ВИКОРИСТАННЯ ШТУЧНОГО ІНТЕЛЕКТУ У ПРОГНОЗУВАННІ ПОТРЕБ У МАТЕРІАЛЬНИХ РЕСУРСАХ

The integration of Artificial Intelligence (AI) and digital connectivity can revolutionize material and human resources management in secondary schools. The study focused on human and material resources management in education in Nigeria and its impact on students’ academic performance in secondary schools, in Ebonyi State, Nigeria. Subjects for the study were two hundred (200) students and hundred (100) teachers randomly selected from five secondary schools in Afikpo, Ohozara, and Ezza South Local Government Areas of Ebonyi State. Questionnaire was used to collect relevant data for the study. Pearson Product Moment Correlation was used to analyses the formulated hypotheses. The findings of the study revealed that there was significant positive relationship between human resources management and students’ academic performance (r = 0.814; p < 0.05). Also, significant relationship exists between material resources management and students’ academic performance (r = 0.629; p < 0.05). The implication is that effective material resource management may bring about improvement in student’s academic performance. While it was recommended that effective practice of both human and material resources is to be well implemented for effective performance of students at any level of educational system in Nigeria.

Motivation is one of the many factors that contribute to employee performance. Teachers’ motivation in public secondary schools has been highlighted as one of the factors that contribute to employee performance (Matoke, Okibo & Nyamongo, 2015). There is however a dearth of literature on the effects of teachers’ level of participation in management of physical and material resources on their motivation. The purpose of this study was to investigate the effects of teachers’ levels of participation in management of physical and material resources on teachers’ motivation in public secondary schools in Kiambu, Machakos and Kajiado Counties, Kenay. The study sought to establish whether there was a significant relationship between the teachers’ level of participation in management of physical and material resources on teachers’ motivation in secondary schools. The study used Kurt Lewin change management model (1951) and Herzeberg Motivation Hygiene Theory (1959). Correlation design was adopted with a sample size of 58 principal and 345 subject teachers. Data was collected using questionnaires for the teachers and principals and self-administered observation guide. Stratified random sampling technique was used to get the sample size of the respondents. Validity was established through expert consultation and reliability determined using cronbach alpha. Means were computed to compare the teachers’ and principals’ opinions on the teachers’ level of participation in management of physical and material resources. Mean of below 2.00 was considered low level, mean between 2 to 3.5 was moderate whereas as mean above 3.5 was regarded high level. Independent t-test was used to establish whether there was a statistical mean difference between the teachers and school principals’ opinion on the teachers’ level of teachers’ participation in management of physical material resources. Simple regression analysis was carried out in order to establish the effect of teachers’ participation in the management of physical and material resources on their motivation. The study established that teachers were moderately involved in management of physical and material resources. The independent sample t-test results shows that there was a significant difference in the mean difference between the means of principals’ and teachers’ views on teachers’ participation in management of physical and material resources t (377) = -6.073, p = 0.00. The study revealed a statistical significant relationship between teachers’ level of participation in the management of physical facilities and their motivation ( = .399, p-value< .01).There is need to embrace participative structures that encourage teachers’ participation in management of physical facilities and material resources. Through such ventures the teachers will highly be motivated to work and consequently improving the quality of education in Kenya.

Demand forecasting is a critical function in Enterprise Resource Planning (ERP) systems, directly influencing inventory management, production planning, and overall operational efficiency. Traditional statistical models often fall short in handling the complexity and variability of modern supply chains. This study investigates the application of Artificial Intelligence (AI), specifically Machine Learning (ML) algorithms, to enhance demand forecasting accuracy within ERP environments. I conduct a comparative analysis of four widely used ML models: Linear Regression, Random Forest, Support Vector Regression (SVR), and Long Short-Term Memory (LSTM) networks. Using real-world ERP datasets, each model is evaluated based on Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and computational performance. The results reveal that while Random Forest and LSTM models outperform others in terms of accuracy, their complexity and training time vary significantly. My findings highlight the trade-offs between model accuracy and computational efficiency, offering practical insights for ERP stakeholders. This study contributes to the growing field of AI-driven enterprise analytics and provides guidance on selecting appropriate ML techniques tailored to specific forecasting needs within ERP systems.

