Review
Engineering, Industrial
Junliang Wang, Chuqiao Xu, Jie Zhang, Ray Zhong
Summary: This paper provides a comprehensive review of big data analytics (BDA) for intelligent manufacturing systems, covering the concepts, methodologies, and applications. BDA has shown great potential in improving the efficiency and outcomes of product design, manufacturing, and maintenance. However, there are still challenges and opportunities that need further research and exploration.
JOURNAL OF MANUFACTURING SYSTEMS
(2022)
Article
Computer Science, Interdisciplinary Applications
Angelo Corallo, Anna Maria Crespino, Mariangela Lazoi, Marianna Lezzi
Summary: In the smart manufacturing environment of Industry 4.0, the adoption of Big Data and analytics allows the manufacturing industry to extract valuable information and knowledge from industrial processes, production systems, and sensors. A model-based framework in the Big Data Analytics pipeline can better address user configuration requirements and provide transparency on the execution of workflows and data processing. However, there is a lack of application of a model-based framework in the manufacturing domain, making a case study necessary to describe and analyze its configuration and execution.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2022)
Article
Computer Science, Information Systems
Dheeraj Malhotra, Omprakash Rishi
Summary: This research aims to address limitations of conventional search and page ranking systems in E-Commerce by providing personalized page ranking to assist customers in making online purchase decisions. The study reveals the inadequacy of traditional search systems for big data analysis in modern E-Commerce environment and proposes an innovative page ranking algorithm. The proposed approach meets critical parameters expected from next-generation big data processing systems and shows efficiency and effectiveness in experimental evaluation.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2021)
Review
Green & Sustainable Science & Technology
Mihai Andronie, George Lazaroiu, Mariana Iatagan, Iulian Hurloiu, Irina Dijmarescu
Summary: This article reviews previous research indicating that cyber-physical production systems shape social sustainability performance technologically, contributing to the literature on sustainable smart manufacturing. Through a quantitative literature review, the study identified key components of data-driven sustainable smart manufacturing and emphasized the importance of Industry 4.0-based technologies in ensuring the sustainability of production systems. Future research should focus on exploring the use of Internet of Things sensing networks and deep learning-assisted smart process planning in achieving sustainability in manufacturing.
Article
Computer Science, Interdisciplinary Applications
Arfan Majeed, Yingfeng Zhang, Shan Ren, Jingxiang Lv, Tao Peng, Saad Waqar, Enhuai Yin
Summary: The paper presents the development and application of the sustainable and smart additive manufacturing (SSAM) framework, aiming to improve the production efficiency and sustainability of additive manufacturing enterprises. By combining big data analytics, additive manufacturing, and sustainable smart manufacturing technologies, energy consumption and product quality are effectively controlled.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2021)
Article
Chemistry, Analytical
Jenniffer S. Guerrero-Prado, Wilfredo Alfonso-Morales, Eduardo F. Caicedo-Bravo
Summary: The paper introduces a Data Analytics/Big Data framework applied to AMI data in Smart Cities, encompassing architectural view, methodological view, and human expertise as a binding element. This framework aims to support optimal decision-making and transform knowledge into wisdom efficiently.
Article
Green & Sustainable Science & Technology
Sarah Kaleem, Adnan Sohail, Muhammad Usman Tariq, Muhammad Asim
Summary: The exponential growth of the Internet of Things has revolutionized intelligent transportation systems, and this paper presents a personalized architecture utilizing federated learning for efficient and real-time big data analytics in IoT-enabled ITSs. The proposed architecture addresses critical challenges and demonstrates superior performance in terms of scalability, real-time decision-making capabilities, and data privacy preservation.
Article
Engineering, Industrial
Yifu Li, Xinwei Deng, Shan Ba, William R. Myers, William A. Brenneman, Steve J. Lange, Ron Zink, Ran Jin
Summary: The study proposes an unsupervised data filtering method for reducing manufacturing big data sets, and introduces a filtering information criterion to balance the tradeoff between the filtered data size and the information preserved. The effectiveness of the proposed method is demonstrated through a case study and a simulation study in a babycare manufacturing context.
