Demand Forecasting Research Articles

Artificial Intelligence in Climate Change Mitigation: A Review of Predictive Modeling and Data-Driven Solutions for Reducing Greenhouse Gas Emissions

Artificial Intelligence (AI) is increasingly recognized as a powerful tool for addressing the challenges of climate change. Its ability to process vast amounts of data and generate advanced predictive models positions AI as a key player in efforts to reduce greenhouse gas (GHG) emissions and develop sustainable solutions. This review delves into the multifaceted role of AI in climate change mitigation, highlighting its potential in several critical areas. Firstly, AI is revolutionizing predictive climate modeling by providing more accurate forecasts and simulations, enabling better-informed policy and decision-making. Secondly, it is optimizing energy systems through smart grid management, demand forecasting, and the integration of renewable energy sources, thereby enhancing energy efficiency and reducing reliance on fossil fuels. Furthermore, AI is advancing carbon capture and storage technologies by improving the identification of optimal sites and enhancing process efficiency. In environmental monitoring, AI-driven solutions are enabling real-time detection and analysis of environmental data, contributing to more effective conservation efforts. This review also presents case studies and data that demonstrate the tangible impact of AI applications in driving progress towards global emission reduction targets. However, the adoption of AI in this domain is not without challenges. Issues such as data privacy, algorithmic transparency, and the ethical implications of AI deployment need to be carefully addressed. The paper concludes by outlining future research directions and emphasizing the need for interdisciplinary collaboration to fully harness the potential of AI in combating climate change.

Theoretical foundations and practical implementation of supply chain optimization a metal fabrication business model

In the dynamic landscape of modern business, effective management of supply chains stands as a cornerstone of organizational success and competitive edge. This study investigates the theoretical principles and practical implementations of optimization models aimed at refining supply chain operations in the metal fabrication business, giving the maximum profit. It begins with a comprehensive survey of optimization theory, encompassing linear programming, integer programming, and stochastic optimization. It proceeds to their application in addressing diverse challenges encountered in supply chain management. The discussion includes deterministic models, such as those optimizing facility location under capacity constraints and transportation logistics, alongside stochastic models designed to manage uncertainties inherent in demand forecasting and inventory control. Advanced methodologies like metaheuristic algorithms and multi-objective optimization techniques are examined for their capacity to navigate the intricate and often conflicting objectives within supply chain networks. Through comprehensive theoretical analysis and a study of the supply chain model developed for the metal fabrication business, this paper contributes to advancing knowledge in mechanical engineering and supply chain management, offering insights pertinent to practitioners and researchers alike. Ultimately, it emphasizes the critical role of optimization models in optimizing efficiency, reducing costs, improving profit margin, reducing CO2 emission, and enhancing competitiveness in modern supply chain operations.

Enhancing supply chain security and transparency with ai and blockchain integration for future-proof solutions

The objective of this review paper is to examine the integration of Artificial Intelligence (AI) and blockchain technology to enhance supply chain security and transparency, providing future-proof solutions for global industries. As the digital landscape evolves, traditional supply chain management methods face increasing vulnerabilities and inefficiencies. This paper reviews existing literature to assess the current challenges and potential benefits of integrating AI and blockchain in supply chains. The review highlights how AI enhances predictive analytics, demand forecasting, and operational efficiency, while blockchain ensures immutable, decentralized ledgers for real-time tracking and verification of goods. The synergy between these technologies offers unprecedented levels of transparency, enabling stakeholders to detect and respond to anomalies swiftly. Case studies across various industries demonstrate significant improvements in reducing fraud, counterfeiting, and human errors, alongside enhanced compliance with regulatory standards. Conclusions drawn from this review underscore the transformative potential of AI and blockchain in creating resilient, transparent, and secure supply chains. The integration of these technologies not only addresses current vulnerabilities but also lays the foundation for adaptive, intelligent supply chain networks capable of withstanding future disruptions. This paper provides actionable insights and practical frameworks for businesses and policymakers aiming to implement AI and blockchain solutions, emphasizing the importance of strategic planning, cross-industry collaboration, and continuous innovation in achieving long-term supply chain sustainability and security.

