Big data analytics for forecasting cycle time in semiconductor wafer fabrication system

Wang, Junliang; Zhang, Jie

doi:10.1080/00207543.2016.1174789

Cited by 89 publications

(34 citation statements)

References 30 publications

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“…The information systems literature broadly conceptualizes BDPA as organizational capability (Srinivasan and Swink, 2018), to process large volumes and varieties of data with the velocity required to gain relevant insights, thereby enabling organizations to gain competitive advantage Fosso Wamba et al, 2015;Gupta and George, 2016;Pauleen and Wang, 2017;Srinivasan and Swink, 2018). The term 'big data' is often used to describe massive, complex and real-time data that requires sophisticated management, analytical and processing techniques to extract management insights (Gupta and George, 2016;Jin et al, 2016;342 R. Dubey et al Khan and Vorley, 2017;Sumbal, Tsui and See-To, 2017;Wang and Zhang, 2016;. The predictive analytics statistical models try to predict future behaviour based on the assumption that what has happened in the past will continue to happen in the future.…”

Section: Introductionmentioning

confidence: 99%

Big Data and Predictive Analytics and Manufacturing Performance: Integrating Institutional Theory, Resource‐Based View and Big Data Culture

Dubey

Gunasekaran

Childe

et al. 2019

British J of Management

534

395

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The importance of big data and predictive analytics has been at the forefront of research for operations and manufacturing management. The literature has reported the influence of big data and predictive analytics for improved supply chain and operational performance, but there has been a paucity of literature regarding the role of external institutional pressures on the resources of the organization to build big data capability. To address this gap, this paper draws on the resource‐based view of the firm, institutional theory and organizational culture to develop and test a model that describes the importance of resources for building capabilities, skills and big data culture and subsequently improving cost and operational performance. We test our research hypotheses using 195 surveys, gathered using a pre‐tested questionnaire. Our contribution lies in providing insights regarding the role of external pressures on the selection of resources under the moderating effect of big data culture and their utilization for capability building, and how this capability affects cost and operational performance.

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Section: Introductionmentioning

confidence: 99%

Big Data and Predictive Analytics and Manufacturing Performance: Integrating Institutional Theory, Resource‐Based View and Big Data Culture

Dubey

Gunasekaran

Childe

et al. 2019

British J of Management

534

395

View full text Add to dashboard Cite

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“…The analysis of data coming from heterogeneous sources, as machines operating at different conditions, is a challenge. In general, there is an elemental algorithm used by several authors [13,15], to deal with maintenance data. This algorithm is taken as the structure for this methodological proposal and it is composed of the following stages or phases: (1) data acquisition (to define the object of study and to collect data), (2) data preprocessing (to extract, transform, and prepare data), (3) data analysis (to obtain a diagnosis about the reliability of the system), and (4) prediction and application (to generate a prognosis analysis for decision making).…”

Section: Proposition For Methodologymentioning

confidence: 99%

“…This algorithm is taken as the structure for this methodological proposal and it is composed of the following stages or phases: (1) data acquisition (to define the object of study and to collect data), (2) data preprocessing (to extract, transform, and prepare data), (3) data analysis (to obtain a diagnosis about the reliability of the system), and (4) prediction and application (to generate a prognosis analysis for decision making). According to Wang and Zang [13] the prediction accuracy improves when data is increased in size.…”

Section: Proposition For Methodologymentioning

confidence: 99%

“…One of the classic tools for the analysis of systemic reliability is the RCM approach (Reliability Centered Maintenance), which describes the operation of equipment arranged in a logical configuration and that allows its modeling and understanding, enabling formulation of suitable maintenance policies [12]. Given that the multifunction problem includes an important size of data of multiple states and transitions, it is necessary to have a structured methodology for its reliability study, which is based on the typical process for data analysis [13][14][15] composed of four phases: (1) data acquisition, (2) data preprocessing, (3) data analysis, and (4) prediction and application. In particular, the RCM approach and the Universal Generating Function (UGF) will be used during the second and third stages to deal with the complexity of the data dimension [16] and to obtain the reliability performance of the entire Multistate System (MSS) based on the performance of its elements, using algebraic procedures [17].…”

Section: Introductionmentioning

confidence: 99%

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Reliability Assessment Methodology for Massive Manufacturing Using Multi‐Function Equipment

et al. 2018

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Experience reveals that reliability varies depending on the characteristics of operation. The manufacturing process based on multifunction equipment gives a usual case of variation in operating conditions. This work presents a methodology for the reliability analysis of multifunction processes, using the RCM approach, and a modification of the Universal Generating Function (UGF) under a massive manufacturing context. The result is a characterization of reliability, for each piece of equipment and for the production system. The methodology is applied in a workshop of a textile industry, where there is prior evidence that the failure behavior varies according to the type of function executed by multifunction machines.

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“…e correlation can be quantified by the uncertainty of the effect caused by the geometric error terms. e method of feature selection suitable for the analysis of a complex correlation relationship [46,47] can effectively identify the key parameters affecting the fluctuations in geometric accuracy. e steps for identifying key geometric error terms based on feature selection are as follows:…”

Section: Key Parameter Identification Modelling Based On Featurementioning

confidence: 99%

A Recognition Methodology for the Key Geometric Errors of a Multi‐Axis Machine Tool Based on Accuracy Retentivity Analysis

2019

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This paper proposes a recognition methodology for key geometric errors using the feature extraction method and accuracy retentivity analysis and presents the approach of optimization compensation of the geometric error of a multiaxis machine tool. The universal kinematics relations of the multiaxis machine tool are first modelled mathematically based on screw theory. Then, the retentivity of geometric accuracy with respect to the geometric error is defined based on the mapping between the constitutive geometric errors and the time domain. The results show that the variation in the spatial error vector is nonlinear while considering the operation time of the machine tool and the position of the motion axes. Based on this aspect, key factors are extracted that simultaneously consider the correlation, similarity, and sensitivity of the geometric error terms, and the results reveal that the effect of the position-independent geometric errors (PIGEs) on the error vectors of the position and orientation is greater than that of the position-dependent geometric errors (PDGEs) of the linear and rotary axes. Then, the fruit fly optimization algorithm (FOA) is adopted to determine the compensation values through multiobjective tradeoffs between accuracy retentivity and fluctuation in the geometric errors. Finally, an experiment on a four-axis horizontal boring machine tool is used to validate the effectiveness of the proposed approach. The experimental results show that the variations in the precision of each test piece are lower than 25.0%, and the maximum variance in the detection indexes between the finished test pieces is 0.002 mm when the optimized parameters are used for error compensation. This method not only recognizes the key geometric errors but also compensates for the geometric error of the machine tool based on the accuracy retentivity analysis results. The results show that the proposed methodology can effectively enhance the machining accuracy.

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Big data analytics for forecasting cycle time in semiconductor wafer fabrication system

Cited by 89 publications

References 30 publications

Big Data and Predictive Analytics and Manufacturing Performance: Integrating Institutional Theory, Resource‐Based View and Big Data Culture

Big Data and Predictive Analytics and Manufacturing Performance: Integrating Institutional Theory, Resource‐Based View and Big Data Culture

Reliability Assessment Methodology for Massive Manufacturing Using Multi‐Function Equipment

A Recognition Methodology for the Key Geometric Errors of a Multi‐Axis Machine Tool Based on Accuracy Retentivity Analysis

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