Steinberg, Fabian
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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:Article4 15 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Open Access , Machine learning-based prediction of missing components for assembly - a case study at an engineer-to-order manufacturerFor manufacturing companies, especially for machine and plant manufacturers, the assembly of products in time has an essential impact on meeting delivery dates. Often missing individual components lead to a delayed assembly start, hereinafter referred to as assembly start delayers. Identifying the assembly start delayers early in the production process can help to set countermeasures to meet the required delivery dates. In order to achieve this, we set up 24 prediction models on four different levels of detail utilizing different machine learning-algorithms - six prediction models on every level of detail - and applying a case-based research approach in order to identify the model with the highest model quality. The modeling approach on the four levels of detail is different. The models on the coarsest level of detail predict assembly start delayers utilizing a classification approach. The models on the three finer levels of detail predict assembly start delayers via a regression of different lead times and subsequent postprocessing operations to identify the assembly start delayers. After training of the 24 prediction models based on a real data set of a machine and plant manufacturer and evaluating their model quality, the classification model utilizing a Gradient Boosting classifier showed best results. Thus, performing a binary classification to identify assembly start delayers was the best modelling approach. With the achieved results, our study is a first approach to predict assembly start delayers and gives insights in the performance of different modeling approaches in the area of a production planning and control.Source Type:Article2 9 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Open Access , Machine learning-based prediction of missing parts for assembly(2024)Manufacturing companies are faced with the challenge of managing increasing process complexity, while at the same time having to meet ever higher demands in terms of on-time delivery and product costs. Especially at points in the value chain such as assembly, where different material flows converge, it is often not possible to provide the components required for an order in a timely and synchronized manner. Early identification of missing parts at the beginning of assembly can help to take countermeasures to meet the required delivery dates. To achieve this, this thesis develops machine learning based prediction models that can predict potential missing parts at the start of assembly at an early stage in the value chain. The development of the models was carried out as case studies at manufacturing companies in the machine industry. As a basis for the development, an extensive systematic literature search was conducted on existing approaches for the prediction of lead times of production orders. The result was that no approach exists that takes into account the full complexity of manufacturing companies. In particular, with regard to the data used, it became clear that information about the product to be manufactured—so-called material data—has not been used up to now. Based on the systemic review, a model for predicting missing parts from inhouse production was implemented. It was shown that classification approaches achieve the best possible model quality for components from in-house production. With the defined modeling approach—classification—it was then verified that material data has a significant influence on the model quality and is therefore relevant for the prediction of missing parts at the start of assembly. Finally, a model for predicting delivery delays in the purchasing process was implemented, which makes it possible to predict potential missing parts from suppliers at the time of ordering. The case studies show that the use of machine learning for the prediction of missing parts in both in-house production and the purchasing process can identify delays in the start of assembly at an early stage. The developed models are therefore suitable as a support system for production planners and controllers as well as purchasing departments to improve material availability at the start of assembly.Source Type:Doctoral Thesis5 16
