This 'intelligent data' provides insights that help companies make more reliable cash flow predictions and manage their working capital more predictably and effectively.
FREMONT, CA: Businesses need cash flow for inventory, labor, product enhancements, and shipping. Customers' orders, credit approvals, invoicing, receipts, and collections must all be managed efficiently. The Order-To-Cash (O2C) period is a crucial mechanism that impacts a variety of financial and customer-related activities. In addition to using conventional process management tools like Six Sigma or Lean to improve their O2C processes, businesses may use Artificial Intelligence (AI) and advanced real-time analytics to improve their operations and financial outcomes.
Collections, conflicts, and cash flow are the most important O2C issues that businesses face. Organizations should consider the following intelligent finance techniques to increase their O2C cycle, pace payments, and ensure continuity.
Optimize the Collections by Knowing Who to Aim and When to Do So
Enhancing collections by more reliably projecting whether and when consumers can pay helps companies shorten the time it takes to finance inventory or receivables and accelerate the O2C cycle. By learning payment trends based on historical payment data and other variables, machine learning may classify the main drivers of late or unpaid invoices (such as seasonal impacts, specific products, or salespeople associated with payment issues).
Accounts receivable departments will more efficiently target accounts that pose the greatest danger by using pattern detection and predictive characteristics and prioritizing follow-up accordingly. Collections teams can increase cash flow, minimize unpaid invoices, and reduce bad debt expenses by using more reliable predictors.
Cash Flow Forecasting: Intelligent Data Hastens Working Capital Management
Cash is king in the corporate world, so finance departments must constantly create cash flow forecasts to assess and handle working capital needs. When operating in a steady-state, traditional forecasting models such as year-over-year patterns, sequential rolling forecasts, and seasonality can be sufficient. On the other hand, traditional forecasting methods are unlikely to achieve the desired level of precision for more competitive companies, such as those undergoing rapid growth or introducing new products. Furthermore, macroeconomic pressures can often cause nearly all businesses to reconsider how they forecast cash flow; the COVID-19 pandemic is a good example.
Companies may use accurate receivables and payables data to detect evolving cash flow trends using a machine learning approach to forecasting. This 'intelligent data' provides insights that help companies make more reliable cash flow predictions and manage their working capital more predictably and effectively.
