Мотивация и стимулирование персонала на современных предприятиях

In a modern, rapidly developing world, the human factor is perhaps the most important factor of production. Therefore, the management company must maintain competent personnel policies that will enable employees to realize their full potential in the organization. Motivation and stimulation of personnel is the cornerstone of modern management. The results of the work of the organization depends on the engagement of staff in labour, the desire to benefit

Sensor retrofit for a coffee machine as condition monitoring and predictive maintenance use case

The concept of Industry 4.0 provides promising approaches to reducedowntime and increase overall equipment efficiency in manufacturing processesthrough interconnected devices in the industrial internet of things (IIoT). As theprocurement of new IIoT-ready machines is costly, the retrofit of old machinescan be an idea worth exploring. In this paper, we designed a simple experimentsetup using affordable sensors and a coffee machine (due to the absence ofmachinery) to measure grinding vibrations and to predict the last coffee beforegrinder no-load. Microsoft Azure Machine Learning Studio was used to deploymachine learning techniques in order train prediction models. While predictionaccuracy in this experiment was non-satisfactory, our results nonetheless indicatethat retrofit is indeed a proper approach to make an older machine park smart,provided that sensors (especially their sample rate) are suitable for theapplication

The decoded information from the Hc-4 molar in Equus stenonis requires renewing the Linnaeus paradigm

Owing to the uncertainties and anomalies that are historically constants in the Linnaean paradigm, it happens that the phylogenetic data obtained from crown molars, although these morphologies are inherited, have a complementary scientific value with regard to the biochemical data. The Hc¿4 molar (Betic Cordillera, Spain) is analyzed in order to obtain new data using two techniques. Its crown wear section is a biomineralized embryonic morphology (retrogerminative technique), and its enamel line draws hexagonal marks (superimposition technique). These data are the foundations of the mitosis area loop development hypothesis during morphogenesis. The tooth structure is a germination process of the embryonic dermal masses (mitosis areas), and in relationship to (1) the moment they were born during loop process, (2) size, and (3) location when they constitute a specific cusps crown when they are petrified by an enamel mantle. In conclusion, Linnaean characteristics (morphology) are associated with two parameters: frequency percentage with which the cusps are inherited and their functional role. This parameters group is called ¿Biological Nature¿. The Reference Series in each molar is the biological nature values group positioned in linear order. The Reference Series of each linnaean holotype imply phylogenetic relationships using similarity percentages and the Linnaean uncertainties disappear from the phylogeny. If I express phenotype with the reference series and also the genotype (DNA) is displayed with a numerical sequence, then it happens that we have two numerical sequences and between them exist cause and effect relationshipPeer Reviewe

Optimization approach for the combined planning and control of an agile assembly system for electric vehicles

For some years now, the automotive industry has been challenged by growing market dynamics, shorter product lifecycles and customers' increasing demands for individualization. In order to cope with this development, the automotive assembly needs to adapt quickly to changing demands with a low level of investment in the future. Under the current circumstances, the traditional line assembly for high volume production is reaching its limits in terms of adaptability and scalability. A promising solution to address the current challenges is the concept of the agile assembly. The concept of agile assembly breaks up the rigid linkage of assembly stations and, thus, enables full flexibility in the sequence of assembly operations only limited by the precedence graph. Therefore, the routing of electric vehicles in the agile assembly is based on the availability of resources such as assembly stations and automated guided vehicles that handle the material supply. Further, by transferring the transport function to the vehicle itself, investments for convey or systems are eliminated. This research work presents an optimization approach for the machine scheduling and transportation planning, which derives instructions for electric vehicles, assembly stations as well as automated guided vehicles. For each electric vehicle, an optimized route is calculated, taking into account product-specific precedence graphs and minimizing the overall makespan. In addition, the machine scheduling and transportation planning is integrated into a combined planning and control concept which covers the allocation of resources and the assignment of capabilities of the entire assembly system. The approach is implemented and applied to a practical case of a compact electric vehicle. Thus, the work contributes to the evaluation of agile assembly systems in automotive production

Development of a matching platform for the requirement-oriented selection of cyber physical systems for SMEs