Designing circular supply chains with digital technologies for competitive sustainability: An operation management perspective

The objective of this research is to investigate how digital technologies, specifically blockchain, artificial intelligence (AI), and the Internet of Things (IoT), can be integrated into circular supply chains to enhance sustainability and preserve a competitive advantage. The goals of circular supply chains, which are founded on the ideas of the circular economy, are to reduce waste, promote resource regeneration, and lessen their negative effects on the environment. The study highlights the critical role that digital technologies play in guaranteeing transparency, promoting real-time monitoring, and optimizing resource distribution. Blockchain offers traceability and trust, IoT boosts visibility and operational efficiency within supply chains, and AI improves demand forecasting and resource efficiency. Though the benefits are obvious, there are significant obstacles to overcome, including high implementation costs, complex technological issues, scalability issues, and regulatory barriers. The research demonstrates how these digital technologies can effectively promote sustainability while enhancing operational efficiency through case studies of well-known companies like HP, Walmart, and Philips. The report also highlights important future trends and makes suggestions for companies looking to develop their workforces, invest in digital technologies, and work together on circular economy platforms. In the end, digital tool adoption is essential for creating supply chains that are more sustainable and competitive in the modern global economy.

AI-driven business analytics for SMES: Unlocking value through predictive and prescriptive analytic

Introduction: With an emphasis on predictive and prescriptive analytics, this study examines the revolutionary implications of AI-driven analytics on small and medium-sized organizations (SMEs). SMEs play a crucial role in the global economy and require advanced solutions to improve decision-making and operational efficiency. The research aims to explore how AI-powered analytics, particularly in predictive and prescriptive forms, can add value to SMEs by enhancing demand forecasting, customer behavior insights, and financial planning. To determine how AI-driven analytics might affect SMEs, a thorough assessment of the literature was undertaken. The study reveals that SMEs implementing predictive analytics experience notable improvements in areas such as inventory management, revenue generation, and overall operational efficiency. Furthermore, businesses that leverage prescriptive analytics benefit from optimized resource allocation, enhanced marketing strategies, and better risk management practices. These findings highlight the potential for AI to overcome key challenges faced by SMEs, including budget constraints and limited data availability. AI-driven analytics can provide valuable insights that allow SMEs to streamline operations and foster growth. With future trends pointing to greater accessibility and developments in machine learning, natural language processing, and the integration of AI with other cutting-edge technologies, like blockchain, AI-powered analytics offers substantial prospects for small and medium-sized enterprises.

Selection of Artificial Intelligence Technology For Digitalization of Order Fulfillment on Drug Factory Using Multi Criteria Decision Making

Objective: This paper aims to identify the most suitable Artificial Intelligence (AI) technology for the order fulfillment process in pharmaceutical factory, along with the key criteria and sub-criteria for successful AI implementation. Theoretical Framework: The study integrates theories of digital transformation, process optimization, and decision-making. Method: The research assesses digital readiness to evaluate a company's preparedness for digital transformation. The as-is process is mapped using BPMN to identify potential AI applications. AI technologies are then ranked using BWM and PROMETHEE methods, followed by scenario analysis to determine the optimal technology. Results and Discussion: Demand Forecasting is the top AI technology for adoption in the pharmaceutical factory's order fulfillment process, reducing cycle time by 29.50% to 21.80 hours. Research Implications: The research contributes to the understanding of selecting the most effective AI technology for optimizing the order fulfillment process in pharmaceutical companies, providing valuable insights for improving efficiency. Originality/Value: The study proposes a perspective on applying AI to optimize the order fulfillment process in pharmaceutical factory, offering valuable insights for improving operational efficiency through digital transformation.

