Digitalization and Early Internationalization- Systematic Literature Review Analyses

2023 | HICSS | Citations: 0

Authors: Appiah, Emmanuel; Gabrielsson, Peter

Abstract: Substantial anecdotal evidence has been garnered to make it uncontentious to con ... Expand

Abstract: Substantial anecdotal evidence has been garnered to make it uncontentious to concede to studies that stress on the influence of digital technology on early internationalization of firms. Having an interest to study how studies on this phenomenon has progressed overtime. The present paper systematically evaluates internationalization literature in which issues of digitalization feature as a component underlying the causes, processes, and outcomes of early internationalization. This enables us to outline the development of major research areas in terms of themes, theoretical and methodological approaches. We contribute by proposing several future research directions. Collapse

Topics: information technology capability internet technology electronic business mobile system marketing management

Methods: literature sample structured literature research theory development cross sectional research literature study

CAPABILITIES FOR DATA ANALYTICS IN INDUSTRIAL INTERNET OF THINGS (IIOT)

2023 | European Conference On Information Systems | Citations: 0

Authors: Bagheri, Samaneh; Dijkstra, Jesse

Abstract: ... Expand

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Topics: analytical information system internet of things mobile system organizational value cloud computing

Methods: qualitative interview machine learning literature study personal interview computational algorithm

Harnessing the Potential of Digital Twins: Lessons from Two Mature case studies

2023 | HICSS | Citations: 0

Authors: Benbya, H.; Belbaly, N.

Abstract: Many enterprises have not progressed their DigitalTwin Projects to full scale. ... Expand

Abstract: Many enterprises have not progressed their DigitalTwin Projects to full scale. Daunting interrelated managerial challenges related to developing living learning models, introducing flexible processes, and scaling and integration impede progress. We describe the strategies that General Electrics and Philips are pursuing to address these challenges. Collapse

Topics: digital twin innovation management supply chain management production management

Methods: qualitative interview machine learning

Household Digital Twin for Disaster Response

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Bonke, Chaitali; Sun, Jun; Owusu, Gabriel; Wang, Yin

Abstract: Using simulation of digital twins can help save lives and reduce the impact of ... Expand

Abstract: Using simulation of digital twins can help save lives and reduce the impact of disasters on households. By accessing a virtual process and making recommendations to families before a disaster hits, the risk of harm can be minimized. Developing a household digital twin for disaster response can lay the foundation for effective household disaster management. This study proposes a design of household digital twin for disaster response and demonstrates how the platform can be used to simulate hazardous processes before they occur for damage control. Collapse

Topics: production management digital twin hurricane recommender system emergency management

Methods: design artifact simulation

Adoption of Artificial Intelligence and Cutting-Edge Technologies for Production System Sustainability: A Moderator-Mediation Analysis

2023 | Information Systems Frontiers | Citations: 0

Authors: Chatterjee, Sheshadri; Chaudhuri, Ranjan; Kamble, Sachin; Gupta, Shivam; Sivarajah, Uthayasankar

Abstract: Cutting-edge technologies like big data analytics (BDA), artificial intelligence ... Expand

Abstract: Cutting-edge technologies like big data analytics (BDA), artificial intelligence (AI), quantum computing, blockchain, and digital twins have a profound impact on the sustainability of the production system. In addition, it is argued that turbulence in technology could negatively impact the adoption of these technologies and adversely impact the sustainability of the production system of the firm. The present study has demonstrated that the role of technological turbulence as a moderator could impact the relationships between the sustainability the of production system with its predictors. The study further analyses the mediating role of operational sustainability which could impact the firm performance. A theoretical model has been developed that is underpinned by dynamic capability view (DCV) theory and firm absorptive capacity theory. This model was verified by PLS-SEM with 412 responses from various manufacturing firms in India. There exists a positive and significant influence of AI and other cutting-edge technologies for keeping the production system sustainable. Collapse

Topics: green IS artificial intelligence organizational productivity blockchain quantum computing

