Manufacturing is moving towards complexity, large integration, digitalization and high flexibility. A combination of these characteristics is a basic for forming a new kind of production system, known as Cyber Physical System (CPS). CPS is a board range of complex, multidisciplinary, physically-aware next generation engineered systems that integrates embedded computing technologies. Those integrated manufacturing systems usually consist of four levels: network, enterprise, production system and workplace. In this article we are concentrated to the workplace level, examining the implementation of the most suitable robot-cell and integration it into the production system and enterprise structure. The problem is actual for the big companies such as automobile industry, but very important is also for small and medium sized enterprises (SMEs) that tend to produce for example; small tractors, air conditioners for high speed trains or even different type of doors for houses. In all cases the best solution to response the situation is the implementation of robot-based manufacturing cell into a production system, which is not only a challenge but also need a lot of specific knowledge. Designing and selecting optimal solutions for robot-based manufacturing systems is suitable to carry out by a computer-based decision support systems (DSS). DSS typically works by ranking, sorting or choosing among the alternatives. This article emphasis to the problem of integration the DSS with the artificial intelligence (AI) tools. For this objective, the study has been focused to development of a conceptual model for assessing robot-based system by means of technical and functional capabilities, which is combined with cell efficiency based on process Key Performance Indicators (KPIs) and enterprise Critical Success Factors (CSFs). The elaborated model takes into consideration system design parameters, product specific indicators, process execution data, production performance parameters and estimates how the production cell objective can be achieved. Ten different types of companies were selected and their robot-based manufacturing systems were mapped by qualitative and quantitative factors based on the model, whereas executives were interviewed to determine companies’ strategic objectives. The study results comprise of an approach that helps SMEs to gain additional economic-technical information for decision making at different levels of a company.
In most cases, the complexity of installation work, such as the induction of a collaborative robot at metal-working enterprises exceeds the complexity of machining and significantly exceeds the labour costs for all other types of production. Today the most assembly jobs in the manufacturing domain of Small and Medium-sized Enterprises (SMEs) are still performed by hand due to high mix and low volume orders. The interaction of humans and robots may increase the efficiency in complex assembly processes. The flexibility and variability of assembly processes require close cooperation between the worker and the automated production system. Automation of production is not an easy process for an enterprise, which requires high investment and additional skills, but it is necessary to improve working conditions and product quality. This article provides an efficiency analysis of collaborative robots usage in one of the Estonian enterprise.
Fast changing market conditions force SMEs to collaborate dynamically with each other for carrying out projects accordingly to customer requirements in a competitive way. In order to simplify the cooperation, a Collaborative Network (CN) of enterprises forms Partner Networks (PN), where partners define the rules of collaboration and sign collaboration agreements. This paper provides a conceptual model of a sustainable realization of collaborative projects for SME-s from the machinery domain that follows criteria for involving new members to a PN. Furthermore, the authors describe the initiation of a PN joining process and introduce the business processes classifier for enterprises partners. The main objective of research is to enable the commencement of new projects, or Virtual Organization (VO) faster for the price proposal preparation. Today there is a gap exists in the state-of-the-art with respect to a comprehensive lifecycle of PN-initiation and collaborative project management, starting from its very inception. The primary purpose of this paper is to introduce a novel model that enables a faster PN initiation process; the novel process of PN initiation covers the aspects of composing the partner network, the PN-initiation lifecycle and a corresponding architecture for PN initiation.
Troubleshooting process is often used to diagnose the cause of failed products in the production systems. This method is employed to repair rejected products and to find root causes of problems so that failed products can be usable again. Troubleshooting is one of the most important ways of identifying symptoms of specific problem in a product and also helps to provide remedial action regarding the causes of those symptoms. This research aims to analyze a troubleshooting process and provide enhancement in decision making procedure of troubleshooters which leads to increase the overall productivity of production process. A framework for business process improvement and data cleaner application are developed for this purpose that facilitates to improve the quality of data, quality of decision making process of troubleshooters and reduce the waste of materials. Moreover, AS-IS analysis of troubleshooting process is made based on that analysis TO-BE process flow is also described while a data cleaner application is developed to support the TO-BE process. The outcome of this research is mainly beneficial for manufacturing industry, however further studies may expand the findings to other streams such as service industry.
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