21st-century logistics is facing Herculean challenges. Logistics systems in warehousing, dispatch and transportation are struggling to keep up with ever-growing demands in terms of flexibility, efficiency and supplier deadlines. Most logistics companies dream of predictive, swift and optimum scheduling based on the best possible forecast of external factors such as requirements, incoming orders and transportation times. To get as close to this and avoid bottlenecks as much as possible, companies must make predictions in complex and dynamic situations, reacting just in time and exercising the right amount of control. AIM is now at this year's CeMAT to show them how it's done with predictive logistics and machining learning.
Innovative machine learning models for predictive logistics are designed to use relevant data in real time to automatically forecast external factors as precisely as possible. What’s more, they learn how to imitate planning control and optimize the process, taking the relevant criteria such as throughput, the cost of switching and supplier deadlines into account. The imitation alone can take a great deal of strain off employees. The ultimate goal is to use predictive control and avoid bottlenecks so as to maintain the highest possible level of logistics flow. Staff and resources such as machines and vehicles should also be deployed as efficiently and considerately as possible. Further possibilities include automating the routine tasks in compiling the various documentation throughout the supply chain. Last but not least, intelligent document processing has the potential to read analog and digital documentation from different sources completely automatically and transfer the data to existing systems.