JOURNAL OF QUALITY TECHNOLOGY
(2022)
Article
Engineering, Industrial
Sinan Kahveci, Bugra Alkan, Mus'ab H. Ahmad, Bilal Ahmad, Robert Harrison
Summary: This paper presents an IoT-based big data analytics platform that integrates various tools and methods to support decision-making and improve process and product qualities in smart manufacturing environments. The platform has been deployed in an electric vehicle battery module assembly automation system and demonstrates its feasibility and effectiveness.
JOURNAL OF MANUFACTURING SYSTEMS
(2022)
Article
Computer Science, Information Systems
Claudio Agostino Ardagna, Valerio Bellandi, Michele Bezzi, Paolo Ceravolo, Ernesto Damiani, Cedric Hebert
Summary: The paper proposes an approach based on Model-Driven Engineering technology to support automation of Big Data Analytics. By defining an abstract Big Data platform and smart engines to meet customer requirements, the Big Data pipeline is able to execute analytics on a specific platform. This method is experimentally evaluated in the real-world scenario of SAP's threat detection system, showing promising results.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2021)
Article
Biotechnology & Applied Microbiology
Stephen Goldrick, Haneen Alosert, Clare Lovelady, Nicholas J. Bond, Tarik Senussi, Diane Hatton, John Klein, Matthew Cheeks, Richard Turner, James Savery, Suzanne S. Farid
Summary: Cell line development is a crucial stage in biopharmaceutical development, and failure to fully characterize the lead clone during initial screening can lead to delays and compromise manufacturing success. This study proposes a novel cell line development methodology called CLD (4), which uses four steps to autonomously select the lead clone based on data. CLD (4) incorporates digitalization, machine learning, and natural language generation to generate an automated report summarizing relevant statistics.
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
(2023)
Article
Business
Usama Awan, Saqib Shamim, Zaheer Khan, Najam Ul Zia, Syed Muhammad Shariq, Muhammad Naveed Khan
Summary: Big data analytics (BDA) plays a crucial role in enhancing decision-making quality within organizations, supporting the circular economy (CE) paradigm. Firms can drive decision-making quality through data-driven insights, business intelligence and analytics (BI&A), and BDA capability.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2021)
Article
Chemistry, Analytical
Eduardo A. Hinojosa-Palafox, Oscar M. Rodriguez-Elias, Jose A. Hoyo-Montano, Jesus H. Pacheco-Ramirez, Jose M. Nieto-Jalil
Summary: The paper aims to support the design of analytics Big Data solutions for industrial cyber-physical systems (iCPS), considering new Big Data modeling analytics techniques and proposing an architecture that integrates from IIoT environment, communications, and the cloud. The architectural design presented considers data as an invaluable asset in iCPS, and a fault diagnosis case study illustrates the application of the reference architecture for Big Data analytics in iCPS.
Article
Computer Science, Artificial Intelligence
Chia-Yen Lee, Chen-Fu Chien
Summary: This study aims to develop a five-phase analytics framework to investigate pitfalls for intelligent manufacturing and suggest protocols for the practical application of data science methodologies in various contexts.
JOURNAL OF INTELLIGENT MANUFACTURING
(2022)
Article
Urban Studies
Jens Kandt, Michael Batty
Summary: The analysis of big data is expected to define a new era in urban research, planning, and policy-making, promising smoother decision-making processes as part of a more evidence-based and smarter urbanism. However, there are also risks and challenges associated with over-reliance on data. Currently, there is limited research on how big data can realistically contribute to addressing urban policy problems.