Leveraging Artificial Intelligence in Supply Chain Execution in E-commerce

Modern perspective of consumer satisfaction has changed due to the proliferation of online businesses. E-commerce has opened up a whole new world to the consumers. The consumers can now get what they want, when they want, delivered at their doorstep. While the ease of online shopping has increased consumerism and consumer demand, it poses significant challenges for the e-commerce players as the consumers are seldom willing to shell out money for availing the convenience of doorstep deliveries. Under such circumstances, the e-commerce players are left with little choices but to significantly enhance their supply chain efficiencies through the improvement of logistics and the delivery services they offer to their customers. Artificial Intelligence or AI, as it is popularly known, is altering the ways and means of these e-commerce businesses. It is changing the manner in which supply chains are managed. Through the use of smart algorithms, data analysis, and automation, it is possible for businesses to employ AI effectively for the optimization of their supply chain management involving inventory and logistics management, demand forecasting, and customer service. This article focuses on logistics management and the application of AI to optimize costs and efficiency in first mile, middle mile and last mile delivery.

AI-driven optimization of agricultural water management for enhanced sustainability

Optimizing agricultural water resource management is crucial for food production, as effective water management can significantly improve irrigation efficiency and crop yields. Currently, precise agricultural water demand forecasting and management have become key research focuses; however, existing methods often fail to capture complex spatial and temporal dependencies. To address this, we propose a novel deep learning framework that combines remote sensing technology with the UNet-ConvLSTM (UCL) model to effectively integrate spatial and temporal features from MODIS and GLDAS datasets. Our model leverages the high-resolution spatial data from UNet and the temporal dependencies captured by ConvLSTM to significantly improve prediction accuracy. Experimental results demonstrate that our UCL model achieves the best R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document} compared to existing methods, reaching 0.927 on the MODIS dataset and 0.935 on the GLDAS dataset. This approach highlights the potential of AI and remote sensing technologies in addressing critical challenges in agricultural water management, contributing to more sustainable and efficient food production systems.

Evaluation of Managerial Economics Applications Strategy using the SPSS Method

Managerial Economics is an economy branch is, this economic theory and size using methods solve real-world managerial problems. It helps businesses and organizations make informed decisions by using economic analysis. Here are some common applications of managerial economics: Demand Analysis and Forecasting: Businesses use managerial economics to understand consumer behavior and forecast future demand for their products or services. This involves studying factors that affect demand, such as price, income levels, and consumer preferences. Production and Cost Analysis: Managerial economics helps businesses optimize production processes and minimize costs. It involves analyzing production functions, cost structures, and economies of scale to determine the most efficient way to produce goods or services. Pricing Strategies: A product or the right price for the service It is up to a company to decide Crucial to profitability. Managerial economics helps businesses set prices by considering factors like production costs, competition, and consumer willingness to pay. Market Structure Analysis: Understanding the type of market, a company operates in (e.g., perfect competition, monopoly, oligopoly) is essential for making strategic decisions. Managerial economics helps analyses market structures and determine appropriate strategies for each. Risk and Uncertainty Analysis: Businesses often face uncertainty in various aspects, such as demand fluctuations, production costs, and market conditions. Managerial economics provides tools to assess and manage risks, including techniques like decision tree analysis and expected value calculations. Capital Budgeting and Investment Analysis: Managerial economics assists in evaluating investment opportunities and capital projects. It involves methods Payback period analysis of investments. Regulatory and Government Policy Analysis: Managerial economics helps businesses understand and navigate government regulations and policies that affect their operations. This can include areas like taxation, environmental regulations, and trade policies. Strategic Planning: Using economic analysis, businesses can develop long-term strategies for growth and sustainability. This may involve assessing market trends, competitive dynamics, and potential expansion opportunities. Game Theory and Competitive Strategy: Managerial economics incorporates game theory to analyze strategic interactions between competitors. It helps businesses formulate optimal strategies in competitive environments. Resource Allocation: Efficient allocation of resources, including labor, capital, and technology, is crucial for a company's success. Managerial economics provides tools to distribute funds in a manner that optimisesoverall productivity and profitability. Demand Analysis and Forecasting, Production and Cost Analysis, Pricing Strategies, Market Structure Analysis, Risk and Uncertainty Analysis, Capital Budgeting and Investment Analysis and Regulatory and Government Policy Analysis. The Cranach’s Alpha Reliability result. The overall Cranach’s Alpha value for the model is .744 which indicates 74% reliability. From the literature review, the above 50% Cranach’s Alpha value model can be considered for analysis.

