Koke, Benjamin
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Koke, Benjamin
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Publication Access status: Open Access , Approaches for the prediction of lead times in an engineer to order environment - a systematic reviewThe interest of manufacturing companies in a sufficient prediction of lead times is continuouslyincreasing - especially in engineer to order environments with typically a large number of individual parts andcomplex production processes. A multitude of approaches have been proposed in the literature for predictinglead times considering different data and methods or algorithms from operations research (OR) and machinelearning (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-twopublications were analyzed and synthetized: Based on a developed framework considering the used dataclass (e.g. product data or system status), the data origin (master data or real data) and the used methodand algorithm from OR and ML, the publications are classified. Based on the classification, a descriptiveanalysis is performed to identify common approaches in the existing literature as well as implications forfurther research. One result is, that mostly order data and the status of the production system are used forpredicting lead times whereas material data are used seldom. Additionally, ML approaches primarily useartificial neural networks and regression models for predicting lead times, while OR approaches use mainlycombinatorial optimization or heuristics. Furthermore, with increasing model complexity the use of realdata decreased. Thus, we identified as an implication for further research to set up a complex data modelconsidering material data, which uses real data as data origin.Source Type:Article3 14 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Open Access , Survey based dataset on automation decisions for assembly systems in GermanyThe data provided in the present article provides information on the importance of a list of monetary and non-monetary influencing factors on decisions regarding the automation of an assembly system. A survey among German industry representatives conducted between July 2018 and October 2018 is the basis for this dataset. It contains the characteristics of industrial companies based in Germany that participated in the survey as well as their attitude towards the development of the automation level in assembly systems. The focus of the survey lies on the influencing factors of the production and production environment and their influence on the automation level. The participants were able to evaluate the factors on a six-step ordinal scale from “no influence” to “very strong influence”. Interpretation of this data can be found in the research article titled “Automation decisions in flow-line assembly systems based on a cost-benefit analysis”Source Type:Article2 10
