How Big Data and AI Help Logistics Companies Flourish

Artificial intelligence is going mainstream because of two parallel events: unprecedented availability of computing power and big data analysis. This article explores how AI models capitalize on big data to reinvent logistics.

Madhu Janardhan

Logistics accelerates the movement of goods and provides a stimulus to global trade. Logistics enterprises function in a borderless, global network in which businesses must be empowered to make timely and informed decisions.

The supply chain generates voluminous data to be harnessed across the entire journey of goods from origin to destination, but logistics enterprises and their many partners aren’t perfect. Shipments are misdirected, stuck in transit, or cannot be traced. Machine learning can convert this logistical challenge into an opportunity. In fact, the compounding nurture of big data and artificial intelligence (AI) can create hyper-efficient logistics enterprises operating in a smart ecosystem. The physical flow of materials across the supply chain leaves a trail of data, which is usually in an unstructured format and scattered across the ecosystem. This is a sweet spot for AI, which depends on large volumes of data to extract knowledge and learn through self-analysis. AI can gather data through remote sensing, the Internet of Things (IoT), telematics and geospatial mapping. In fact, through the deft use of sensors, information can be embedded into products and the vehicles transporting them, giving insight into the origin, destination, journey and recipients of goods.

Last-Mile Efficiency

Route optimization rationalizes the cost of last mile delivery, a sizable cost in logistics expenditure. AI algorithms can leverage historical trip sheets and real-time statistics to estimate the delivery time for each shipment. Data-driven operating models are already helping food distributors, retailers and logistics providers provide same-day delivery—the Holy Grail in B2C logistics service. 

AI platforms optimize the route of every delivery vehicle in real time. Streams of geographical, environmental, traffic and shipment data are correlated with designated delivery time windows and vehicle information to sequence delivery and generate the best delivery route for each shipment. The next best point of delivery or a modified route is calculated based on constraints or events and are displayed on a live map. The most optimal delivery route is shared with the driver via the onboard navigation system of the vehicle. Schmitz Cargobull, a German trailer and truck body manufacturer, monitors maintenance requirements, cargo transported and delivery routes of trailers to minimize vehicle breakdown. 

Data-laden dashboards help logistics facility managers make informed decisions. They can also help to monitor the performance of drivers and specific facilities. Real-time visibility into key performance indicators, such as units moved per-hour for each category of product (parcel or pallet), average vehicle speed and total travel time, help benchmark and improve service planning.  

Warehouse Network Optimization 

Large warehouses stand to gain better operations and efficiencies thanks to real-time data from both automated materials handling systems and smart-equipment. For example, optimizing the route for clamp trucks and forklifts handling inbound and outbound cargo expedites movement and can both save fuel and ensure safety. The smart use of real-time data in warehouses can also help organizations realize their respective omni-channel initiatives. 

Since omni-channel marketing is imperative for enterprises, the location and layout of warehouses need to be designed for anytime, anywhere delivery. Big data can help enterprises, government agencies refine the optimal locations needed in a robust distribution infrastructure. The World Bank, for example, is using big data optimization methods to develop a multi-modal transport network in India. Open spatial information helped create and validate a pilot model for identifying locations of multi-modal ports for the organization.  

Predictive AI algorithms and analytics can also help logistics companies improve productivity as well as resource utilization at warehouses and distribution centers. For example, predictive maintenance for trucks, conveyors, forklifts, and trailers rationalizes warehousing and distribution costs. 

The deft use of data can also enable AI-driven analytical tools to help logistics providers aggregate customer demand, simplify distribution networks and manage inventory. Intuitive systems optimize the distribution network and ensure smooth warehouse operations by instantly mapping capacity and availability of equipment. It can also help allocate manpower with workload and provide visibility across warehouse and transportation processes. Such an analytical approach helps to improve stow accuracy and maximize asset use, including conveyors and rack systems.

Freight Consolidation 

AI models also offer insights into products, volume and number of shipments by location, customer, season, mode of freight, preferred delivery timeframes, and transport prerequisites such as ambient temperature or humidity. By understanding these various factors in or at near real time, logistics enterprises can consolidate shipments to reduce transit time, control costs and improve customer service. Significantly, it maximizes capacity utilization despite variability in demand for B2B and B2C shipments. Small parcels, for example, can be converted into Less-Than-Truckload (LTL) shipments, and LTL freight into minimal stop truckloads. 

Advanced logistics applications integrate simulation and AI to help logistics service providers implement cost optimization strategies. Damage claims can be analyzed across delivery routes and modes of transport. It supports rate negotiations for high-risk cargo and enhances damage mitigation approaches. Rules-based AI solutions detect fraud and errors by tracking supply chain events and documents. 

Resource Utilization

Automated systems can help allocate and maximize resources as well. For example, these systems can help in tracking pickup and delivery orders, job schedules and crew availability and streamline the overall logistics network. Machine learning systems can also deliver long-term value by predicting constraints in the ecosystem and mitigating process bottlenecks in real-time.  

AI rules could even evaluate job priority, cargo type, weather, traffic, and resource capabilities to make business decisions about movement of freight. These benefits can extend to air, land, and ocean logistics service providers seeking to maximize resource utilization, including manpower, cargo handling equipment, transport vehicles and space. And, as time goes on, self-learning systems and AI frameworks will become integral in logistics as autonomous vehicles and drones used in last mile delivery alter the distribution landscape. 

The effects of AI resource allocation engines are already being seen today. For example, one such system optimizes daily schedules and manages engineering as well as maintenance activities for rail lines in Hong Kong. The Hong Kong Airport Authority, too, deploys an AI scheduling system to allocate parking slots to aircraft based on flight schedules and shifts in operational dynamics.

The central goal of AI and big data-driven initiatives in logistics is perfecting backend events and operations for the benefit of company, consumer and everyone between. The continued use of big data and AI is pushing logistics to evolve in new, more environmentally friendly ways. But the biggest benefits of AI-driven decision making are happening within providers deploying it, since it provides them with the opportunity to monitor and refine shipments on almost any scale without draining resources and personnel. 

Whether flying to Hong Kong, shipping freight over rail or even dropping a package off via drone, AI and big data are going to be the central drivers of logistics for the foreseeable future.