How This Workflow Works
This workflow forecasts future inventory demand, applies scenario-based changes to key business parameters, and compares the resulting impacts on stock levels, profit, and risk. It enables users to simulate extreme or hypothetical situations, quantify their effects, and make informed decisions based on clear, data-driven insights.
Key Features:
- Forecast future inventory demand and value using machine learning
- Estimate inventory value and stock coverage based on forecasted demand
- Simulate the impact of changes in price, cost, or lead time on inventory and profitability
- Compare baseline and scenario outcomes to identify risks and opportunities
- Visualize key metrics such as profit variation, stock duration, and stock imbalances
Step-by-step:
1. Generate Baseline Inventory Forecasts:
The workflow uses historical inventory and sales data to forecast future demand for each product. A machine learning model predicts expected quantities, while additional calculations estimate baseline inventory value and stock coverage over time.
2. Apply Scenario-Based Adjustments:
Users can simulate changes in business parameters such as price increases, cost fluctuations, or lead time shifts by adjusting scenario controls. The workflow recalculates inventory and financial metrics under these new conditions, providing a direct view of potential impacts.
3. Compare Baseline and Scenario Outcomes:
The workflow compares the original forecasts with scenario-adjusted results. It highlights differences in key metrics such as profit, stock duration, and inventory levels, allowing users to identify areas of increased risk or opportunity.
4. Visualize and Share Insights:
An interactive dashboard presents the results, including charts for current stock, price per unit, estimated profit, stock coverage over time, and scenario impacts on net profit and stock duration. These visualizations support clear communication and data-driven decision-making.