Enhancing hosting capacity for electric vehicles in modern power networks using improved hybrid optimization approaches with environmental sustainability considerations

The increasing adoption of electric vehicles (EVs) presents both opportunities and challenges for power networks. While EVs have the potential to reduce carbon emissions, accommodating their growing power demand requires careful planning to prevent overloading and mitigate environmental impacts. This paper introduces an integrated hosting capacity model to facilitate higher EV penetration while maintaining environmental standards. In addition to EV charging stations, the model incorporates transmission lines, reactive power compensators, energy storage systems, and thyristor-controlled series compensators to ensure a reliable power supply. The model aims to maximize EV charging station deployment, minimize greenhouse gas emissions, and optimize net present value through hosting capacity strategies. Three hosting capacity plans are proposed to analyze the impact of prioritizing one of these objectives over the others in network configurations. Accurate EV demand forecasting is critical for this model, and a swarm intelligence forecasting algorithm is proposed to explore various forecasting approaches. The model is complex and involves nonlinear multi-objective optimization. To solve it, a new hybrid optimization algorithm is introduced, combining the features of the Marine Predators Algorithm and the Honey Badger Algorithm. Three hybridization schemes—Series Hybrid Scheme, Population Division Scheme, and Switching Strategy Scheme—are developed to address the optimization challenges effectively. The results show that the first and second hybridization schemes are the most effective for solving the EV load forecasting models, with a robustness of at least 90%. In contrast, the robustness of the third scheme reaches only 30% in some models. Simulation studies on the IEEE 9-bus network and the IEEE 30-bus system validate the model’s effectiveness in integrating EVs while achieving environmental sustainability objectives. The findings show the superiority of the proposed hybrid schemes in solving the hosting capacity model in terms of finding optimal solutions. However, the third scheme required less computing time than the others, with its convergence time being at least 33.3% shorter.

Technological Innovation for Sustainable Supply Chain Management in the Food Industry

This study explores the critical role of technological innovations—specifically blockchain, Internet of Things (IoT), and artificial intelligence (AI)—in enhancing sustainable supply chain management within Malaysia's food industry. As the industry faces increasing pressure to adopt sustainable practices due to environmental concerns, regulatory demands, and consumer expectations, these technologies offer significant potential for improving traceability, reducing waste, and optimizing operational efficiency. The research highlights how blockchain enhances transparency and ethical sourcing, IoT enables real-time monitoring to minimize waste, and AI optimizes demand forecasting and inventory management, reducing overproduction and resource waste. However, the study also identifies substantial barriers to the adoption of these technologies, particularly for small and medium-sized enterprises (SMEs). These challenges include high implementation costs, a shortage of skilled labor, and concerns over data security and privacy. To address these issues, the study recommends starting with pilot projects to mitigate risks, investing in continuous workforce training, enhancing data security measures, and leveraging government support and industry collaborations. By overcoming these challenges, Malaysia's food industry can effectively integrate these innovations, leading to more sustainable, efficient, and resilient supply chains. This research underscores the importance of strategic planning and continuous improvement in fully realizing the benefits of technological advancements, thereby positioning Malaysia’s food industry as a leader in sustainable practices within the global market.

Explainable artificial intelligence to improve the resilience of perishable product supply chains by leveraging customer characteristics

AbstractOptimizing costs and profits while meeting customer demand is a critical challenge in the development of perishable supply chains. Customer-centric demand forecasting addresses this challenge by considering customer characteristics when determining inventory levels. This study proposes a solution framework comprising two steps: (a) segmentation using customer characteristics and (b) demand forecasting for each segment using transparent and responsible artificial intelligence techniques. We employed k-means, hierarchical clustering, and explainable AI (XAI) to segment, model, and compare several machine-learning techniques for demand forecasting. The results showed that support vector regression outperformed the autoregressive models. The results also showed that the two-step segmentation and demand forecasting process using hierarchical clustering and LSTM outperforms (Weighted average RMSE across segments = 61.57) the conventional single-step unsegmented forecasting process (RMSE overall data = 238.18). The main implication of this study is the demonstration of XAI in enhancing transparency in machine learning and an improved method for reducing forecasting errors in practice, which can strengthen the supply chain resilience for perishable products.

