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Consumers' dramatic demand has a pernicious effect throughout the supply chain. It exacerbates inventory distortion because of significant revenue loss caused by stock-level issues. Despite the availability of several forecasting techniques, large organisations, manufacturing firms, and e-commerce websites collectively lose around $1.8 trillion annually to inventory distortion. If this problem is solved, sales may increase by 10.3 percent. The businesses are concerned about mitigating this loss. Artificial intelligence (AI) can play a significant role in building resilient supply chains. However, developing AI models consumes time and cost. In this paper, we propose a No Code Artificial Intelligence (NCAI) enabling non-technical companies to build machine learning models based on production quantity and inventory replenishment. The development of the NCAI model is fast and inexpensive. However, little research deals with applying NCAI to operations and supply chain problems. Addressing the existing gap, we show the application of NCAI in the retail industry. [ABSTRACT FROM AUTHOR]

Blockchain-based crowdfunding is a form of crowdfunding that uses blockchain technology to facilitate the fundraising process. Blockchain technology provides a decentralized, transparent, and secure platform for crowdfunding by allowing the creation of smart contracts and the issuance of digital tokens. However, Blockchain-based crowdfunding systems suffer from a few security issues, such as the possibility of fraud, risk assessment, smart contracts bugs, and cyber-attacks. This paper proposes integrating artificial intelligence models to prevent smart contract vulnerabilities and anomaly detection. Thus, we will deploy Graph Neural Networks models to protect Blockchain-based crowdfunding platforms from smart contracts-based attacks such as reentrancy and infinite loop attacks. Then, we will use a machine learning model for anomaly detection and prevent attacks such as advanced persistent threats, malware, and distributed denial of service attacks. An experimental study is conducted in a real crowdfunding platform to prove the feasibility of our framework and to draw lessons from the real-life implementation of such models. Our results show that our approach can accurately identify both normal and abnormal traffic and classify correctly specific types of attacks. We also evaluate the performance of our framework using various evaluation metrics to ensure its effectiveness in detecting anomalies. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) offers a promising solution for building and promoting more resilient supply chains. However, the literature is highly dispersed regarding the application of AI in supply-chain management. The literature to date lacks a decision-making framework for identifying and applying powerful AI techniques to build supply-chain resilience (SCRes), curbing advances in research and practice on this interesting interface. In this paper, we propose an integrated Multi-criteria decision-making (MCDM) technique powered by AI-based algorithms such as Fuzzy systems, Wavelet Neural Networks (WNN) and Evaluation based on Distance from Average Solution (EDAS) to identify patterns in AI techniques for developing different SCRes strategies. The analysis was informed by data collected from 479 manufacturing companies to determine the most significant AI applications used for SCRes. The findings show that fuzzy logic programming, machine learning big data, and agent-based systems are the most promising techniques used to promote SCRes strategies. The study findings support decision-makers by providing an integrated decision-making framework to guide practitioners in AI deployment for building SCRes. [ABSTRACT FROM AUTHOR]

Increasing pollution is causing adverse environmental effects, leading to increased interest in combating this issue. There has been a significant interest in minimizing the pollution caused by combustion engine vehicles, with high research and development investments in hybrid and electric vehicle (EV) batteries. The innovations in EVs have a high potential to contribute to an optimized transportation sector while also playing a crucial role in reducing greenhouse gas emissions. This study contributes to the EV industry by precisely predicting the power demand at a particular charging station and identifying the optimal charging station characteristics. We proposed a modified business process based on digital technologies to maximize customer engagement and operational efficiency. Our research has incorporated technologies like artificial intelligence (AI) and machine learning (ML). This study addresses the issues of EV infrastructure facilities, the issues raised by the lack of service features for EVs, and the optimal power requirement for charging stations. The proposed framework has managerial and technological implications, suggesting that the system must promptly receive, store, and analyze substantial volumes of data and demonstrate adaptability in response to environmental factors, such as the availability of EVs and the utilization of renewable energy sources. Despite the challenges, there is potential promise in developing decision assistance systems for electric vehicle power demands based on AI and ML. • This study aims to predict electric vehicle charging demand and identify the optimal charging station characteristics in the automobile industry. • The proposed framework utilizes artificial intelligence and machine learning to analyze large volumes of data and adapt to environmental factors such as the availability of electric vehicles and renewable energy sources. • This research addresses issues with electric vehicle infrastructure and the lack of service features for electric vehicles, as well as the optimal power requirement for charging stations. • This study has implications for both management and technology, and the proposed framework may help reduce the negative impact of increased combustion engine vehicle pollution. [ABSTRACT FROM AUTHOR]

