Supply Network Research Articles

Navigating Modern Logistics: Innovations and Challenges in the Era of New Retail

This study explores modern logistics innovations and challenges in the new retail era. With the rapid development of science and technology, the traditional logistics model can no longer meet the market demand. Consumers' demands for speed of product delivery, service quality and personalization are constantly rising, and traditional logistics processes and management methods can no longer adapt to this fast-changing market environment. As a result, companies need to innovate and reform to improve logistics efficiency, reduce operating costs, and respond more flexibly to the challenges of globalized competition. Through literature review and empirical analysis, this paper provides an in-depth analysis of the problems faced by the modern logistics industry, including the inefficiency of the traditional model, the promotion of digital intelligence, and the need to improve the level of logistics in the new era. On this basis, targeted recommendations include strengthening standardization, promoting digital intelligence improvement and optimizing the supply network. This study is expected to provide useful reference and guidance for developing the modern logistics industry.

System for monitoring building heat consumption

Relevance. Energy consumption and efficient use of district heating networks affect the environment, society and the economy. Solutions are needed that can significantly reduce specific thermal energy losses. Using a technically and economically feasible way to assess energy efficiency allows these decisions to be made. Currently, assessing the energy efficiency of buildings raises significant questions among specialists. There is not much modern specialized literature in this area. The problem of energy efficiency requires more rigorous attention to urban planning issues. Thus, refined methods for assessing the energy efficiency of buildings make it possible to simultaneously save capital investments and ensure efficient consumption of thermal energy. Aim. To develop a system for assessing the real heat consumption of buildings to ensure optimization of dispatching of centralized heat supply systems in real time. Methods. Computer modeling of the state of heat consumption of buildings with various characteristics and schemes for their connection to centralized heat supply networks; methods of geographic information analysis. Results. The authors have proposed the system for monitoring the heat consumption of buildings. This system allows assessing the energy efficiency of buildings through the maximum use of computer and software technologies. It is shown that the calculated heat flows of buildings used to predict savings when modernizing buildings do not always correspond to the actual heat loads, which can lead to an incorrect assessment of investment projects. The authors obtained graphical dependences of the quantities influencing specific heat losses on various primary independent factors. Conclusions. The constructed system for monitoring the heat consumption of buildings allows, together with the use of geoinformation analysis methods, obtaining a fairly complete picture of the state of heat consumption for city areas and an assessment of the energy characteristics of buildings.

Upgrading the industrial structure for optimizing water-energy-carbon nexus in regional trade network

To take actions for the SDGs (Goals 6, 7, 9, 12, and 13), the world is undergoing intense industrial transformation and upgrading. However, there has been limited analysis of water-energy-carbon (WEC) nexus within regional trade network to uncover the effects of industrial restructuring. Here, this study explores the trade-related WEC flows in the Yangtze River Delta area (YRD) from 2012 to 2017 by four aspects (spatiotemporal status, sectors, WEC flowing synergies, and environmental-economic trade-off) based on an environmentally extended input-output analysis. The main results show that the resource supply network in the YRD has been optimized, with enhanced synergies of WEC flows. Resource trade in the YRD has shifted from traditional heavy industry (HV Ⅰ) to high-tech manufacturing industry (HV Ⅱ). The share of trade-related WEC in demand of HV Ⅱ among all sectors increased by 10, 5, and 15 percentages, while the share in demand of HV Ⅰ decreased by 15, 10, and 10 percentages during this transition period. This study also discloses an environmental-economic contradiction arising in regional trade: low-carbon and high-value-added economic development in some regions may be premised on relocating environmental resource pressures. Hence, this study could assist policymakers in analyzing sustainable industrial transition models for regional integration clusters and in formulating targeted interregional resource trade policies to alleviate development gaps.

Tracing the geographic origin of Atlantic cod products using stable isotope analysis.

Increasing demand for fish and seafood means that the traceability of marine products is becoming ever more important for consumers, producers and regulators. Highly complex and globalised supply networks create challenges for verifying a stated catch region. Atlantic cod is one of the most commercially important species in the northeast Atlantic. Several regional fisheries supply cod into the trade network, of which some are at greater risk of overexploitation than others. Tools allowing retrospective testing of spatial origin would significantly assist sustainable harvesting of fish, reducing incentives for illegal fishing and fraud. Here, we investigate whether stable isotope ratios of carbon, nitrogen and sulphur can be used to retrospectively identify the catch region of Atlantic cod (Gadus morhua). We measured the isotopic composition of muscle tissue from 377 cod from 10 catch regions across the northeast Atlantic and then applied three different assignment methods to classify cod by region of most likely origin. The assignment method developed was subsequently tested using independently sourced, known-origin samples. Individual cod could be traced back to their true origin with an average assignment accuracy of 70-79% and over 90% accuracy for certain regions. Assignment success rates comparable to those using genetic techniques were achieved when assigning among restricted and pre-selected regions. However, assignment accuracy to the fishery region estimated from independent samples across the whole geographic range of cod averaged ~25% overall, highlighting the need for careful application of isotope-based approaches. Stable isotope techniques can provide effective tools to test for origin in Atlantic cod, but not all catch regions are isotopically distinct. Stable isotopes could be combined with genetic techniques to result in higher assignment accuracy than could be achieved using either method independently. Assignment potential can be estimated from reference datasets, but estimates of realistic assignment accuracy require independently collected data.