Article
Engineering, Industrial
D. G. Mogale, Arijit De, Abhijeet Ghadge, Manoj Kumar Tiwari
Summary: This study aims to develop a sustainable freight transportation network using capacitated cross-docks to minimize overall supply chain costs. A mathematical model is developed to consider challenges such as time-dependent demand, multiple products and sourcing, and distribution. The study utilizes a two-level self-adaptive variable neighbourhood search algorithm to solve this complex problem.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Management
Rosalin Sahoo, Bhaskar Bhowmick, Manoj Kumar Tiwari
Summary: This paper addresses a freight forwarder service problem by optimizing the total shipping cost and considering factors such as inventory, processing, transportation, perishability, capacity, and reduced carbon emission. The study develops a mathematical model and uses two variants of the evolutionary algorithm to minimize the total cost incurred in transporting goods via air cargo. The results are validated and sensitivity analysis is performed to assess the impact of various factors.
INTERNATIONAL JOURNAL OF LOGISTICS-RESEARCH AND APPLICATIONS
(2023)
Article
Engineering, Industrial
D. G. Mogale, Abhijeet Ghadge, Naoufel Cheikhrouhou, Manoj Kunnar Tiwari
Summary: This study attempts to address the limited storage capacity issue in India's food grain production by developing a mathematical model that considers various social sustainability factors. The results indicate the importance of establishing an adequate number of modernized silos for improving environmental impact and social factors.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Engineering, Industrial
Mayank Shukla, S. P. Sarmah, Manoj Kumar Tiwari
Summary: This paper proposes a framework based on breach databases and textual information processing to identify cyber risk, threat, and countermeasure. It uses multi-objective optimization to find a trade-off between cyber risk and investment, and extracts relationships among categorized factors using natural language processing. The model helps in effective decision-making by finding vulnerable suppliers and pairing categorized factors.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Food Science & Technology
Anirban Adak, Biswajeet Pradhan, Nagesh Shukla, Abdullah Alamri
Summary: The demand for food delivery services has increased during the COVID-19 crisis, and customer reviews on internet platforms have become crucial for evaluating company performance. However, due to the high volume of feedback and limited resources, only a small portion of customer opinions are addressed. This study utilized deep learning techniques for sentiment analysis and explained the predictions using explainable AI methods.
Article
Geosciences, Multidisciplinary
Husam A. H. Al-Najjar, Biswajeet Pradhan, Ghassan Beydoun, Raju Sarkar, Hyuck-Jin Park, Adbullah Alamri
Summary: AI techniques are increasingly used in landslide modeling, emphasizing the importance of fairness and transparency. This paper introduces an explainable artificial intelligence approach for landslide prediction, demonstrating the superiority of the random forest model and the key features in landslide prediction.
Article
Computer Science, Artificial Intelligence
Jyotirmoy Nirupam Das, Manoj Kumar Tiwari, Ashesh Kumar Sinha, Vivek Khanzode
Summary: This study proposes a multiobjective formulation to solve the problem of warehouse assignment and carton configuration optimization. A novel DECIPACO model, integrating IPACO and DEC algorithms, is developed to solve the problem. The test results show that the DECIPACO model provides an optimal solution in all cases.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Industrial
Ajit Sharma, Manoj Kumar Tiwari
Summary: With the increasing adoption and demand for electric vehicles, the lack of sufficient battery production has become a critical bottleneck in the supply chain. This study proposes a three-stage digital twin design and analysis method to develop robotic workcells for fast and cost-effective assembly of electric vehicle battery modules. The use of digital twin design and simulation allows for the development of optimized assembly line configurations and evaluation of speed and cost.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Business
Aishwarya Dash, Sarada Prasad Sarmah, M. K. Tiwari, Sarat Kumar Jena
Summary: This study aims to use digital technology to curb counterfeiting, considering consumer characteristics and the random distribution of counterfeits. The results reveal that brand product manufacturers can earn maximum revenue by adopting digital technology counterstrategies when consumers are proactive, informed, and value-conscious. Therefore, brand product manufacturers should focus on the digital supply chain, product redesign, and product tracking to empower informed, value-conscious, and proactive consumers.