Multidisciplinary project-based learning (PBL) allows multifaceted real-world problems to be resolved with solutions addressing scientific, technical, business, and social aspects. However, such a method can be very challenging in terms of project management (including planning, coordinating, and management of human, material and financial resources) and time-consuming, especially when involving several partners (academic and industrial partners).
 This paper provides firsthand experience of multidisciplinary PBL implemented in France in engineering education through a case study on urban drone conception. This research project has been proceeded within a collaborative framework in which 3 specialties (electrical energy systems, industrial computing, and mechanics and systems design) of a graduate school of engineering and a start-up company are jointly involved for the first time.
 This article discusses knowledge and skills that engineering students are expected to acquire at the end of the research project, and the methods of assessment. The designing, manufacturing, and experimental validation of the urban drone are described. The various steps in the project management, and the interactions with the students and partners (teachers and industrial partner) are particularly highlighted. 
 Qualitative and quantitative data have been collected through questionnaires, student logbooks and reports. The results demonstrate that this experience is overwhelmingly positive since it empowers and enhances student learning.

This abstract delves into the realm of manufacturing productivity enhancement through the review of AI-driven supply chain management (SCM) optimization and Enterprise Resource Planning (ERP) systems integration. As industries strive for operational excellence, the convergence of artificial intelligence (AI) and supply chain management emerges as a transformative force in driving efficiency, agility, and competitiveness. Through a comprehensive analysis, this abstract examines the synergistic relationship between AI-driven SCM optimization and the integration of ERP systems, elucidating their collective impact on manufacturing productivity. AI-driven SCM optimization encompasses a spectrum of technologies and methodologies, including predictive analytics, machine learning, and autonomous decision-making systems, aimed at optimizing various facets of the supply chain, from demand forecasting and inventory management to production planning and logistics optimization. By harnessing the power of AI, manufacturers can enhance forecasting accuracy, reduce lead times, optimize inventory levels, and mitigate supply chain disruptions, thereby improving overall productivity and customer satisfaction. Integration of ERP systems plays a complementary role in manufacturing productivity enhancement by providing a centralized platform for data management, process automation, and cross-functional collaboration. Through seamless integration with AI-driven SCM optimization tools, ERP systems enable real-time data exchange, actionable insights, and end-to-end visibility across the supply chain, facilitating informed decision-making and agile response to dynamic market conditions. Drawing insights from case studies and industry examples, this abstract highlights best practices, challenges, and emerging trends in AI-driven SCM optimization and ERP systems integration. Strategies for successful implementation, including organizational readiness assessment, change management, and stakeholder engagement, are discussed to guide manufacturers in unlocking the full potential of these transformative technologies. In conclusion, the convergence of AI-driven SCM optimization and ERP systems integration offers a compelling pathway for enhancing manufacturing productivity, driving operational excellence, and sustaining competitive advantage in the digital era.. Keywords: Artificial Intelligence, Supply Chain Management, Enterprise Resource Planning, Manufacturing Productivity, AI Integration, Predictive Analytics.

Enterprise resource planning (ERP) systems claim to meet the information needs of organizations. These off-the-shelf software packages replace hard to maintain solutions created by IS departments or older off-the-shelf packages that often provided only piecemeal solutions to an organization’s information needs. ERP systems evolved from material requirements planning (MRP) systems and manufacturing resources planning (MRP II) systems. ERP serves the entire enterprise, not just manufacturing and inventory control as with its predecessors. ERP integrates information for the entire organization in a single database. But ERP implementations are often complex and experience serious problems. Failures, abandoned projects, and general dissatisfaction have been well publicized in the business press. ERP systems are “expensive and difficult to implement, often imposing their own logic on a company’s strategy and existing culture” (Pozzebon, 2000, p. 1015).

The integration of Artificial Intelligence (AI) and cloud computing has revolutionized enterprise systems, particularly in predictive marketing. AI-powered enterprise solutions enable businesses to analyze vast amounts of data in real-time, enhancing decision-making, customer engagement, and operational efficiency. Predictive analytics allows companies to anticipate consumer behavior, refine marketing strategies, and optimize customer interactions. Cloud computing further supports AI-driven predictive marketing by providing scalable and cost-effective solutions that enhance data processing capabilities and business intelligence. AI-integrated enterprise resource planning (ERP) and customer relationship management (CRM) systems facilitate automated decision-making, improving supply chain management and personalized marketing campaigns. Despite its advantages, AI adoption in enterprise systems and predictive marketing presents challenges such as data privacy concerns, cybersecurity risks, and ethical considerations. The complexity of AI integration requires substantial investment in infrastructure and regulatory compliance to mitigate biases and ensure transparency in AI-driven decisions. Explainable AI (XAI) is increasingly necessary to build trust and accountability in enterprise applications. Future advancements in AI, including blockchain, augmented reality (AR), and quantum computing, will enhance predictive analytics and business intelligence, further transforming marketing automation and decision-making processes. The convergence of AI and blockchain is particularly promising in securing digital transactions and improving data transparency in enterprise operations. As AI continues to reshape enterprise systems and predictive marketing, businesses must adopt responsible AI practices, strengthen cybersecurity measures, and comply with global regulations to maximize its benefits. Companies that leverage AI-driven insights will gain a competitive edge by improving customer engagement, optimizing marketing strategies, and driving sustainable growth in the evolving digital economy.

