scholarly journals Polymethyl Methacrylate Quality Modeling with Missing Data Using Subspace Based Model Identification

Processes ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1691
Author(s):  
Nikesh Patel ◽  
Kavitha Sivanathan ◽  
Prashant Mhaskar
Keyword(s):  
Missing Data ◽  
Least Squares ◽  
Partial Least Squares ◽  
Polymethyl Methacrylate ◽  
Model Identification ◽  
Principal Component ◽  
Quality Modeling ◽  
The Impact ◽  
Quality Measurements

This paper addresses the problem of quality modeling in polymethyl methacrylate (PMMA) production. The key challenge is handling the large amounts of missing quality measurements in each batch due to the time and cost sensitive nature of the measurements. To this end, a missing data subspace algorithm that adapts nonlinear iterative partial least squares (NIPALS) algorithms from both partial least squares (PLS) and principal component analysis (PCA) is utilized to build a data driven dynamic model. The use of NIPALS algorithms allows for the correlation structure of the input–output data to minimize the impact of the large amounts of missing quality measurements. These techniques are utilized in a simulated case study to successfully model the PMMA process in particular, and demonstrate the efficacy of the algorithm to handle the quality prediction problem in general.

scholarly journals eWOM in C2C Platforms: Combining IAM and Customer Satisfaction to Examine the Impact on Purchase Intention

2021 ◽  
Vol 16 (5) ◽  
pp. 1612-1630
Author(s):  
Salvador Bueno ◽  
M. Dolores Gallego
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Purchase Intention ◽  
Structural Equation ◽  
Source Credibility ◽  
Positive Impact ◽  
Equation Modeling ◽  
Research Model ◽  
Argument Quality ◽  
The Impact

This study is focused on communications that come from consumer-to-consumer (C2C) ecommerce relationships. This topic is directly associated with the electronic word-of-mouth (eWOM) phenomenon. eWOM is related to the set of positive or negative opinions made by potential, actual, or former customers about a seller. The present study proposes a structural equation modeling with partial least squares (PLS-SEM) research model to analyze consumers’ opinions impact on attitude toward purchasing. This model is based on the Information Adoption Model (IAM) in combination with an ecommerce satisfaction perspective, comprising five constructs: (1) service quality, (2) ecommerce satisfaction, (3) argument quality, (4) source credibility and (5) purchase intention. The model was tested by applying the Smart Partial Least Squares (SmartPLS) software for which 116 effective data from customers of the Taobao C2C platform were used. The findings reveal that all of the defined relationships were supported, confirming the positive impact of all the proposed constructs on the purchase intention. In this respect, the findings suggest that C2C platforms should strengthen the analyzed connections to grow the business and to promote transactions. Finally, implications and limitations related to the explanatory capacity and the sample are identified.

scholarly journals A Novel Mutual Information and Partial Least Squares Approach for Quality-Related and Quality-Unrelated Fault Detection

Processes ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 166
Author(s):  
Majed Aljunaid ◽  
Yang Tao ◽  
Hongbo Shi
Keyword(s):  
Fault Detection ◽  
Mutual Information ◽  
Least Squares ◽  
Partial Least Squares ◽  
Principal Component ◽  
False Alarms ◽  
Total Quality ◽  
Process Variables ◽  
Tennessee Eastman Process ◽  
Value Decomposition

Partial least squares (PLS) and linear regression methods are widely utilized for quality-related fault detection in industrial processes. Standard PLS decomposes the process variables into principal and residual parts. However, as the principal part still contains many components unrelated to quality, if these components were not removed it could cause many false alarms. Besides, although these components do not affect product quality, they have a great impact on process safety and information about other faults. Removing and discarding these components will lead to a reduction in the detection rate of faults, unrelated to quality. To overcome the drawbacks of Standard PLS, a novel method, MI-PLS (mutual information PLS), is proposed in this paper. The proposed MI-PLS algorithm utilizes mutual information to divide the process variables into selected and residual components, and then uses singular value decomposition (SVD) to further decompose the selected part into quality-related and quality-unrelated components, subsequently constructing quality-related monitoring statistics. To ensure that there is no information loss and that the proposed MI-PLS can be used in quality-related and quality-unrelated fault detection, a principal component analysis (PCA) model is performed on the residual component to obtain its score matrix, which is combined with the quality-unrelated part to obtain the total quality-unrelated monitoring statistics. Finally, the proposed method is applied on a numerical example and Tennessee Eastman process. The proposed MI-PLS has a lower computational load and more robust performance compared with T-PLS and PCR.