The evolution of digital platforms and related ecosystems has led to a new and essential context of entrepreneurship. Previous studies have revealed that although high-tech new ventures possess cutting-edge technology, they face significant challenges because of vital resource constraints and limited management capability. Design thinking practices have emerged as a prominent problem-solving approach offering innovation benefits during high uncertainties. However, in the context of high-tech ventures, more research is needed on linking the relationship between design thinking principles, innovation theories (effectuation and causation), and firm performance. The present study investigates how the design thinking principles influence innovation theories and whether it leads to improved high-tech venture performance. Based on a survey of 291 platform-based start-ups in India, the results show that design thinking ways are well integrated into the theories of effectuation and causation, facilitating the growth of platform-based ventures. However, the influence of effectuation and causation on platform-based venture performance is found to be mediated through the digital capabilities acquired by these ventures. • This study assesses how the design thinking principles influence innovation theories. • Digital capabilities (DCs) are critical in new platform-based ventures to drive performance. • DCs mediate between effectuation and causation's effect on venture performance. • Design thinking ways are well integrated into the theories of effectuation and causation. • Platform-based venture performance is affected by the experience of the entrepreneur. [ABSTRACT FROM AUTHOR]

• The research mainly addresses digital twin for sustainable manufacturing supply chains. • A systematic literature review of 70 research papers on various dimensions of a digital twin for sustainable manufacturing is presented. • Technologies such as IoT, cloud computing, and blockchain have increased the potential of digital twin and the digital twin should include the "things" and "humans" from the entire supply chain. • A sustainable digital twin implementation framework for the supply chain is presented as an outcome of this study. A digital twin is an integration of virtual and physical systems using disruptive technologies. More precisely, it is a method of developing sustainable, intelligent manufacturing systems for attaining robust quality, reducing time, and customized products using real-time information throughout the product life cycle. This paper presents a systematic literature review of 98 research papers on various digital supply chain twin dimensions with sustainable performance objectives. The selected papers were reviewed and classified into three broad categories: components of the digital twin, applications in the manufacturing supply chain, and sustainability. Based on the review and future perspectives from the study, we suggest that advancements in technologies such as IoT, cloud computing, and blockchain have increased the potential of digital twin applications in the supply chain. The results indicate that a digital supply chain twin should include the things and humans from the entire supply chain and not be restricted to the local manufacturing systems. Based on our review findings, we present a sustainable digital twin implementation framework for supply chains. The proposed framework will guide future practitioners and researchers. [ABSTRACT FROM AUTHOR]

Healthcare supply chain management (HCSCM) is a network of different systems, tools, and processes. This system works in a seamless manner to ensure healthcare related materials such as medicines, hospital supplies, and so on. These materials are manufactured in the firms, distributed through agents, and eventually reach patients on time. During COVID-19, HCSCM was severely impacted in many countries. Many patients had to face severe difficulties during the pandemic. But post pandemic and after the adoption of several modern technologies like Industry 4.0, the performance of HCSCM was more reliable. Not many studies are available which have examined the influence of Industry 4.0 in HCSCM during such a crisis. In such context, the study aims at examining the influence of Industry 4.0 in HCSCM with moderating role of environmental dynamism. A conceptual model is proposed with the support of theories and literature. This was later validated by PLS-SEM approach considering 312 respondents from people of HCSCM system. The study ascertained that Industry 4.0 has greatly improved the performance of HCSCM system after COVID-19 crisis. In addition, this study highlighted that environmental dynamism has a significant moderating role on the linkage between adoption of industry 4.0 technology with HCSCM. [ABSTRACT FROM AUTHOR]