Purpose Algorithm bias refers to repetitive computer program errors that give some users more weight than others. The aim of this article is to provide a deeper insight of algorithm bias in AI-enabled ERP software customization. Although algorithmic bias in machine learning models has uneven, unfair and unjust impacts, research on it is mostly anecdotal and scattered. Design/methodology/approach As guided by the previous research (Akter et al., 2022), this study presents the possible design bias (model, data and method) one may experience with enterprise resource planning (ERP) software customization algorithm. This study then presents the artificial intelligence (AI) version of ERP customization algorithm using k-nearest neighbours algorithm. Findings This study illustrates the possible bias when the prioritized requirements customization estimation (PRCE) algorithm available in the ERP literature is executed without any AI. Then, the authors present their newly developed AI version of the PRCE algorithm that uses ML techniques. The authors then discuss its adjoining algorithmic bias with an illustration. Further, the authors also draw a roadmap for managing algorithmic bias during ERP customization in practice. Originality/value To the best of the authors’ knowledge, no prior research has attempted to understand the algorithmic bias that occurs during the execution of the ERP customization algorithm (with or without AI).

Artificial intelligence (AI) is rapidly diffusing and becoming increasingly popular in the business world, as the most vital driving force to transform the business process in recent years. The applications of AI now are impacting all aspects of business operations, which also include Supply Chain Management (SCM). This research examines and discusses the current AI technologies used in logistics and SCM, including AI methods like Machine Learning (ML) and Advanced Data Analytics. We have conducted comprehensive research from literature research on available AI-based industrial applications today and explored some significant AI challenges for the SCM in business and related careers. The discussions cover AI's primary functions, including supply chain configuration, agility, demand forecasting, inventory control, scheduling, and solving SCM logistics issues. To sum up the discussions, we provided managerial implication suggestions for SCM professionals and researchers.

With the deepening of globalization and technological innovation, the concept of new quality productivity has gradually come onto the agenda, profoundly affecting the efficiency improvement of logistics supply chains. This study systematically analyzes the application and strategies of emerging technologies such as information technology, artificial intelligence, and big data analysis in enhancing the efficiency of logistics supply chains from the perspective of new quality productivity development. By constructing a Data Envelopment Analysis (DEA) model and a multi-indicator comprehensive evaluation method, this paper quantitatively assesses the role of new quality productivity in improving supply chain efficiency and verifies the effectiveness of related strategies through simulation optimization. The research results show that the Internet of Things (IoT) and big data analysis play a key role in supply chain information sharing, inventory management, and demand forecasting, while artificial intelligence and machine learning technologies significantly improve supply chain responsiveness by optimizing the decision-making process. In addition, this paper explores the potential of blockchain technology in ensuring supply chain transparency and security and proposes precise improvement plans for the supply chain agglomeration effects in different regions. This study provides insights for formulating improvement strategies and guiding supply chain performance management and offers decision-making references for policymakers. Future research can further explore new supply chain management models under competition and cooperation, assess the impact of new economic forms on the stability of supply chain systems, and predict the long-term trends of global supply chains in the context of changes in the global political and economic landscape.

Supply chain automation represents an essential approach for attaining operational excellence while strengthening resilience and sustainability across global logistics. The paper integrates research from over 100 peer-reviewed studies and industry reports published through 2020 to explore how automation technologies are applied across supply chain functions along with their benefits and challenges. Automation includes multiple technologies such as artificial intelligence (AI), machine learning (ML), robotic process automation (RPA), and the Internet of Things (IoT). The implementation of these technologies extends to warehouse management along with inventory control, demand forecasting, logistics optimization and additional essential sectors as identified by Atzori et al., 2017 and Choi et al., 2018 and Fosso Wamba et al., 2018. Studies on automated systems demonstrate enhancements in operations through productivity increases of 20% to 30% and substantial cost savings (Frank et al., 2019; Lacity & Willcocks, 2016). The implementation of AI-driven demand forecasting methods can cut forecast errors down by 50%, which leads to better inventory management and lower carrying costs (Tiwari et al., 2019). The integration of automated warehouse systems with autonomous mobile robots (AMRs) and automated guided vehicles (AGVs) results in faster and more precise order fulfillment while cutting labor costs according to Zhong et al. (2017). Automation adoption faces obstacles such as significant upfront costs to implement systems and challenges integrating new solutions with existing legacy infrastructures while requiring specialized technical expertise (Strandhagen et al., 2017). Ethical issues surrounding AI decision-making in supply chains and job displacement potential continue to gain recognition as important concerns (Ghobakhloo, 2018). The research explores the role of automation in developing resilient supply chains which have gained significance after global disruptions like COVID-19 (Ivanov & Dolgui, 2020). Through their findings researchers, practitioners and policymakers can gain important information about supply chain automation's benefits and challenges which forms a basis for well- informed technology adoption strategies. Keywords Supply Chain Automation, Artificial Intelligence, Machine Learning, Robotic Process Automation, Internet of Things, Warehouse Automation, Demand Forecasting, Logistics Optimization, Supply Chain Resilience

