Accurate Demand Forecasting Research Articles

Sensitivity analysis and comparative assessment of novel hybridized boosting method for forecasting the power consumption

This research focuses on the crucial task of accurately forecasting electricity consumption, a key concern in modern societies where electricity is essential for industries, healthcare, and transportation. The study explores the complex factors influencing electricity usage, aiming to improve the accuracy of consumption forecasts. Recognizing the limitations of current forecasting techniques, which often fail to account for the detailed interplay of variables affecting consumption, it introduces innovative methods to address these challenges. By employing sensitivity analysis and advanced hybrid boosting algorithms, such as LightGBM and CatBoost optimized with Arithmetic Optimization and Fruit Fly Optimization Algorithms, the research analyzes time-series data on electricity use, weather conditions, and usage timing across three power distribution networks. The sensitivity analysis reveals the significant impact of customer behavior and temperature on consumption patterns. The predictive models, especially the optimized LightGBM, demonstrate remarkable accuracy, with rates of 0.9996 and 0.9968 for training and testing datasets, respectively. These results highlight the effectiveness of the proposed methodologies in providing accurate and reliable forecasts of electricity demand, offering valuable insights for the energy sector to manage resources more efficiently and meet consumption needs effectively.

Tackling Uncertainty: Forecasting the Energy Consumption and Demand of an Electric Arc Furnace with Limited Knowledge on Process Parameters

The iron and steel industry significantly contributes to global energy use and greenhouse gas emissions. The rising deployment of volatile renewables and the resultant need for flexibility, coupled with specific challenges in electric steelmaking (e.g., operation optimization, optimized power purchasing, effective grid capacity monitoring), require accurate energy consumption and demand forecasts for electric steel mills to align with the energy transition. This study investigates diverse approaches to forecast the energy consumption and demand of an electric arc furnace—one of the largest consumers on the grid—considering various forecast horizons and objectives with limited knowledge on process parameters. The results are evaluated for accuracy, robustness, and costs. Two grid connection capacity monitoring approaches—a one-step and a multi-step Long Short-Term Memory neural network—are assessed for intra-hour energy demand forecasts. The one-step approach effectively models energy demand, while the multi-step approach encounters challenges in representing different operational phases of the furnace. By employing a combined statistic–stochastic model integrating a Seasonal Auto-Regressive Moving Average model and Markov chains, the study extends the forecast horizon for optimized day-ahead electricity procurement. However, the accuracy decreases as the forecast horizon lengthens. Nevertheless, the day-ahead forecast provides substantial benefits, including reduced energy balancing needs and potential cost savings.

Enhancing Demand Prediction: A Multi-Task Learning Approach for Taxis and TNCs

Taxis and Transportation Network Companies (TNCs) are important components of the urban transportation system. An accurate short-term forecast of passenger demand can help operators better allocate taxi or TNC services to achieve supply–demand balance in real time. As a result, drivers can improve the efficiency of passenger pick-ups, thereby reducing traffic congestion and contributing to the overall sustainability of the program. Previous research has proposed sophisticated machine learning and neural-network-based models to predict the short-term demand for taxi or TNC services. However, few of them jointly consider both modes, even though the short-term demand for taxis and TNCs is closely related. By enabling information sharing between the two modes, it is possible to reduce the prediction errors for both. To improve the prediction accuracy for both modes, this study proposes a multi-task learning (MTL) model that jointly predicts the short-term demand for taxis and TNCs. The model adopts a gating mechanism that selectively shares information between the two modes to avoid negative transfer. Additionally, the model captures the second-order spatial dependency of demand by applying a graph convolutional network. To test the effectiveness of the technique, this study uses taxi and TNC demand data from Manhattan, New York, as a case study. The prediction accuracy of single-task learning and multi-task learning models are compared, and the results show that the multi-task learning approach outperforms single-task learning and benchmark models.

