Supply Network Management Research Articles

Water Demand Forecasting in Multiple District Metered Areas Based on a Multi-Scale Correction Module Neural Network Architecture

Short-term water demand forecasting (STWDF) for multiple spatially and temporally correlated District Metering Areas (DMAs) is an essential foundation for achieving more refined management of urban water supply networks. However, due to the greater uncertainty associated with specific DMA demand compared to overall water usage, accurately predicting STWDF poses significant challenges. This study introduces an innovative network architecture—the multi-scale correction module neural network, built upon Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN) enhanced with Attention mechanisms—for simultaneous STWDF with a temporal resolution of one hour over a week for 10 DMAs located in a single city in northern Italy. This framework utilizes multivariate corrections to refine and enhance the output accuracy. The results reveal that, in comparison to traditional Gated Recurrent Unit or LSTM models, the proposed model with integrated correction modules, particularly those that leverage inter-DMA correlations, improves performance across all evaluation metrics by an average of 5%-20% per DMA. Additionally, it consistently delivers superior accuracy across three scenarios: single DMA forecasting, total water demand, and extreme conditions, while maintaining stable performance throughout. Furthermore, the interpretability analysis underscores the feasibility of this innovative structure and highlights the contribution of meteorological features to the predictive model in some DMA-level STWDF. The unified input-output framework elegantly simplifies the STWDF process across multiple DMAs, providing new insights and methodologies for future research in this domain.

Combining wavelet-enhanced feature selection and deep learning techniques for multi-step forecasting of urban water demand

Abstract Urban water demand (UWD) forecasting is essential for water supply network optimization and management, both in business-as-usual scenarios, as well as under external climate and socio-economic stressors. Different machine learning and deep learning models have shown promising forecasting skills in various areas of application. However, their potential to forecast multi-step ahead UWD has not been fully explored. Modelling uncertain UWD patterns and accounting for variations in water demand behaviors require techniques that can extract time-varying information and multi-scale changes. In this research, we comparatively investigate different state-of-the-art machine learning- and deep learning-based predictive models on 1-day- and 7-day-ahead UWD forecasting, using daily demand data from the city of Milan, Italy. The contribution of this paper is two-fold. First, we compare the forecasting performance of different machine learning and deep learning models on single- and multi-step daily UWD forecasting. These models include Artificial Neural Network (ANN), Support Vector Regression (SVR), Light Gradient Boosting Machine (LightGBM), and Long Short-Term Memory network with and without an attention mechanism (LSTM and AM-LSTM). We benchmark their prediction accuracy against autoregressive time series models. Second, we investigate the potential enhancement in predictive accuracy by incorporating the wavelet transform and feature selection performed by LightGBM into these models. Results show that, overall, wavelet-enhanced feature selection improves the model predictive performance. The hybrid model combining wavelet-enhanced feature selection via LightGBM with LSTM (WT-LightGBM-(AM)-LSTM) can achieve high levels of accuracy with Nash-Sutcliffe Efficiency larger than 0.95 and Kling–Gupta Efficiency higher than 0.93 for both 1-day- and 7-day-ahead UWD forecasts. Furthermore, performance is shown to be robust under the influence of external stressors causing sudden changes in UWD.

Fractal Characteristics of Water Outflows on the Soil Surface after a Pipe Failure

Water pipe failures result in real water losses in the form of water outflowing into the porous medium, such as the surrounding soil. Such an outflow may result in the creation of suffosion holes. The appropriate management of the water supply network may contribute to reducing the number of failures, but due to their random nature, it is not possible to completely eliminate them. Therefore, alternative solutions are being sought to reduce the effects of the failures. This article presents a fragment of the results from a broader scope of the research, which attempted to determine the outflow zone in relation to the fractal characteristics of water outflows. The research included the analysis of the actual geometric structures created by the water outflows, which were simplified into linear structures using isometric transformations. The structures were analyzed in terms of the parameters characterizing them, including their fractal dimensions. As a result, it was found that there was no relationship between the analyzed fractal parameters and the leakage area or hydraulic pressure in the water pipe. However, the influence of the number of points forming each linear structure on the analyzed parameters was shown. This allowed for the determination of further research aimed at estimating the size of the water outflow zone after the unsealing of an underground water supply pipe.

