A logistics company wants to reduce delivery delays using predictive analysis. What should they focus on?

Explanation: Predictive analysis in logistics is invaluable for anticipating traffic patterns and optimizing delivery routes. By analyzing historical traffic data and real-time updates, predictive models can forecast congestion and suggest alternative routes. For example, integrating weather and traffic data can help prevent delays caused by unforeseen conditions. This approach not only improves delivery punctuality but also reduces fuel costs and enhances customer satisfaction. Predictive analysis thus transforms logistics into a more efficient and reliable operation. Option A: Predicting inventory levels is part of supply chain optimization but does not address delivery delays. Option B: Visualizing routes aids planning but lacks the forward-looking perspective provided by predictive analysis. Option D: Surveys provide customer insights but do not directly reduce delays. Option E: Chatbots improve customer communication but cannot predict or prevent delays.