How Data Analytics Can Significantly Help Manage Working Capital?

One of the strongest measures of a company's financial health is its working capital. Businesses should consider this as a critical KPI.

CIOL Bureau
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Anshuman Bhar 2 1 1

One of the strongest measures of a company's financial health is its working capital. Businesses should consider this as a critical KPI; managing working capital should be a core part of operational excellence.


Finance executives should strive for long-term improvements in the inventory management, accounts receivable, and accounts payable processes to effectively manage working capital and to boost return on the capital used.

Data-driven strategies that help organizations manage the working capital effectively

Using data analytics to improve working capital effectiveness is a critical step. A data-driven approach to this financial activity can assist firms in properly managing working capital, since analytics-driven solutions would focus on the following areas:

  1. Identifying and preventing revenue leaks

Revenue leaks happen due to multiple reasons and the most common causes are either flawed processes or bad data, e.g., process problems with disjointed systems, unenforced policies, disputes, invalid customer deductions with a disproportionately high volume and low value, auto-approved write-offs, inaccurate or outdated customer information, spreadsheets, etc.

Many large enterprises struggle with revenue leakage, and to top it, many companies do not even take the necessary steps to stop it from happening in the first place.


Advanced analytics can be used to overcome this major challenge and even reverse the revenue leakage. The ways companies can combat revenue leakage include reconsidering outdated processes, reducing the use of bad or inaccurate data and turning to smarter software and better technology.

We usually speak about an example here. Suppose there is a customer who uses low dollar value deductions as a strategy to strengthen its cash flow. In such a situation, it is difficult to track low-dollar value deductions as it can really be a small number and below the acceptable tolerance/ threshold. This would be like finding a needle in a haystack.

However, when such deductions are aggregated at a customer level over a period, it can be utterly amazing to see how certain group of customers are using this strategy to cause a significant cash flow leakage for the company.


To track such events, there are advanced clustering algorithms which can tell about the customers who are consistently using this strategy. These insights can then help the A/R team to work on the revenue recovery in a strategic way.

  1. Identifying high risk customers and undertaking recommended actions for faster collection

Understanding the behaviour and financial health of a large customer that has thousands of interactions with an organisation may be quite challenging. This leads to past due payments or even the write-off of receivables. For finance professionals to keep on top of their customers and manage outstanding receivables, it is essential to identify and prioritise high-risk customers.


Advanced classification algorithms can be used, and customers can be categorized into different risk buckets. They can then be contacted before their unpaid invoices pile up on past-due AR.

This way, advanced analytics would assist organizations in taking proactive measures to identify customers who pose a significant risk and gradually limit their exposure to them.

  1. Providing modern inventory management solutions

Inventory management is a major challenge in an organization as it directly affects fulfilling customer requirements. Managing inventory effectively requires lots of data; stored in a central warehouse, connected to a modern inventory management solution. To extract value from data in several ways, various analytics techniques, like descriptive, diagnostic, predictive, and prescriptive analytics, are deployed to inventory data.

Modern inventory management solutions that are based on AI/ML help monitor goods throughout the supply chain, from raw materials to the sale of final products.

By applying these analytics, it is possible to gain insightful information about Stock keeping units (SKU) and the variables that are related to them, such as minimum order quantity, lead times, replenishment frequency, and safety stocks. Advanced classification algorithms, along with predictive capabilities can help to keep the inventory issues such as supply mismanagement, deadstock, and wastage under strict control. With accurate forecasting, data-driven decisions prevent inventory shortages, overselling and shrinkage. Since these variables increase costs and directly impact profitability. So, controlling them using advanced analytics will help manage the working capital effectively.


Laying the foundation for effective management of working capital

As per a BCG report, companies that take data driven steps to manage their working capital can add as much as 5% back to their bottom lines. So, to lay the foundation of efficient working capital, organizations should focus on three key areas: a) Setting up an effective data infrastructure b) Definingclear, granular, and measurable key performance indicators (KPIs) to monitor while implementing solutions to manage working capitals c) Moving from retrospective reporting to a forward-looking mindset.

Authored By: Anshuman Bhar, CEO & Co-founder, Aays Analytics