I would approach this by first using data analytics to review historical demand patterns, current stock levels, and supplier performance. Based on my experience, it’s crucial to segment inventory into categories like high-demand, slow-moving, or critical items. This helps prioritise adjustments. I’d also leverage predictive analytics to forecast future disruptions and align inventory levels accordingly. Collaborating closely with suppliers and using real-time dashboards ensures swift action. It’s about balancing just enough stock to meet demand while avoiding overstocking or shortages.

I would leverage historical data to look for seasonalities, where the demand is high or irregular. Then as an advancing ecosystem, the forecast for demand, could be very insightful, for datqd

To adjust inventory levels during supply chain disruptions using data analytics, start by leveraging historical and real-time data to assess demand patterns and identify critical stock-keeping units (SKUs). Use predictive analytics to model potential disruption impacts and simulate scenarios to optimize inventory allocation. Implement dynamic safety stock calculations that account for lead time variability and demand fluctuations. Monitor supplier performance metrics to identify risks and adjust reorder points accordingly. Integrate demand sensing tools to prioritize high-demand or essential items while scaling back on non-critical stock, ensuring a balance between availability and minimizing overstock.

1) Analyzing Demand Patterns: Use historical and real-time data to forecast demand and adjust inventory proactively. 2) Segmenting Inventory: Focus on critical items (e.g., ABC analysis) to prioritize stock replenishment. 3) Dynamic Safety Stocks: Set flexible safety stock levels based on supply variability and lead times. 4) Scenario Planning: Simulate disruption scenarios to plan for contingencies and maintain optimal inventory.

Include value that customer willing to spend on each product category into data analysis as additional criteria for more accurate forecast of inventory. This can avoid buying up items that not many are willing to spend and ended up bringing products that lead to under selling. Perceived value of the product is crucial in today purchase decision.