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The interest of manufacturing companies in a sufficient prediction of lead times is continuously increasing -especially in engineer to order environments with typically a large number of individual parts and complex production processes. A multitude of approaches have been proposed in the literature for predicting lead times considering different data and methods or algorithms from operations research (OR) and machine learning (ML). In order to provide guidance at setting up prediction models and developing new approaches, a systematic review of the available approaches for predicting lead times is presented in this paper. Forty-two publications were analyzed and synthetized: Based on a developed framework considering the used data class (e.g. product data or system status), the data origin (master data or real data) and the used method and algorithm from OR and ML, the publications are classified. Based on the classification, a descriptive analysis is performed to identify common approaches in the existing literature as well as implications for further research. One result is, that mostly order data and the status of the production system are used for predicting lead times whereas material data are used seldom. Additionally, ML approaches primarily use artificial neural networks and regression models for predicting lead times, while OR approaches use mainly combinatorial optimization or heuristics. Furthermore, with increasing model complexity the use of real data decreased. Thus, we identified as an implication for further research to set up a complex data model considering material data, which uses real data as data origin.

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