Industry Supply Chain Research Articles

Delineating the embodied CO2 emissions in Canada's exports: Routes, drivers, and paths

AbstractThis study combined the World Input–Output Database and Asian Development Bank's Multiregional Input–Output database to investigate Canada's embodied CO2 emissions in exports (EEE) for the period of 2000–2018. We examined the key drivers and paths through structural decomposition analysis and structural path analysis. First, the results showed that embodied emissions in the intermediate exports were the major contributor to Canada's EEE, and emission paths involving more than three countries were on the rise, indicating that the expansion of the global industrial supply chains has complicated the paths of Canada's EEE. Second, the factors such as emission intensity of sectors, export structure, and export scale, had varying influences on Canada's EEE over time. For several sectors, the benefit from reduced emission intensity was largely offset by the additional emissions from the increased export scale. Hence, the design of emission regulations should consider the heterogeneity of industrial sectors in order to mitigate emissions for the diverse industries in Canada. Third, energy and resource industries (e.g., electricity, petroleum, wood, metals, and so on) played an essential role in Canada's exports. A significant amount of embodied emissions was transferred from these sectors to the downstream sectors along the supply chain, indicating that abatement measures should be adopted from the whole life cycle perspective of a product/service through an integrated governance of the supply chain. This article met the requirements for a Gold–Silver JIE data openness badge described at http://jie.click/badges.

The Development of Risk Assessments and Supplier Resilience Models for Military Industrial Supply Chains Considering Rare Disruptions

Background: Supply chain risk and resilience in non-profit-seeking industries involving governmental agencies and quasi-governmental agencies have been under-studied. This paper focuses on the military industrial supply chain to demonstrate the development of risk assessment and supplier resilience models considering one-off disruption events such as the COVID-19 disruption. Methods: We establish relevant resilience-based categories through a literature review, supported by the experiences of supply chain experts within the military industry. We quantify the severity of the identified resilience categories, their detectability, and their occurrence probabilities. The failure modes and effects analysis technique is used to evaluate the risk priorities for the resilience categories to develop a risk assessment model. The risk assessment model is then extended to a supplier resilience model by incorporating specific rare disruption factors, which can act as a scenario planning tool. Results: It is found that (i) the top four resilience sub-categories are financial, topical data, business continuity planning, and supply chain mapping, while cost reduction strategies and green material usage are the least important; (ii) the main areas requiring focus are topical data, supply chain depth awareness, business continuity management, and internal risk management; and (iii) suppliers have least resilience in the areas of ‘topical information’ and ‘business continuity strategy’. Conclusions: The tool developed can help military industrial supply chains identify the main areas to enhance resilience from multiple perspectives of severity, occurrence probability, detectability, and suppliers.

The Role of Digital Supply Chain in Enhancing Industry 4.0 Capabilities Influence for Better Strategies of Transportation Logistics: An Applied Study on the Shipping and Transportation Services Sector in Egypt

The Role of Digital Supply Chain in Enhancing Industry 4.0 Capabilities Influence for Better Strategies of Transportation Logistics: An Applied Study on the Shipping and Transportation Services Sector in Egypt

Prediction of the process capability for compression rubber part forming in the automotive supply chain

The paper proposes predicting production process capability for the compression rubber part in automotive supply chain management. Delivery of parts to tier 1 and OEM on time is the most important part of supply chain management, together with the delivery of on-quality and on-cost control to maintain the competitiveness of the supply chain. There are many suppliers to produce many automotive parts for tier 1. Therefore, the simulation approach properly predicts and prevents the process from getting into trouble during the actual production time. Production process quality control is critical to ensure that the good quality of the parts purchased can be delivered on time. Rubber parts are used widely in automotive, motorcycles, trucks, and other types of vehicles, with small sizes but in huge quantities to support general OEM brands and specific parts. The rubber part manufacturing process is complex and uncertain with compression moulding and rubber curing conditions. Therefore, good conditions can predict the production process's capability to commission and deliver on schedule.A neuro-fuzzy system is adopted and developed to deal with the uncertain process capability under multi-criteria decision-making.The methodology development can be used in the actual rubber part manufacturing supply chain environment and can predict uncertain problems that might occur in the subcontractor factories.The prediction of the production process capability of the rubber part supply chain might be more effective on the real-time monitoring control system and can be tracking location part progress for further planning both success or rescheduling.The platform can be applied to audit and test the actual industrial supply chain, and problem and research questions are brought about from the real supply chain in the local country.The methodology development was originally created for the particular supply chain in rubber automotive parts that can replace the existing system to obtain a more efficient performance evaluation process.