Probabilistic forecast of electric vehicle charging demand: analysis of different aggregation levels and energy procurement

Electric vehicles (EVs) are expected to be vital in transitioning to a low-carbon energy system. However, integrating EVs into the power grid poses significant challenges for grid operators and energy suppliers, especially regarding the uncertainty and variability of EV charging demand. Accurate forecasting of EV charging demand is essential for optimal power system integration, yet previous studies have often only considered point predictions that are inadequate for risk assessment. Therefore, this paper compares different probabilistic forecasting models for the short-term prediction of EV charging demand at various aggregation levels, using a large and novel dataset of over 350,000 charging processes at more than 500 locations across Germany. The performance of both machine learning and deep learning methods is evaluated against a naïve benchmark model, and the impact of data availability on the forecasting models is investigated. Further, the paper examines the effects of forecast accuracy on energy procurement, which has so far received minor attention in the literature. The results show that machine learning methods such as Ada Boosting and Random Forest yield robust results with a normalized root mean square error of 0.42 and 0.41 and a mean absolute scaled error of 0.36 and 0.34 at the highest aggregation level. Furthermore, the results show the influence of different site compositions on the forecast quality and how many charging points are likely to yield a robust forecast. Energy and fleet managers can use the described method to reliably predict the required energy quantities for fleets of sufficient size and procure them at low risk.

Big Data Enables the Intelligent Manufacturing Industry

With the rapid development of big data technology, the intelligent manufacturing industry is ushering in unprecedented opportunities. Big data enables intelligent manufacturing, helping to achieve more accurate demand forecasting, production planning and supply chain management, and improve production efficiency and product quality. At the same time, big data analysis can provide real-time monitoring and early warning system, timely find and solve problems in the production process, and reduce operating costs. Through big data technology, intelligent manufacturing enterprises can better grasp consumer needs, optimize product design, provide personalized customized services, and enhance market competitiveness. In the future, big data will continue to play an important role in intelligent manufacturing, helping enterprises to realize digital transformation and upgrade the industrial value chain.

Forecasting Raw Material Demand for Battery Breaker Production Process

This study aims to identify the most effective forecasting method for predicting raw material demand in the tin smelting industry, addressing the challenge of uncertainty in material arrival and inaccurate demand forecasting. Three methods, namely moving averages with n = 3 and n = 5, and exponential smoothing, were evaluated using historical data. Results indicate that exponential smoothing with α = 0.2 outperformed the other methods, yielding the smallest error rate with a Mean Absolute Percentage Error (MAPE) of 23%, Mean Absolute Deviation (MAD) of 411, and Mean Squared Error (MSE) of 293303. The implication of these findings underscores the importance of employing appropriate forecasting techniques to optimize inventory management and mitigate shortages in critical industries reliant on volatile raw material supplies. Highlights : Accurate demand forecasting is crucial for companies engaged in smelting to prevent shortages and inventory increases. Three methods were used to determine the most appropriate forecasting method for raw material demand based on historical data: moving average with n = 3 and n = 5, and exponential smoothing with α = 0.2. The Exponential Smoothing Method with α = 0.2 had the smallest error rate, with a MAPE value of 23%, MAD of 411, and MSE of 293303, and can be used to optimize demand forecasting for the next period. Keywords: demand forecasting, smelting, raw materials, historical data, moving average, exponential smoothing.

AI-DRIVEN PREDICTIVE ANALYTICS IN AGRICULTURAL SUPPLY CHAINS: A REVIEW: ASSESSING THE BENEFITS AND CHALLENGES OF AI IN FORECASTING DEMAND AND OPTIMIZING SUPPLY IN AGRICULTURE

This study provides a comprehensive review of the integration and impact of Artificial Intelligence (AI) in agricultural supply chains, focusing on its role in enhancing demand forecasting and optimizing supply. The primary objective was to assess how AI-driven predictive analytics transforms agricultural practices, addressing challenges, and shaping future trends. A systematic literature review and content analysis methodology were employed, utilizing academic databases and digital libraries to source peer-reviewed articles and conference papers published between 2014 and 2024. The inclusion criteria focused on studies related to AI applications in agricultural supply chains, while exclusion criteria filtered out non-peer-reviewed and irrelevant literature. Key findings reveal that AI significantly improves the accuracy and efficiency of demand forecasting and supply chain operations in agriculture. AI technologies, including machine learning and big data analytics, have led to advancements in real-time data analysis, predictive maintenance, and resource optimization. However, challenges such as data quality, infrastructure development, and skill gaps among agricultural professionals persist. The future landscape of AI in agriculture is marked by growth opportunities and challenges, including the need for equitable AI technology access and ethical considerations. The study recommends that industry leaders and policymakers invest in infrastructure, promote AI research and development, and provide training to facilitate AI adoption. Future research should focus on developing robust AI models tailored to agriculture, exploring AI's integration with emerging technologies, and assessing AI's long-term socio-economic impacts. This study contributes to understanding AI's current applications and future potential in transforming agricultural supply chains, offering valuable insights for stakeholders in the agricultural sector.
 Keywords: Artificial Intelligence, Agricultural Supply Chains, Predictive Analytics, Demand Forecasting.

