With the holiday rush at its peak, efficient delivery logistics and traffic management are essential to keeping operations on schedule. During the holiday season, overall road traffic (including delivery fleets) increases by 36%.

That’s why STV’s digital advisory team is exploring how AI, coding and interactive visualization can transform delivery performance analysis for construction, infrastructure and urban logistics projects across the United States.

Reimagining Delivery Performance Analytics

Traditional dashboards, such as those built with Power BI, require analysts to manually plot data points: a process that can take hours and limits the time spent interpreting the insights themselves.

During a recent team-building experiment, STV’s digital advisory team developed an AI-powered, interactive app that creates predictive models for manufacturing and delivery performance. This app demonstrates how in-depth operational analysis can identify bottlenecks and enhance delivery planning.

Using a synthetic dataset reflecting regional traffic in a community, as well as weather and holiday conditions, our team found:

• Traffic patterns drive the largest delays: Normal weeks mix light, moderate and heavy traffic, but during holiday periods, every route experiences heavy traffic, adding 15-20 minutes per delivery.
• Days leading up to holidays amplify delays: Days prior to Christmas and New Year’s add +30-35 minutes; days prior to Thanksgiving +20-25 minutes and summer holidays +12-18 minutes averaged across all highways and roadways in the dataset.
• Longer delivery routes are disproportionately affected: From the distribution center to the final destination, the dataset found a 55-mile route adds +20 minutes, whereas a 300+ mile route adds +70 minutes.

With such insights, AI-driven analytics can optimize supply chains, construction material delivery and municipal infrastructure logistics.
AI models can generate dashboards and predictions, but the real value lies in subject-matter experts validating the results and applying them to practical scenarios. At STV, our team integrates domain knowledge with AI tools, enabling predictive modeling that supports actionable decisions: helping clients manage project timelines, identify risks and optimize resources for local communities.

Opportunities Beyond Deliveries

The same techniques our team employs can be applied to logistics planning for large-scale infrastructure projects, optimizing the delivery of construction materials or equipment and congestion modeling for urban projects.

AI and interactive applications are reshaping the way infrastructure and construction projects are planned, analyzed and delivered. This case study demonstrates that by automating data visualization, analysts can spend more time interpreting results, testing scenarios and providing actionable recommendations so clients understand the “why” behind the numbers.