The Impact of Blockchain Technology on Transparency and Traceability in the Supply Chain of the Food Industry

Abstract: This thesis explores the revolutionary potential of blockchain technology, emphasizing its implications for improving transparency and traceability. Food safety, authenticity, and sustainability are major issues that the food industry must deal with. These issues can have a big impact on customer confidence and safety. In order to properly handle these issues, traditional supply chain systems frequently lack the traceability and transparency features that are required. But blockchain technology provides a decentralized, immutable database that safely and transparently records transactions, which is a promising option. This study investigates the theoretical underpinnings and real-world uses of blockchain technology while conducting a critical analysis of the state of the food industry's supply chain using secondary data from published academic articles. It assesses the effect of blockchain technology on improving transparency and traceability in food supply chains by combining findings from other academic publications, taking into account elements like data integrity, information sharing, and stakeholder participation. This thesis emphasizes how blockchain technology has the possibility to revolutionize supply chain transparency and traceability in the food business. Through the utilization of blockchain's intrinsic characteristics, such decentralization and cryptographic security, stakeholders can augment consumer trust, alleviate hazards, and cultivate increased accountability along the food supply chain

Correction: Industrial internet supply chain emergency capacity improvement model based on stochastic differential game

Correction: Industrial internet supply chain emergency capacity improvement model based on stochastic differential game

Insights into the performance of green supply chain in the Chinese semiconductor industry

To achieve the global carbon neutrality goal by 2050, businesses are urged to take the lead in adopting sustainable practices. Recently, there has been a growing interest among both academics and practitioners in utilizing artificial intelligence (AI) for digital transformation. However, measuring the impact of digital transformation on achieving carbon neutrality goals is still in its infancy, particularly in the context of the semiconductor industry. Therefore, this study aims to explore the nexus between AI capabilities, digital transformation, and carbon neutrality in enhancing green supply chain performance. A partial least squares structural equation modeling, bootstrapping, and importance-performance map analysis were employed to test the proposed research model. The data was obtained through a structured questionnaire from 426 respondents from semiconductor firms in China. The results revealed that AI capabilities positively impact the digital transformation of Chinese semiconductor firms. Furthermore, the findings demonstrated that digitally transformed firms are better equipped to achieve carbon neutrality objectives. Lastly, the study found a positive correlation between carbon neutrality and the overall performance of green supply chains in semiconductor manufacturing firms. These results serve as a valuable resource for logistics and supply chain managers, providing insights into how AI capabilities can be harnessed to enhance the performance of green supply chains.

How e-commerce can boost China’s high-quality agricultural exports

IntroductionExport quality improvement is a crucial path for a country or region to climb up the advanced division of labor in the global value chain. For developing countries that have long been at the lower end of the global value chain division of labor, the dominance of agricultural trade is often controlled by international retailers, leading to higher costs of agricultural trade and hindering the upgrading of the quality of agricultural exports. As a traditional agricultural exporting country, China still needs a competitive advantage despite the large scale of its agricultural exports. Currently, e-commerce can provide buyers and sellers with comprehensive transaction information and technical support, allowing them to realize rational allocation of resources and export upgrading. Therefore, studying how e-commerce can promote the quality upgrade of agricultural exports is crucial for China to build a trade power.MethodsThis paper clarifies the mechanism of e-commerce’s theoretical impact on the quality of agricultural exports and then empirically tests it using a high-dimensional fixed-effects model with Chinese customs data from 2000 to 2020 as a sample.ResultsThe results show that e-commerce significantly promotes the quality of agricultural exports in all regions of China, and this conclusion still holds after various robustness tests; the heterogeneity test shows that e-commerce fosters the quality of agricultural exports in eastern China, densely populated regions, and economically underdeveloped regions, especially in the case of heterogeneous products; the mechanism analysis confirms that e-commerce promotes the quality of agricultural exports mainly through the optimization of the agricultural industry chain and supply chain, and the enhancement of the level of servicing. Mechanism analysis confirms that e-commerce improves agricultural export quality mainly through optimizing the agricultural industry chain and supply chain, improving the level of servitization, and docking the demand information of the consumer market, etc. Expanded analysis shows that constructing cross-border e-commerce platforms also significantly improves the quality of micro enterprises’ agricultural exports. This paper enriches the research on e-commerce and agricultural trade.DiscussionThis paper provides an essential reference for constructing a robust agricultural trade country and developing high-quality agriculture. Despite encompassing data up to 2020, in the context of today’s global economic volatility directly affecting agricultural trade, future investigations will broaden the scope to delve deeper into how e-commerce can help countries cope with global economic uncertainty.

