The Impact of Different Internet Application Contexts on Knowledge Transfer between Enterprises

This paper investigates the effect of different Internet application scenarios on the forward and reverse knowledge transfer between architecture enterprises and component enterprises in the context of modular production in manufacturing enterprises. After dividing the Internet contexts into three the perspectives of Internet tools, Internet platforms, and Internet resources, this paper adopts a system dynamics approach to construct a causality model and a system flow diagram of the influence of different Internet contexts on the knowledge transfer between architecture enterprises and component enterprises, and uses Vensim PLE software to simulate and conduct a sensitivity analysis of the model. The results show that the three Internet contexts, namely Internet tools, Internet platforms, and Internet resources, can promote both positive and negative knowledge transfer between architecture enterprises and component enterprises, and exhibit a steadily growing trend of promotion; Internet tools and Internet resources are more conducive to positive knowledge transfer from architecture enterprises to component enterprises, and Internet platforms have a stronger facilitation effect on the reverse knowledge transfer from the component enterprises to the architecture enterprises. Our research offers useful recommendations for businesses on how to utilize various Internet settings to promote and assist effective knowledge transfer.

Implementation of Zero Emission Fast Shortsea Shipping

The paper describes the implementation of state-of-the-art “Industry 4.0” methods and tools, a holistic ship design optimization and modular production methods, as well as advanced battery technologies to enable a fully electrical, fast zero-emission waterborne urban transport. The design of a fast catamaran passenger ferry demonstrator planned for operation as a waterborne shuttle in the Stavanger/Norway area and of a replicator for operation at Thames River/London are elaborated, including infrastructural issues for their operation. The presented research is in the frame of the H2020 funded project “TrAM – Transport: Advanced and Modular” (www.tramproject.eu)

Poultry subcomplex of Kazakhstan: production of turkey meat

The goal-is to analyze the state of development of poultry farming in Kazakhstan, identify the range of main problems and propose mechanisms for their solution. Methods - economic and mathematical modeling, economic and statistical, dialectical. Results - the advantages of turkey meat production over other types of poultry industry were revealed. The indicators of the poultry sector, as well as the production of feed in the leading regions of the republic in dynamics from 2019 to 2021 are given. An important advantage in obtaining turkey meat is a large meat yield from one carcass compared to other poultry, this indicates a high economic efficiency. The authors note that in the poultry subcomplex, meat direction has an advantage over the egg one, since poultry meat can be sold from all its varieties. The necessity of rational use of the fodder base in the production of turkey meat has been determined. Methods of using feed, namely wet, dry and combined processing, are considered, their structure is shown. So, for example, mixes are subjected to wet processing, that is, ground cereals, grains, concentrated supplements with protein content by adding water, whey, milk, juicy feed, broth and vitamins. Dry feed consists of complete feed in granules or ground form, enriched with essential minerals and vitamins. It has been determined that the use of innovative technologies for obtaining turkey meat, such as a floor system of growing and heating of a gas type, can reduce heating tariffs compared to a conventional system, reduce energy costs by more than 20%. Conclusions. Development of the poultry sector is socially beneficial and the most promising direction in ensuring food security of the Republic of Kazakhstan. Recommendations have been developed for improving the feed base, reimbursing part (30%) of financial costs associated with purchase of machinery and special equipment, the construction of modular production structures for meat and egg farms, preferential subsidies for investment activities of poultry enterprises, as well as the formation of stabilization funds for feed grain.

Alignment of safety and security risk assessments for modular production systems

In order to ensure the safety and security of industrial systems with regard to all life cycle phases from development through operation to disposal, specific regulatory and normative requirements are imposed. Due to the digitalization, interconnection, and constantly increasing complexity of manufacturing systems in the context of Industrie 4.0, the manual effort necessary to achieve the required safety and security is becoming ever greater and almost impossible to manage, especially for small and medium-sized enterprises. Therefore, this paper examines the existing challenges in this area in more detail and gives an outlook on the possible solutions to ensure safety and security much quicker and with less manual effort. The overall vision is a (partially) automated risk assessment of modular systems with respect to safety and security, including the alignment of the corresponding processes from both domains and the formalization of the information models needed.

