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Artificial intelligence (AI) and big data are two emerging technological concepts in supply chain management. The current study has merged AI and big data as artificial intelligence-driven big data analytics capability (AI-BDAC). The integration of resource-based theory (RBT) and contingency theory (CT) has provided to formulate conceptual framewor...
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... Key technologies for logistics 4.0 and digital supply chain include the Internet of Things, Blockchain, digitalization, artificial intelligence, machine learning, cyberphysical, augmented reality, virtual reality, robotics, additive manufacturing, and cloud computing [8][9][10][11][12]. However, studies on logistics 4.0 and digital supply chains have often focused on individual technologies such as artificial intelligence [13][14][15], blockchain [16], Internet of Things [17][18][19], big data [15,[20][21][22], supply chain analytics [23], and wearable technologies [24]. The literature found inconsistencies in the use of information technology in digital supply chain and logistics 4.0. ...
... Key technologies for logistics 4.0 and digital supply chain include the Internet of Things, Blockchain, digitalization, artificial intelligence, machine learning, cyberphysical, augmented reality, virtual reality, robotics, additive manufacturing, and cloud computing [8][9][10][11][12]. However, studies on logistics 4.0 and digital supply chains have often focused on individual technologies such as artificial intelligence [13][14][15], blockchain [16], Internet of Things [17][18][19], big data [15,[20][21][22], supply chain analytics [23], and wearable technologies [24]. The literature found inconsistencies in the use of information technology in digital supply chain and logistics 4.0. ...
... The study explored the relationship among logistics 4.0, digital supply chain, artificial intelligence, Internet of Things, digitalization, big data, circular economy, green logistics, and green supply chain management. The focus was only on four key technologies: Artificial intelligence [13][14][15], Internet of Things [17][18][19], big data [15,[20][21][22], and digitalization [51] These technologies are common in logistics 4.0 and digital supply chains, and most literature focuses on their economic, environmental, and social benefits. ...
... According to comprehensive research, organizations adopting AI technologies have experienced a 32% improvement in operational efficiency and a 28% reduction in supply chain costs [11]. Studies indicate that companies implementing integrated AI solutions have achieved a 45% increase in process automation and a 37% enhancement in decision-making accuracy [12]. The analysis of current implementation trends reveals that organizations following structured AI adoption frameworks have demonstrated a 41% higher success rate in digital transformation initiatives compared to those using ad-hoc approaches. ...
... However, companies that have successfully navigated these challenges through strategic planning have reported a 39% improvement in overall supply chain performance. Studies indicate that organizations implementing comprehensive change management strategies have achieved a 43% higher user adoption rate and a 35% reduction in implementation-related disruptions [12]. The research emphasizes that successful AI implementation requires a balanced approach considering both technological capabilities and organizational readiness. ...
... Research projects that by 2025, approximately 60% of supply chain organizations will have implemented advanced AI capabilities in their core operations [11]. Studies indicate that organizations investing in nextgeneration AI solutions expect to achieve a 50% improvement in forecast accuracy and a 33% reduction in supply chain disruptions [12]. The integration of machine learning algorithms with existing supply chain systems has shown potential for a 40% improvement in process efficiency and a 25% reduction in operational costs. ...
This article explores the transformative impact of Machine Learning (ML) and Artificial Intelligence (AI) on supply chain management and logistics operations. It examines how these technologies revolutionize traditional processes through advanced demand forecasting, intelligent route optimization, and warehouse automation. This article investigates the integration of AI-driven predictive analytics in supply chain planning, highlighting how businesses can leverage these technologies to create more Sriker Reddy Palla https://iaeme.com/Home/journal/IJITMIS 889 editor@iaeme.com resilient and adaptive supply networks. It demonstrates that ML algorithms, when applied to supply chain operations, significantly enhance operational efficiency, reduce costs, and improve customer satisfaction through precise demand prediction and optimized delivery systems. It suggests that AI-powered solutions are particularly effective in managing complex supply chain disruptions and market uncertainties. This article provides insights into the current state of AI adoption in supply chains and presents a framework for understanding future developments in this rapidly evolving field.
... According to comprehensive research, organizations adopting AI technologies have experienced a 32% improvement in operational efficiency and a 28% reduction in supply chain costs [11]. Studies indicate that companies implementing integrated AI solutions have achieved a 45% increase in process automation and a 37% enhancement in decision-making accuracy [12]. The analysis of current implementation trends reveals that organizations following structured AI adoption frameworks have demonstrated a 41% higher success rate in digital transformation initiatives compared to those using ad-hoc approaches. ...
... However, companies that have successfully navigated these challenges through strategic planning have reported a 39% improvement in overall supply chain performance. Studies indicate that organizations implementing comprehensive change management strategies have achieved a 43% higher user adoption rate and a 35% reduction in implementation-related disruptions [12]. The research emphasizes that successful AI implementation requires a balanced approach considering both technological capabilities and organizational readiness. ...
