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.
The article presents tools, methods and systems used in mechanical engineering that in combination with information technologies create the grounds of Industry 4.0. The authors emphasize that mechanical engineering has always been the foundation of industrial activity, while information technology, the essential part of Industry 4.0, is its main source of innovation. The article discusses issues concerning product design, machining tools, machine tools and measurement systems.
Rescheduling is a frequently used reactive strategy in order to limit the effects of disruptions on throughput times in multi-stage production processes. However, organizational deficits often cause delays in the information on disruptions, so rescheduling cannot limit disruption effects on throughput times optimally. Our approach strives for an investigation of possible performance improvements in multi-stage production processes enabled by realtime rescheduling in the event of disruptions. We developed a methodology whereby we could measure these possible performance improvements. For this purpose, we created and implemented a simulation model of a multi-stage production process. We defined system parameters and varied factors according to our experiment design, such as information delay, lot sizes and disruption durations. The simulation results were plotted and evaluated using DoE methodology. Dependent on the factor settings, we were able to prove large improvements by real-time rescheduling regarding the absorption of disruption effects in our experiments.
The article refers to the idea of using the software defined network (SDN) as an effective hardware and software platform enabling the creation and dynamic management of distributed ICT infrastructure supporting the rapid prototyping process. The authors proposed a new layered reference model remote distributed rapid prototyping that allows the development of heterogeneous, open systems of rapid prototyping in a distributed environment. Next, the implementation of this model was presented in which the functioning of the bottom layers of the model is based on the SDN architecture. Laboratory tests were carried out for this implementation which allowed to verify the proposed model in the real environment, as well as determine its potential and possibilities for further development. Thus, the approach described in the paper may contribute to the development and improvement of the efficiency of rapid prototyping processes which individual components are located in remote industrial, research and development units. Thanks to this, it will be possible to better integrate production processes as well as optimize the costs associated with prototyping. The proposed solution is also a response in this regard to the needs of industry 4.0 in the area of creating scalable, controllable and reliable platforms.
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.
This study presents cause-effect dependencies between inputs and outputs of business transitions that are software objects designed for processing information-decision state variables in integrated enterprise process control (EntPC) systems. Business transitions are elementary components of controlling units in enterprise processes that have been defined as self-controlling, generalized business processes, which may serve not only as business processes but also as business systems or their roles. Business events, which have zero durations by definition, are interpreted as executions of business actions that are main operations of business transitions. Any ordered set of business actions, performed in the controlling unit of a given enterprise process and attributed to the same discrete-time instant, is referred to as ‘the information-decision process’. The i-d processes may be substituted by managerial business processes, performed on the lower organizational level, where durations of activity executions are greater than zero, but discrete-time periods are considerably shorter. In such a case, procedures of business actions are performed by corresponding activities of managerial processes, but on the level of business transitions the durations of their executions are imperceptible, and many different business events may occur at the same discrete-time instant. It has been demonstrated in the paper how to control business actions to ensure that a given i-d state variable may not change more than once at a given instant. Furthermore, the rules of designing the i-d process structures, which prevent random changes of transitory states, have been presented.
The objective of the milk-run design problem considered in this paper is to minimize transportation and inventory costs by manipulating fleet size and the capacity of vehicles and storage areas. Just as in the case of an inventory routing problem, the goal is to find a periodic distribution policy with a plan on whom to serve, and how much to deliver by what fleet of tugger trains travelling regularly on which routes. This problem boils down to determining the trade-off between fleet size and storage capacity, i.e. the size of replenishment batches that can minimize fleet size and storage capacity. A solution obtained in the declarative model of the milk-run system under discussion allows to determine the routes for each tugger train and the associated delivery times. In this context, the main contribution of the present study is the identification of the relationship between takt time and the size of replenishment batches, which allows to determine the delivery time windows for milkrun delivery and, ultimately, the positioning of trade-off points. The results show that this relationship is non-linear.
With the increasing demand of customisation and high-quality products, it is necessary for the industries to digitize the processes. Introduction of computers and Internet of things (IoT) devices, the processes are getting evolved and real time monitoring is got easier. With better monitoring of the processes, accurate results are being produced and accurate losses are being identified which in turn helps increasing the productivity. This introduction of computers and interaction as machines and computers is the latest industrial revolution known as Industry 4.0, where the organisation has the total control over the entire value chain of the life cycle of products. But it still remains a mere idea but an achievable one where IoT, big data, smart manufacturing and cloud-based manufacturing plays an important role. The difference between 3rd industrial revolution and 4th industrial revolution is that, Industry 4.0 also integrates human in the manufacturing process. The paper discusses about the different ways to implement the concept and the tools to be used to do the same.