Nowadays, in the domain of production logistics, one of the most complex planning processes is the accurate forecasting of production and assembly efficiency. In industrial companies, Overall Equipment Effectiveness (OEE) is one of the most common used efficiency measures at semi-automatic assembly lines. Proper estimation supports the right use of resources and more accurate and cost-effective delivery to the customers. This paper presents the prediction of OEE by comparing human prediction with one of the techniques of supervised machine learning through a real-life example. In addition to descriptive statistics, takt time-based decision trees are applied and the target-oriented OEE prediction model is presented. This concept takes into account recent data and assembly line targets with different weights. Using the model, the value of OEE can be predicted with an accuracy of within 1% on a weekly basis, four weeks in advance.

Nowadays, one of the important and indispensable conditions for the effectiveness and competitiveness of industrial companies is the high efficiency of manufacturing and assembly. These enterprises based on different methods and tools systematically monitor their efficiency metrics with Key Performance Indicators (KPIs). One of these most frequently used metrics is Overall Equipment Effectiveness (OEE), the product of availability, performance and quality. In addition to monitoring, it is also necessary to predict efficiency, which can be implemented with the support of machine learning techniques. This paper presents and compares several supervised machine learning techniques amongst other polynomial regression, lasso regression, ridge regression and gradient boost regression. The aim of this article is to determine the best estimation method for semiautomatic assembly line and large batch size. The case study presented with a real industrial example gives the answer as to which of the cumulative or rolling horizon prediction methods is more accurate.

Nowadays, a lot of data is generated in production and also in the domain of assembly, from which different patterns can be extracted using machine learning methods with the support of data mining. With the support of various modern technical and Information Technology (IT) tools, the recording, storage and processing of large amounts of data is now a routine activity. Based on machine learning, efficiency metrics including Overall Equipment Effectiveness (OEE), can be partially predicted, but industrial companies need more accurate and reliable methods. The analyzed algorithms can be used in general for all production units or machines where production data is recorded by Manufacturing Execution System (MES) or other Enterprise Resource Planning (ERP) systems are available. This paper presents and determinates which most used machine learning methods should be combined with each other in order to achieve a better prediction result.

Nowadays, a lot of data is generated in production and also in the domain of assembly, from which different patterns can be extracted using machine learning methods with the support of data mining. With the help of the revealed patterns, the assembly operations and processes can be further optimized, thus the profit achieved can be increased. This article attempts to explore the patterns related to the most used Key Performance Indicator (KPI) in manufacturing, the Overall Equipment Effectiveness (OEE) metric. The patterns and relationships discovered will be sorted into Assembly Pattern Catalogue (APC). Firstly, a literature review demonstrates scientific relevance. Secondly, it examines the circumstances and methods of samples in the Manufacturing Execution System (MES) data source and Enterprise Resource Planning (ERP) systems. In the third section, the detailed pattern catalogue is defined in the area of assembly. The novelty of the article is that beyond the generalization of patterns, it characterizes the pattern catalogue with mentioning practical industrial examples.

In this study, we first performed a comprehensive structural analysis of four models of radiosondes (devices intended for use as the meteorological probe of a sounding balloon) manufactured by three different companies – Graw, Vaisala and Meteomodem. The radiosondes were disassembled for visual inspection and manual measurement, three-dimensional computed tomography images were taken of their inner structure, and the outer shapes of the radiosondes were scanned with a structured-light three-dimensional scanner. The structural properties of the radiosondes thus identified were then compared to one other, based on which the Meteomodem M10 was ranked as the least harmful in a potential collision. Next, the Meteomodem M10 radiosonde was used in collision tests with a heavy target and with a pumpkin model, in order to evaluate the possible damage caused by and to the radiosonde in different types of collisions.