The determination of planned order lead times is still an unsolved issue in companies with complex production structures. As a result, companies often have problems delivering to customers on time. In addition to insufficient adherence to schedules due to late work processes, premature work processes lead to avoidable inventory. Precise planned order lead times are required to confirm a feasible deadline to the customer, to plan production capacities and to better organize procurement.In practice, however, rigid methods for determining the planned order lead times are often used, which do not react sufficiently to changing environmental influences. Possible environmental influences are e.g. short term sick calls or fluctuations of the load of production. Classical approaches for the determination of planned order lead times include, in particular, methods based on estimates, historical values, logistic models and simulation. These methods take into account different information and data for the determination. The limitations of most of the classical methods are especially in too simplified assumptions, which makes a high planning quality difficult. Approaches such as simulation counteract these limitations, but lead to a very high application effort. Especially in complex production environments like shop floor production (e.g. tool and special machine manufacturing), precise planning is not implemented in practice due to scattering work content and a fluctuating number of operations per job. In addition, for contract manufacturers, insufficient determination of the planned lead times has a direct impact on the adherence to schedules to customers. The central input factors influencing the order lead times in a shop floor production are not yet systematically investigated.Therefore, the aim of this project is to identify and quantify central input factors for the determination of planned order lead times in shop floor production from company data of six contract manufacturers. Machine learning (ML) will be used in the project to identify patterns in data and to derive generalized assumptions. The insights into the central cause-effect relationships will help the investigated companies to more precisely forecast planned order lead times and thus improve their ability to meet their customers' deadlines.