scholarly journals Hierarchical Factor Analysis Methodology for Intelligent Manufacturing

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Hyun Sik Sim
Keyword(s):  
Factor Analysis ◽  
Product Quality ◽  
Structural Model ◽  
Product Yield ◽  
Intelligent Manufacturing ◽  
Difficult Problem ◽  
Process Equipment ◽  
Key Factors ◽  
Analysis Methodology ◽  
Hierarchical Factor Analysis

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.

scholarly journals Structural Relationship of Key Factors for Student Satisfaction and Achievement in Asynchronous Online Learning

2021 ◽  
Vol 13 (12) ◽  
pp. 6734
Author(s):  
Sohee Kim ◽  
Dae-Jin Kim
Keyword(s):  
Online Learning ◽  
Student Engagement ◽  
Student Satisfaction ◽  
Survey Data ◽  
Structural Model ◽  
Student Interaction ◽  
Structural Relationship ◽  
Key Factors ◽  
Course Structure ◽  
Instructor Presence

This study examines the structural relationship among key factors influencing student satisfaction and achievement in online learning. A structural model was developed by considering course structure, student–student interaction, instructor presence, student engagement, student satisfaction and achievement as key factors. In order to verify the effectiveness of the developed structural model, we utilized the survey data collected from a total of 250 students enrolled in two asynchronous online courses offered at Kyung Hee University in Korea in the fall semester of 2020. Then, the collected survey data were analyzed using the structural equation model. The verification of the statistical analysis results indicates that the course structure has a more significant effect on the student satisfaction and achievement than the other key factors such as the student–student interaction, instructor presence and student engagement. It also reveals that the student engagement affects only the student satisfaction and has a mediated effect between student–student interaction and student satisfaction.

Model and optimal operational windows for hydrodynamic fiber fractionation

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Thomas Schmid ◽  
Stefan Radl
Keyword(s):  
Product Quality ◽  
Energy Demand ◽  
Optimization Procedure ◽  
Product Yield ◽  
Short Fiber ◽  
Design Tool ◽  
Quality Product ◽  
Fractionation Model ◽  
Micro Hole ◽  
Fiber Fractionation

AbstractBased on fitted experimental data, an empirical fractionation model for mini-channel hydrodynamic fiber fractionation (miniFrac) is presented. This model, combined with an optimization procedure, is then used as a design tool to synergize competing fractionation performance characteristics, i. e., product quality, product yield and energy demand. Based on this model, miniFrac is compared to state-of-the-art fiber fractionation technology with respect to (i) long fiber-short fiber fractionation and (ii) fines-fiber fractionation. In terms of fines-fiber fractionation, miniFrac is outperformed by typical micro-hole pressure screening regarding the purity of fines fraction. However, a comparison with a slotted (slot width of 0.2 mm) and a smooth-holed pressure screen (hole diameter of 0.8 mm) shows, that miniFrac is capable of outperforming both systems regarding product quality and energy demand at a comparable product yield. If, in the case of fines-fiber fractionation, reject purity (i. e., fines exclusion) is more important than fines purity (i. e., long fiber remain in the reject), miniFrac is an interesting tool with some key advantages over pressure screens.

KEY FACTORS FOR REDUCING LIVE POULTRY LOSSES DURING TRANSPORTATION

2021 ◽  
Vol 113 ◽  
pp. 115-131
Author(s):  
Valery KURGANOV ◽  
Mikhail GRYAZNOV ◽  
Egor TIMOFEEV ◽  
Liliya POLYAKOVA
Keyword(s):  
Mathematical Model ◽  
Factor Analysis ◽  
Economic Impact ◽  
Processing Plant ◽  
Poultry Farm ◽  
Key Factors ◽  
Live Poultry

The results of this study on the loss of live poultry at various stages of delivery from the farm to the processing plant by road are given. A factor analysis of the reasons for the loss of livestock delivered from the farm to the processing plant was carried out. The dependencies of livestock losses on loading delays and the duration of the movement of the loaded poultry farm to the processing plant were established. Methodological recommendations for rationing the number of injuries observed during delivery were developed. The study of losses of live birds during delivery to the processing plant from the density of stocking in shipping boxes was carried out; the economic and mathematical model for optimizing the landing of live birds in shipping boxes was proposed. The calculation of the economic impact of the implementation of the results of the study is given.

scholarly journals Defining the Frontiers of Synergism between Cellulolytic Enzymes for Improved Hydrolysis of Lignocellulosic Feedstocks

Catalysts ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1343
Author(s):  
Mpho S. Mafa ◽  
Brett I. Pletschke ◽  
Samkelo Malgas
Keyword(s):  
Value Added ◽  
Product Yield ◽  
Cellulose Hydrolysis ◽  
Cellulolytic Enzymes ◽  
Cellulolytic Enzyme ◽  
Key Factors ◽  
Cellulose Conversion ◽  
Lignocellulosic Feedstocks ◽  
Polysaccharide Monooxygenases ◽  
Hydrolysis Of

Lignocellulose has economic potential as a bio-resource for the production of value-added products (VAPs) and biofuels. The commercialization of biofuels and VAPs requires efficient enzyme cocktail activities that can lower their costs. However, the basis of the synergism between enzymes that compose cellulolytic enzyme cocktails for depolymerizing lignocellulose is not understood. This review aims to address the degree of synergism (DS) thresholds between the cellulolytic enzymes and how this can be used in the formulation of effective cellulolytic enzyme cocktails. DS is a powerful tool that distinguishes between enzymes’ synergism and anti-synergism during the hydrolysis of biomass. It has been established that cellulases, or cellulases and lytic polysaccharide monooxygenases (LPMOs), always synergize during cellulose hydrolysis. However, recent evidence suggests that this is not always the case, as synergism depends on the specific mechanism of action of each enzyme in the combination. Additionally, expansins, nonenzymatic proteins responsible for loosening cell wall fibers, seem to also synergize with cellulases during biomass depolymerization. This review highlighted the following four key factors linked to DS: (1) a DS threshold at which the enzymes synergize and produce a higher product yield than their theoretical sum, (2) a DS threshold at which the enzymes display synergism, but not a higher product yield, (3) a DS threshold at which enzymes do not synergize, and (4) a DS threshold that displays anti-synergy. This review deconvolutes the DS concept for cellulolytic enzymes, to postulate an experimental design approach for achieving higher synergism and cellulose conversion yields.

