEP-based cash conversion cycle is predicated on the notion that OBEP relates more to the operational efficacy of a firm which hinges on its intrinsic capability and ability to utilize its resources than the mere conjecture behind the expectations of timely conversion of inventories, receivables, and delay in remitting payables. This thinking and approach is in line with the work of Nobanee (2009) which dwelt more on an optimal cash conversion cycle and a balanced approach to the use of the CCC.

2. Methodology

To study the effectiveness of the DIO, DSO, DPO usage in the CCC computation and introduce the use of the OBEP-based metric, the study employed an ex-post facto research design to obtain operational data from the published financial statements of 83 firms listed in India and Nigeria within a period of five financial seasons.  Multi-factor regression analysis was used to analyze and compare the period-based CCC (CCC-X) and the OBEP-based CCC (CCC-B) with the profit after tax (PAT) and the return on assets (ROA) of the firms using the ValuStats (VSP 2.0) software.

3. Results and Findings

Findings from the review of extant literatures indicate that all the studies examined affirmed the fact that effective working capital management impacts positively on the financial performance of companies with liquidity, profitability, and leverage playing very important roles. The findings are show that the cash conversion cycle (CCC) is a veritable tool for an effective working capital management. Where the CCC is wrongly estimated, the consequence will impact negatively on the financial performance of the entity with the attendant loss of goodwill in the hands of suppliers and fund providers. However, the work of Nobanee (2009) seems to have provided a useful ameliorative solution with the suggested approach to the optimal CCC measurement system. This latter suggestion was in line with the solution introduced in this study using the operational breakeven point (OBEP) approach to computing CCC. OBEP recognizes the need for optimality in working capital management, that is the reason for the introduction of the working capital required (WCR) at a firm’s operational breakeven point (Enyi, 2021). 

Tables 1-4 show the results of the multi-factor regression analysis using ValuStats (version 2.0).

Table 1 shows the OLS Regression results on profit after tax (PAT) with Working Capital (WKC) as a Predictor. The F-statistics (F_(79,3) = 6.977, p =  .000) show that the distribution is a perfect fit. Notwithstanding the R² and Adj. R² of 0.209 and 0.179 respectively, the CCC-X returns a coefficient of 0.8683, standard error of 1.833, (t₍₈₃₎ = 0.474, p = .637), whilst the CCC-B returns a coefficient of -4.6799, standard error of 1.520, (t₍₈₃₎ = -3.079, p = .003). WKC returns a coefficient of 0.191, standard error of 0.058, (t₍₈₃₎ = 3.279, p = .002). The model constant reveals similar statistics to the CCC-B: (t₍₈₃₎ = 3.144, p = .002).

Table 2 shows the OLS Regression results on return on assets (ROA) with Working Capital (WKC) as a Predictor. The F-statistics (F_(79,3) = 9.898, p =  .000) also reveal that the distribution is a perfect fit. Notwithstanding the R² and Adj. R² of 0.273 and 0.246 respectively, the CCC-X returns a coefficient of 0.0027, standard error of 0.007, (t₍₈₃₎ = 0.39, p = .698), whilst the CCC-B returns a coefficient of -0.0268, standard error of 0.006, (t₍₈₃₎ = -4.688, p = .000). WKC returns a coefficient of 0.0005, standard error of 0.000, (t₍₈₃₎ = 2.401, p = .019) with the model constant statistics showing t₍₈₃₎ = 6.402, p = .000.

Table 3 shows the OLS Regression results on profit after tax (PAT) with Working Capital (WKC) as a Mediator. The F-statistics (F_(79,3) = 18.860, p =  .000) show that the distribution is a perfect fit. Notwithstanding the R² and Adj. R² of 0.550 and 0.521 respectively, the CCC-X returns a coefficient of 1.8359, standard error of 1.407, (t₍₈₃₎ = 1.305, p = .196), whilst the CCC-B returns a coefficient of -3.3968, standard error of 1.187, (t₍₈₃₎ = -2.861, p = .005). WKC returns a coefficient of 3.5431, standard error of 0.445, (t₍₈₃₎ = 7.969, p = .000) with the model constant statistics at t₍₈₃₎ = 2.089, p = .040.

Table 4 shows the OLS Regression results on return on assets (ROA) with Working Capital (WKC) as a Mediator. The F-statistics (F_(79,3) = 7.571, p =  .000) reveal that the distribution is a perfect fit. Notwithstanding the R² and Adj. R² of 0.330 and 0.286 respectively, the CCC-Xreturns a coefficient of 0.0041, standard error of 0.007, (t₍₈₃₎ = 0.611, p = .543), whilst the CCC-B returns a coefficient of -0.0244, standard error of 0.006, (t₍₈₃₎ = -4.286, p = .000). WKC returns a coefficient of 0.0057, standard error of 0.002, (t₍₈₃₎ = 2.655, p = .010). The model constant will reveal similar statistics to the CCC-B: (t₍₈₃₎ = 5.740, p = .000).

