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The Role of Artificial Intelligence in Enhancing Procurement Processes and Supply Chains
Full text linkThe integration of Artificial Intelligence (AI) into procurement and supply chain management has significantly transformed traditional operations by enhancing efficiency, resilience, and decision- making. As businesses increasingly adopt AI-driven solutions, procurement processes and supply chain functions have evolved to become more automated, data-driven, and responsive to market dynamics. This study conducts a systematic literature review (SLR) to explore the role of AI in optimizing procurement processes and improving overall supply chain performance. The research examines key AI technologies, including machine learning, predictive analytics, robotic process automation (RPA), and natural language processing (NLP), highlighting their applications in procurement, logistics, and risk management. Findings suggest that AI enhances supplier selection, automates procurement workflows, improves demand forecasting, and strengthens supply chain resilience, particularly in response to disruptions such as the COVID-19 pandemic. By leveraging AI, organizations can minimize operational inefficiencies, enhance real-time decision- making, and improve supply chain sustainability. Despite its numerous benefits, the study identifies key challenges hindering widespread AI adoption in procurement and supply chains. These challenges include high implementation costs, data security concerns, workforce resistance to AI-driven automation, and the complexity of integrating AI with existing systems. Addressing these barriers requires strategic investments in AI infrastructure, enhanced data governance, and workforce training initiatives to ensure smooth AI adoption. The study concludes that while AI presents transformative opportunities for procurement and supply chains, businesses must develop comprehensive AI strategies to maximize its potential. Future research should focus on overcoming adoption barriers, improving AI-driven decision-making frameworks, and exploring emerging innovations to further enhance procurement and supply chain efficiency
Evaluating Supply Chain Transformation of Passenger Vehicle Industry Towards Manufacturing in Bangladesh
Full text linkBangladesh is classified as a lower-middle-income economy by the World Bank, with a Gross National Income (GNI) per capita of $2,824. It is also designated as a Least Developed Country (LDC) by the United Nations, based on low income, human asset deficits, and economic vulnerability. However, in February 2021, the UN Committee for Development Policy (CDP) recommended Bangladesh for LDC graduation, recognizing its progress in income growth, human development, and economic resilience. Bangladesh, which is the second largest economy in South Asia gained independence in 1971. Nearly 54 years have passed, yet customers in the passenger vehicle segment predominantly rely on used, reconditioned cars imported from Japan. These vehicles, allowed for import up to five years after manufacture, continue to dominate the market. However, there is a growing need to shift toward locally manufactured, brand-new vehicles to advance the country's automobile sector. Establishing a domestic automobile manufacturing industry could drive employment, strengthen the local economy, and improve living standards. Thus, many prime points come into limelight like consumer behaviour, government support and infrastructural capability. This study explores the future of the automotive supply chain in Bangladesh, focusing on identifying key factors that could drive its transformation. Utilizing a hypothesis-driven research design, the study analyzes survey responses to uncover consumer preferences and emerging trends shaping the industry. The findings provide critical insights into the potential shift from a reliance on imported reconditioned vehicles to localized manufacturing, highlighting opportunities and challenges within the evolving automotive landscape of Bangladesh
Real-Time Visibility for Building Adaptive Resilience
Full text linkSupply chain resilience has become critical in today's volatile global landscape. Recent disruptions have exposed vulnerabilities in interconnected supply networks, highlighting the need for new approaches to managing complex, rapidly evolving challenges. This research investigates how real-time visibility facilitates adaptive resilience in global supply chains through an integrated theoretical framework. Using a mixed- method approach, we analyze five major recent supply chain disruptions including the Red Sea crisis, Panama Canal drought, COVID-19 outbreaks, Baltimore bridge collapse, Israel-Iran conflict. We develop the Real-Time Adaptive Resilience (RTAR) model, a four- layer framework that explains how visibility transforms data into adaptive capabilities. Our findings reveal three key mechanisms through which real-time visibility enhances resilience: information velocity, system-wide transparency, and predictive capability. The research contributes to supply chain management theory by integrating Resource-Based View, Dynamic Capabilities, and Systems Dynamics perspectives while providing practical guidance for building adaptive supply chain capabilities
Role of Blockchain Technology in Supply Chain Management
