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Leverage Machine Learning to improve your Order-to-Cash Process
By Terence Leung
Many organizations are not able to induce their customers to pay on time. Their Days Sales Outstanding (DSO) is suffering and actually increasing, as revenue grows. A popular action is to give discounts to late paying customers or worse, to undergo tougher tacks. Working capital and margin therefore continue to suffer.
For organizations that have many steps in the order-to-cash process (some has 50+ steps and some have even bigger number of steps), even if they have the best transaction and reporting systems, they cannot identify other corrective actions. But there are so many other things that can go incorrectly every day, as mentioned in this McKinsey article. And many other causes are operational in nature: fulfillment quality may be consistently sub-optimal at a division or location, invoicing timing may not be configured correctly for a product line, etc. There may be chronic issues that have gone undetected for a long period of time.
Predictive Analytics and Machine Learning (ML) solutions, such as Balance Sheet of the Future™, have the intellectual power to analyze the issues in any business granularity, look for gaps, and let us know the best actions to take and the actual impact (upside) and risk (downside). The scenario analysis for risk and impact are designed for business users. This will help Finance and Operations easily discuss, plan and take actions together.
In addition to DSO and margin, DIO, DPO and a variety of Key Risk Indicators (KRIs) and Key Performance Indications (KPIs) can be improved using ML. Thoughts?
A note about the author: At Pathlock Technologies, Terence Leung conceptualizes and manages analytical solutions for Finance, which serves the increasing needs of the Office of the CFO on strategic decision-making critical to processes, operations and transformations. He was previously at Deloitte Consulting’s Finance, Operations and Strategy practice and at solution providers including i2 Technologies (now part of JDA) that optimize company performance and processes. Terence really enjoys interacting with industry practitioners on topics such as business value, technology, business models, and especially analytics.