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Working capital analytics in the time of Covid-19

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Working capital analytics in the time of Covid-19 1

By Sachin Bagla, AVP and DNA Practice Lead, Europe, Infosys

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.

With the onset of COVID-19, working capital management is firmly in the spotlight. Businesses that fail to manage their cash flow position will find it difficult to survive. This excess cash is needed to fund operational expenditures and other expenses, according to Gartner. Tales from industry support this finding. With oil and gas demand side fluctuations rippling through the energy industry in March, Shell, the Anglo-Dutch group, reduced working capital to free up cash flow. Similarly, Countrywide PLC, the UK residential leasing agent, said that it would “carefully balance payment obligations between smaller and larger suppliers to manage the working capital cycle.”

Put simply, working capital is a metric to measure how much excess cash a firm has, and is used by CFOs to determine the ongoing health and liquidity of their business. The equation adds the money tied up in goods (inventory) with money owed from customers (receivables) and then subtracts the money owed to suppliers (payables). Optimizing this equation over time, based on internal efficiencies, external markets and other influences, gives CFOs a complete picture of where and how they should reinvest capital. The strategy might mean borrowing more from financial institutions or negotiating better deals with suppliers that are in less risky financial situations. By addressing substandard practices, in 2019, PwC found that globally listed firms could free up €1.3 trillion from their balance sheets. And now, with cash flow risk through the roof due to COVID-19, getting at that cash has never been more important.

The working capital disconnect

Sachin Bagla

Sachin Bagla

But there is a problem. Working capital is often disconnected from real-time intelligence, and financial officers base their strategies on mere intuition rather than concrete data science models. There is a reason for this. Historically, collating data to make real-time decisions has been difficult. Supply chains are complex. So too is the data landscape in large firms, with many using multiple ERP systems and capital management solutions. Even when data-driven insights are used, siloed business units perform analysis without input from each other, on different platforms, and in different formats. For instance, the Sales department may calculate receivables, while the Procurement team calculates payables, leading to suboptimal decisions on investments and borrowings. Also, data discovery and preparation is a cumbersome art, and slows down the decision-making process. The data is rarely granular enough, and forgoes the keen intelligence that can be found from big data such as market trend patterns or customer sentiment.

Done well, however, such analysis could be used to determine risks from both buyers and suppliers. This would give firms a sure-fire strategy even during black swan events; they might decide they need to offer discounts to buyers, or sell an invoice to a bank to mitigate cash flow risk.

Getting the right data

So what data is need exactly? To get up to speed quickly, firms should build a data ingestion framework that pulls in data from corporate finance. These datasets include granular master and transaction data that dig right down to the invoice level. Examples include:

  • Current assets. These are account receivables in the form of sales invoices, stock inventory and cash balance.
  • Current liabilities. These are account payables, including credit notes, interest on loans and purchase invoices.
  • Data from supply chain finance like borrowing rates and business processes like “order to cash” and “procure to pay”.
  • The target days sales outstanding (DSO) and days payable outstanding (DPO)
  • Big data from external sources, including FX and commodity rates and COVID-19 impact by region.

Generating actionable insights

An integrated platform should pull in this data, and assign weights and scores to each input. Using machine learning, CFOs can continuously refine the insights that are derived. The platform should enable the following:

  • Strategic DSO and DPO can be changed in real-time, giving the CFO a way to set goals for each quarter.
  • From analytics on payables and receivables, the system should tell the CFO the order in which to pay or collect invoices for optimum cash flow. The system should allow them to input their own payment/collection cycles if they want to restrict recommendations to just these cycles.
  • The COVID-19 impact should drill down to the customer and supplier level so that the CFO can make appropriate forecasting decisions in the event of default or delays.

For a large business, this platform would augment the intelligence of ERP systems, and for smaller firms, it would enable a quick way to optimize both payables and receivables. In either case, the CFO office would be transformed. Less time would be spent trying to get the insights, leaving more time to negotiate mission critical working capital conversations with the wider ecosystem.

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