Transport&Logistics

Transport and Logistics

The arrival and spread of big data usage dramatically changed the way businesses use to work with their analytics. Companies can anticipate slow and busy periods, potential future supply shortage and act accordingly. The supply chain is more transparent, but also automated, deliveries are optimised, and inefficiencies reduced. All these insights help in making better strategic decisions that provide a real competitive advantage.
Big Data has also generated significant insight into the end-consumer base. Companies can tap into social sentiment data generated by the boatloads from platforms such as Facebook, Instagram, or Twitter to gain a deeper understanding of customers.This way, companies can examine that sentiment, match with sales records, and anticipate a rise (or drop) in demand. Then, they can coordinate with their supply chain manager to make sure they're not wasting money shipping superfluous units or leaving money on the table by not meeting demand.
But there are other ways logistics companies are using Big Data to make consumers happier. Companies know that the delivery of tangible goods often involves face-to-face contact with customers. DHL points out that companies are gathering all kinds of data about these touchpoints in order to make the process more user-friendly for the consumer.Using Big Data means that companies now have unprecedented analytics into the preferences and buying behavior of their customers.

Sample Architecture

Accurate Business Demand Planning

Network resource planners need to accurately forecast future business demand to assure they have the right fleet resources and crews in the right place to meet promised delivery schedules. They also need to assure they have the right inventory in the right distribution centers and warehouses.

Route Optimization

The key to on-time delivery and optimal load factors is optimal crew and equipment routing.Airlines, trucking companies, and railroads need to understand the implications of changing weather conditions,missed connections, crew work time limits, maintenance schedules, and a host of other factors. Sensors arebecoming a significant new source of critical data. Telematics can be applied to insurance to introduce new data monetization opportunities. The claims-to-premiums ratio can be reduced by analyzing risk profiles based on realtime driving statistics for people. Also, additional location based services can be provided based on routing and route optimization.

Increased Customer Wallet Share

Customer service professionals must deliver goods in time to meet service level agreements and be seen as an integrated partner in supply chains and delivery. Where customer needs go outside the network, partnerships become critical to offering a more complete solution.

Risk Analysis

Logistics companies must understand risk associated with lengthy and complicated crew work schedules and potential losses caused when perishable items are delayed in delivery or subject to temperature extremes, or when items are mishandled and damaged. Vehicle and driver risk analysis based on sensor and geo data will improve driver safety and reduce warranty costs by avoiding potential costly recalls.

IT operational Efficiency

Not unique to logistics management and transportation companies and rarely driven from the lines of business (but a possible reason for embarking on extended architectures that include Hadoop) is the need to move data staging and transformation to a schema-less platform for more efficient processing and leveraging of IT resources. IT operational efficiency is often difficult to prove but is sometimes an initial justification that IT organizations gravitate toward when deploying these types of solutions.

Demand Prediction and Dynamic Pricing

Dynamic pricing is a very powerful tool that empowers pricing managers to optimize prices with dynamic pricing based on sales and demand. Thanks to advanced machine learning models it's now possible to totally automate the pricing process, using prices based on sales and demand, and therefore increasing margins and sales, and saving money on operative costs.