Methods: partial least squares path modeling theoretical contribution conceptual modelling theory development partial least squares regression

Theories: dynamic capabilities theory absorptive capacity theory

The Core Industry Manufacturing Process of Electronics Assembly Based on Smart Manufacturing

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Chen, Rongli; Chen, Xiaozhong; Wang, Lei; Li, Jianxin

Abstract: This research takes a case study approach to show the development of a diverse a ... Expand

Abstract: This research takes a case study approach to show the development of a diverse adoption and product strategy distinct from the core manufacturing industry process. It explains the development status in all aspects of smart manufacturing, via the example of ceramic circuit board manufacturing and electronic assembly, and outlines future smart manufacturing plans and processes. The research proposed two experiments using artificial intelligence and deep learning to demonstrate the problems and solutions regarding methods in manufacturing and factory facilities, respectively. In the first experiment, a Bayesian network inference is used to find the cause of the problem of metal residues between electronic circuits through key process and quality correlations. In the second experiment, a convolutional neural network is used to identify false defects that were overinspected during automatic optical inspection. This improves the manufacturing process by enhancing the yield rate and reducing cost. The contributions of the study built in circuit board production. Smart manufacturing, with the application of a Bayesian network to an Internet of Things setup, has addressed the problem of residue and redundant conductors on the edge of the ceramic circuit board pattern, and has improved and prevented leakage and high-frequency interference. The convolutional neural network and deep learning were used to improve the accuracy of the automatic optical inspection system, reduce the current manual review ratio, save labor costs, and provide defect classification as a reference for preprocess improvement. Collapse

Topics: artificial intelligence smart manufacturing production management Python internet of things

Methods: experiment deep learning case study convolutional neural network Bayesian network

EMBRACING CONCEPTUAL MODELLING TO ENABLE THE MEDIATION ROLE OF ENTERPRISE INFORMATION SYSTEMS

2023 | European Conference On Information Systems | Citations: 0

Authors: Chis, Andrei; Ghiran, Ana-Maria

Abstract: Nowadays, digital environments are critical regarding the way enterprise operat ... Expand

Abstract: Nowadays, digital environments are critical regarding the way enterprise operations are conducted and how enterprises are being developed. Traditional information systems used by enterprises need to evolve to accommodate the shift of their role from representation to mediation, role that has been recently conceptualized but not sufficiently researched to identify means that enable its operationalization across domains and purposes. This work identifies what are the key enablers and how should these be applied regardless of the domain of interest. For this, our paper showcases as a proof-of-concept artifact that operationalizes the before mentioned new role of the IS. Our proposal relies on Conceptual Modelling together with all the other related enablers like Digital Twins, metamodelling platforms or model-driven operation. Our example concerns the modern organization that continuously aims to become more innovative and gain competitive advantage therefore, it employs smart devices like drones in different business operations. Collapse

Topics: Python systems development digital twin usability production management

Methods: design science design artifact business process model and notation modeling language metamodel

Introduction to the Special Issue on Smart Systems for Industry 4.0 and IoT

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Chen, Mu-Yen; Thuraisingham, Bhavani; Egrioglu, Erol; Rubio, Jose De Jesus

Abstract: INTRODUCTIONThe development of big data applications is driving the dramatic gr ... Expand