AutoDetect: Novel Autoencoding Architecture for Counterfeit IC Detection

The global electronic supply chain industry is a complex and interconnected network of companies, organizations, and individuals that collaborate to produce and distribute electronic devices and components. It plays a critical role in the global economy, as electronic devices have become an essential part of modern life. Electronic components and devices are produced and distributed across the world, with different regions and countries specializing in different aspects of the supply chain. This global nature of the electronic supply chain industry also poses challenges, particularly in terms of quality control and supply chain transparency involving counterfeit components owing to its interconnected nature. Counterfeiting of integrated circuits (ICs) and semiconductor devices is a significant challenge that poses threats to the safety and reliability of electronic devices, the global economy, intellectual property rights, and the overall sustainability of the electronic supply chain industry. To address this challenge, we propose a novel autoencoding architecture for counterfeit IC detection using a labeled database. The proposed architecture achieves an overall accuracy of 83% which is ≈\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\approx$$\\end{document} 20% more than the existing transfer learning approaches.

Key embrace factors for designing sustainable supply chains in Egyptian Industry 4.0

PurposeThis study explores the key embrace factors for designing supply chains in Industry 4.0 to improve sustainability practices with respect to the triple bottom line.Design/methodology/approachThe theoretical underpinnings of this study were strengthened by a qualitative method that included an interview process within the framework of a holistic case study. Data collected through 38 remote interviews with representatives of 17 Egyptian companies that are actively using Industry 4.0.FindingsThe thematic analysis identified 6 main categories in addition to 32 economic, environmental, and social key embrace factors. Further, an integrated model was established to show how various key embrace factors can be included in the evaluation of the Supply chain. 64.7% of interviewees affirmed that these factors were deemed to be the most important and influential key factors.Practical implicationsThe global contribution is mainly to facilitate additional eco-friendly initiatives to gain a competitive advantage through environmentally responsible practices, which reflects the strengthening of the sustainable supply chain with the involvement of all the interested parties.Social implicationsThis study expands on previous research papers to underline 20 new global key embrace factors, including 9 social, 7 economic, and 4 environmental key embrace factors.Originality/valueThe Key embrace factors discovered and addressed in this paper provide a unique theoretical guideline to plan and popularize this integration process. The integrated approach assists in the review of existing supply chains or the creation of new ones.

Industrial internet supply chain emergency capacity improvement model based on stochastic differential game

Industrial internet supply chain emergency capacity improvement model based on stochastic differential game

Role of IT on the Effectiveness of Logistics and Supply Chain Management Systems in the Pharmaceutical Sector in Saudi Arabia Vision 2030

The main objective of this study is to evaluate how IT might improve the efficacy and efficiency of supply chain management and logistics systems in the pharmaceutical sector because the pharmaceutical industry is experiencing a transformation by the lofty objectives outlined by Saudi Arabia Vision 2030. The study was conducted using 150 executives working in the Saudi Arabian pharmaceutical industry's logistics and supply chain division. An open-ended survey questions were used to determine how IT technology affects pharmaceutical logistics and supply chain using non-probability sampling, logistics managers' LinkedIn accounts were selected for the sample, irrespective of the organization's size, location, or gender. SPSS statistical tools were used to analyse the data. It was found that the integration of IT tools and systems has emerged as a crucial enabler in this process. By harnessing the power of data analytics, IoT (Internet of Things), and automation, pharmaceutical companies can gain real-time insights into their supply chains. This, in turn, allows them to develop agile responses to changing market dynamics and customer expectations. Such strategic decisions, driven by IT, enable businesses to adapt quickly, reorganize their operations, and offer value-added services tailored to the specific needs of the market. Moreover, the creative application of IT solutions facilitates efficient inventory management, demand forecasting, and tracking of pharmaceutical products from manufacturing facilities to end consumers results also show that companies may simply increase the pace of the supply chain with the aid of innovative chances in major choices and judgments.