Estimating Store Brand Shelf Space: A New Framework Using Neural Networks and Partial Least Squares

2009 ◽  
Vol 51 (2) ◽  
pp. 1-19 ◽  
Author(s):  
Monica Gomez ◽  
Shintaro Okazaki
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Structural Equation ◽  
Statistical Treatment ◽  
Principal Component ◽  
Future Research ◽  
Store Brand ◽  
Shelf Space ◽  
Managerial Implications ◽  
Space Data

Despite abundant research that examines the effects of store brands on retail decision making, little attention has been paid to the predictive model of store brand shelf space. This paper intends to fill this research gap by proposing and testing a theoretical model of store brand shelf space. From the literature review, 11 independent variables were identified (i.e. store format, reputation, brand assortment, depth of assortment, in-store promotions, leading national brands’ rivalry, retailers’ rivalry, manufacturers’ concentration, store brand market share, advertising, and innovation) and analysed as potential predictors of the dependent variable (i.e. store brand shelf space). Data were collected for 29 product categories in 55 retail stores. In designing the statistical treatment, a three-phase procedure was adopted: (1) interdependence analysis via principal component analysis; (2) dependence analysis via neural network simulation; and (3) structural equation modelling via partial least squares. The findings corroborate our proposed model, in that all hypothesised relationships and directions are supported. On this basis, we draw theoretical as well as managerial implications. In closing, we acknowledge the limitations of this study and suggest future research directions.

scholarly journals The optimal number of partial least squares components in genomic selection for pork pH

2017 ◽  
Vol 47 (1) ◽  
Author(s):  
Fernanda Gomes da Silveira ◽  
Darlene Ana Souza Duarte ◽  
Lucas Monteiro Chaves ◽  
Fabyano Fonseca e Silva ◽  
Ivan Carvalho Filho ◽  
...  
Keyword(s):  
Least Squares ◽  
Genomic Selection ◽  
Multiple Regression ◽  
Partial Least Squares ◽  
Predictive Accuracy ◽  
Principal Component ◽  
Theoretical Background ◽  
Optimal Number ◽  
Number Of Components ◽  
Regression Techniques

ABSTRACT: The main application of genomic selection (GS) is the early identification of genetically superior animals for traits difficult-to-measure or lately evaluated, such as meat pH (measured after slaughter). Because the number of markers in GS is generally larger than the number of genotyped animals and these markers are highly correlated owing to linkage disequilibrium, statistical methods based on dimensionality reduction have been proposed. Among them, the partial least squares (PLS) technique stands out, because of its simplicity and high predictive accuracy. However, choosing the optimal number of components remains a relevant issue for PLS applications. Thus, we applied PLS (and principal component and traditional multiple regression) techniques to GS for pork pH traits (with pH measured at 45min and 24h after slaughter) and also identified the optimal number of PLS components based on the degree-of-freedom (DoF) and cross-validation (CV) methods. The PLS method out performs the principal component and traditional multiple regression techniques, enabling satisfactory predictions for pork pH traits using only genotypic data (low-density SNP panel). Furthermore, the SNP marker estimates from PLS revealed a relevant region on chromosome 4, which may affect these traits. The DoF and CV methods showed similar results for determining the optimal number of components in PLS analysis; thus, from the statistical viewpoint, the DoF method should be preferred because of its theoretical background (based on the "statistical information theory"), whereas CV is an empirical method based on computational effort.

Intellectual capital and supply chain resilience

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Muhammad Shujaat Mubarik ◽  
Nick Bontis ◽  
Mobasher Mubarik ◽  
Tarique Mahmood
Keyword(s):  
Supply Chain ◽  
Least Squares ◽  
Partial Least Squares ◽  
Intellectual Capital ◽  
Structural Equation ◽  
Content Type ◽  
Supply Chain Resilience ◽  
Structural Capital ◽  
The Impact

PurposeThe main objective of this study is to test whether firms with a higher level of intellectual capital (IC) perform better in terms of their supply chain resilience compared to those with lower levels of IC. Likewise, the study also examines the impact of IC (characterized by human capital, relational capital and structural capital) on supply chain resilience directly and through supply chain learning.Design/methodology/approachData were collected from the 159 processed-food sector firms using a close-ended questionnaire during the corona virus 2019 (COVID-19) pandemic. Partial least squares structural equation modelling (PLS-SEM), partial least squares multigroup analysis (PLS-MGA) and one-way analysis of variance (ANOVA) were used to test a set of hypotheses emanating from a conceptual model of IC and supply chain resilience.FindingsEmpirical results revealed a significant influence of all dimension of IC on a firm's supply chain learning and supply chain resilience. Likewise, findings also exhibit a momentous role of supply chain learning in reinforcing the impact of IC on supply chain resilience. Cross-firm size comparison reveals that supply chain resilience of firms with a higher level of IC performed significantly better than those with lower levels of IC. Firms with a higher level of structural capital had a highly resilient supply chain.Practical implicationsFindings of the study imply that IC and supply chain learning should be considered as a strategic tool and should be strategically developed for uplifting a supply chain performance of a firm. The development of IC and supply chain learning (SCL) not only improves the supply chain resilience of a firm but also can help to integrate the internal and external knowledge for harnessing supply chain resilience.Originality/valueThis research study was conducted during the COVID-19 pandemic which provides a unique setting to examine resiliency and learning.

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