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EN
The market of consumer goods requires nowadays quick response to customer needs. As a consequence, this is transferred to the time restrictions that the semi-finished product manufacturer must meet. Therefore the cost of manufacturing cannot determine how production processes are designed, and the main evaluation function of manufacturing processes is the response time to customers’ orders. One of the ideas for implementing this idea is the QRM (Quick Response Manufacturing) production organization system. The purpose of the research undertaken by the authors was to develop an innovative solution in the field of production structure, allowing for the implementation of the QRM concept in a Contract Manufacturer, which realizes its tasks according to engineering-to-order (ETO) system in conditions defined as High Mix, Low Volume, High Complexity. The object of the research was to select appropriate methods for grouping products assuming that certain operations will be carried out in traditional but well-organized technological and/or linear cells. The research was carried out in one of the largest producers of sheet metal components in Europe. Pre-completed groupings for data obtained from the company had indicated that – among the classical methods – the best results had been given by the following methods: King’s Algorithm (otherwise called: Binary Ordering, Rank Order Clustering), k-means, and Kohonen’s neural networks. The results of the tests and preliminary simulations based on the data from the company proved that the implementation of the QRM concept does not have to be associated with the absolute formation of multi-purpose cells. It turned out that the effect of reducing the response time to customer needs can be obtained by using hybrid structures that combine solutions characteristic of cellular systems with traditional systems such as a technological, linear, or mixed structure. However, this requires the application of technological solutions with the highest level of organization.
2
EN
The aim of this paper is to present the possibilities and purposefulness of the application of fuzzy set theory to the valuation of real options. Owing to temporal fluctuations in the market, some input parameters in a model of a real option cannot always be expressed in a precise sense. Therefore, it is natural to consider them as a fuzzy numbers. Such an approach allows us to keep more information about the possible value of real options. A hybrid (fuzzy-stochastic) model for valuing a switch option is presented. Under these assumptions, the value of a switch option will be a fuzzy random set. This article assesses the incremental benefit of product switch options in steel plant projects. Such options are valued by Monte Carlo simulation and modelling the prices of and demand for steel products using fuzzy geometric Brownian motion. Finally, the value of a product switch option is defined by the upper and lower probability distribution function.
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