Hierarchical Factor Analysis Methodology for Intelligent Manufacturing

To realize intelligent manufacturing, a controllable factory must be built, and manufacturing competitiveness must be achieved through the improvement of product quality and yield. The yield in the micromanufacturing process is gaining importance as a management factor used in deciding the production cost and product quality as product functions becomes more sophisticated. Because the micromanufacturing process involves manufacturing products through multiple steps, it is difficult to determine the process or equipment that has encountered failure, which can lead to difficulty in securing high yields. This study presents a structural model for building a factory integration system to analyze big data at manufacturing sites and a hierarchical factor analysis methodology to increase product yield and quality in an intelligent manufacturing environment. To improve the product yield, it is necessary to analyze the fault factors that cause low yields and locate and manage the critical processes and equipment factors that affect these fault factors. However, yield management is a difficult problem because there exists a correlation between equipment, and in the sequence of process equipment that the lot passed through, the downstream and the upstream cause complex faults. This study used data-mining techniques to identify suspected processes and equipment that affect the yield of products in the manufacturing process and to analyze the key factors of the equipment. Ultimately, we propose a methodology to find the key factors of the suspected process and equipment that directly affect the implementation of the intelligent manufacturing scheme and the yield of the product. To verify the effect of key parameters of critical processes and equipment on the yield, the proposed methodology was applied to actual manufacturing sites.

Parallel analysis and MBI-HSS: How many factors?

It has been only recently possible to validate the Maslach Burnout Inventory-Human Services Survey (MBI-HSS)1 among health professionals of Cali2, an important step for using this instrument with local empirical support in regard to its reliability of scoring and internal structure. However, two aspects of this analysis can be considered as methodological weaknesses. First, the Cronbach alpha coefficient was calculated for the total group of items, and this is absolutely inappropriate because: a)the authors did not demonstrate empirical support for accomplishing this (e.g., a hierarchical factor analysis), b) the literature indicates that factors in the MBI-HSS are generally independent, a characteristic also reported by Córdoba et al. 2,

Fluid and Crystallized Intelligence and the Berlin Model of Intelligence Structure (BIS)

Summary: Assessment of intelligence is often based on fluid (gf) and crystallized intelligence (gc), and - in the German-speaking countries - the Berlin Model of Intelligence Structure (BIS). As yet, however, the two approaches have not been systematically related to each other. The present study therefore aims to identify possible relationships between the approaches. We hypothesize that gf is related to “processing capacity” and “memory” in the BIS, whereas gc is related to “fluency” and “knowledge” and, to a lesser degree, to “processing capacity.” We also assume “processing speed” to be related to both gf and gc. All components of the BIS that are relevant to the present study were measured by means of the BIS-r-DGP test, which, together with “knowledge” scales, was administered to 9,520 persons in the context of personnel selection. The following results were obtained: First, the BIS was replicated by factor analysis of the BIS-r-DGP test. Second, “knowledge” was shown to form an additional component. Third, gf and gc emerged clearly from hierarchical factor analysis. Finally, with the exception of the relation of “fluency” to gc, all hypotheses were confirmed by confirmatory factor analysis.