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Predictive logistics are based on massive data-driven learning analytics. This helps logistics specialists to boost their productivity and quality by anticipating the demand of their customers before ordering. A lack of patience for long delivery times is the principal influencer of anticipatory logistics. Customers still want to balance their experience of online shopping with the ease of quick delivery. In this region, all parties involved in the supply line profit from anticipatory logistics by predicting demand, enabling companies to invest their money before demand shoots up.

AI expects consumer demand to grow for the new model, which will then boost the manufacturer’s production of that particular model. In the field of risk control, forward-looking strategies even operate well. AI tools predict safety features and potential risks closely linked to the management forecasts of infrastructure. The automotive and transport system utilizes AI technology to repair vehicles and facilities. Predictive maintenance in this case is based on the sensor data obtained from smart machines and vehicles.

In order to evaluate infrastructure conditions and other properties, KONUX, a Munich based IIoT company combines smart sensor systems with an AI based analysis, allowing preventive modeling. One way is to track and examine switches by rail operators. The computer controls the mechanical wear and detects anomalies in time. This avoids the failure of the railway switch.