Advancing Organizational Effectiveness Through Strategic Workforce Planning and Technology Integration

Abstract: This comprehensive examination delves into the pivotal roles of strategic workforce planning and technological advancements in bolstering organizational effectiveness. Spanning various critical dimensions such as human resource demand forecasting, strategic recruitment and selection, employee training and development, diversity and inclusivity, and the integration of technology in HR practices, the paper articulates a multi-faceted approach to optimizing workforce capabilities. By leveraging predictive analytics, organizations can forecast future employment needs, while AI-enhanced recruitment processes streamline candidate selection, ensuring alignment with organizational goals. The deployment of advanced training methodologies, coupled with a focus on creating a diverse and inclusive work environment, not only enriches organizational culture but also drives innovation and improves overall performance. The paper further explores the strategic use of HR analytics, AI in recruitment, and digital platforms in employee development and engagement, highlighting their significance in adapting to the evolving work landscape. Through an in-depth analysis, it becomes evident that the integration of strategic HR planning and cutting-edge technologies is not merely an option but a necessity for organizations aiming to thrive in the competitive global market.

Improving efficiency and sustainability via supply chain optimization through CNNs and BiLSTM

Supply chain management is changing rapidly due to increasing complexity, uncertain demand, and the requirement for sustainable methods. Advanced technologies like Bidirectional Long Short-Term Memory networks (BiLSTM) and Convolutional Neural Networks (CNNs) can enhance supply chain processes. This paper proposes integrating CNNs and BiLSTM models to improve supply chain efficiency and sustainability. The proposed model employs CNNs to optimize resource allocation, uncover trends, and evaluate supply chain spatial linkages. Using BiLSTM models to capture temporal correlations allows accurate demand forecasting and proactive decision-making. Combining these models explains supply chain dynamics. CNNs and BiLSTM models' adaptive learning and real-time monitoring boost efficiency by responding quickly to changing situations. Predictive analytics optimizes inventory, lowers stock outs, and cuts lead times. Sustainability factors include transportation route optimization, carbon footprint minimization, and intelligent green-sourcing decision assistance. The proposed Hybrid Model achieved 94.65 % Specificity, 96.57 % Accuracy, 95.67 % Sensitivity and 0.85 % MCC. The result analysis demonstrates that the proposed model significantly improved the accuracy level. This research sheds light on supply chain difficulties from all sides. CNNs and BiLSTM models can boost operational efficiency and link supply chain practices with sustainability goals to produce a more sustainable global supply network.

Research on tourism demand forecast based on BP neural network -- Taking the number of tourists in Mulan Scenic spot in Huangpi District for example

With the development of the economy and society, the public's demand for tourism is getting higher and higher, followed by the environmental carrying capacity of the destination, the bearing capacity of traffic reception facilities, and the psychological carrying capacity of tourism residents being affected by different degrees. This paper took the analysis of the number of tourists in Mulan Scenic Spot in Huangpi District for the May Day holiday as an example, combined with the BP neural network algorithm, forecast the tourism demand, and put forward the local government's suggestions on holiday tourism management based on the data research.

A short-term load demand forecasting: Levenberg–Marquardt (LM), Bayesian regularization (BR), and scaled conjugate gradient (SCG) optimization algorithm analysis

A short-term load demand forecasting: Levenberg–Marquardt (LM), Bayesian regularization (BR), and scaled conjugate gradient (SCG) optimization algorithm analysis