Application of Participatory Concept in Minimizing Food Packaging Waste (FPW) in Manufacturing Firms

All the industrialized countries lately focus on the need for the best solutions for the reduction of food waste at the various stages of the supply chain by conducting the participatory concept. It is a solution and stakeholder-oriented concept providing information on how to analyze the status quo identification of organization-specific problems and with the involvement of relevant stakeholders, develop solutions in the processes along the food value chain. The objective of this study is to present a participatory (five-phase) concept to diminish FPW in manufacturing firms. This concept is adapted to the continuous improvement (PDCA cycle) applied in Total Quality Management (TQM), which involves a participatory approach where workers and stakeholders are collaborated to detect causes for inefficiency and advance measures to counteract FPW in the Healthy Food Drink powder Manufacturing firm. The research was conducted by analyzing the data related to FPW such as Aluminum Wrappers (AlWs), Bag in Boxes (BIBs), and Plastic Bottles (PBs) using Microsoft Excel and Power BI software. Also, it paid attention to related operational and supporting processes within the organization and the behavior of the staff to get an impression of the working atmosphere and attitudes of staff. The analysis was done as a comparison with the collected FPW data for 2 months before and after developing, and implementing upgraded, targeted management practices. This analysis provided the basis for the developmental framework with the focus of increasing the effectiveness of FPW management practices by assessing the structure and selecting the best waste management methodologies for FPW. According to the analyzed data, the total wastage of AlWs, BIBs, and PBs showed 5.81%, 1.82% and 1.96% respectively. With the participatory concept, the organizational weaknesses which caused the occurrence of FPW were identified, and developmental measures were implemented to obtain significant reductions of waste of Alws, BIBs, and PBs up to 2.06%, 0% and 0.43% consequently. Counteractive measures, such as join in workforces into the developing and implementing waste reduction measures, workforce training, scheduled preventive maintenance on machines, utilization of removable ink-to-print date codes, efficient demand planning and accurate demand forecasting, enhance workforce motivation and commitment, establishing trust among all employees and proper communication flows within departments which influencing the occurrence of FPW have been proposed to avoid losses. It affirms that the participatory concept either contributes to reducing FPW or enhancing resource efficiency in the food industry, as this would enable companies to benefit economically. 
 Keywords: Food packaging waste, Participatory concept, Continuous improvement, Integration, Resource efficiency

Hierarchical Time Series Forecasting in Emergency Medical Services

Accurate forecasts of ambulance demand are crucial inputs when planning and deploying staff and fleet. Such demand forecasts are required at national, regional, and sub-regional levels and must take account of the nature of incidents and their priorities. These forecasts are often generated independently by different teams within the organization. As a result, forecasts at different levels may be inconsistent, resulting in conflicting decisions and a lack of coherent coordination in the service. To address this issue, we exploit the hierarchical and grouped structure of the demand time series and apply forecast reconciliation methods to generate both point and probabilistic forecasts that are coherent and use all the available data at all levels of disaggregation. The methods are applied to daily incident data from an ambulance service in Great Britain, from October 2015 to July 2019, disaggregated by nature of incident, priority, managing health board, and control area. We use an ensemble of forecasting models and show that the resulting forecasts are better than any individual forecasting model. We validate the forecasting approach using time series cross-validation.

