Assembly supply chain systems are becoming increasingly complex and, as a result, there is more and more need to design and manage them in a way that benefits the producers and also satisfies the interests of community stakeholders. The structural (static) complexity of assembly supply chain networks is one of the most important factors influencing overall system complexity. Structures of such networks can be modeled as a graph, with machines as nodes and material flow between the nodes as links. The purpose of this paper is to analyze existing assembly supply chain complexity assessment methods and propose such complexity metric(s) that will be able to accurately reflect not only specific criteria for static complexity measures, but also selected sustainability aspects. The obtained results of this research showed that selected complexity indicators reflect sustainability facets in different ways, but one of them met the mentioned requirements acceptably.

The paper presents quantitative approach for management decisions of the manufacturing system for production of fireplaces, related to evaluation of key parametersproductivity and throughput, which most authors and methodologies consider to be substantial. Methodology was based on creating the simulation model of the fireplace production line in software Witness; optimizing the production capacity by selecting constraints, based on results from simulation model; evaluating the simulation experiments with the goal to increase productivity; setting production to maximize sales profits using Simplex method. Simulation model was built according to a technological process of fireplaces in a semi-automated production. Improvement in a production process within theory of constraints philosophy is complemented by mathematical modelling -Simplex method, that estimate profit maximization in case the company management decides to produce more product variants.

Simulations are widely used in manufacturing system design, production planning and decision making. The aim of this paper is to present the possibility of using Monte Carlo simulations in the production plan optimizing and in the project risk management. Optimization is accomplished through two different approaches which principles and results are mutually compared. According to the first approach, production optimization is performed via a deterministic model using the Generalized Reduced Gradient algorithm. The second approach is based on the stochastic model. The optimized production plan is submitted to risk analysis. Two approaches are demonstrated in order to reduce the rate of risk. The first way is modifying the production plan to increase the forecast reliability; the second approach is limiting the uncertainty of key variables. The detailed methodology enables implementing presented approaches in solving various optimization tasks.

The main goal of the paper is to propose solution toward capacity improvement of assembly process on a selected production line. The major content is focused on Basic Most method and its application in improvement process. There is a theoretical expectation that after applying Basic Most method to the assembly process, the capacity of production line would increase about twenty percentages. Motion and Time studies are often applied for improvements in efficiency of assembly processes. First, Process Screen method is used for gaining the information about process behaviour and its individual, partial and total duration. The final solution consists of new layout design of working station at the selected production line.

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For safe, reliable and convenient facility operation, it is eligible to install a control system with ambient intelligence. Building automation with its functions as keeping building climate within a specified range, lighting rooms based on an occupancy schedule, monitoring performance and device failures in all systems, providing malfunction alarms (usually via email and/or sms text notifications) to building engineering/maintenance staff, has become more affordable and simple through smartphone, tablet connectivity, evolved touch control panels. Building automation systems reduce operation energy and maintenance costs comparing to non-controlled building systems; advantage embodies pre-emptive maintenance and quick detection of breakdowns. The paper deals with new features in designing ambient intelligence in monitoring and control processes for tangible manufacturing operations. Intelligent building systemsA continual process of managed change, constant adaptation of internal practices and external relationships to new customer opportunities, that is according to Goldman [1] expression of agility. Evolution results in a phenomenon that markets of all kinds are fragmenting at an accelerating pace. Advances in automation, technology * Daniela Onofrejova. Tel.

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