Advanced supply chain optimization for emerging market healthcare systems

The complexities of pharmaceutical supply chains in emerging markets present significant challenges that directly impact healthcare outcomes, particularly in underserved regions. This research focuses on the optimization of supply chain systems for the healthcare sector, with an emphasis on enhancing pharmaceutical delivery. By leveraging data analytics and innovative procurement strategies, the study seeks to improve efficiency, reduce operational costs, and ensure the timely distribution of essential medications. Drawing on practical experiences at eMedic Technologies Africa, the research highlights the need for advanced models that integrate real-time data to optimize inventory management, forecast demand, and address bottlenecks in last-mile delivery. The study proposes a multi-faceted approach to addressing common challenges in emerging market supply chains, including unreliable infrastructure, fluctuating demand, and limited access to technology. By utilizing predictive analytics, the proposed models aim to improve accuracy in demand planning, reducing stockouts and overstock situations. Additionally, innovative procurement strategies, such as centralized procurement and strategic partnerships with local suppliers, are explored as key methods for enhancing cost-effectiveness and streamlining supply chain operations. A critical component of the research is the focus on last-mile delivery, which remains a significant hurdle in rural and underserved areas. The study advocates for the use of digital platforms and mobile technologies to enhance delivery tracking and coordination, ensuring that essential medications reach their destination efficiently. Furthermore, it emphasizes the importance of fostering collaboration between governments, non-governmental organizations, and private sector stakeholders to build resilient supply chain systems capable of adapting to local conditions. By providing actionable insights and scalable models for supply chain optimization, this research aims to contribute to improved healthcare delivery in emerging markets. The proposed strategies not only reduce costs and improve efficiency but also ensure a reliable and consistent supply of medications, ultimately leading to better health outcomes in regions most in need. Keywords: Supply Chain Optimization, Data-Driven Procurement, Last-Mile Delivery, Healthcare Efficiency, Emerging Markets

A Sporadic Classification and Regression-Based Approach to Intermittent Demand Forecasting in Smart Supply Chain

A Sporadic Classification and Regression-Based Approach to Intermittent Demand Forecasting in Smart Supply Chain

The limitation of machine learning methods for water supply and demand forecasting: A case study for Greater Melbourne, Australia

ABSTRACT Many cities around the world have faced water scarcity due to climate change, population growth, and urbanization. Accurate water supply and demand forecasting is critical for sustainable urban water management. Machine learning (ML) models provide new possibilities for forecasting compared with traditional models in handling non-linearity. This study aims to address the efficacy of ML models, long short-term memory (LSTM), stacked LSTM, bidirectional LSTM (Bi-LSTM), and multilayer perceptron (MLP), for forecasting water supply and demand in Greater Melbourne, Australia. The ML modelling utilized daily water supply and demand, and climatic variables (rainfall and maximum temperature) recorded by Melbourne Water and the Bureau of Meteorology from 1990 to 2019. The stacked LSTM performs better than other models in forecasting with R2, RMSE, and MAPE values of 0.908, 335.74 ML, and 23.5% for water supply and 0.791, 94.88 ML, and 5.3% for water demand, respectively. The inclusion of climatic variables enhanced the accuracy of forecasting by improving R2 and reducing RMSE and MAPE. The results indicate effectiveness of ML models, particularly LSTM-based architectures, in forecasting water supply and demand. However, these models have limitations, particularly in forecasting extreme values, emphasizing the need to improve ML models for more reliable and accurate water management.

Mechanism Study on Innovative Influences of AI and Blockchain on Supply Chain Logistics: Case Study of JD and Alibaba

This study explores the innovative impact mechanisms of artificial intelligence (AI) and blockchain in supply chain and logistics through the case study of JD and Alibaba. The successful cases of JD and Alibaba in supply chain management show that by integrating AI and blockchain technologies, companies can automate, smarten and make supply chain logistics more efficient, thereby reducing costs, improving efficiency and enhancing overall competitiveness. Applying AI in supply chain logistics, such as demand forecasting, inventory optimization and transport route planning, can provide accurate data analysis and decision support. It can help reduce inventory costs, shorten transportation time and optimize resource allocation in the supply chain. However, AI and blockchain technologies still face several challenges, such as data privacy and security issues, technology costs and standardization. Future research and practice will continue to work on solving these challenges, further optimizing and improving the application of AI and blockchain in supply chain logistics, and promoting innovation and development in supply chain logistics.