The problem of ranking and supplier selection in the uncertain environment is part of a purchasing plan and its relationship has a critical effect on the competitive advantage (high-quality products at lower cost with higher customer satisfaction) of each industrial organization. The considered problem can be stated as a multicriteria decision problem which includes both quantitative and qualitative criteria. The criteria present supplier performances which are defined by the purchasing Management Team depending on the size of industrial organizations and on production type. In this paper, the Management Team, using European Union (EU) recommendations, made a choice of criteria for supplier evaluation. The fuzzy rating weights of each pair of the considered criteria and uncertain criteria values are described by linguistic expressions which are modelled by triangular fuzzy numbers. The fuzzy extent approach for the synthetic extent values of the pairwise comparison for handling fuzzy analytic hierarchical process (AHP) is used to calculate the weight vector. The extension of the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) is applied to rank the suppliers. The proposed model is illustrated by an example. It is shown that the developed model is highly suitable as a decision-making tool for reaching decisions about supplier selection.

The determination of the optimal purchasing strategy in enterprise that is a part of global supply chain could be performed in two steps. In step one, a classification of potential suppliers is performed in order to determine the optimal portfolio of suppliers. This is delivered by using the fuzzy multicriteria proposed ABC classification method. Uncertainties in relative importance of criteria and their values are described by linguistic expressions. Modelling of linguistic expressions is based on the fuzzy sets theory. In the second step, ranking of optimal portfolio of suppliers is performed by using the modified ELECTRE method. The obtained results represent valuable input for determining the long time purchasing strategy and building partnership with the best suppliers. The developed two-step model is verified on real life data. The obtained results indicate good compliance with the opinions management in this type of industry. It is worth to mention that the proposed model can be easily extended and adopted to the analysis of other issues of management which could be applicable in different research areas.

This paper reports the results of investigations on manufacturing cycle times for special-purpose products. The company performs serial production characterized by complex and diverse technologies, alternative solutions and combined modes of workpiece movement in the manufacturing process. Because of various approaches to this problem, an analysis of previous investigations has been carried out, and a theoretical base is provided for the technological cycle and factors affecting the manufacturing cycle time. The technological and production documentation of the company has been analysed to establish the technological and real manufacturing cycle times, total losses and flow coefficients. This paper describes the original approach to production cycle scheduling on the grounds of investigations of manufacturing capacity utilization levels and causes of loss, in order to measure their effects and to reduce the flow coefficient to an optimum level. The results are a segment of complex studies on the production cycle management of complex products, accomplished in the company in the period from 2010 to 2012.

The aim of this article is to indicate that methods of monitoring capacity utilization applied in the processing industry such as cement production can be used in the metalworking industry that has a high level of capacity utilization. The process approach to establishing the level of capacity utilization can be employed in conditions of balanced line production, or when there are components of large-scale production, as well as short production cycles. The results of the analysis indicate that when the level of capacity utilization is high, this variable can be observed per day as stochastic, while, per machine, it can be a random variable. In the metalworking industry it is possible to monitor a larger number of factors related to operating and not operating machines, whereas in the processing industry the factor of work is generally only a single-machining time and factors of not operating are connected only to breakdowns or regular maintenance. To monitor capacity utilization in the processing industry, it is possible to apply a work-sampling method as well as the continual streaming method. The possibility common for both industries is to represent the monitoring of capacity utilization over time and to test the mastering of that stochastic function via control limits that are optimal when defined by 2SD.

occupational safety risk assessment uncertainty fuzzy sets