Artificial intelligence for last-mile logistics - Procedures and architecture

Artificial Intelligence (AI) is becoming increasingly important in many industries due to its diverse areas of application and potential. In logistics particular, increasing customer demands the growth shipment volumes are leading difficulties forecasting delivery times, especially for last mile. This paper explores potential using AI improve forecasting. For this purpose, a structured theoretical solution approach method improving presented. doing so, phases Cross-Industry Standard Process Data Mining (CRISP-DM) framework, standard process data mining, adopted discussed detail illustrate complexity importance each task such as preparation or evaluation. Subsequently, by embedding described into an overall system architecture information systems, ideas integration real systems given.