Industry 4.0 and the associated idea of society 4.0 pose specific challenges for the concept of sustainable development. These challenges relate, inter alia, to responsibility, in which the changes to date have overall entailed: • a transition from ex post responsibility to ex ante responsibility (H. Jonas); • a transition from individual responsibility to corporate social responsibility. In the context of society 4.0 there is a need for shared responsibility. The problem of justice and therefore the implementation of sustainable development not only becomes an open problem, but also requires constant updating and specifi c optimisation.
This paper points out assumptions and reasons for using digital technologies, the importance of using digital technologies in teaching and management. It also refers to the digital technologies and digital competences as an essential part of the competency model of a teaching staff in education. It also points out the fact that existing competency models need to be further explored, decomposed, and formulated as an illustration by the digital competences extensions.
A product is referred to as robust when its performance is consistent. In current product robustness paradigms, robustness is the responsibility of engineering design. Drawings and 3D models should be released to manufacturing after applying all the possible robust design principles. But there are no methods referred for manufacturing to carry and improve product robustness after the design freeze. This paper proposes a process of inducing product robustness at all stages of product development from design release to the start of mass production. A manufacturing strategy of absorbing all obvious variations and an approach of turning variations to cancel one another are defined. Verified the application feasibility and established the robustness quantification method at each stage. The theoretical and actual sensitivity of different parameters is identified as indicators. Theoretical and actual performance variation and accuracy of estimation are established as robustness metric. Manufacturing plan alignment to design, complimenting the design and process sensitivities, countering process mean shifts with tool deviations, higher adjustable assembly tools are enablers to achieve product robustness.
The application of the 5S methodology to warehouse management represents an important step for all manufacturing companies, especially for managing products that consist of a large number of components. Moreover, from a lean production point of view, inventory management requires a reduction in inventory wastes in terms of costs, quantities and time of non-added value tasks. Moving towards an Industry 4.0 environment, a deeper understanding of data provided by production processes and supply chain operations is needed: the application of Data Mining techniques can provide valuable support in such an objective. In this context, a procedure aiming at reducing the number and the duration of picking processes in an Automated Storage and Retrieval System. Association Rule Mining is applied for reducing time wasted during the storage and retrieval activities of components and finished products, pursuing the space and material management philosophy expressed by the 5S methodology. The first step of the proposed procedure requires the evaluation of the picking frequency for each component. Historical data are analyzed to extract the association rules describing the sets of components frequently belonging to the same order. Then, the allocation of items in the Automated Storage and Retrieval System is performed considering (a) the association degree, i.e., the confidence of the rule, between the components under analysis and (b) the spatial availability. The main contribution of this work is the development of a versatile procedure for eliminating time waste in the picking processes from an AS/RS. A real-life example of a manufacturing company is also presented to explain the proposed procedure, as well as further research development worthy of investigation.