A hybrid LSTM method for forecasting demands of medical items in humanitarian operations

The inventory management of medical items in humanitarian operations is a challenging task due to the intermittent nature of their demand and long replenishment lead-times. An effective response to humanitarian emergencies is often associated with excess inventories, which leads to high costs. Henceforth, using accurate demand forecasts to control the inventories of these medical items is shown to be a significant lever to reduce inventory costs while keeping high service levels. This paper investigates the effectiveness of parametric and non-parametric demand forecasting methods that are commonly considered to deal with items characterised with intermittent demand patterns. Moreover, we propose a new hybrid deep learning method that combines the long short-term memory neural network and the support vector regression. Such combination enables to deal with complex and non-linear data. We conduct an empirical investigation by means of data related to 523 medical items managed in two warehouses of a major humanitarian organisation. The forecast accuracy and the inventory efficiency of the methods are analysed. The empirical results show the high performance of the deep learning methods. These insights are valuable not only in the context of medical items in humanitarian operations, but also for any items with intermittent demand patterns.

Stability And Risk Management of Global Supply Chains in Foreign Trade Economy

This article provides an in-depth analysis of the key role of global supply chain stability in the international trade economy and the core role of efficient risk management in this process. Driven by the wave of globalization, supply chains have become a key link connecting the economies of various countries. This article provides a comprehensive analysis of the current status of global supply chains and an in-depth exploration of the many factors affecting supply chain stability, including supplier reliability, logistics system efficiency, demand forecast accuracy, and inventory management effectiveness. It further delves into many aspects of supply chain risk management, including risk identification, assessment, mitigation and monitoring. Analyze and formulate contingency plans for emergencies as well as regular monitoring and evaluation strategies and measures to improve the effectiveness and value of risk management. Finally, this study emphasizes the importance of supply chain stability and efficient risk management in the global foreign trade economic system, and provides suggestions for future research directions and possible improvement strategies. This research has far-reaching significance for enhancing the transparency, resilience and sustainability of global supply chains, and is crucial to the stable development of the global economy.

Ecological modernization of enterprises’ logistics

The article designated the Relevance of introducing environmentally friendly technologies into enterprises' logistics in the context of the European Green Course policy. The research aimed to develop proposals and practical recommendations for improving logistics efficiency by implementing green technologies and optimizing the management of supply chains in trade. The object of the research is the logistics of "SILPO-FOOD" LLC, Ukraine. Calculation, analysis, chronological, generalization, forecasting, expert, observation, monographic surveys, and other methods were used in the research. In political instability, rising inflation, decreasing population demand, and growing competition, the feasibility of introducing modern ecologically oriented technologies for optimizing logistics operations, particularly the Webfleet GPS, in LLC "SILPO-FOOD" is substantiated. Proposed main stages of its implementation: definition of goals and planning; selection and coordination of functions with the supplier; preparation of vehicles, installation, and adjustment of equipment; employee training; system testing; monitoring and optimization of system operation. The main economic results of implementing this system are reducing fuel costs by 15%, maintenance by 28%, and insurance by 5%. In general, using the specified GPS will help reduce the harmful impact on the environment due to the optimization of routes, achieve savings in fuel and lubricants, and ensure better control of the technical condition of vehicles and the environmental friendliness of logistics processes. For further ecological modernization of logistics, it is advisable to focus research on implementing the Digital Twins technology of the supply chain. Its use will contribute to optimizing stocks and routes, better managing product safety and quality, improving customer service, and more accurate forecasting of demand. Keywords: Ecological modernization, enterprise, trade, logistics, technology, vehicle, expenses, route.

Comparative study of univariate and multivariate strategy for short-term forecasting of heat demand density: Exploring single and hybrid deep learning models

Accurate short-term forecasting of heating energy demand is needed for achieving optimal building energy management, cost savings, environmental sustainability, and responsible energy consumption. Furthermore, short-term heating energy prediction contributes to zero-energy building performance in cold climates. Given the critical importance of short-term forecasting in heating energy management, this study evaluated six prevalent deep-learning algorithms to predict energy load, including single and hybrid models. The overall best-performing predictors were hybrid models using Convolutional Neural Networks, regardless of whether they were multivariate or univariate. Nevertheless, while the multivariate models performed better in the first hour, the univariate models often were more accurate in the final 24 h. Thus, the best-performing predictor of the first timestep was a multivariate hybrid Convolutional Neural Network–Recurrent Neural Network model with a coefficient of determination (R²) of 0.98 and the lowest mean absolute error. Yet, the best-performing predictor of the final timestep was the univariate hybrid model Convolutional Neural Network–Long Short-Term Memory with an R² of 0.80. Also, the prediction accuracy of the best-performing multivariate hybrid models reduced faster per hour compared to the univariate models. These findings suggest that multivariate models may be better suited for early timestep predictions, while univariate models may be better suited for later time steps. Hence, combining the models can enhance accuracy at various timesteps for achieving high fidelity in forecasting and offering a comprehensive tool for energy management.

