Why Cash Allocation is the top priority in your order-to-cash process.
A best-in-class order-to-cash (O2C) software solution needs to be best-in-class at all stages of the O2C process if you are to ever zero down on your delinquent DSO days, maximise your AR cashflow position and collections efficiency index.
But doing everything all at once is not typically the best way to go. Better to master one thing before moving on to another. Have a fully automated end-goal in mind but aim to get there in integrated modular stages thus giving you a better chance of carrying along disparate parts of the organisation with you.
There are 5 key stages to the O2C process. The cradle-to-grave order in which they should be prioritised is debatable but typically it might be as follows;
- Risk Management – Assessing the potential bad debt risk of customers before you accept any orders based either on historical internal data or via external credit risk agencies or a combination of multiple (big) data sources.
- Billing (E-Invoicing) – The process of getting invoices out and delivered on-time and in the right format. Moving from print and post to electronic means and the use of 24/7 self-serve customer portals to retrieve invoices.
- Cash Allocation – The process of aligning payments already made with unpaid invoices either via manual spreadsheets or via more advanced AI and machine learning tools.
- Credit Management – The day-to-day process of chasing due debt via dunning, disputes and customer relationship management tools. Typically the heart of the O2C solution.
- Collections and Bailiff agencies – The passing of bad debt to specialised debt recovery companies when all other forms of recovery have been exhausted or failed.
Each individual step of the process has a direct impact on your DSO performance as well as your credit collection efficiency. Unallocated cash is particularly problematic as it remains as due debt until it can be matched to an open invoice or order. Agents are therefore chasing customers who have already paid thereby wasting both company and customer time in the process and potentially adding customer dissatisfaction into the bargain. For this reason, many companies place Cash Allocation as the top priority in the O2C process as the direct and indirect gains on cashflow, DSO and collector efficiency are quickly realised.
Manual cash allocation can become overwhelming and require large teams of allocation agents taking long periods of time to match payments with potential invoices. In most cases the end result can still leave a significant balance of unallocated cash as well as errors that need correcting.
“Cash before breakfast”
Companies that have adopted advanced cash allocation systems utilizing AI, RPA and machine learning techniques can complete the daily cash allocation task within minutes or hours rather than days and sometimes weeks. This enables their allocation agents to start the day with a collections book that does not contain scheduled calls to chase overdue debt that has already been paid. This alone can have a significant impact on staff and customer VAT (value-add-time) i.e. less time spent on the unproductive onerous task of cash allocation and more time spent on better communications with customers as well as internal departments on more productive issues such as dispute and complaint resolution that can be a barrier to payment.
Onguard’s MatchMaker is a sophisticated, cost-effective, rule-based AI tool that can reconcile debit and credit invoices across multiple currencies with initial automation rates of up to 85% achievable. This helps you minimise unallocated payments and allocation costs as well as the ability to process remittance advices and keep the accounting ledger up to date. User friendly dashboards and suggested match views make the exceptions process simple and easy to use.
Integrated within your Total AR solution
Onguard is a world leader in order-to-cash solutions with over 750 customers worldwide. Using MatchMaker as a stand-alone module within your O2C system or as part of an integrated platform can be implemented within a matter of weeks.
