Analytics for Transportation and Logistics

You can get a 360-degree view of fleet performance by tracking average time on route, load utilization rate, engine idle time, and other KPIs, and identify root causes of fuel waste and high emissions (e.g., by establishing connections between emission levels and certain vehicle types and routes). With IoT-based vehicle health analytics, it is also possible to conduct predictive maintenance.

You can monitor on-time delivery rate, perfect order rate, fill rate, and other SCM KPIs. It is also possible to segment the established routes (e.g., by delivery time, costs) and use what-if simulations to help identify more cost- and time-efficient routes. More advanced solutions can automatically adjust routes based on weather and traffic conditions and provide case-specific recommendations (e.g., optimal transportation means for last-mile delivery).

With insights into inventory size and turnover, bin location, and box utilization businesses can optimize storage usage and major warehouse operations such as pick and pack, receiving, and putaway operations. Predictive analytics can be used to forecast warehouse labor demand as well as inventory replenishment and equipment maintenance needs.

Analytics solutions help to monitor the condition of damage-prone, hazardous, and perishable goods during transportation and send alerts on safety deviations. Using real-time and historical data on incidents, driver behavior, near misses, and safety policy violations, it is also possible to detect recurrent issues and identify their root causes, which helps minimize warehouse and transportation incidents.

By monitoring inventory metrics (e.g., stock level by location, turnover ratio) and employing multidimensional segmentation (e.g., by product type, supplier performance), businesses can identify slow-moving or obsolete stock. To prevent over- and under-stocking, T&L companies can build demand forecasts and receive real-time alerts on inventory levels falling below the defined threshold to prevent over- and under-stocking.

Tracking procurement metrics such as spend under management and cost of purchase order helps to optimize procurement costs. You can also get insights into procurement process optimization (e.g., with process mapping visualization and cycle time tracking). Another purpose of procurement analytics is to assist in choosing optimal suppliers. You can use it to benchmark supplier performance and get smart recommendations on case-specific suppliers.

Analytics helps to improve order management processes by tracking and analyzing order fulfillment metrics such as order accuracy, fulfillment rates, cycle time, and return rates. By establishing connections between order return reasons and other order-specific details (e.g., the responsible driver, the product batch), it is possible to identify return root causes and minimize returns in the future.

Breaking down costs (e.g., by type, transportation mode) and revenue (e.g., by product, customer segment) enables businesses to identify profitability drivers and bottlenecks. Financial forecasting and modeling enable informed budget planning, cost optimization, and risk mitigation.

Customers can be segmented by different characteristics (e.g., by industry and location for B2B customers, by demographics and order frequency for B2C customers). This allows businesses to tailor marketing and retention strategies. Tracking customer journeys and related KPIs like CLV, churn, and satisfaction rates helps to identify high-value customer groups, prevent churn, and drive service personalization.

You can monitor employee performance and satisfaction to ensure fair compensation, identify attrition drivers, and minimize employee turnover. Workforce analytics also includes employee qualification monitoring, hiring strategies analysis, smart recommendations for labor cost optimization, and what-if modeling for efficient workforce allocation.