Full text linkBlockchain technology has drew interest for its capability in supply chain management to has prompt efficiency in terms of improved transparency. Blockchain has received a lot of attention with its promises of improving supply chain through accountability. This paper aims to evaluate the benefits and risks of incorporating blockchain in supply chain management networks with especial focus on peer-reviewed articles, conference papers, and literatures documented between 2015 to 2021. From the paper, it is apparent that blockchain brings critical advantages, such as real-time tracking of product movement, less fake products, and fewer errors, which is stored in the shared platform for every stakeholder’s view. Also, the use of smart contracts promotes automated execution of a contract so that the responsibility and performance of agreements are enhanced as well as operationalism. Combine examples of Walmart and IBM’s operation solution Food Trust and the shipping logistics platform Trade Lens to demonstrate the use of blockchain for the optimistic outcome in different fields. However, the broad implementation of blockchain has some barriers; these are; reluctance by the supply chain executor to adopt new structures and systems among partners, high initial costs, and diversity with blockchain solutions that does not support operational norms across platforms. These challenges therefore call for more concentrated efforts in enhancing the understanding of education on blockchain technology and supply chain actors, setting of standard practices for the ecosystem and associating the pertinent players to allow for the considerably more effective operation of the technology. This research is useful for those academics as well as practitioners who are planning to pursue the strategy of blockchain implementation in supply chain management. The direction for further research should be to address the mentioned challenges and identify the state-of-the-art approaches, formats and standards, and the ways of implementing them in practice
Intelligent Demand Forecasting for Sustainable Spare Parts Reuse in a Rational Consumption Environment
Full text linkThis study explores the development of intelligent demand forecasting methods in the context of the rise of rational consumption awareness, to establish the optimal spare parts reuse plan. As consumers pay more attention to sustainability and environmental protection, price and quality are no longer the only considerations; global inflation has also made consumer behavior more conservative, emphasizing budget control and long-term value. In response to the sluggish market demand, this study proposes a intelligent demand forecasting method to help companies understand consumer demand and design product reuse plans that are in line with their values. This study adopts the concept of "integrated forecasting" and designs two methods: (1) Hybrid Stacking (HS) Method: Combining traditional time series and machine learning techniques to improve forecast accuracy; (2) External Information Integration (EI-HS) Method: Incorporating external information into the HS model as an improvement baseline to further reduce forecast errors. Finally, the results of MASE and RGRMSE indicate that the methods proposed in this study hold strong application potential. The research results emphasize that the development of intelligent demand forecasting methods in the era of rational consumption is of key significance and points out the direction of sustainable improvement and expansion in the future, providing companies with more accurate forecasting tools and spare parts reuse decision-making basis
Predictive Analytics for Inbound Logistics: Optimizing Lead Times and Vendor Reliability
Full text linkLead time variability has significant impact on supply chain management (SCM) and is a critical factor that affects operational efficiency, cost management, and logistical aspects of a business. This coupled with vendor reliability is the key to quality assurance, waste reduction and cost rationalization. This article delves into the manner in which variations in lead time impact two important aspects of supply chain performance, lead time optimization and vendor management. The focus is on application of predictive analytics in this respect. The article underscores the strong potential that predictive analysis has in addressing the key threats and opportunities faced by modern supply chains. The key contribution of this research is addition to the pool of literature that covers the relatively less widely discussed areas - inbound logistics and the application of sophisticated modern technologies towards improvement of supply chain visibility for enhancing efficiency of supply chain management. The article establishes that predictive analytics can be effectively used to facilitate data-driven decision-making in supplier management thus making decisions more proactive than reactive which significantly improves supply chain resilience
A Scope of Implementation intellectual Capital Accounting and Supply Chain Management in Telecommunication Companies: Evidence from Iraq
Full text linkResearch positioned in the intersection between management accounting and supply chain management is increasing. So, the core purpose of this research study is to identify the range of harmonization between theoretical concepts of Intellectual Capital (IC) around the three aspects and empirical practices required from international and local accounting standards (IAS).
The current research follows the positivist paradigm and quantitative in nature. Secondary data was used to measure the compatibility between the theoretical concepts of intellectual capital and the items that were accepted in international accounting standards across ratios, and to test these variables with TIST between the average actual practices of companies and the objective value (theoretical concepts of intellectual capital). The items will later include the items in the International Accounting Standards and the actual practices of telecommunications companies in Iraq.