Mathematical model of the topological structure of the urban water supply network

To effectively manage urban water supply networks, utility companies providing water supply and drainage (hereinafter referred to as KP "Vodokanal") need to solve the following problems: - use of topographic maps of scales 1:500, 1:1000, 1:2000, which include both ground-based buildings , as well as adjacent underground communications - water supply and sewerage, electrical networks, gas supply networks, heat supply, communications, etc.; - hydraulic calculations of the water supply network to determine the condition, analysis and optimization of the network structure; - prompt elimination of emergency situations and re-sumption of the network after the emergency is eliminated; - control and management of drinking water quality; - availability of pressure, flow and piezometer monitoring devices; - automated control of pumping stations; - saving energy resources; – network inventory. A comprehensive solution to these problems is provided by a geoinformation automated water supply network management system. Automated network management is carried out using geoinformation technologies, mathematical and geoinformation models of the water supply network. The work proposes a mathematical model of the topological structure of the water supply network, defines geospatial data bases about network objects and the interrelation-ships of these objects, taking into account the topological structure of the network to provide geoinformation and mathematical modeling of the hydraulic state of the water supply net-work, solving problems of emergency processing, monitoring the quality of drinking water, and optimizing the network, energy saving. To construct a digital model of the branched-ring structure of a topological graph, digital vector topographic maps are used as follows: - based on the results of geodetic surveys, water supply network facilities are marked on the topog-raphic map with symbols; - analysis of information-graphic diagrams of well details indicates the presence of pressure/flow regulators and hydrants. If there are regulators, their symbols are applied to the polyline of the water supply section at the symbol of the well at the point where the water pipe is inserted into the well, their diameters and the percentage of opening are indicated; - if there is a hydrant, the symbol for a well without a hydrant is replaced with the symbol for a well with a hydrant; - for all objects of the water supply network, the values of technical and graphic parameters are determined; - the orientation of the graph is deter-mined - the direction of water flow; – a matrix of incidents of vertices and contours is formed.

Water distribution system modelling of GIS-remote sensing and EPANET for the integrated efficient design

Abstract Urban settlement depends on water distribution networks for clean and safe drinking water. This research incorporates geographic information systems (GIS), remote sensing (RS), and hydraulic modelling software EPANET to analyse and construct water distribution systems in Bota town, India. Satellite images and hydrological data have been utilized for the management of the Bota town's water supply network, sources to cater the demand for urban centres. EPANET simulates hydraulic behaviour in the water distribution system under different operating situations. EPANET simulation shows network leaks, low pressure, and substantial head loss. These findings have advised for water distribution system improvements by analysing network shortcomings. Booster pumps, new pipelines, and repairing of existing leakages are examples of such improvements. GIS, RS, and EPANET provided a comprehensive water distribution system study and more accurate and efficient improvement identification. This study emphasizes the necessity of new technologies in water distribution system analysis and design. The study solves Bota town's water distribution system problems of low pressure, high head loss, and leaks utilizing GIS, RS, and EPANET. The findings of this research can help in enhancing the water delivery systems in other towns with comparable issues.

