Data analytics has become a cornerstone in modern supply chain network design, fundamentally transforming how companies plan, operate, and optimize their supply chains. A supply chain network comprises suppliers, manufacturers, warehouses, distribution centers, and retail outlets, all interconnected to ensure that products reach the end consumer efficiently. The complexity of managing these nodes, along with fluctuating customer demand, global sourcing challenges, and cost pressures, necessitates a data-driven approach. Data analytics provides the tools and methodologies to interpret large volumes of data generated across the supply chain, enabling informed decisions for network design and operational optimization. In supply chain network design, data analytics plays multiple critical roles. First, it helps in demand analysis by identifying consumption patterns across regions, product lines, and time periods. Companies can leverage historical sales data, market trends, demographic information, and external factors such as economic indicators to model expected demand more accurately. This demand-driven insight allows supply chain managers to determine the optimal number, size, and location of warehouses and distribution centers. For instance, if data analytics reveals that a particular metropolitan area has consistently high demand for certain products, a strategically placed warehouse in proximity to that area can reduce transportation costs and improve delivery times. Conversely, regions with lower demand may be served more effectively through cross-docking or centralized distribution centers, reducing excess capacity and storage costs. Second, data analytics supports transportation network optimization by analyzing routing, lead times, and transportation modes. Advanced analytics can integrate data from GPS tracking, shipment histories, traffic patterns, fuel costs, and carrier performance to identify the most efficient logistics routes. Predictive models can anticipate delays due to weather, port congestion, or seasonal demand spikes, allowing planners to reroute shipments proactively. Machine learning algorithms can continuously refine these predictions as new data becomes available, improving the resilience and responsiveness of the supply chain network. Third, inventory optimization is a critical area where data analytics has a transformative impact. Forecasting models, which form the core of predictive analytics, are employed to estimate future demand and determine appropriate inventory levels. Statistical methods such as time-series analysis, regression models, and machine learning techniques like neural networks and random forests can account for seasonal variations, promotional activities, and other influencing factors. By predicting demand more accurately, companies can balance the trade-off between holding costs and stockout risks. For example, a retailer using predictive analytics might forecast a surge in demand for a specific electronic product during the holiday season. Based on the forecast, the company can allocate sufficient inventory across regional warehouses, reducing the risk of lost sales while avoiding overstocking. Capacity planning also benefits significantly from data-driven forecasting. Production facilities and distribution centers have finite capacities in terms of labor, machinery, storage, and transportation. Analytics can help in determining the required capacity to meet forecasted demand without overburdening resources. Scenario analysis, enabled by data analytics, allows planners to model multiple “what-if” situations, such as sudden spikes in demand, supplier delays, or logistical disruptions. For example, a