Enterprise Resource Planning (ERP) and Supply Chain Management (SCM) systems have been growing in popularity across various segments of the electronic industry over the past few years. New initiatives in resources planning, electronic commerce and extended supply chain are fuelling the trend among corporations toward integrating strategic business applications. ERP systems assist enterprises in automating and integrating corporate cross-functions. SCM systems support information flow from various players in the supply side, reducing transaction cost and facilitating supplier-customer interactions. Given the popularity and importance of ERP and SCM systems, this research aims to examine and compare these two systems from the standpoint of organisational and technological implementation and explores the trend and opportunity of their system integration. Furthermore, the authors present and analyse two potential approaches to linking ERP and SCM systems, which indeed promote their integration. This article is concluded with the challenges and implications of the ERP and SCM system integration.

This research paper delves into the integration of Artificial Intelligence (AI) in Customer Relationship Management (CRM) systems within Enterprise Resource Planning (ERP) frameworks, with a focus on personalized interactions, predictive modeling, and service automation. The primary objective was to empirically investigate how AI enhancements in CRM functionalities can contribute to more efficient and effective customer relationship management within ERP systems. A quantitative research design was adopted, utilizing a structured questionnaire distributed among CRM managers and IT professionals across various industries, with the data analyzed using the Statistical Package for the Social Sciences (SPSS). Key findings from the study reveal significant positive impacts of AI integration on CRM functionalities, notably in personalized customer interactions, predictive modeling accuracy, and service automation efficiency. These enhancements were found to contribute to improved customer satisfaction and operational efficiency, suggesting that AI - driven CRM strategies within ERP systems offer substantial benefits to organizations. The research fills a critical gap in existing literature by providing empirical evidence of the specific benefits of AI in CRM and underscores the strategic value of AI integration in enhancing CRM functionalities. The broader implications of this study highlight the transformative potential of AI in CRM, encouraging organizations to leverage AI technologies to gain a competitive edge in customer relationship management.

Tight competition encourages textile companies to increase their competitiveness to efficient in all fields, especially by reducing material stock to its optimum figures without disrupting production operations. Currently this textile manufacture has implemented SAP as an enterprise resource planning (ERP) software. SAP is currently used as material resources planning (MRP), purchasing and inventory control software. Purchases are made automatically using reorder point (ROP) and inventory control using the ABC analysis method. In time being there is no relationship between the ABC analysis method to inventory control and ROP. With this condition, the purchase order for material is raised when the stock level is lower than ROP even these unnecessary, causing the stock to become slow moving and dormant when it reaches six months laying on the warehouse and causing excess inventory. This study aims to design and implement how the ABC method can be used to determine purchasing decisions and which ROP calculations should be used. The ABC multi criteria analysis in this study shows that ABC categorization is acceptable for deciding ROP determination. This method of inventory management has the potential to reduce inventory levels up to 17% and eliminate dormant and slow moving

Enterprise Resource Planning (ERP) systems offer various functionalities to support an organization’s core functions. However, many anticipated benefits often need to materialize due to business context changes and users’ high expectations. Continuous adaptation and improvement are necessary to address user disappointments. This research focuses on ERP systems, exploring key factors influencing the success of their implementation. Recognizing challenges in ERP system implementation, this study provides a comprehensive literature review, identifying essential and contemporary Critical Success Factors (CSFs) influenced by technological advancements. Addressing challenges specific to the wood industry, this research introduces additional industry-adapted CSFs, including industry adaptability, integration with production machinery, effective warehouse management, and supply chain tracking. Furthermore, this paper emphasizes the need for continuous adaptation and improvement of ERP systems, especially in light of current trends and technological achievements. This study recommends a holistic approach, considering traditional or essential CSFs while adapting to new trends. Critical success factors in ERP implementation in the next decade involve considering cloud technology, artificial intelligence and machine learning, data security, mobile access, IoT integration, user experience, and training. The main objective of this paper is to identify the latest CSFs in ERP implementation. This research highlights essential success factors in ERP implementation, and contemporary trends in ERP implementation with a particular focus on the specifics of wood industry. While organizations should aim to maximize the potential of ERP systems, they should also acknowledge the crucial role played by human intervention in the effective and responsible implementation of artificial intelligence.