The Infosys Logistics practice adopts predictive analytics to streamline processes and rationalize costs. Our analytical solutions for hub-and-spoke networks, haulage companies, distributors, freight forwarders, and 3 / 4 PL operators span customer, network, demand-supply, and fleet analytics. Predictive insights into customer preferences, cargo condition, resource availability, and the logistics network enable informed decisions to enhance last mile transportation, customer service and resource utilization.
Our algorithms collate, analyze and extrapolate historical data, delivery records, telemetry data from transport units / vehicles, and streaming data from Internet of Things (IoT) devices and sensors embedded in pallets / warehouses to anticipate variables across processes. It correlates events and stakeholders to resolve issues, recommend action, or trigger automated response. Constraints may range from driver performance, vehicle condition, weather, product, packaging, pickup and delivery timeframes, and traffic / port congestion to warehouse capacity. The output is leveraged by optimization engines for load planning, route optimization, vehicle maintenance scheduling, workforce allocation, and customer notification systems.
Simulation models and predictive analytics enable logistics managers to prevent downstream bottlenecks in the event of supply chain disruptions. Further, near real-time insights enable prompt action to mitigate risks. Significantly, it prevents under-utilization of resources even while accepting orders for less-than-truck / container load freight.
Point of View: Machine learning drives seamless logistics
Analytical tools help logistics providers aggregate global demand, while predictive maintenance of heavy equipment rationalizes warehousing and distribution costs.
Big data solutions harvest a huge volume of data from diverse sources, an imperative for establishing correlations between datasets and understanding underlying business issues.
Decision support systems combine historical data and real-time patterns, which helps adjust delivery schedules and mitigate risks due to disruptions.
Accurate analysis and data visualization help devise supply chain strategies to respond to fluctuating demand.