Task Decomposition Based on Cloud Manufacturing Platform

As a new service-oriented manufacturing paradigm, cloud manufacturing (CMfg) realizes the optimal allocation of resources in the product manufacturing process through the network. Task decomposition is a key problem of the CMfg system for resource scheduling. A high-quality task decomposition method can shorten product development time, reduce costs for resource service providers, and provide technical support for the application of CMfg. However, a cloud manufacturing system has to manage the allocation the correct amount of manufacturing resources, complex production processes, and highly dynamic production environments. At the same time, the tasks issued by service demanders are usually asymmetric and tightly coupled. We solve the complex task decomposition problem by using the traditional methods, that are hard to complete in CMfg. To overcome the shortcomings of CMfg, this paper proposed a task decomposition method based on the cloud platform. For achieving modular production, this approach creatively divides the product production process into four stages: design, manufacturing, transportation, and maintenance. Then a hybrid method, which combines with depth-first search algorithm, fast modular optimization algorithm, and artificial bee colony algorithm, is introduced. The method can obtain a multi-stage task optimization decomposition plan in CMfg. Simulation results demonstrate the proposed method can achieve complex task optimization decomposition in a CMfg environment.

Modular production control using deep reinforcement learning: proximal policy optimization

EU regulations on $$\textit{CO}_2$$ CO 2 limits and the trend of individualization are pushing the automotive industry towards greater flexibility and robustness in production. One approach to address these challenges is modular production, where workstations are decoupled by automated guided vehicles, requiring new control concepts. Modular production control aims at throughput-optimal coordination of products, workstations, and vehicles. For this np-hard problem, conventional control approaches lack in computing efficiency, do not find optimal solutions, or are not generalizable. In contrast, Deep Reinforcement Learning offers powerful and generalizable algorithms, able to deal with varying environments and high complexity. One of these algorithms is Proximal Policy Optimization, which is used in this article to address modular production control. Experiments in several modular production control settings demonstrate stable, reliable, optimal, and generalizable learning behavior. The agent successfully adapts its strategies with respect to the given problem configuration. We explain how to get to this learning behavior, especially focusing on the agent’s action, state, and reward design.

Design of a self-learning multi-agent framework for the adaptation of modular production systems

This paper presents the design of a multi-agent framework that aids engineers in the adaptation of modular production systems. The framework includes general implementations of agents and other software components for self-learning and adaptation, sensor data analysis, system modelling and simulation, as well as human-computer interaction. During an adaptation process, operators make changes to the production system, in order to increase capacity or manufacture a product variant. These changes are automatically captured and evaluated by the framework, building an experience base of adjustments that is then used to infer adaptation knowledge. The architecture of the framework consists of agents divided in two layers: the agents in the lower layer are associated with individual production modules, whereas the agents in the higher layer are associated with the entire production line. Modelling, learning, and adaptations can be performed at both levels, using a semantic model to specify the structure and capabilities of the production system. An evaluation of a prototype implementation has been conducted on an industrial assembly system. The results indicate that the use of the framework in a typical adaptation process provides a significant reduction in time and resources required.

Optimal ordering decisions in mass customization supply chain with overconfident retailer

In this paper, we analyze the optimal order-quantity decisions in a supply chain with mass customization (MC) manufacturer and overconfident retailer. First, we consider a newsvendor model in which an unbiased retailer sells mass customized products. The retailer needs to make order quantity decisions before the selling season. Meanwhile, the supplier is a mass customization manufacturer and implements modular production. The supply process is uncertain, as the real quantity the retailer received is the order quantity multiplied by a random yield rate. Second, two overconfident models are considered and theorems are proposed. In the first model, the behavioral bias of overconfidence only affects the retailer's judgment of variance of market demand. In the second model, the behavior bias of overconfidence affects not only the retailer's cognition of the variance of market demand, but also his cognition of the expectation of market demand. In addition, the relationship between the optimal decisions and the modularity level is obtained. Finally, we provide managerial insights for the decision makers of the retailers and the manufacturers on order quantity and modularity level, respectively.