This paper addresses the challenge of a systematic requirementoriented configuration and selection of cyber physical systems (CPS) for SMEs. As the key technologies of realizing the digitalization and interconnection of production processes, manufacturing companies have realized the potential benefits brought by CPS. However, due to the complexity and fast development of CPS technology, it is difficult for SMEs, which lack expertise and financial resources, to select the appropriate CPS technologies meeting both functional and financial requirements. To overcome the issue, an online matching platform is developed to let SMEs express their needs and assist them conceptualize the individual CPS. This paper presents the matching methodology of the matching platform, which can not only match technical characteristics but also evaluate economic potentials. Then, it was demonstrated by a tracking and tracing use casein the end-of-line assembly of a small-sized German electric automobile manufacturer

Conceptualization of the use of Artificial Intelligence for Interdependencies Analysis in Requirements Engineering

The efficiency in product development is largely determined by the quality of the requirements and the ability of the product design and production planner to analyze them. Interdependencies between multiple requirements identified at an early stage enable a sustainable design of the product as well as the corresponding production system by increasing process efficiency as well as the effectiveness of development processes. However, the necessary analysis of complex interdependencies between requirements of a product and the corresponding production system is time-consuming, error-prone, and highly inefficient when performed manually. Current development processes are based on such manual processes for analyzing requirements in natural language and must therefore be adapted. This paper describes a methodical approach based on a semi-systematic literature review making the complexity of the interdependencies manageable by using existing approaches and methods in the field of model-based systems engineering (MBSE) as well as natural language processing (NLP). Thereby, a transition from informal requirements represented in natural language to analyzable and structured information, which enable interdependencies modeling for requirement chains, is described. A corresponding framework for analyzing interdependencies in the requirements engineering process is derived

Classification Of Flow-Based Assembly Structures For The Planning Of Flexible Mixed-Model Assembly

The increasing product variance due to the growing individualization of customer requirements leads to smaller batch sizes and higher process time spreads in mixed-model assembly. The resulting decline in efficiency pushes conventional, less flexible assembly lines to the limits of their economic viability. Matrix assembly is an approach to increase flexibility and efficiency by decoupling workstations and dissolving cycle time constraints while maintaining flow. Both matrix and line assembly are flow-based assembly structures characterized by assembly objects moving according to the flow principle. Due to the numerous design options of flow-based assembly structures and the need to consider flexibility as a central decision criterion, the complexity of structural planning increases. The variety of the design options as well as their compatibility make it challenging for assembly planners to decide which configuration provides sufficient flexibility for their use case. This paper presents a novel level-based classification for flow-based assembly structures that identifies the relevant configurations, ranks them according to provided flexibility, and breaks down the characteristics as well as their compatibility. The classification enables planners to efficiently compile, evaluate and select the flow-based structure configurations suitable for the individual use case during assembly structure planning. Planning efficiency and results are improved by transparently providing all configurations and their characteristics' compatibility to the planner without any research effort. The configuration selection focusing on flexibility by means of the classification can be the starting point of a subsequent simulation of the system behavior concerning efficiency

Bridging data gaps in the food industry – sensor-equipped metal food containers as an enabler for sustainability

In recent years, Machine Learning (ML) applications for manufacturing have reached a high degree of maturity and can be considered as a suitable tool for improving production performance. In addition, ML applications can be used in many other production areas to enhance sustainability within the manufacturing process. One area is storing and transporting bulk materials with metal Intermediate Bulk Containers (IBC). These IBCs are currently used solely for their primary purpose of storage and transportation of raw and finished goods. Hence, while in use , IBCs are often a black box that does not provide additional value to manufacturers. Equipping IBCs with sensor technology can provide such value: new data can be generated along the entire supply chain and production processes, taking the sustainability of production to a new level. Within the research project smart.CONSERVE, we use this additional data, for example, to monitor the stored food's critical characteristics or to establish predictive maintenance for IBCs. Thus, storing produced goods in defective IBCs can be avoided and wasting resources can be prevented. This publication describes how smart IBCs in the food industry can increase supply chain visibility and reduce food waste. To illustrate this, we present possible use cases enabled by new data availabilities. Additionally, we provide insights into how these use cases can be transferred to other industries. Besides, we exemplify the many opportunities for manufacturers to develop new smart services and ML applications based on the collected data - and how this can support manufacturers in achieving higher levels of sustainability