Being Resilient: Working Capital Management In The Time Of Covid-19

By Sachin Baglar, Shashidhar Ramakrishnaiah, Harry Keir Hughes August 2020   |   POV   |   12 min read   |   Email this article   |   Download
COVID-19 has brought disruption to capital plans and business models. CFOs must act quickly and deliberately to adapt to what has changed. A data-centric analytical solution using artificial intelligence and agile data management can help.
Harmonized retail blocks

Management of working capital is in the spotlight. Businesses that can’t manage working capital effectively are going to find it ever more difficult to survive. According to Gartner, ensuring the availability of cash to fund operating expenditures and other expenses is one of the most significant challenges that CFOs face in tackling the aftermath of COVID-19.1

Many firms both big and small are changing their working-capital strategy to finance their business. With the oil and gas industry still reeling from demand-side risk and daily price fluctuations, Shell, the Anglo-Dutch group, reduced working capital in March to free up cash flow, a strategy bolstered by cutting share buyback programs, cutting capital expenditures, and reducing operating costs.2

Separately, Countrywide PLC, the UK residential leasing agent, released a statement at the end of April. It said that, along with reducing discretionary spend and deferring taxes, it would meet its obligations by moving all receivables to monthly payments, in line with many other firms in the property services industry. It also said that it would “carefully balance payment obligations between smaller and larger suppliers to manage the working capital cycle.”3

Put simply, working capital describes how much excess cash a firm has. By adding the amount of money owed from customers (receivables) to money tied up in goods (inventory) and subtracting money owed to suppliers (payables), CFOs can determine the ongoing liquidity and health of their business - and what options they have to improve the picture.

Options include reinvesting capital in the business, borrowing more from financial institutions, and negotiating better deals with suppliers that are in a less risky financial position. The crudest form of the strategy is to reduce the waiting time for payments from buyers while increasing the time the business takes to pay suppliers. This improves liquidity, operational efficiency, and ultimately profits.4

Data on working capital can also be used in liquidity reporting by financial services firms and by other large corporations, giving regulators insight into how much cash flow will be available in worst-case scenarios. Such stress testing increases the health of the overall financial landscape and goes a long way toward improving the investor sentiment and credit ratings of a business.

The significance of good working-capital management has gone up with the onset of the virus

Understanding these cash flow positions was important before the pandemic (in 2019, PwC reported that globally listed firms could release €1.3 trillion from their balance sheets by addressing substandard working-capital performance5). But the significance of good working-capital management has only gone up with the onset of the virus.

Cash flow risk across the supply chain is through the roof, with firms unable to determine whether producers are able to operate at full capacity or whether buyers actually want the goods produced. Understanding how much cash can be put aside to hedge against market uncertainty has never been more important.

The working-capital disconnect

However, there is a problem: something that COVID-19 has brought to the foreground. Working-capital management is often disconnected from real-time market and ecosystem intelligence. In fact, most working-capital management decisions are based more on intuitive judgment than on objective data science practices.

What’s needed are good, reliable, data-driven insights — into payables, receivables, and inventory — that can inform CFOs of the cash flow position in or near real time. But collating this data is made difficult by the increasing complexity of supply chains, where disparate practices across geographic nodes (both onshore and offshore) pull data into siloed systems.

Even in organizations where data-driven decision-making is a priority, the data landscape is complicated, consisting of diverse enterprise resource planning products and capital management solutions. Teams often perform analysis without input from each other, on different platforms, and in different formats. Payment terms with buyers and suppliers are often negotiated in business unit siloes, leading to inferior results. Solutions are often metrics-oriented (e.g., based on how much spend can be reduced in a single business unit) without a holistic view of how a given metric affects another unit somewhere else. In such a model, the Procurement Department may calculate payables while the Sales Department calculates receivables, usually on old data that offers limited perspectives. These insights are then crunched together by the CFO’s office, leading to suboptimal decisions on investments and borrowings.

Another problem is that the use of analytics in cash flow decisions — if it happens — occurs usually on an ad-hoc basis . This not only slows things down but also calls for huge effort and cost for every data science life cycle. Data discovery and data preparation are cumbersome to manage in this scenario, which severely affects the time to market of insights. Also, the insights themselves are suboptimal, as data is ingested from black-box systems that are siloed across the organization and decisions are made without a coherent explanation as to why certain strategies are recommended at all.