A STUDY ON IMPROVING PICKING EFFICIENCY IN THE DHL SUPPLY CHAIN USING PICK TO LIGHT TECHNOLOGY

This study delves into the integration of Pick to Light (PTL) innovation inside the complicated system of the DHL supply chain, pointing to illustrate its effect on operational productivity and vital arranging. Analysing information assembled from 221 members, the inquire about utilizes a vigorous strategy including relapse, chi-square, and ANOVA tests to supply comprehensive experiences. The comes about uncover a compelling relationship (β = 0.7864, p < 0.05) between the seen benefits of PTL innovation and the defence of speculation, showing a coherent arrangement between seen preferences and long-term taken a toll reserve funds. In addition, a essential affiliation (χ² = 264.62, p < 0.05) develops between recognitions of arrange precision and the defence of innovation costs, underscoring the essential part of exactness in forming recognitions of innovation esteem. The ANOVA test encourage complements the critical effect of PTL innovation on post-integration workforce evaluation (F = 388.76, p < 0.05), emphasizing its adequacy in upgrading workforce execution and operational effectiveness. These discoveries collectively emphasize the transformative potential of PTL innovation inside the DHL supply chain, enlightening its part in optimizing operations and vital decision-making forms. By leveraging PTL innovation, DHL stands balanced to cultivate effectiveness, upgrade competitiveness, and solidify its position as a pioneer within the energetic and advancing scene of the supply chain industry.

Assessing the cascading impact of industry 4.0 disruption on supply chain analytics through the lens of dependency concepts

Nowadays, Industry 4.0 (I4.0) is regarded as one of the most sophisticated technologies that businesses need to have to stay competitive. I4.0 infrastructure system may be seen to function as a network of interdependent elements connected with multiple networks through various means. One such network is the supply chain which has an interrelation with Industry 4.0. Any disruption to Industry 4.0 will ripple through its entire supply chain and will lead to cascading effects. Thus, there is a need to examine how to minimize the disruption of I4.0 and make its dependent supply chain more functional and cost-saving. To that end, the aim of this research is threefold: (i) modeling the dependence between fourth industrial revolution and supply chain performance using the Bayesian network, (ii) capturing how a disruption in one network may propagate through the entire network and impact its overall robustness, (iii) conducting a series of advance analyses including belief propagation, sensitivity analysis, and information theory approach to validate the result and draw further insights. As a result of these analyses, it was found that technological factors and supplier quality are vital to the I4.0 disruption and performance of supply chain performance.

A responsive closed-loop supply chain network design under demand uncertainty

A closed-loop supply chain (CLSC) is important in developing a green industrial supply chain and a sustainable economy. This study addresses the challenge of designing a closed-loop supply chain network (CLSCN) under demand uncertainty by integrating forward and reverse logistics. A continuous approximation (CA) approach is proposed to solve a responsive CLSCN model. An inventory-sharing strategy is applied in the continuous approximation (CA) approach to further solve uncertain demand in a CLSCN. We conduct sensitivity analysis to demonstrate how the optimal design characteristics and the responsive supply chain network (SCN) design depend on various scenarios. A responsive CLSCN's network density is higher than a responsive SCN's. The changes in network density of the distribution center (DC) are not significantly affected by increased risks of uncertainties. The proposed model has demonstrated substantial cost savings compared to the traditional responsive CLSCN model.

