Cash flow management relies heavily on maintaining optimal inventory levels. To optimize inventory, identify trends to predict and adjust to avoid inventory difficulties such as deadstock, supply mismanagement, or wasted stock.
Fremont, CA: The good news is that data analytics provides the potential to evaluate current data to unearth vital information for individuals who may not have a solid management strategy in place.
Data analytics will assist organizations in better understanding and managing sales, inventories, accounts receivable, and customer segmentation, which is critical throughout the recovery process.
Receivables are the most important cash flow variables.
Management can use data analytics to see how internal rules and practices affect the collection of unpaid invoices. For example, even in the absence of a pandemic wreaking havoc on the economy, companies might become complacent about receivables. In this case, data analytics can aid by:
Receivables and invoicing programs are linked to ensure timely delivery.
Predicting payment dates for people with a history of late payments, thereby enhancing cash flow statement accuracy
Identifying scenarios where offering discounts to encourage early settlement makes sense
Inventory
Cash flow management relies heavily on maintaining optimal inventory levels. To optimize inventory, identify trends to predict and adjust to avoid inventory difficulties such as deadstock, supply mismanagement, or wasted stock. In this scenario, the analytics begins with some difficult questions:
What is the company's understanding of the supply chain? There are frequently six or more degrees of separation between the firm and the supplier. As a result, it's critical to understand the entire supply chain and start planning.
Companies that sell high-demand products, such as disinfectants, are looking for additional stock in today's market, especially since usual suppliers may be nearing capacity. Those who use data analytics correctly can acquire meaningful insights on product categories and suppliers, helping businesses plan for eventualities.
Analytics can help forecast when inventory is about to run out, allowing companies to communicate with customers to suggest substitutions or postpone orders. In addition, data analytics supports informed decision-making in the following areas
Obtaining the necessary quantity
Reserving inventory for specific consumers
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These decisions are strongly reliant on data availability, accuracy, and utility. Companies in a severe downturn, on the other hand, might utilize analytics to make difficult decisions about how to best decrease inventory costs and cash outflow
