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Fleet operators rely on forecasts of future user requests to reposition empty vehicles and efficiently operate their vehicle fleets. In the context of an on-demand shared-use autonomous vehicle (AV) mobility service (SAMS), this study analyzes the trade-off that arises when selecting a spatio-temporal demand forecast aggregation level to support the operation of a SAMS fleet. In general, when short-term forecasts of user requests are intended for a finer space–time discretization, they tend to become less reliable. However, holding reliability constant, more disaggregate forecasts provide more valuable information to fleet operators. To explore this trade-off, this study presents a flexible methodological framework to evaluate and quantify the impact of spatio-temporal demand forecast aggregation on the operational efficiency of a SAMS fleet. At the core of the methodological framework is an agent-based simulation that requires a demand forecasting method and a SAMS fleet operational strategy. This study employs an offline demand forecasting method, and an online joint AV-user assignment and empty AV repositioning strategy. Using this forecasting method and fleet operational strategy, as well as Manhattan, NY taxi data, this study simulates the operations of a SAMS fleet across various spatio-temporal aggregation levels. Results indicate that as demand forecasts (and subregions) become more spatially disaggregate, fleet performance improves, in terms of user wait time and empty fleet miles. This finding comes despite demand forecast quality decreasing as subregions become more spatially disaggregate. Additionally, results indicate the SAMS fleet significantly benefits from higher quality demand forecasts, especially at more disaggregate levels.

This study introduces and evaluates the Optimal Feature Selection Convolutional LSTM (OFSCL) model, a hybrid deep learning architecture designed for accurate short-term energy demand forecasting at distribution substations using real-world data. Unlike traditional statistical models that struggle with non-linear and high-dimensional patterns, OFSCL integrates automatic feature selection within convolutional layers and employs LSTM units to effectively capture temporal dependencies. This architecture achieves superior predictive accuracy, robustness to noise, and adaptability across varying demand patterns, outperforming classical machine learning approaches and advanced deep learning models such as LSTM and CNN. After extensive hyperparameter tuning, OFSCL achieved an R² of 90%, an MAE of 0.55 MWh, and an RMSE of 0.74 MWh, demonstrating strong forecasting performance. The model captures both spatial and temporal dynamics in load data. Additionally, gradient-based sensitivity analysis identifies air temperature, month, and relative humidity as key contributors to forecasting accuracy, enhancing robustness against environmental variability and supporting informed feature prioritization.

Purpose: This study presents a systematic literature review that provides a broad and holistic view of how machine learning can be used and integrated to enhance decision-making in various areas of the supply chain, highlighting its combination with other techniques and models.Design/methodology/approach: An exhaustive literature review used three sets of keywords in the Scopus and Web of Science (WoS) databases. Through a rigorous filtering process, 70 articles were selected from an initial total of 410, focusing on those that specifically addressed the intersection of machine learning and decision-making in supply chain management.Findings: Machine learning has proven to be an essential tool in the supply chain, with applications in inventory management, logistics, and transportation, among others. Its integration with other techniques has led to significant advances in decision-making, improving efficiency in complex environments. Combining machine learning methods with traditional techniques has been particularly effective, and integration with emerging technologies has opened up new application possibilities.Originality/value: Unlike previous studies that focused on specific areas, this study offers a broad perspective on the application of machine learning in the supply chain. Additionally, combining machine learning techniques with other models is highlighted, representing added value for the scientific community and suggesting new avenues for future research.

Purpose: This work aims to evaluate demand forecasting models to determine if using exogenous factors and machine learning techniques helps improve performance compared to univariate statistical models, allowing manufacturing companies to manage demand better.Design/methodology/approach: We implemented a multivariate Auto-Regressive Moving Average with eXogenous input (ARMAX) statistical model and a Neural Network-ARMAX (NN-ARMAX) hybrid model for forecasting. Later, we compared both to a standard univariate statistical model to forecast the demand for electrical products in a Colombian manufacturing company.Findings: The outcomes demonstrated that the NN-ARMAX model outperformed the other two. Indeed, demand management improved with the reduction of overstock and out-of-stock products.Research limitations/implications: The findings and conclusions in this work are limited to Colombian manufacturing companies that sell electrical products to the construction industry. Moreover, the experts from the company that provided us with the data also selected the external factors based on their own experiences, i.e., we might have disregarded potential factors.Practical implications: This work suggests that a model using neural networks and including exogenous variables can improve demand forecasting accuracy, promoting this approach in manufacturing companies dealing with demand planning issues.Originality/value: The findings in this work demonstrate the convenience of using the proposed hybrid model to improve demand forecasting accuracy and thus provide a reliable basis for its implementation in supply chain planning for the electrical/construction sector in Colombian manufacturing companies. 

Electricity demand forecasting is a critical task for efficient, reliable and economical operation of the power grid, which is one of the most essential building blocks of smart cities. Accurate forecasting allows grid operators to properly maintain the balance of supply and demand as well as to optimize operational cost for generation and transmission. This article proposes a novel neural network architecture PowerNet which can incorporate multiple heterogeneous features such as historical energy consumption data, weather data and calendar information for the demand forecasting task. Using real-world smart meter dataset, we conduct an extensive evaluation to show the advantages of PowerNet over recently-proposed machine learning methods such as Gradient Boosting Tree (GBT), Support Vector Regression (SVR), Random Forest (RF) and Gated Recurrent Unit (GRU). PowerNet demonstrates notable performance in reducing both the median and worst-case prediction errors when forecasting demands of individual residential households. We further provide empirical results concerning the two operational considerations that are crucial when using PowerNet in practice: the time horizon the model can predict with a decent accuracy and the frequency of training the model to retain its modeling capability. Finally, we briefly discuss a multi-layer anomaly/electricity-theft detection approach based on PowerNet demand forecasting.