Optimization of Ship Power Supply Network and Intelligent Energy Management Strategy Under Multiple Energy Modes

<p>Purpose: This article aims to address the issues of incomplete management models and slow convergence speed of optimization models in energy management in ship multi energy systems, and to construct a comprehensive dynamic optimization objective model. Method: Firstly, establish an optimization design model with the objective functions of energy storage system cost, grid power fluctuation smoothing, and energy supply and demand balance; Then, in the optimization process of the objective function, a bus voltage coordination control strategy is adopted, and for the parameter optimization problem in the strategy, a cuckoo search algorithm based on population feature feedback is used for optimization. Result: Through simulation experiments, the method proposed in this article provides effective guidance for capacity configuration and energy management of multi energy ship microgrids, improving quality and efficiency.</p> <p> </p>

상수관망 관리를 위한 3D 공간정보 시스템 및 모델 프로토타입 개발

Recently, water supply problems and the advent of the 4th Industrial Revolution, smart water supply network management using digital twin technology is becoming needs. In this study, to develop a prototype of the model and system 3D visualization information system for water supply network management based on own technology without restrictions on licenses. Based on the data from the ME(2017), an inventory DB according to the Level classification was established, and for 3D visualization based on 2D information systems, 9-steps of implementation process and verification were conducted. Comparing and analyzing the values of latitude and longitude of polygons, it was confirmed that the reliability and accuracy of the two-dimensional information system implemented in this study were secured. Therefore, 3D visualization was performed using this, and the process of designing data linkage plans within the program and implementing 3D visualization was established. The results of this study are considered insignificant in the construction of a network management space server with high economic benefits and high reliability and accuracy of GIS.

Hydraulic and Water Quality Simulation in a Drinking Water Supply Network: A Case Study of the Ain Regada Network

Abstract To ensure the quantitative and qualitative safety of the drinking water supply to urban and rural areas, drinking water supply network managers are always concerned to improve the quality of the service provided to consumers and the continuity and quality of the water distributed. They are also concerned to ensure better management of all water supply systems. The development of computer software has made network modelling an essential part of the design and management of water supply systems. In this context, this study focuses on the operation of the Ain Regada water supply network. The simulation of the operation of the network in its current state is carried out using Epanet software.

Circular economy supply network transition phases management dynamics

AbstractThis paper discusses transitions towards circular economy, developing a circular economy supply network transition phases management dynamics framework. We studied seven circular supply networks in Brazil through process analysis and system dynamics modeling. Primary data were collected through 35 manager's interviews and companies' plant and their suppliers' visits. We suggest that transition phases management are pre‐development, learning, expansion, leadership, stabilization and self‐renew with cooperative and competitive challenges, characterized by specific circular factors over time. The simulation and transition phases models provide strategic tools to make decision on which circular factor, project team should focus applying multi‐tier supply chain management and hybrid leadership to stimulate internal and integrate external stakeholders to adopt circularity, unveiling challenges and average time to achieve each phase. We also enrich circular economy supply network management theoretical background defining it in three dimensions: behavioral as supply ecosystem, structural as supply network and contextual as complex adaptive system.

Artificial Intelligence and Internet of Things-Based Leak Detection Method for the Water Supply Network

The good management and safe operation of the urban water supply network are of great significance to residents’ lives and industrial production. In view of the difficulties in supervision and leakage location of the urban water supply network, based on the technology of Internet of things and artificial intelligence algorithm, a leakage detection method of the urban water supply network is proposed. First of all, low-power, low-cost terminal detection equipment and gateway monitoring equipment are developed for remote data transmission through WiFi or cellular data networks. The data organization, storage, release and control are realized by using the data center software platform. Second, the leakage location model of the water supply network is established by using remote pressure monitoring data, and the accurate location of pipe network leakage is realized. Based on ALO and PSO optimization algorithms, the water supply network in an industrial area of a city in China is solved. Finally, the performance of the two optimization algorithms is compared and analyzed. The results show that the designed intelligent monitoring system of the water supply network can monitor the pipe network well. In addition, on the problem of leakage detection, the ALO algorithm is superior to the PSO algorithm in terms of optimization ability and search efficiency. The leakage monitoring method of water supply networks proposed in this study can provide a reference for the design and management of urban water supply networks.

