A comparative study of similarity measures for manufacturing cell formation

We introduced a spectral clustering algorithm based on the bipartite graph model for the Manufacturing Cell Formation problem in [Oliveira S, Ribeiro JFF, Seok SC. A spectral clustering algorithm for manufacturing cell formation. Computers and Industrial Engineering. 2007 [submitted for publication]]. It constructs two similarity matrices; one for parts and one for machines. The algorithm executes a spectral clustering algorithm on each separately to find families of parts and cells of machines. The similarity measure in the approach utilized limited information between parts and between machines. This paper reviews several well-known similarity measures which have been used for Group Technology. Computational clustering results are compared by various performance measures. (C) 2008 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.

A spectral clustering algorithm for manufacturing cell formation

A graph clustering algorithm constructs groups of closely related parts and machines separately. After they are matched for the least intercell moves, a refining process runs on the initial cell formation to decrease the number of intercell moves. A simple modification of this main approach can deal with some practical constraints, such as the popular constraint of bounding the maximum number of machines in a cell. Our approach makes a big improvement in the computational time. More importantly, improvement is seen in the number of intercell moves when the computational results were compared with best known solutions from the literature. (C) 2009 Elsevier Ltd. All rights reserved.

Economic performance of Brazilian manufacturing firms: a counterfactual analysis of innovation impacts

This article assesses if innovators outperform non-innovators in Brazilian manufacturing during 1996-2002. To do so, we begin with a simple theoretical model and test the impacts of technological innovation (treatment) on innovating firms (treated) by employing propensity score matching techniques. Correcting for the survivorship bias in the period, it was verified that, on an average, the accomplishment of technological innovations produces positive and significant impacts on the employment, the net revenue, the labor productivity, the capital productivity, and market share of the firms. However, this result was not observed for the mark-up. Especially, the net revenue reflects more robustly the impacts of the innovations. Quantitatively speaking, innovating firms experienced a 10.8-12.5 percentage points (p.p. henceforth) higher growth on employment, a 18.1-21.7 p.p. higher growth on the net revenue, a 10.8-11.9 p.p. higher growth on labor productivity, a 11.8-12.0 p.p. higher growth on capital productivity, and a 19.9-24.3 p.p. higher growth on their market share, relative to the average of the non-innovating firms in the control group. It was also observed that the conjunction of product and process innovations, relative to other forms of innovation, presents the stronger impacts on the performance of Brazilian firms.

Trade and environment: Evidence from China's manufacturing sector

This paper focusing on the Chinese manufacturing sector assesses the environmental impact of trade liberalization in China. The results show that China's experience with the trade liberalization-environment nexus is consistent with international evidence. On one hand, trade liberalization has had various positive effects on the environment. Firstly, it promoted specialization in areas of comparative advantage, which, in general, included industries that contributed less to environmental degradation. Secondly, it allowed China to access and adopt the best international practices in pollution abatement technology. Thirdly, it enabled China to transfer environmental costs to other countries by importing intermediate products whose production contributed to environmental degradation. On the other hand, these positive effects were overwhelmed by a negative scale effect, which was the result of a huge increase in the demand for Chinese exports. The paper concludes that if China is to prevent pollution from reaching a critical threshold, environmental regulations need to be tightened. Copyright (C) 2002 John Wiley & Sons, Ltd and ERP Environment.

Productivity growth in Australian manufacturing sector: some new evidence

This article examines the productivity performance of Australia's manufacturing sector by decomposing its output growth into input growth, technological progress and gains in technical efficiency. This three-way decomposition is done with an improved version of the stochastic frontier model using eight, two-digit industry level data from 1968/9 to 1994/5. Empirical evidence shows that input growth fueled output growth from 1968/9 to 1973/4, but since then, total factor productivity (TFP) growth has been the main contributor of output growth. While the trend of TFP growth was found to be promising for most industries with positive and increasing technological progress, the negative gains from technical efficiency over time is however cause for concern.