The use of analytics in cash flow decisions often only occurs on an ad-hoc basis

Finally, the data itself is not granular enough and often doesn’t include big data such as market trend patterns or customer sentiment. Comparative analysis is rarely done in order to judge how likely a firm is to go into arrears and default on a payment or whether it needs extra days to come up with the cash. With such analysis, firms could more confidently offer discounts to buyers, or determine when it’s best to sell an invoice to a bank to mitigate cash flow risk.

To do this effectively however, one needs to bring different data sources together from across the firm and the wider market, using big data to augment decision-making capability.

Getting the right data

So what data is needed exactly? To truly understand the working-capital situation, CFOs must pull in granular master and transaction data from corporate finance. These real-time insights should dig right down to the invoice level so that the firm can make decisions about which ecosystem partners to go after for early returns. Examples include:

  1. Current assets. These are accounts receivables in the form of sales invoices, stock inventory, and cash balance. A complete picture here would include related data like payment terms and purchase orders.
  2. Current liabilities. These are accounts payables in the form of purchase invoices, credit notes, and interest on loans.
  3. Data from supply chain finance. These include funding, borrowing rates, cost of capital, billing/payment cycles, working-capital requirements, and business processes like “order to cash” and “procure to pay.”
  4. Demographic and firmographic data on customers and suppliers.
  5. Data from any other existing financial solutions , such as cash flow forecasting and sales forecasting.
  6. The target days sales outstanding (DSO) and days payable outstanding (DPO).
  7. Support staff costs and other costs set by the CFO office for the financial year/quarter.
  8. Big data from external sources that can enrich insights and recommendations, including:
    • FX and commodity rates.
    • Industry and region DSO and DPO.
    • Firmographic data of customers and suppliers, with risk and credit ratings.
    • COVID-19 impact, by region and other categories.

Generating actionable insights

Once the data is in place in an integrated platform, it helps to be able to assign weights or scores to each input — as well as to override any specific inputs on an as-needed basis. Adding machine learning to the system can continuously and automatically refine the insights that are derived.

Machine learning can be used to continuously and automatically refine insights

Within this context, any platform that is built for this purpose should enable the following:

  1. Financial managers should be able to input their strategic DSO and DPO in order for the recommendation engine to set goals.
  2. From analytics on payables and receivables, the system should give recommendations on the order in which to pay or collect invoices for optimal cash flow and how to maximize benefits by optimizing collections and payments decisions. With this in mind, the system should:
    • Filter out individual customers or suppliers, an entire industry sector, or an entire region from being considered for the invoice payment/collection recommendation engine. This will mean that preferred customers (or those customers from a particular industry/region who can’t or won’t change their behavior) won’t be considered in some recommendations, thus enabling the business to reach a target goal with a smaller dataset.
    • Allow financial managers to input their own payment/collection cycles if they want to restrict recommendations to just these cycles instead of extending cycles to an optimal date.
  3. There should be insights on customer/supplier business details like revenue/purchases over the past 30 days/past 60 days/past 90 days/past year — or the lifetime of the business — so that CFOs can measure risk and act accordingly, tweaking the algorithm if needed. Other types of data that can help here include payables/receivables, discounts, and penalties involved.
  4. The manager should be able to see a COVID-19 impact drilled down to the customer/supplier level and based on demographic/industry scores that take into account the supply chain. This will enable CFOs to make appropriate forecasting decisions in the event of default or in the case of requests to delay payments.

Rather than being entirely automated, the solution should augment decision-making capability while allowing the financial manager to override recommendations, based on previous experience and in-depth understanding of black swan events.

The solution allows the financial manager to override recommendations based on previous experience

For a large business, this would positively augment the functions of ERP systems, and for SMBs, it could be a quick means to optimizing both payables and receivables.

In either case, the CFO office would be transformed, spending more time negotiating working-capital conversations rather than scrambling around to get the right insights.

“You may not get rich by using all the available information, but you surely will become poor if you don’t,” said Jack Treynor, former editor of the CFA Institute’s Financial Analyst Journal. Developing a working-capital management solution that understands key data and parses it in real time is one way to make hay, even as it rains.

References
  1. What CFOs Need to Know About Working Capital in Response to COVID-19, March 31, 2020, Gartner
  2. Shell and Total slash costs and suspend buybacks, Anjli Raval and David Keohane, March 23, 2020, Financial Times
  3. Countrywide update including response to COVID-19, April 30, 2020, Financial Times
  4. Five Tips for Effectively Managing Working Capital, Mary Duffy, April 24, 2019, SoftCo
  5. Ibid.