4. Discussions of the findings

The data displayed in the preceding section summarizes the analysis of how working capital affects the financial performance of the firms studied. Essentially, it probes if and whether working capital management can predict profitability.

1. The F-statistics [F(79,3) = 6.977, p = .000), (F_(79,3) = 9.898, p =  .000), (F_(79,3) = 18.860, p =  .000), (F_(79,3) = 7.571, p =  .000)] - all show that the model used to predict profitability and return on assets fits the data very well, and the results are statistically significant.

2. R² and Adjusted R² (ranging from 0.209 and 0.179 to 0.550 and 0.521) - indicate that around 20% to 55% of the changes in profitability and return on assets can be explained by the working capital and other factors considered in the model.

3. The CCC-X’s coefficients, standard errors, t statistics and the p values for all the four analyses indicate that the period-based cash conversion cycle (CCC-X) has a small positive but statistically insignificant impact on profitability.

4. The CCC-B’s coefficients, standard errors, t statistics and the p values for all the four analyses suggest that the operational breakeven-based cash conversion cycle (CCC-B) has a significant negative impact on profitability. Meaning that the lower the CCC-B, the higher a firm’s profitability and vice versa.

5. The WKC’s coefficients, standard errors, t statistics and the p values for all the four analyses also reveal that working capital itself has a small but statistically significant positive impact on profitability. This means that a firm’s working capital also contributes positively to the firm’s financial performance.

In clear terms, the study found that managing working capital can influence profits, but the way profits are affected differs based on the methods used to measure cash conversion cycles. The period-based cash conversion cycle (CCC-X) method seems less impactful, while the operational-breakeven-point-based formula (CCC-B) method shows a significant negative effect on profits, indicating strongly that the CCC-B aligns inversely as expected with companies’ financial performance. Working capital, on the other hand, also proved to be in line with the feasibility expectations of firms as it has a positive impact on profitability.

5. Conclusions   

The cash conversion cycle is an indispensable tool for assessing and enhancing working capital management. By evaluating its components and implementing strategic practices, organizations can significantly improve their cash flow and operational efficiency. Ultimately, a well-optimized CCC can lead to increased profitability and a sustainable competitive advantage, reinforcing the importance of this metric in financial management. This study has demonstrated how the operational breakeven theory can help to establish a better and more efficient formula for measuring cash conversion cycles which are important metrics for effective working capital management.

6. Recommendations for Enhancing the Cash Conversion Cycle 

To optimize working capital management and improve the effectiveness of the CCC, businesses can adopt several best practices including the use of the operational breakeven-based metric and others including:

Implementing just-in-time (JIT) inventory systems can minimize DIO by reducing excess stock and improving turnover rates (Gaur & Goyal, 2009). Establishing robust credit policies and enhanced collection strategies can effectively reduce DSO. Early payment discounts and clear invoicing processes can encourage faster payments (Lind, 2012).

Companies should also negotiate favorable payment terms with suppliers to extend DPO without damaging supplier relationships. This strategy allows for improved liquidity (Baños-Caballero et al., 2014). Firms should consider implementing Integrated Financial Management Systemsby leveraging technology to automate financial processes which can lead to improved data accuracy and quicker decision-making regarding cash management (Baker et al., 2013).

Companies needs to introduce continuous tracking of the CCC and its components to enable timely identification of potential issues and the implementation of corrective measures. Regular reviews can help align working capital strategies with overall business objectives (Richards & Laughlin, 1980).

Ethical Approval and Consent to Participate: This research did not involve any human or animal subjects. The study exclusively utilized publicly available financial statements of companies listed on the Nigerian and Indian stock exchanges, which do not require specific permissions for use. According to the Babcock University Research and Ethics Committee (BUREC) guidelines, research using publicly available information is exempt from ethical scrutiny that applies to studies involving direct contact with humans or animals.

Consent for Publication: No personalized details, images, or videos of any individuals were used in the preparation of this document, thus no consent for publication is required.

Funding: No external funding was received for this study. All the costs associated with data sourcing and analysis were covered by the authors’ contributions.

Data Availability: The data used for the analysis in this study are available from the corresponding author upon request.

Authors’ Contributions: The first author wrote the manuscript, the second author supplied the literature, the third author supplied the financial statements from which data were extracted, the fourth author performed the analysis, and the last author proofread the manuscript and effected the pre-submission suggested corrections.

Conflict of Interest: The authors have no conflict of interest to declare.

Declaration of Generative AI and AI-assisted Technologies: This study has not used any generative AI tools or technologies in the preparation of this manuscript.