New energy power demand prediction and optimal scheduling based on artificial intelligence in smart grid

Abstract With the development of smart grid, the demand for new energy power increases. Improving the accuracy of new energy power demand forecast is an important basis for the orderly operation of power system. This article presents a new energy power demand forecasting method based on DESSA-NESN algorithm. First, differential evolution algorithm (DE) and sparrow search algorithm (SSA) are combined, and operations such as mutation, crossing and screening are introduced into the population updating process of SSA. The internal state function of the savings pool of the standard echo state network (ESN) is replaced by the hyperbolic tangent function to obtain the nonlinear echo state network (NESN). Then, the parameters of deep echo state network (DESN) are optimized using DESSA algorithm. The DESSA-DESN prediction model is established. Finally, the mean absolute percentage error (MAPE) and root mean square error (RMSE) of DESSA-NESN were 15.84 and 0.12%, respectively, and the prediction effect was better than other comparison models.

A Study on Artificial Intelligence – Driven Inventory Solutions and Enhancing Manufacturing Efficiency

Artificial Intelligence is pivotal in transforming Manufacturing and Inventory management practices and maintaining this delicate inventory balance. AI-driven predictive forecasting, for instance, considers historical data, seasonality, and market dynamics to provide exceptionally accurate demand forecasts. Managing inventory has become a complex puzzle in today’s fast-paced business world. As companies expand their reach and cater to a global audience, tracking inventory and maintaining an accurate “available-to-promise” status in real time has become a challenge. In the current competitive landscape, retailers prioritise a seamless approach to inventory management, especially with the growing importance of omnichannel fulfilment. The art of balancing supply and demand while keeping costs in check has become a high-stakes game where every piece of the inventory puzzle must fit perfectly. In this article, we explore the remarkable role of Artificial intelligence in Manufacturing and Inventory management. We will discuss how businesses can harness the power of AI to optimize their efficiency in Manufacturing and Inventory processes.

Forecasting High Speed Diesel Demand in India with Econometric and Machine Learning Methods

According to International Energy Agency (IEA), India is expected to surpass China by 2024 to become the second largest consumer of oil in the world followed by the United States. High-Speed Diesel (HSD) has the biggest share in the total petroleum products consumed in India accounting for around 38% of the total consumption. Considering the volatile global oil market and an oil import dependency ratio of more than 80% during the last four years, the probability of supply disruptions is high in the Indian context. As any uncertainty about the supply of diesel can affect the smooth functioning of the economy and may create inflationary pressures. Accurate forecasting of HSD demand will be essential for appropriate supply management arrangements. Artificial Neural Networks (ANN) with Multi-Layer Perceptron (MLP) and extreme learning machines is used for forecasting diesel demand in this study. Demand forecasting has been carried out using monthly HSD demand data drawn from the “Indiastat” database for the period 1991-2022. Comparison of ANN with traditional forecasting methods of Autoregressive Integrated Moving Average(ARIMA)and Exponential Smoothing has also been undertaken in this study. This study identifies the deep learning technique of ANN with MLP as the best diesel demand forecasting technique.

An integrated approach for modern supply chain management: Utilizing advanced machine learning models for sentiment analysis, demand forecasting, and probabilistic price prediction

In the contemporary business landscape, effective interpretation of customer sentiment, accurate demand forecasting, and precise price prediction are pivotal in making strategic decisions and efficiently allocating resources. Harnessing the vast array of data available from social media and online platforms, this paper presents an integrative approach employing machine learning, deep learning, and probabilistic models. Our methodology leverages the BERT transformer model for customer sentiment analysis, the Gated Recurrent Unit (GRU) model for demand forecasting, and the Bayesian Network for price prediction. These state-of-the-art techniques are adept at managing large-scale, high-dimensional data and uncovering hidden patterns, surpassing traditional statistical methods in performance. By bridging these diverse models, we aim to furnish businesses with a comprehensive understanding of their customer base and market dynamics, thus equipping them with insights to make informed decisions, optimize pricing strategies, and manage supply chain uncertainties effectively. The results demonstrate the strengths and areas for improvement of each model, ultimately presenting a robust and holistic approach to tackling the complex challenges of modern supply chain management.

