![\"Four-phase](\"https:\/\/www.cbs-consulting.com\/wp-content\/uploads\/transition-approach-grafic.png\")
- \n
- What is happening?<\/strong> Descriptive analysis establishes transparency of the current state.<\/li>\n
- What will happen?<\/strong> Predictive modeling uses historical data and operational drivers to forecast how working capital metrics may evolve.<\/li>\n
- What is wrong?<\/strong> Diagnostic assessment focuses on analyzing outliers and irregular behaviors.<\/li>\n
- What should we do?<\/strong> Prescriptive simulation provides a scenario-building environment to test the impact of potential interventions before execution.<\/li>\n<\/ol>\n
Taken together, these phases form a continuous loop: insights feed predictions, predictions guide diagnostics, and simulations turn options into decisions.<\/p>\n
3. The four phases in practice<\/h2>\n
3.1. Descriptive analysis: Understanding the current state<\/strong><\/h6>\n
Traditional finance dashboards provide transparency into NWC development across customers, suppliers, and materials. These cover core KPIs such as Days Sales Outstanding (DSO), Days Inventory Outstanding (DIO), and Days Payables Outstanding (DPO).
\nTypical insights include:<\/p>\n- \n
- DSO increased by 30 days for certain Customer segment in last quarter<\/li>\n
- DIO exceeded policy thresholds by 15 days for certain product groups<\/li>\n
- DPO dropped by 10 days below target in the last quarter for certain vendor groups<\/li>\n<\/ul>\n
This stage lays the foundation by revealing correlations and interrelated drivers, forming the basis for AI-driven analysis.<\/p>\n
3.2. Predictive modeling: Forecasting what\u2019s ahead<\/strong><\/h6>\n
Machine-learning frameworks (such as XGBoost) are increasingly applied to NWC modeling to forecast how key metrics like DSO, DPO, and inventory levels will evolve under different business scenarios. They combine historical patterns with operational drivers to predict future behavior.<\/p>\n
Examples:<\/p>\n
- \n
- Predicting whether DSO will remain elevated based on customer payment history, billing cycles, and invoice error rates<\/li>\n
- Forecasting DIO trends using inputs such as sales demand, planning buffers, and procurement policies<\/li>\n
- Estimating DPO behavior linked to payment discipline, supplier terms, and sourcing behavior<\/li>\n<\/ul>\n
Model explainability tools (like as SHAP) help finance teams understand the drivers behind each forecast, validate results, and sharpen analysis.<\/p>\n
![\"DSO](\"https:\/\/www.cbs-consulting.com\/wp-content\/uploads\/dso-example-forcasting.png\")
DSO example: Forecasting what\u2019s ahead by highlighting the key drivers of future DSO.<\/figcaption><\/figure>\n 3.3. Diagnostic assessment: Analyzing the anomalies<\/strong><\/h6>\n
Once patterns are predicted, the next step is to examine anomalies and deviations that fall outside expected ranges. Outlier analysis enables the identification of irregular behaviors (policy-related, behavioral, or process-driven) and process gaps that may bias predictions or indicate where corrective action is required.
\nExample findings:<\/p>\n- \n
- Increase in DSO due to unusually long delays among a small customer group<\/li>\n
- Increase in inventory due to sudden stock build-up in a single product line<\/li>\n
- Decrease in DPO due to extremely short payment cycles for certain suppliers<\/li>\n<\/ul>\n
Together, prediction and diagnostics create a cycle that not only projects outcomes but also explains why variances occur.<\/p>\n
![\"DSO](\"https:\/\/www.cbs-consulting.com\/wp-content\/uploads\/DSO-example-Analyzing-anomalies-by-via-residuals-and-outliers.png\")
DSO example: Analyzing anomalies by via residuals and outliers.<\/figcaption><\/figure>\n 3.4. Prescriptive simulation: Testing impact before acting<\/strong><\/h6>\n
With root causes identified, the focus shifts from explanation to simulation. Instead of prescribing fixed solutions, the main aim is to provide a scenario-building environment where finance teams can test the effects of potential adjustments before execution.
\nSimulated actions may include:<\/p>\n- \n
- Reducing invoice errors to accelerate collections and lower DSO.<\/li>\n
- Adjusting safety stock in stable supply chains to reduce excess inventory.<\/li>\n
- Aligning payment terms across supplier groups to influence DPO.<\/li>\n<\/ul>\n
- What will happen?<\/strong> Predictive modeling uses historical data and operational drivers to forecast how working capital metrics may evolve.<\/li>\n
![\"DSO](\"https:\/\/www.cbs-consulting.com\/wp-content\/uploads\/2-DSO-example-testing-impact-before-acting-by-simulating-improvement-measures-on-average-DSO.png\")