Sustainable Business Model Based on Digital Twin Platform Network: The Inspiration from Haier’s Case Study in China

Li, Xiao; Cao, Jiarou; Liu, Zheng Gang; Luo, Xiangfeng

doi:10.3390/su12030936

Cited by 58 publications

(25 citation statements)

References 64 publications

(79 reference statements)

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“…Business models need to be analyzed as to their future readiness and sustainability impact, as both economic and environmental sustainability are an intertwined challenge for businesses that cannot be tackled in isolation [68,69]. In doing so, "digital" plays a vital role in contributing towards the goals of the United Nations Sustainable Development Goals, where economic and environmental issues are at the heart of solving the challenges of the future [70].…”

Section: Discussion: a Conceptual Framework For Research And Practicementioning

confidence: 99%

“…In doing so, "digital" plays a vital role in contributing towards the goals of the United Nations Sustainable Development Goals, where economic and environmental issues are at the heart of solving the challenges of the future [70]. For firms to be able to navigate these complex challenges, transforming their existing business models into Digital Business Models, with short-and long-term effects for resource allocation and reduced resource utilization towards a circular economy, becomes paramount for sustainable market activities [69,71]. The concept of a circular economy supports firms in operationalizing sustainable developments, which can be fostered through "digital" [72,73]: Through digital business models, potentially alongside intermediary integration where unavoidable, supply chains are enabled to reduce parts needed and to avoid waste, as well as to digitally transform the business model for sustainable value capture and value creation beyond mere resource consumption, while also being able to strengthen and deepen customer interaction [72,74,75].…”

Section: Discussion: a Conceptual Framework For Research And Practicementioning

confidence: 99%

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Digital Business Model, Digital Transformation, Digital Entrepreneurship: Is There A Sustainable “Digital”?

2020

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Digitalization plays a major role in contributing towards the United Nations Sustainable Development Goals. Without transformation of existing businesses, both economic and environmental challenges of the future cannot be solved sustainably. However, there is much confusion on interrelationships and terms dealing with digitization or digitalization: Digital business model, digital transformation, digital entrepreneurship. How do these terms interrelate with and to digitalization, and how do they support firms to grow sustainably? To answer this question, we identified seven core digital-related terms based on a structured literature search within the management and economics domain, namely: Digital, Business Model, Digital Business Model, Digital Technology, Digital Innovation, Digital Transformation, and Digital Entrepreneurship. Thereafter, we analyzed prior literature for deriving a common understanding and definition as a basis for interrelations within a conceptual framework. Definitions were presented in a case study setup with twelve innovation and research and development (R&D) managers from various business units of a German high-tech company. Based on these insights, we propose a conceptual framework on how Digital Readiness, Digital Technology, and Digital Business Models might sustainably relate to Innovation, moderated by a Digital Transformation Process. With this approach, we aim to equip practitioners and researchers alike in handling and addressing change through digitalization sustainably.

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Section: Discussion: a Conceptual Framework For Research And Practicementioning

confidence: 99%

Section: Discussion: a Conceptual Framework For Research And Practicementioning

confidence: 99%

Digital Business Model, Digital Transformation, Digital Entrepreneurship: Is There A Sustainable “Digital”?

2020

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“…These are business models based around resource sharing as well as knowledge, ownership, and wealth creation. Li et al (2020) investigated how co-creation can deliver sustainable value through a digital twin platform network. Such a platform can provide a comprehensive overview of products, manufacturing processes, supply chain, customer experience, and profitability.…”

Section: Sustainable Social Value Creationmentioning

confidence: 99%

Sustainable Business Models: A Systematic Review of Approaches and Challenges in Manufacturing

Agwu

Bessant

2021

Rev. adm. contemp.

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Objective: there is an ongoing need for businesses to strive to maintain ideals within environmental, economic, and social values - commonly known as the triple bottom line. Manufacturing as a sector has advanced drastically and the literature on sustainable business models in this sector has emerged. The purpose of this paper is to analyze sustainable business models in manufacturing and the approaches and challenges faced in creating and implementing them. Methods: this paper uses a systematic approach to review the literature. We identify sustainable business models and classify them within different industry areas while strategies and challenges emerge from the literature. Results: the findings indicate that there is some empirical work done in investigating the social and environmental dimensions of sustainability in manufacturing. Overall, there is an ongoing transition to sustainable business models in varying divisions of industry. However, the creation and delivery of sustainable economic value is still unexplored. Conclusions: the research provides insight to researchers and practitioners on how organizations implement sustainability while delivering value to their stakeholders. It also provides new avenues for conducting research in unexplored strategies of sustainable business modeling.

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“…Image segmentation is one of the necessary steps of brain image fusion, so it is necessary to segment and extract the features of the image. There are many approaches to extract brain image features; currently trendy ones include Region Growing (RG), Level Set, Fuzzy Clustering, and Machine Learning (ML) (Aheleroff et al, 2020;Angin et al, 2020;Li et al, 2020;Pan et al, 2021). However, these approaches require manual image segmentation, consuming lots of human resources and materials.…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Support Vector Machine for Digital Twins Based Brain Image Fusion

et al. 2021

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The purpose is to explore the feature recognition, diagnosis, and forecasting performances of Semi-Supervised Support Vector Machines (S3VMs) for brain image fusion Digital Twins (DTs). Both unlabeled and labeled data are used regarding many unlabeled data in brain images, and semi supervised support vector machine (SVM) is proposed. Meantime, the AlexNet model is improved, and the brain images in real space are mapped to virtual space by using digital twins. Moreover, a diagnosis and prediction model of brain image fusion digital twins based on semi supervised SVM and improved AlexNet is constructed. Magnetic Resonance Imaging (MRI) data from the Brain Tumor Department of a Hospital are collected to test the performance of the constructed model through simulation experiments. Some state-of-art models are included for performance comparison: Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), AlexNet, and Multi-Layer Perceptron (MLP). Results demonstrate that the proposed model can provide a feature recognition and extraction accuracy of 92.52%, at least an improvement of 2.76% compared to other models. Its training lasts for about 100 s, and the test takes about 0.68 s. The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of the proposed model are 4.91 and 5.59%, respectively. Regarding the assessment indicators of brain image segmentation and fusion, the proposed model can provide a 79.55% Jaccard coefficient, a 90.43% Positive Predictive Value (PPV), a 73.09% Sensitivity, and a 75.58% Dice Similarity Coefficient (DSC), remarkably better than other models. Acceleration efficiency analysis suggests that the improved AlexNet model is suitable for processing massive brain image data with a higher speedup indicator. To sum up, the constructed model can provide high accuracy, good acceleration efficiency, and excellent segmentation and recognition performances while ensuring low errors, which can provide an experimental basis for brain image feature recognition and digital diagnosis.

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Sustainable Business Model Based on Digital Twin Platform Network: The Inspiration from Haier’s Case Study in China

Cited by 58 publications

References 64 publications

Digital Business Model, Digital Transformation, Digital Entrepreneurship: Is There A Sustainable “Digital”?

Digital Business Model, Digital Transformation, Digital Entrepreneurship: Is There A Sustainable “Digital”?

Sustainable Business Models: A Systematic Review of Approaches and Challenges in Manufacturing

Semi-Supervised Support Vector Machine for Digital Twins Based Brain Image Fusion

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