РОЛЬ ИНФОРМАЦИОННЫХ СИСТЕМ В ОПТИМИЗАЦИИ ЛОГИСТИКИ И ЦЕПОЧЕК ПОСТАВОК

With accelerated globalisation and ever-changing market conditions, effective logistics and supply chain management is becoming a critical success factor for companies of all sizes. Information systems enable more accurate demand forecasting, inventory optimisation and shorter delivery times, which ultimately contribute to the competitiveness of enterprises. An important component of effective logistics supply chain management is the integration of digital technologies. The research conducted analyses five key technologies – CRM, SCM, APS, MES and MRP, identifying their advantages and disadvantages. The impact of artificial intelligence in optimising logistics processes is also explored. The article also examines the process of global digitalisation in the logistics sector, including the adoption of the latest technologies and an analysis of possible threats to data security. Successful examples of the use of these technologies in various logistics companies are highlighted, and recommendations are given on the development of information technology infrastructure of enterprises and support for innovative projects in the field of logistics for effective adaptation to the rapidly changing digital requirements of society.

Forecasting air passenger traffic and market share using deep neural networks with multiple inputs and outputs.

In this study, we address the challenge of accurate time series forecasting of air passenger demand using historical market demand data from the U.S. commercial aviation industry in the 21st century. Commercial aviation is a major contributor to the U.S. economy, directly or indirectly generating ~US$1.37 trillion annually, or 5% of annual GDP, and supporting more than 10 million jobs (Airlines for America, 2024). Over 1 billion passengers flew through U.S. airports in 2023 (Bureau of Transportation Statistics, 2024a). Using multiple correlated time series inputs predicts future values of multiple interrelated time series and leverages their mutual dependencies to enhance accuracy. In this study, we introduce a two-stage algorithm employing a deep neural network for correlated time series forecasting, addressing scenarios where multiple input variables are interrelated. This approach is designed to capture the influence that one time series can exert on another, thereby enhancing prediction accuracy by leveraging these interdependencies. In the first stage, we fit four Recurrent Neural Network (RNN) models to generate accurate univariate forecasts, each functioning as a single input-output model to predict aggregated market demand. The Gated Recurrent Unit (GRU) model was the top performer for our dataset overall. In the second stage, we apply the best fitted model (GRU Model) from Stage 1 to each individual competitor (disaggregated from the market) and then merge all input tensors using the Concatenate function. We hope to contribute to the relevant body of knowledge with a deep neural network framework for forecasting market share among competitors in the U.S. commercial aviation industry, as no similar approach has been documented in the literature. Given the importance of the industry, there is potentially great value in applying sophisticated forecasting techniques to achieve accurate predictions of air passenger demand. Moreover, these techniques may have wider applications and can potentially be employed in other contexts.

Demand Forecasting for Multi-Variety and Small-Batch Materials Based on Attention to Degree

Abstract Optimal production planning based on accurate market demand forecasting is crucial for cost control, inventory management, and market response in the multi-variety and small-batch production modes. Considering factors such as market demand, unit selling price, market demand trends, and product saturation within the attention cycle, an attention degree model for multi-variety and small-batch materials is constructed using historical market demand data from an electronic product manufacturing enterprise. A method for prioritizing and screening small-batch materials based on attention degree to formulate production plans is proposed. The demand for prioritized small-batch materials is predicted using the autoregressive integrated moving average model, multilayer perceptron, and bidirectional long short-term memory network. The optimal prediction results are selected to calculate the attention degree, which is then used to formulate the production plan for the next attention cycle to achieve orderly production. Taking the electronic product manufacturing enterprise as an example, the effectiveness and feasibility of the proposed model and method are verified by applying the prioritized production of small-batch materials screened based on attention degree.