Tech Talks: Use of Data Analytics in improving working Capital Management

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What is working capital management?

Working capital is an organization's utilization of money to cover its daily needs, such as paying for raw materials, supplies, and salaries. The term can also be applied to individuals. Working Capital Management is defined as "managing cash flow so that it fulfills all the business needs".

For example, if you have $10 in your wallet but need $100 worth of groceries today, your working capital would be negative $90. Working capital management takes care of the flow of funds within the organization. It ensures that funds are available to meet short-term obligations without having to borrow or sell assets. It's essential for all businesses because it affects the growth and the profits of the company.

Without sufficient working capital, companies will fail before utilizing their full potential. Working capital management is a critical function for every company. Whether you are operating in the manufacturing or service industry, managing your working capital will impact your ability to grow and succeed. 

How does it help the organizations?

Data analytics can help organizations measure how much money they need for their working capital based on their current situation. This way, they can improve their working capital management by minimizing risks such as overinvesting or underinvesting in one area while neglecting others.

There is a need for both MSMEs and large manufacturers to remotely manage their supply chain, cash flows, etc. This has led to a rapid and massive shift away from manual processes. This is where automation comes into play - Accounts Receivable Automation (ARA) was developed as an alternative solution by many companies who needed more control in this area of their business while still managing all aspects with less workforce. These systems allow businesses to deal directly with suppliers and it drastically cuts down processing between payments and delivery. 

Application in the real world 

In the past few years, the use of Data Analytics has been steadily increasing as a way for organizations to understand their customers better and identify trends. 

In today's world, data analytics is indispensable as it facilitates the efficient working of an organization. The proper recording and analysis of every activity related to the manufacturing cycle of the products help in having visibility of the processes. 

One particular area where Data Analytics can be applied is in examining customer payment patterns, such as when customers pay or don't pay on time. For example, one company found that because of the customers who paid late, they were losing roughly about $21 million annually due to delayed payments from other clients. It also examined the reasons why people pay late. They came up with several insights like cash crunch during month-ends, etc. that stopped them from making these payments. 

Using a company's balance sheet and cash flow statement, a financial analyst can determine when the business has excess funds and also the times when they need more money. This analysis can then be used to establish an appropriate financing strategy that balances the company's needs with its ability to repay the debt over time. Data Analytics makes the entire process smoother and better.

Conclusion

To maintain change, it is imperative to differentiate between noise and signal. This is done by developing measurable, granular  KPIs that are monitored strictly. Carefully analyzing historical data can provide valuable insights into managing networking capital by quickly finding and dealing with emerging issues.

Contact us today if you want to be well equipped when it comes to dealing with such situations. With a digital analytics course, implementing these tactics in your business becomes easier.

With a data science course, you become aware of the techniques that go into it. The course comes along with a placement opportunity so that you're all set to apply your business analytics knowledge in managing operations.

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