DEVELOPING A TAXONOMY FOR IDENTIFYING ENTREPRENEURIAL RISK: AN EMPIRICAL STUDY ON ENTREPRENEURIAL RISK PATTERNS OF BUSINESS ENTERPRISES

2014 ◽  
Vol 19 (01) ◽  
pp. 1450002 ◽  
Author(s):  
D. ANTHONY MILES
Keyword(s):  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Predictor Variable ◽  
Multivariate Regression Analysis ◽  
The Body ◽  
Business Failure ◽  
Behavioral Characteristics ◽  
Industry Type ◽  
Analysis Methodology ◽  
Business Enterprises

Most of the prior research on entrepreneurial risk concentrates on entrepreneur behavioral characteristics, personality traits and characteristics. Very little of the body of research examines the market behavior of the firm and its risk patterns that can cause business failure. The purpose of this research was to develop a taxonomy of entrepreneurial risk behaviors and examine their effect on small business enterprises (SME). The study was the development and administration of the Entrepreneurial Risk Survey (ERS) instrument. ERS was used to empirically examine a sample (N = 201) of SMEs across 11 industries for the proposed taxonomy of 22 risk variables. An exploratory factor analysis methodology was conducted for the study. The principle component analysis was conducted and resulted in an eight-factor solution. A multivariate regression analysis was also used to measure the industry type as a predictor variable. The results of the exploratory factor analysis (EFA) indicated there are eight factors of entrepreneurial risk that affect SMEs.

CRM Scale Development and Validation in Indian Telecom Sector

2013 ◽  
Vol 4 (3) ◽  
pp. 1-19
Author(s):  
Arun Kumar Agariya ◽  
Deepali Singh
Keyword(s):  
Factor Analysis ◽  
Structural Equation ◽  
Business Performance ◽  
Structural Model ◽  
Customer Relationship ◽  
Success Factor ◽  
Multidimensional Construct ◽  
Critical Success ◽  
Telecom Sector ◽  
Development And Validation

This paper develops a reliable and valid CRM (Customer relationship management) scale regarding the Indian telecom sector. A review of literature on CRM was followed by depth interview and questionnaire survey. The exploratory factor analysis is performed with the first half of the data to identify the major CRM dimensions based on which authors have proposed a construct, which is confirmed through confirmatory factor analysis and validated through Structural equation modelling by using the other half of the data. The covariance model shows CRM in Indian telecom sector as a multidimensional construct comprising of factors namely competitiveness and reliability, support feature, relationship quality, transmission quality, technological edge and reputation. The structural model validates the previously extracted factors along with their indicators. The findings of this study validate the belief that CRM is a multidimensional construct and serves as a critical success factor for business performance. The proposed scale helps to identify issues that contribute to CRM in Indian telecom sector and formulating strategies accordingly, resulting in efficient (in terms of cost) and effective (outcomes) CRM practices.

scholarly journals Modeling Product Manufacturing Reliability with Quality Variations Centered on the Multilayered Coupling Operational Characteristics of Intelligent Manufacturing Systems

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5677
Author(s):  
Anqi Zhang ◽  
Yihai He ◽  
Xiao Han ◽  
Yao Li ◽  
Xiuzhen Yang ◽  
...  
Keyword(s):  
Product Quality ◽  
Manufacturing Process ◽  
Manufacturing Systems ◽  
Coupling Effect ◽  
Intelligent Manufacturing ◽  
Intelligent Manufacturing Systems ◽  
Operational Characteristics ◽  
Product Manufacturing ◽  
Quality Variation ◽  
Variation Model

For intelligent manufacturing systems, there are many deviations in operational characteristics, and the coupling effect of harmful operational characteristics leads to the variations in quality of the work-in-process (WIP) and the degradation of the reliability of the finished product, which is reflected as a loss of product manufacturing reliability. However, few studies on the modeling of product manufacturing reliability and mechanism analysis consider the operating mechanism and the coupling of characteristics. Thus, a novel modeling approach based on quality variations centered on the coupling of operational characteristics is proposed to analyze the formation mechanism of product manufacturing reliability. First, the PQR chain containing the co-effects among the manufacturing system performance (P), the manufacturing process quality (Q), and the product manufacturing reliability (R) is elaborated. The connotation of product manufacturing reliability is defined, multilayered operational characteristics are determined, and operational data are collected by smart sensors. Second, on the basis of the coupling effect in the PQR chain, a multilayered product quality variation model is proposed by mining operational characteristic data obtained from sensors. Third, an integrated product manufacturing reliability model is presented on the basis of the variation propagation mechanism of the multilayered product quality variation model. Finally, a camshaft manufacturing reliability analysis is conducted to verify the validity of the proposed method. The method proposed in this paper proved to be effective for evaluating and predicting the product reliability in the smart manufacturing process.

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