JOURNAL OF BUSINESS & INDUSTRIAL MARKETING
(2023)
Article
Computer Science, Interdisciplinary Applications
Vishal Gupta, Rony Mitra, Frank Koenig, Maneesh Kumar, Manoj Kumar Tiwari
Summary: This article explores the maintenance of airports' baggage handling systems and examines the use of predictive maintenance as an alternative to periodic maintenance. The unique challenges faced by baggage handling systems, such as random noise from IoT sensors and complex interconnected components, are discussed. The article proposes a scalable and cost-effective maintenance 4.0 solution using real-time sensor data. An algorithm is presented to differentiate between anomaly detection and outlier detection and remove irrelevant data. Integrated machine learning techniques are employed to detect and diagnose defects early on. The article also compares the performance of different machine learning algorithms and suggests future research directions.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Editorial Material
Green & Sustainable Science & Technology
Dedi I. Inan, Ghassan Beydoun, Siti Hajar Othman
Article
Operations Research & Management Science
Sumeetha Natesan, Deepika Thakur, Goutam Dutta, Manoj Kumar Tiwari
Summary: In this study, we develop both dynamic and static pricing models for home loans for a bank. The models aim to optimize the net present value of money available at the end of 15 years, taking into account pricing limits and cash flows. We collect real data from a leading nationalized bank in India to establish the relationship between interest rate (price) and number of loans sanctioned (demand). Various versions of the demand function (linear, exponential, and rectangular hyperbola) are assumed. We also analyze the impact of changes in the parameters of the demand equations on the results of the dynamic pricing model.
Article
Business
Vishal Kumar Gupta, Shrinath Dakare, Kiran Jude Fernandes, Lakshman S. Thakur, Manoj Kumar Tiwari
Summary: This article presents a model for solving the joint production, pricing, and inventory control problem in an omnichannel environment. The study finds that enabling showrooming and webrooming on the drop-shipping channel can generate additional profit in the supply chain. The article proposes solution techniques and validates them through numerical experiments, providing key managerial insights for manufacturers.
IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
(2023)
Proceedings Paper
Automation & Control Systems
Duhita Wani, Ritik Singh, Vivekanand B. Khanapuri, Manoj Kumar Tiwari
Summary: COVID-19 has had a significant impact on supply chains and last-mile logistics. To address the changes in B2C online business and customer behavior, accurately estimating delays is crucial in reducing time-related uncertainty. This study analyzes shipment information from an online business organization and proposes an enhanced hybrid voting-based classification model to predict delays with high accuracy using various parameters. The model shows improved performance and provides strategic, operational, and industrial insights for decision-making in last-mile businesses.
Proceedings Paper
Automation & Control Systems
Prajwal Yadav, Ratnesh Bhosale, Rosalin Sahoo, Vivek Khanzode, Manoj Kumar Tiwari
Summary: Blockchain is a foundational technology with various features that is being used to solve problems across industries. This study proposes a platform using blockchain to address financial issues in the air cargo industry, aiming to improve transparency, trust, and efficiency in the supply chain.
Article
Computer Science, Interdisciplinary Applications
Xiaolin Wang, Liyi Zhan, Yong Zhang, Teng Fei, Ming-Lang Tseng
Summary: This study proposes an environmental cold chain logistics distribution center location model to reduce transportation costs and carbon emissions. It also introduces a hybrid arithmetic whale optimization algorithm to overcome the limitations of the conventional algorithm.
COMPUTERS & INDUSTRIAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Hong-yu Liu, Shou-feng Ji, Yuan-yuan Ji
Summary: This study proposes an architecture that utilizes Ethereum to investigate the production-inventory-delivery problem in Physical Internet (PI), and develops an iterative heuristic algorithm that outperforms other algorithms. However, due to gas prices and consumption, blockchain technology may not always be the optimal solution.
COMPUTERS & INDUSTRIAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Paraskevi Th. Zacharia, Elias K. Xidias, Andreas C. Nearchou
Summary: This article discusses the assembly line balancing problem in production lines with collaborative robots. Collaborative robots have the potential to improve automation, productivity, accuracy, and flexibility in manufacturing. The article explores the use of a problem-specific metaheuristic to solve this complex problem under uncertainty.
COMPUTERS & INDUSTRIAL ENGINEERING
(2024)
