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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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This paper describes the current legal framework and regulations applied to industrial drone's flights established by different countries as a set point on drones design and applications. We present a mathematical approach used to build the structure of an attitude controller for a quadcopter (drone) as to answer the question of 'How to implement an attitude controller on a quadcopter for indoor and outdoor flights as a step forward to support the safety features in factory applications' . The control system was designed for a quadcopter on ' × ' configuration, which allows it to have six degrees of freedom (6DOF) of movement, while commanded by four inputs given by the radio controller (R/C). Accelerometers and gyroscopes were used in order to obtain the data related to the drone's behavior during the flight to be able to control it. A cascade controller P-PID was implemented in order to control the system and reach the stability as well as in indoor or outdoor flights. The theories presented in this paper were analyzed and proved on a real model resulting in the desired flying behavior.