Consumer Value Considerations: Impact of Remanufactured Products in The Supply Chain Industry

Consumer Value Considerations: Impact of Remanufactured Products in The Supply Chain Industry

An Investigative Study to Examine Impact of Digitalisation on Manufacturing Supply Chains

This study delves into how the COVID-19 pandemic impacted the Malaysian glove industry's supply chain and triggered the adoption of digitalization to enhance supply chain performance (SCP). Through surveys and interviews, it was discovered that the pandemic negatively affected business performance but prompted increased digitalization adoption. Commonly utilized digital solutions include Big Data Analytics (BDA), Internet of Things (IoT), and Cloud Computing (CC), while less commonly adopted solutions include Augmented Reality (AR) and Additive Manufacturing (AM). The research underscores a positive link between digitalization and SCP, stressing the significance of digital capabilities in sustaining supply chain resilience and responsiveness. However, challenges such as cultural shifts in work practices and investment apprehensions impede digitalization endeavors. The study provides insights for industry practitioners on harnessing digitalization to alleviate supply chain disruptions and offers recommendations for effective digitalization strategies.

Blockchain-Based Lean Supply Chain: A Simulation Approach

This paper aims to propose a model based on blockchain and cloud computing technologies to support lean production techniques for industrial supply chain management, evaluating how the combination of these technologies provides improvement in the chain performance. The literature review contextualizes the state of the art in studies related to the application of blockchain technology to lean manufacturing processes, as well as identifies research gaps in this field. Besides, this study models and simulates an architecture based on both the Hyperledger Fabric network and a virtual machine in the cloud environment, assessing its applicability and performance in the industrial supply chain. The analysis addresses lean aspects identified as research gaps, including integration of stakeholders in the collaborative production of customized products, conditions of flexibility and adaptability to demand variations, coordinated relationship of networks involving small and medium-sized enterprises, and behavior of various players acting simultaneously and consensually. The proposed model reveals a decentralized and nonlinear information sharing concept that demonstrates a logic of simultaneous participation of all players involved in the supply chain. The low costs and processing times identified in the simulation indicate the robustness of the system even in transactions involving a large number of companies and a large amount of data. This architecture offers an integration solution between stakeholders to support operational processes, providing insights for researchers and specialists in the adoption of innovative, reliable and inclusive information technologies for companies of different sizes or levels of development, with a focus on lean manufacturing, reduction in resource consumption and cost reduction. The developed model, which combines blockchain and cloud computing technologies applied to stages of lean production systems, presents a differentiated solution on the planning of integrated production systems, indicating the differential in this work.

The role of AI-Driven predictive analytics in optimizing IT industry supply chains

This review paper examines the pivotal role of AI-driven predictive analytics in optimizing supply chain operations within the IT industry. By leveraging machine learning, deep learning, and neural networks, predictive analytics can significantly enhance demand forecasting, inventory management, supplier selection, and risk management. Despite its potential to revolutionize supply chains, the integration of AI faces challenges, including data quality, the need for skilled personnel, and organizational resistance. Strategic implementation approaches are discussed, emphasizing robust data infrastructure, stakeholder engagement, and continuous innovation. This paper contributes to the academic discourse by highlighting AI's economic and social implications in supply chains and suggesting directions for future research. It is a comprehensive guide for practitioners and academics navigating the complexities of AI-driven predictive analytics in supply chain optimization. Keywords: AI-driven Predictive Analytics, Supply Chain Optimization, IT Industry, Machine Learning, Strategic Implementation.

ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN OPTIMIZING INVENTORY MANAGEMENT ACROSS GLOBAL INDUSTRIAL MANUFACTURING & SUPPLY CHAIN: A MULTI-COUNTRY REVIEW

This study examines the impact of Artificial Intelligence (AI) and Machine Learning (ML) on inventory management within global industrial manufacturing and supply chains, particularly in the context of Industry 4.0. Through a comparative analysis across several countries, the research analyzes quantitative and qualitative data to assess the adoption and integration of these technologies and their implications for supply chain optimization. The research methodology includes a comprehensive literature review using multiple databases and expert interviews conducted within a specific timeframe. The study identifies a significant favorable influence of AI and ML on enhancing efficiency, reducing costs, and improving real-time data analytics and predictive maintenance. It highlights the evolution from theoretical potential to practical applications, with an increased focus on regulatory compliance and data integrity, reflecting the industry's maturation in digital integration. Furthermore, the study explores the strategic role of AI and ML in process design and the holistic adoption of Industry 4.0 principles across the supply chain. The findings contribute to the academic literature by detailing the benefits and challenges of AI and ML implementation, offering insights for future research and practical applications in the supply chain sector. The conclusion emphasizes the transformative potential of AI and ML, advocating for their strategic implementation to foster resilience and adaptability in supply chain networks.