Design and management of supply networks in retail companies: A bibliometrics review

In this presentation, a systematic review on the design and management of supply networks in retail companies was proposed. Currently, organizations seek to prioritize the management of their supply networks in a comprehensive manner to avoid uncertainty and the intricate connections between the various levels of the supply network, seeking growth in sales, ensuring adequate inventory rotation, supplier management and transportation or inventory-related expenses. In this case, the objective was to promote the concept of design and management of supply networks in retail companies. The review was carried out including the literature of 15 theses and articles in the period 2003-2023 using different reliable databases. Research shows that a good design and management of supply networks in retail companies reduces shrinkage and provides greater control of their stocks, avoiding unnecessary expenses or losses.

Automatized data preparation methods for the asset management of water supply networks

La gestion patrimoniale des infrastructures en eau (GPI) et la compréhension des phénomènes de fuite et de casse des conduites demandent une grande quantité de données stockées dans un format interopérable. C’est particulièrement vrai lorsqu’il s’agit de planifier le renouvellement des conduites grâce aux modèles de prédiction de leurs défaillances tels que ceux implémentés dans le logiciel Casses. Différents systèmes d’information (SI) performants existent pourtant pour stocker ces données chez les gestionnaires de réseaux. Néanmoins, peu de méthodologies ont été pensées pour intégrer des données historiques et non géolocalisées concernant les conduites et leurs défaillances dans ces SI. Dans ce travail, nous présentons un ensemble de méthodes automatisées (scripts en langage R) s’adressant à cette problématique et développées grâce aux données de la Société wallonne des eaux (SWDE) sur le secteur de Mons, dans l’optique d’alimenter le logiciel Casses. Ces algorithmes règlent essentiellement le problème de trouver la conduite du système d’information géographique défaillante, ne connaissant que l’immeuble le plus proche de cette dernière ou seulement la rue où s’est déroulée sa réparation. Pour cela, il a été nécessaire de définir rigoureusement la notion d’appartenance à une rue pour une conduite, ainsi que la manière de sélectionner le tronçon le plus vraisemblablement défaillant parmi les candidats compatibles avec la localisation renseignée, voire avec les caractéristiques connues de la réparation (diamètre, matériau). Ces méthodes, dont les limites sont également discutées, pourraient être utiles pour d’autres gestionnaires de réseaux d’eau potable qui disposent de données historiques encore non exploitées pour la construction des stratégies de GPI ou qui souhaitent faire évoluer leur SI. Enfin, les masses de données pouvant être rendues opérationnelles pour les modèles de défaillance de façon automatique représentent un gain important dans l’optique d’amélioration de la connaissance des processus de fuite et de casse des canalisations.

The Effects of Supply Network’s Social Capitals on Sustainable Supply Network Management Project and Its Performance

The Effects of Supply Network’s Social Capitals on Sustainable Supply Network Management Project and Its Performance

Swimming against the tide: supplier bridging roles in diffusing sustainability upstream and downstream in supply networks

PurposeThis paper aims to investigate the bridging role of first-tier suppliers in diffusing sustainability in supply networks and how this role is facilitated by the procurement function.Design/methodology/approachThe paper is based on an embedded case study of two supply networks of a coffee beans roasting company. The embedded cases focus on coffee beans and packaging supply networks.FindingsThe findings reveal less than expected involvement of the focal company and its procurement function in sustainability implementation with first-tier suppliers. Instead, sustainability diffuses upstream to lower-tier suppliers but also downstream, against the tide, as a result of the various bridging roles performed by first-tier suppliers.Research limitations/implicationsThis paper provides two theoretical contributions. First, it contributes to the sustainable supply network management literature by providing rich insights on sustainability diffusion to lower-tier suppliers and the role of first-tier suppliers in this process. Second, the paper contributes to structural hole theory by revealing a typology of bridging roles that actors, such as suppliers, undertake in the sustainability context.Practical implicationsThe paper provides managers with practical insights on how sustainability can be diffused in the supply network and the different roles that first-tier suppliers can play in this direction.Originality/valueThis paper shows that sustainability diffusion to lower-tier suppliers is possible in the absence of focal company procurement involvement when bridging roles are undertaken by first-tier suppliers and their procurement functions are involved in the implementation process. These bridging roles facilitate sustainability diffusion both upstream and downstream.