Abstract: INTRODUCTIONThe development of big data applications is driving the dramatic growth of hybrid data, often in the form of complex sets of cross-media content including text, images, videos, audios, and time series. Tremendous volumes of these heterogeneous data are derived from multiple IoT sources and present new challenges for the design, development, and implementation of effective information systems and decision support frameworks to meet heterogeneous computing requirements. Emerging technologies allow for the near real-time extraction and analysis of heterogeneous data to find meaningful information.Machine-learning algorithms allow computers to learn automatically, analyzing existing data to establish rules to predict outcomes of unknown data. However, traditional machine learning approaches do not meet the needs for Internet of Things (IoT) applications, calling for new technologies. Deep learning is a good example of emerging technologies that tackle the limitations of traditional machine learning through feature engineering, providing superior performance in highly complex applications. However, these technologies also raise new security and privacy concerns. Technology adoption and trust issues are of timely importance as well.Industrial operations are in the midst of rapid transformations, sometimes referred to as Industry 4.0, Industrial Internet of Things (IIoT), or smart manufacturing. These transformations are bringing fundamental changes to factories and workplaces, making them safer and more efficient, flexible, and environmentally friendly. Machines are evolving to have increased autonomy, and new human-machine interfaces such as smart tools, augmented reality, and touchless interfaces are making interaction more natural. Machines are also becoming increasingly interconnected within individual factories as well as to the outside world through cloud computing, enabling many opportunities for operational efficiency and flexibility in manufacturing and maintenance.An increasing number of countries have put forth national advanced manufacturing development strategies, such as Germany's Industry 4.0, the United States' Industrial Internet and manufacturing system based on CPS (Cyber-Physical Systems), and China's Internet Plus Manufacturing and Made in China 2025 initiatives. Smart Manufacturing aims to maximize transparency and access of all manufacturing process information across entire manufacturing supply chains and product lifecycles, with the Internet of Things (IoT) as a centerpiece to increase productivity and output value. This manufacturing revolution depends on technology connectivity and the contextualization of data, thus putting intelligent systems support and data science at the center of these developments. ACM Reference format: Collapse

Topics: production management computer data storage smart manufacturing differential privacy privacy

Methods: computational algorithm convolutional neural network machine learning

Toward the Sustainable Development of Machine Learning Applications in Industry 4.0

2023 | European Conference On Information Systems | Citations: 0

Authors: Ellenrieder, Sara; Jourdan, Nicolas; Biegel, Tobias; Cassoli, Beatriz; Metternich, Joachim; Buxmann, Peter

Abstract: As the level of digitization in industrial environments increases, companies ar ... Expand

Abstract: As the level of digitization in industrial environments increases, companies are striving to improve efficiency and resilience to unplanned disruptions through the development of Machine Learning (ML)based applications. Still, sustainable deployment and operation beyond proofs-of-concept is a challenging and resource-intensive task in dynamic enviroments such as industry 4.0, often impeding practical adoption in the long-term and thus sustainable ML product development. In this work, we systematically identify these challenges based on the CRISP-ML process model phases by applying a design science research approach. To this end, we conducted 15 interviews with data science practitioners in industry 4.0. Following a qualitative content analysis, design requirements and design principles for the development and sustainable long-term deployment of ML systems are derived to address identified challenges such as robustness to, and management of data drift caused by timedependencies and machine/product differences, missing metadata, interfaces to other IT systems, expectation management, and MLOps guidelines. Collapse

Topics: production management industry 4.0 systems design systems development new product development

Methods: qualitative interview business process modeling design requirement design principle Cross-Industry Standard Process for Data Mining

Design and Implementation of Hierarchical Digital Twins in Industrial Production Environments

2023 | HICSS | Citations: 0

Authors: Finke, C.; Groth, M.; Schumann, M.; Dewitz, P.; Gehrke, J.; Marahrens, T.

Abstract: The increasing requirements for industrial production environments due to custo ... Expand

Abstract: The increasing requirements for industrial production environments due to customer expectations, the implementation of batch size 1, and further automation of production processes are confronting companies with new challenges. In particular, the emergence of cyber-physical systems is influencing and complicating manufacturing processes by capturing an increasing amount of information within production facilities. Digital twins are an interdisciplinary technology that may solve these issues because they serve to monitor, control, and optimize cyber-physical systems by creating a digital representation of realworld objects. Existing concepts for digital twins usually only consider individual machines without their context. This is of limited use for production environments due to a multitude of different machines and associated sensor types. Therefore, we propose a requirements catalog, concept, and prototypical implementation for the hierarchical structuring of digital twins in this paper. Collapse

Topics: digital twin usability web application programming language user interface

Methods: design artifact design science structured literature research simulation literature study