Data-Driven Customer Success

Some use cases to hink about for Qlik AutoML in supply chain management and why are they fundamental:

1. Inventory Stock-outs Prediction: Predicting inventory stock-outs is crucial for ensuring product availability, meeting customer demand, and preventing lost sales. By utilizing AutoML, supply chain managers can analyze historical sales data, inventory levels, seasonality, and external factors (such as weather or promotions) to forecast future demand accurately. This enables proactive inventory management strategies, such as replenishment planning, safety stock optimization, and dynamic ordering, to minimize stock-outs and improve customer satisfaction.
2. Supply Chain Performance/Bottlenecks: Assessing supply chain performance and identifying bottlenecks are essential for optimizing operations, reducing costs, and enhancing efficiency. AutoML can analyze vast amounts of supply chain data, including production metrics, lead times, and supplier performance, to identify patterns and anomalies. By building predictive models, supply chain managers can anticipate potential bottlenecks, optimize resource allocation, and implement process improvements to streamline operations and meet customer demand more effectively.
3. Transportation Optimization: Optimizing transportation logistics is critical for reducing shipping costs, minimizing delivery times, and improving overall supply chain efficiency. AutoML can analyze transportation data, such as shipping routes, carrier performance, and fuel costs, to identify optimization opportunities. By leveraging predictive modeling, supply chain managers can forecast shipping volumes, optimize route planning, and select the most cost-effective transportation modes, leading to reduced transportation costs and improved delivery reliability.

These use cases are fundamental because they directly impact supply chain performance, cost efficiency, and customer satisfaction. By leveraging AutoML technology, supply chain managers can harness the power of predictive analytics to anticipate demand, optimize operations, and mitigate risks, thereby driving operational excellence and maintaining a competitive edge in the market.