Managing supply chains during the Covid-19 crisis: synthesis of academic and practitioner visions and recommendations for the future

PurposeThe Covid-19 pandemic has created an environment of high uncertainty and caused major disruptions in supply chains. The new normal that has emerged during the pandemic is leading to a need to identify new solutions to improve supply chain crisis management in the future. Practitioners require adapted recommendations for solutions to implement. These recommendations are laid out in this paper.Design/methodology/approachA combination of a systematic literature review (SLR), qualitative semi-structured interviews and a questionnaire survey of supply chain practitioners is applied. The interviews provide insights into supply chain practitioners' views of their approaches and, together with the solutions proposed in the literature, provide future recommendations for action for supply chain managers.FindingsDuring the pandemic, companies experienced disruptions in supply, production and demand, as well as interruptions in transportation and distribution. The majority of the solutions proposed in the literature, coincide with the opinions of practitioners. These include collaborative risk management, real-time monitoring and information sharing, supply network management, scenario planning and “what-if” simulations.Research limitations/implicationsAlthough the number of interviews conducted and questionnaires completed is limited, they still serve to supplement the SLR with important practical insights and recommendations.Originality/valueThis paper presents a review of recent academic literature focusing on the impact of Covid-19 on supply chains and the existing solutions to mitigate that impact and manage future crises. It has been expanded to include industry perspectives and experiences. The findings of this study present recommended practices and strategies for better managing supply chains during a crisis.

Resilience in food processing supply chain networks: empirical evidence from the Indian dairy operations

PurposeThis paper identifies sources of disruptions that impede resilience in the dairy supply chain in an emerging economy context.Design/methodology/approachA case study approach is used. The unit of analysis is the Indian dairy supply chain (IDSC). Data were collected from nine major dairy cooperatives and five major private firms operating across the Indian states. A total of 28 face-to-face semi-structured interviews were conducted with two individuals from each case dairy organisation during January 2016 to December 2017.FindingsDisruption sources in the IDSC are both external and internal and impact the quality of products and the distribution network. Compared to developed economies, in an emerging economy context such as India, the number of disruptions is very high. These disruptions negatively impact resilience and affect efficiency, flexibility, responsiveness and product quality.Research limitations/implicationsThe findings stress the importance of integration across upstream and downstream processes in the IDSC. However, contextual factors should also be considered when designing the supply chain configuration. Small supply sources may be conceptualised as distributed sources that can be consolidated on the move using logistics and IT-enabled solutions. Moreover, the underlying processes of the dairy supply chain need to adapt to the external environment, and internal causes of disruptions should be eliminated through process redesign.Practical implicationsThe findings highlight that the efficient operation of the IDSC is challenged by disruptions, the fragmentation of various stages and poor support infrastructure. The findings may be useful in managing supply networks which have linkages in emerging economies.Social implicationsThe upstream stage of the IDSC involves many small- and medium-sized unorganised producers. The overall inefficiency and poor value generation across the entire IDSC constrain the livelihood and interests of these unorganised producers. Therefore, supply chain design needs to be aligned with social context.Originality/valueThe central contribution of this article is to present sources of disruptions that impact dairy supply chain performance in an emerging economy context. Areas requiring process improvement are also highlighted.