The results show that there are significant differences between the theoretical concepts of IC in International Accounting and Reporting Standards - IAS 38. The areas where there is no match related to items that do not meet the condition of the concept of assets or do not achieve the rules of proof of accounting or measured to be reliable. The results were derived from the analysis of the financial statements of Zain and Asia Cell and its annual reports, including analytical tables and explanatory notes.
The confidentiality of some data related to the work of companies is one of the difficulties of conducting such research, as some of these data are linked to the competitiveness of companies and tax matters, as well as the lack of companies operating in this sector in Iraq.
This research is the first modest attempt on social environment of Iraqi knowledge economy, which represents telecommunications companies one of its components and associated components of intellectual capital. This study makes a contribution to the literature by conceptualizing the elements of management accounting in a context of the supply chain and by relating it to supply chain strategy and supply chain relationship structure
Microfinance Cooperation Base On Loan Sharks
Full text linkThis study aims to observe the motivational factors for the moneylender business of the Batak tribe known as shark loans. The discriminant analysis method is used to classify motivation into two different factors based on the pull and push theory. The testing was also carried out on two different groups of respondents, namely the favorable and the unfavorable. These results show that the determining factors for the motivation of borrower customers in the loanshark cooperative in this case are divided into two parts, namely; 1) pulling factors consisting of; independence, money, challenges/achievements, seeing opportunities, and lifestyle. 2) driving factors consisting of; job dissatisfaction, changes in the world of work, assistance from employers, and the needs of children/families. The results of the study prove that overall, all of these determining factors contribute to the Batak moneylender business. The Batak moneylender business based on the motivation factor of the pull and push theory in both community groups that support and oppose the Batak moneylender business shows that its existence is still very much needed, reaching 66.5%. Because the weak economic community in general do not have access to banking and do not have adequate requirements for creditworthiness or bank loans
Use of Machine Learning in Predicting Electric School Bus Battery Range for Optimized Routing
Full text linkThe transition to electric school buses (ESBs) promises significant environmental and economic benefits. However, optimizing their operations remains a challenge due to the limited and variable range of their batteries. This paper contributes to addressing this challenge by introducing a machine learning (ML)-based framework for accurately predicting ESB battery range under diverse operational conditions. By leveraging historical and real-time data on energy consumption, traffic patterns, weather conditions, and charging infrastructure, this study develops predictive models that enhance routing efficiency, reduce operational costs, and improve fleet reliability. Our approach integrates advanced ML techniques such as regression models, ensemble learning, and neural networks to create robust range predictions. The study's key contributions include (1) the development of a comprehensive ML-driven predictive model tailored for ESB fleets, (2) the integration of real-time environmental and operational data for dynamic decision-making, and (3) the demonstration of the model's effectiveness through numerical experiments using both simulated and real-world datasets. The findings illustrate the potential of ML in optimizing ESB routing and reducing energy wastage, paving the way for more sustainable student transportation systems
Unearthing the Contribution of Driver Fatigue to the High Rate of Road Accidents in Zimbabwe: A Critical Analysis
Full text linkThis study sought to investigate the contribution of driver fatigue to the rate of road accidents in Zimbabwe. The attribution of human error to 90% of fatal road accidents motivated the researchers to seek to establish the relationship between driver fatigue and the chances of it contributing to high rate of fatal road accidents which are a major disruption of the supply chain. The Theory of Planned Behaviour (TPB) was used as the anchorage theory for the study while the Theory of Reasoned Action (TRA) was the supporting theory. A Critical Realism philosophy was adopted supported by a cross-sectional design and quantitative approach. A sample of 162 participants was drawn out of a target population of 250 using random sampling from which a response rate of 63% was achieved. Data collection was done using a structured questionnaire; whilst data analysis was done using the Statistical Package for Social Sciences (SPSS) Version 22. The major finding from the study was that driver fatigue significantly contributes to the chances of drivers being involved in fatal road accidents. However, in Zimbabwe this area has been to a greater extent ignored in previous research with more attention having been directed to human error which constitutes 97% of causes of road accidents. The conclusion drawn from thus study is that while Zimbabwe’s statistics give little attention to fatigue, it might be the underlying factor of human error as reflected by meta-data from international studies. This paper contributes new insights into the need for further rigorous studies in Zimbabwe on the actual contribution of driver fatigue to fatal road accidents making use of modern technologies such as artificial intelligence (AI) and new driving technologies