Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor Manufacturing

Moyne, James; Iskandar, Jimmy

doi:10.3390/pr5030039

Cited by 176 publications

(65 citation statements)

References 32 publications

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“…External data from social media outlets and supplier network channels provide a huge influx to augment existing data that combine sensors and machine data coming from the production line. To adapt and evolve within a challenging environment, manufacturing organizations need to bring changes to structure and strategy with fast and effective decision-making.Modern manufacturing industries are going through a data-driven revolution that is transforming traditional manufacturing into a highly optimized manufacturing process [4]. Along with technological advancements for smart manufacturing facilities, industries are focusing on creating manufacturing intelligence from real-time data to support decision-making for the entire organization [5].…”

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confidence: 99%

“…Along with technological advancements for smart manufacturing facilities, industries are focusing on creating manufacturing intelligence from real-time data to support decision-making for the entire organization [5]. The development of advanced digital electronics and computer integrated systems has delivered technologies such as cyber-physical systems (CPSes) and the internet of things (IoT), which can collect and deliver real-time data from an entire manufacturing organization [4,6,7]. The biggest challenge for manufacturing industries is not producing, but handling data appropriately.…”

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confidence: 99%

“…The biggest challenge for manufacturing industries is not producing, but handling data appropriately. This can lead to potential applications of using this data to optimize manufacturing output and eliminate inefficiency [4].The main objectives of process re-engineering (PR) in manufacturing industries are to bring efficiency and increase productivity through changes in processes or functions that are unprecedented given the appearance of new technologies and globalization [8]. These changes are building up inefficiencies, leading to strategic failure [9].…”

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confidence: 99%

“…Modern manufacturing industries are going through a data-driven revolution that is transforming traditional manufacturing into a highly optimized manufacturing process [4]. Along with technological advancements for smart manufacturing facilities, industries are focusing on creating manufacturing intelligence from real-time data to support decision-making for the entire organization [5].…”

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confidence: 99%

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confidence: 99%

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Integration of Data-Driven Process Re-Engineering and Process Interdependency for Manufacturing Optimization Supported by Smart Structured Data

Khan

Butt

Mebrahtu

et al. 2019

Designs

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Process re-engineering and optimization in manufacturing industries is a big challenge because of process interdependencies characterized by a high failure rate. Research has shown that over 70% of approaches fail because of complexity as a result of process interdependencies during the implementation phase. This paper investigates data from a manufacturing operation and designs a filtration algorithm to analyze process interdependencies as a new approach for process optimization. The algorithm examines the data from a manufacturing process to identify limitations through cause and effect relationships and implements changes to achieve an optimized result. The proposed cause and effect approach of re-engineering is termed the Khan-Hassan-Butt (KHB) methodology, and it can filter the process interdependencies and use those as key decision-making tools. It provides an improved process optimization framework that incorporates data analysis along with a cause and effect algorithm to filter out the process interdependencies as an approach to increase output and reduce failure factors simultaneously. It also provides a framework for filtering the manufacturing data into smart structured data. Based on the proposed KHB methodology, the study investigated a production line process using the WITNESS Horizon 22 simulation package and analyzed the efficiency of the proposed approach for production optimization. A case study is provided that integrated the KHB methodology with data-driven process re-engineering to analyze the process interdependencies to use them as decision-making tools for production optimization.

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confidence: 99%

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confidence: 99%

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confidence: 99%

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Integration of Data-Driven Process Re-Engineering and Process Interdependency for Manufacturing Optimization Supported by Smart Structured Data

Khan

Butt

Mebrahtu

et al. 2019

Designs

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Enabling energy‐efficient manufacturing of pharmaceutical solid oral dosage forms via integrated techno‐economic analysis and advanced process modeling

Sampat

Kotamarthy

Bhalode

et al. 2022

J Adv Manuf & Process

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The global pharmaceutical industry is a trillion-dollar market. However, the pharmaceutical sector often lags in manufacturing innovation and automation which limits its potential to maximize energy efficiency. The integration of technoeconomic analysis (TEA) with advanced process models as part of an overarching smart manufacturing platform, can help industries create business models, which can be adapted for manufacturing to reduce energy consumption and operating costs while ensuring product quality which can further enable a more sustainable process operation. In this study, a rational design of experiment on three unitoperations (wet granulation, drying, and milling) was performed on a batch (case 1) and continuous (case 2) pharmaceutical process to obtain experimental data. Process models for predicting product quality and energy efficiency of each of the three-unit operations were developed. The experimental data were used to validate the models and good agreement was observed. The energy consumption of each unit operation was calculated using statistical models relating the power consumption and the process parameters. The developed process models and energy models were further integrated into a TEA framework, which quantified the energy and monetary cost of manufacturing for both batch and continuous manufacturing cases. With this integrated framework, energy costs savings of ~33% was obtained in the continuous manufacturing process (case 2) over the batch process (case 1). K E Y W O R D Sadvanced process models, energy efficiency, Industry 4.0, smart manufacturing, technoeconomic analysis Chaitanya Sampata and Lalith Kotamarthy contributed equally to this study.

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Equipment deterioration modeling and cause diagnosis in semiconductor manufacturing

et al. 2021

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Condition‐based monitoring (CBM) as a new control scheme suggests characterizing the machine condition and triggering the corresponding control actions. CBM includes prognostic and diagnostic modules. In this study, the framework of equipment deterioration modeling and monitoring for batch processes is proposed with two objectives in the semiconductor industry. The first one is to characterize equipment behavior by exploiting the temporal data of batch processes. The second one is to model the deterioration trend with the most related causes. With the best‐fitted mother wavelet, wavelet packet decomposition transforms the temporal data into macro and micro level domains to identify two types of deterioration. The determinant of the correlation matrix of the decomposed signals is calculated as the equipment condition, and the factors that account for the deterioration are identified through a stepwise searching algorithm. A case study shows that the proposed methodology can identify influencing factors and model deterioration.

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Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor Manufacturing

Cited by 176 publications

References 32 publications

Integration of Data-Driven Process Re-Engineering and Process Interdependency for Manufacturing Optimization Supported by Smart Structured Data

Integration of Data-Driven Process Re-Engineering and Process Interdependency for Manufacturing Optimization Supported by Smart Structured Data

Enabling energy‐efficient manufacturing of pharmaceutical solid oral dosage forms via integrated techno‐economic analysis and advanced process modeling

Equipment deterioration modeling and cause diagnosis in semiconductor manufacturing

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