Gamified Learning of Project Business Skills

During the recent years, scholars have paid a lot of attention to game-based and gamified learning. Although recent studies have offered some mixed results, most scholars believe that gamification increases the student engagement, collaboration, and communication. In our study, we applied gamification to learn skills required to manage a large project portfolio with many simultaneous project and project opportunities. In the empirical part of the study, we investigate post-graduate students’ feedback on the project portfolio management (PPM) workshop using both content and sentiment analysis. The complexity of the human nature makes studies on learning extremely challenging and vulnerable to human errors and biases. To increase the accuracy of our study we applied a machine learning assisted approach and used machine learning both to validate the conceptual framework of our study and to verify the correctness of the manual feedback categorization. The results of the experiment indicate that students’ sentiment towards the PPM workshop was positive, and gamification seems to be a suitable method of learning project business skills. The more detailed analysis revealed that a gamified PPM workshop is a motivating method of learning the creation of the management system for an organization and different kinds of portfolio management tasks like resource allocation, scheduling, and prioritization. Similarly, this kind workshop seems to be a less suitable way to study financial management and especially the customer and supplier network management.

Comparing Predictive Machine Learning Models for Short- and Long-Term Urban Water Demand Forecasting in Milan, Italy

Urban water demand forecasting is essential for water supply network optimization and management. In this case study, we comparatively investigate different state-of-the-art predictive models on short- (1 day-ahead) and long-term (7 day-ahead) urban water demand (UWD) forecasting for the city of Milan, Italy. The contribution of this paper is two-fold. First, we compare the forecasting performance of different time series and machine learning models on daily UWD. The tested models include Autoregressive Integrated Moving Average (ARIMA) models, Artificial Neural Networks (ANN), Support Vector Regression (SVR), Light Gradient Boosting Machine (LightGBM), and Long Short-Term Memory (LSTM) networks. Second, we investigate whether coupling a Wavelet Data-Driven Forecasting Framework (WDDFF) with these models further improves predictive capacity. Results show that, in general, WDDFF can improve model predictive performance. LSTM coupled wavelet decomposition technique can achieve high levels of accuracy with R2 larger than 0.9 for both short- and long-term UWD forecasts. LightGBM can efficiently reduce the number of predictors and show the potential to forecast and select important features in the hydrology and water resources field.

International Procurement and Supply Network Management in the Post COVID 19 Era in Nigeria: A Literature Review


 
 Introduction: The global economy has been greatly affected by the outbreak of the COVID-19 pandemic and its impact on the oil and gas industry is also being felt immensely as the industry relies heavily on the importation of the goods and services that they require to run their operations effectively and efficiently and the only way to get to the final destination of these company is through a well-managed supply network.
 Purpose: The purpose of this study was to examine international procurement and supply network management in the post COVID 19 Era in Nigeria. Being a literature review, the paper adopted a desk research methodology.
 Findings: The findings of the review of literature reveal that international procurement significantly influence and predicts supply network management performance. It also shows that COVID-19 has negatively affected both international procurement and supply network management, and that the phrase supply network management, instead of supply chain management, is most appropriate to describe the activities and relationships between and amongst individuals, enterprises, facilities and information, materials and services systems that connects supplier’s suppliers to customer’s customers capturing the multiple and complex relationships that exists amongst them.

Multi-criteria risk classification to enhance complex supply networks performance

Management of complex supply networks is a fundamental business topic today. Especially in the presence of many and diverse stakeholders, identifying and assessing those risks having a potential negative impact on the performance of supply processes is of utmost importance and, as a result, implementing focused risk management actions is a current lively field of research. The possibility of supporting Supply Chain Risks Management (SCRM) is herein explored from a Multi-Criteria Decision-Making (MCDM)-based perspective. The sorting method ELimination Et Choix Traduisant la REalité (ELECTRE) TRI is proposed as a structural procedure to classify Supply Chain Risks (SCRs) into proper risk classes expressing priority of intervention so as to ease the implementation of prevention and protection measures. This approach is intended to offer structured management insights by means of an immediate identification of the most highly critical risks in a wide set of previously identified SCRs. A real-world case study in the field of the automotive industry is implemented to show the applicability and usefulness of the approach.