... Research projects that by 2025, approximately 60% of supply chain organizations will have implemented advanced AI capabilities in their core operations [11]. Studies indicate that organizations investing in nextgeneration AI solutions expect to achieve a 50% improvement in forecast accuracy and a 33% reduction in supply chain disruptions [12]. The integration of machine learning algorithms with existing supply chain systems has shown potential for a 40% improvement in process efficiency and a 25% reduction in operational costs. ...
This article explores the transformative impact of Machine Learning (ML) and Artificial Intelligence (AI) on supply chain management and logistics operations. It examines how these technologies revolutionize traditional processes through advanced demand forecasting, intelligent route optimization, and warehouse automation. This article investigates the integration of AI-driven predictive analytics in supply chain planning, highlighting how businesses can leverage these technologies to create more resilient and adaptive supply networks. It demonstrates that ML algorithms, when applied to supply chain operations, significantly enhance operational efficiency, reduce costs, and improve customer satisfaction through precise demand prediction and optimized delivery systems. It suggests that AI-powered solutions are particularly effective in managing complex supply chain disruptions and market uncertainties. This article provides insights into the current state of AI adoption in supply chains and presents a framework for understanding future developments in this rapidly evolving field.
... The correlation between AI-BDACs and supply chain performance [61] and supply chain analytics and environmental performance [62] was found. Additionally, IoT capability had a direct effect on firm performance with the mediating effects of IoT-enabled supply chain integration and supply chain capability from the retail industry in the United Kingdom [63]. ...
The industrial shift from Industry 4.0 to Industry 5.0 has transformed organizational thinking, moving the focus from purely technological implementation to a more human-centered approach. The current study has focused on the Industry 5.0 technological capabilities to bring into circular economy practices aligned with sustainable development goals, aiming to enhance sustainable performance. Moreover, the resource-based theory has grounded the development of the comprehensive framework on Industry 5.0 technological capabilities (artificial intelligence capabilities, big data analytical capabilities, Internet of Things capabilities, machine learning capabilities, and blockchain technology capabilities) and circular economy practices (eco-design, management system, and investment recovery) to achieve sustainable performance (environmental performance, social performance, and economic performance). Data have been collected from 179 respondents from the Chinese manufacturing industry. Additionally, data have been analyzed using the structural equation modeling technique. The results showed that Industry 5.0 technological capabilities directly affect sustainable performance. Moreover, circular economy practices played a dual, moderating, and mediating role between Industry 5.0 technological capabilities and sustainable performance. The current study has contributed to filling a gap in the literature on Industry 5.0 capabilities, especially in the circular economy and sustainable performance perspective. The practical contribution recommended is that if organizations focused on their Industry 5.0 technological capabilities, it would boost circular economy practices and sustainable performance to achieve sustainable development goals.
... Studies have also considered the mediating effect of green SCC on the relationships of big data analytics and artificial intelligence with environmental performance (Benzidia et al., 2021). The mediating effect of internal integrity between AI-BDAC and SCP was grounded in supply chain literature (Pereira and Shafique, 2023). The mediating relationship of green SCC was found between BDA-AI and environmental performance was found in recent supply chain literature (Gallo et al., 2023). ...
With the global digitalisation, big data has received growing attention from academicians and practitioners. However, only a few empirical studies examined the benefits of big data predictive analytics (BDPA) and its influence on supply chain collaboration (SCC) and supply chain performance (SCP). Addressing the identified gaps of the implementation of organisational information processing theory (OIPT), the current study provided the foundation to develop a conceptual framework. All relevant data were collected from 197 employees in the Chinese logistics industry. Partial least squares-structural equation modelling technique was performed. The obtained empirical results supported top management support and compatibility as critical factors for the adoption of BDPA. Moreover, BDPA exhibited positive influence on SCC and SCP. Additionally, SCC mediated the relationship between BDPA and SCP. This study presented significant theoretical contributions and provided guidelines that can benefit policymakers and organisations in the efforts of implementing BDPA for enhanced SCP. After all, improving SCP would benefit customers and the society in the case of reduction and wastage of resources.
In the food industry, consumer preferences constantly keep on changing and as a business; it is inevitable to have robust supply chain to meet the changing market demands. This dynamic consumer behaviour drives different market trends and demand for expected personalised experiences while using products and services. This chapter will discuss these two important pointers, ever changing landscape of consumer behaviour and role of supply chain agility in dealing with it. Case studies will be used to demonstrate the transformative effect of supply chain agility practices. These practices majorly consist of flexible manufacturing process, real time data analytics and collaborative partnership. Furthermore, need of risk management and resilience is to be considered to put proactive measures in place for seamless supply chain operations. The future trends and challenges will also be covered in this chapter, primarily due to emerging technologies and the changes in regulations shaping the direction of agile supply chains within the food sector
