Manufacturing Debt ageing
Why use AI Manufacturing Debt ageing
Cash, the lifeblood of all business. Poor management of AR can lead to serious cash flow problems. Extending days sales outstanding (DSO)—or in other words, the average number of days that receivables remain outstanding before they are collected — is a common problem in the manufacturing industry. AI is enabling businesses to identify invoices that are very likely to be paid late so that Debt collectors have greater visibility of future overdue invoices and they can focus on the customers with the largest overdue amounts. Therefore, collectors, with the support of an AI module, can lower DSO and provide much greater future cash flow visibility.
Find answers to questions:
- How the collector should decide who he/she will call first?
- Which client is more likely to pay late?
- How collectors keep track of clients behavior?
Did you know that reducing days sales outstanding (DSO) by about five days can reduce trapped cash by 30%? In the language of simplified numbers, if your annual revenue is €10M, reducing DSO by about five days can free up almost €150k in annual cash flow.
Benefits of AI Debt ageing
- Save money on interest rates charged on borrowed money by reducing the number of days sales outstanding (DSO) and reduce trapped cash by 30%.
- Prioritization of clients based on the probability of late payment helps collectors make more effective and efficient decisions. Identify invoices with late payment probability at the moment of creation so users can assign specific payment terms to such an invoice in order to prevent late payment.
- Identify and sort existing invoices with the highest probability of being paid late and take an appropriate action.
About iERP
iERP’s mission is to provide an end-to-end business prediction platform with modules that address multiple business scenarios and ZERO required knowledge of artificial intelligence or machine learning technologies.
We are helping companies to predict sales and inventory demands, what their customers are going to purchase next, or identify customers that are going to be late with the payment.