Purpose: The objective of this research was to implement new inventory management in a footwear company through the analysis of indicators obtained from inventory data collection. Design/methodology/approach: The methods of ABC analysis, demand forecasting, safety stock, reorder point and economic order quantity were applied. The items in inventory were classified by order of financial importance through ABC analysis, and the proposed indicators were analyzed to determine the moment the inventory replenishment should be carried out as well as the purchase lot size for each item. The research also analyzed the behavior of the demand and pointed out the demand forecasting method that came closest to reality. Findings: The study presents a method of implementing inventory management based on indicators derived from the application of ABC curve methods, demand forecasting, safety stock, re-fulfillment point, and economic purchased lot. It also indicates how the ABC classification of stocks can be used to check the most representative materials in stock. The study also highlights that the rejection of modifications can be surpassed by obtaining favorable results. Research limitations/implications: The inventory management applied in this work is based on indicators that resulted in two main data which were able to define the size of the purchase lot to be ordered and the amount of material needed. Practical implications: The methods of ABC analysis, demand forecasting, safety stock, reorder point and economic order quantity were applied. The items in inventory were classified by order of financial importance through ABC analysis, and the proposed indicators were analyzed to determine the moment the inventory replenishment should be carried out as well as the purchase lot size for each item. The research also analyzed the behavior of the demand and pointed out the demand forecasting method that came closest to reality. Originality/value: In this study, a method applied is presented, highlighting the importance of the methodological application for the implementation of inventory management. The study contributes to the encouragement and adoption of methodologies to improve analysis and inventory management in companies.

This paper presents the development of an artificial intelligent water demand forecasting model. The model comprises a single hidden-layer feed-forward neural network trained in using a differential evolution algorithm. Multiple feature selection techniques were employed to identify the minimal subset of features for optimal learning, namely Pearson correlation, information gain, symmetrical uncertainty, Relief-F attribute and principal component analysis. The performance of the feature selection techniques was compared to a baseline scenario comprising a full set of data covering potential casual variables including weather, socioeconomic and historical water consumption data. The performance of the models was evaluated based on accuracy. Results show that the five feature selection techniques outperformed the baseline scenario. More importantly, the subset of features obtained from the Pearson correlation technique produced the most superior model in terms of model accuracy. Findings from the study suggest that the inclusion of weather and socioeconomic variables in water demand modelling could enhance the accuracy of forecasts and cater for the impacts of climate and socioeconomic variations in water demand planning and management.

Despite the fact that the concept of forecasting has emerged in the realm of tourism, studies delving into this sector have yet to provide a comprehensive overview of the evolution of tourism forecasting visualization. This research presents an analysis of the current state-of-the-art tourism demand forecasting (TDF) and combined tourism demand forecasting (CTDF) systems. Based on the Web of Science Core Collection database, this study built a framework for bibliometric analysis from these fields in three distinct phases (1980–2021). Furthermore, the VOSviewer analysis software was employed to yield a clearer picture of the current status and developments in tourism forecasting research. Descriptive analysis and comprehensive knowledge network mappings using approaches such as co-citation analysis and cooperation networking were employed to identify trending research topics, the most important countries/regions, institutions, publications, and articles, and the most influential researchers. The results yielded demonstrate that scientific output pertaining to TDF exceeds the output pertaining to CTDF. However, there has been a substantial and exponential increase in both situations over recent years. In addition, the results indicated that tourism forecasting research has become increasingly diversified, with numerous combined methods presented. Furthermore, the most influential papers and writers were evaluated based on their citations, publications, network position, and relevance. The contemporary themes were also analyzed, and obstacles to the expansion of the literature were identified. This is the first study on two topics to demonstrate the ways in which bibliometric visualization can assist researchers in gaining perspectives in the tourism forecasting field by effectively communicating key findings, facilitating data exploration, and providing valuable data for future research.

Supply chain professionals: master pioneering techniques for integrating demand and supply, and create demand forecasts that are far more accurate and useful! In Demand and Supply Integration, Dr. Mark Moon presents the specific design characteristics of a world-class demand forecasting management process, showing how to effectively integrate demand forecasting within a comprehensive Demand and Supply Integration (DSI) process. Writing for supply chain professionals in any business, government agency, or military procurement organization, Moon explains what DSI is, how it differs from approaches such as S&OP, and how to recognize the symptoms of failures to sufficiently integrate demand and supply. He outlines the key characteristics of successful DSI implementations, shows how to approach Demand Forecasting as a management process, and guides you through understanding, selecting, and applying the best available qualitative and quantitative forecasting techniques. You'll learn how to thoroughly reflect market intelligence in your forecasts; measure your forecasting performance; implement state-of-the-art demand forecasting systems; manage Demand Reviews, and much more.