Beyond profit margins: Orchestrating social, economic, and environmental sustainability within the Norwegian Salmon Food Supply Chain

Food Supply Chains (FSCs) have become increasingly complex with the average distance between producers and consumers rising considerably in the past two decades. Consequently, FSCs are a major source of carbon emissions and reducing transportation costs a major challenge for businesses. To address this, we present a mathematical model to promote the three core dimensions of sustainability (economic, environmental, and social), based on the Mixed-Integer Linear Programming (MILP) method. The model addresses the environmental dimension by intending to decrease the carbon emissions of different transport modes involved in the logistics network. Several supply chain network characteristics are incorporated and evaluated, with a consideration of social sustainability (job generation from operating various facilities). The mathematical model's robustness is demonstrated by testing and deploying it to a variety of problem instances. A real-life case study (Norwegian salmon supply chain) helps to comprehend the model's applicability. To understand the importance of optimizing food supply networks holistically, the paper investigates the impact of multiple supply chain permutations on total cost, demand fluctuations and carbon emissions. To address fluctuations in retail demand, we undertook sensitivity analysis for variations in demand, enabling the proposed model to revamp Norway's salmon supply chain network. Subsequently, the results are thoroughly examined to identify managerial implications.

Multidimensional assessment of a Nature-based Solution for decentralized greywater treatment in Costa Rica

In Costa Rica, water supply networks provide water to over 94% of the country's population. However, only an estimated 14% of wastewater receives proper treatment. The lack of centralized sanitation infrastructure has resulted in the use of septic tanks and the discharge of untreated greywater into rivers, causing environmental degradation of surface waters. Retrofitting conventional centralized sewer networks and treatment plants into the existing urbanization presents extensive social, economic, and technical challenges. Nature-based Solutions (NbS) for greywater treatment can reduce pollutant loads and improve the environmental status of water resources and represent an opportunity for technical leapfrogging towards sustainable, decentralized on-site treatment and reuse. However, the implementation of NbS in urban areas poses significant challenges due to the complex interplay of social, regulatory, and economic factors. Specifically, for on-site greywater treatment systems, meeting several criteria, including efficient pollutant removal, affordability, and public acceptance is essential for successful implementation and operation. This study assesses the technical, socio-economic, and political-regulatory dimensions relevant to implementing NbS for decentralized greywater treatment. To conduct this research, a Real-World Lab was established in the Great Metropolitan Area of Costa Rica, employing a transdisciplinary approach. This approach provided a physical space and societal context to integrate site-specific aspects and understand the various factors that influence the implementation and upscaling of NbS. As part of our methodology, we analyzed the water quality parameters and treatment performance of a NbS prototype for decentralized greywater treatment. Within the Real-World Lab framework, we conducted interviews, surveys, and field observations to investigate socio-economic and political-regulatory aspects. Our results highlight the technical potential of the NbS prototype. However, the limitation lies in the governance scheme and financing mechanisms required for upscaling the NbS as a decentralized on-site technology across the country. Our multidimensional assessment provides insights into the requirements for widespread implementation of NbS, applicable to other regions facing similar retrofitting sanitation challenges.

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.

Leakage assessment of water supply networks in a university based on WB-Easy Calc and night minimum flow: a case study in Xi'an

Leakage assessment of water supply networks in a university based on WB-Easy Calc and night minimum flow: a case study in Xi'an

A two‐stage adaptive robust model for designing a reliable blood supply chain network with disruption considerations in disaster situations

AbstractWe consider multi‐period blood supply chain network design in disaster situations that involve blood donor groups, permanent and temporary blood collection facilities, blood banks, and hospitals. We use a discrete scenario set to model the uncertain blood supply and demand, and the unforeseeable disruptions in permanent blood collection facilities, blood banks, and road links arising from a disaster, where instead of complete failure, disrupted permanent blood collection facilities and blood blanks may only lose part of their capacities. To design a reliable blood supply network to mitigate the possible disruptions, we present a two‐stage adaptive robust model that integrates the location, inventory, and allocation decisions incorporating a blood sharing strategy, where blood can be delivered from a disrupted/non‐disrupted blood bank to disrupted blood banks to enhance the flexibility of the relief network. For this novel problem, we devise an exact algorithm that integrates column‐and‐constraint generation and Benders decomposition and introduce several non‐trivial acceleration techniques to speed up the solution generation process. We conduct extensive numerical studies on random data sets to evaluate the algorithmic performance. We also conduct a case study in Tehran to demonstrate its real‐life applicability and examine the impacts of key model parameters on the solutions. The numerical results verify the benefits of our model over typical benchmarks, that is, deterministic and stochastic models, and the superiority of our solution algorithm over the CPLEX solver and two well‐known solution approaches, that is, column‐and‐constraint generation and Benders decomposition. Finally, based on the numerical results, we derive managerial insights from the analytical findings.

Innovation ecosystems beyond construction projects – a case study of Swedish cross laminated timber building networks

ABSTRACT While innovation ecosystems have been proven effective for driving innovation in high-tech industries, their application in construction with cross laminated timber (CLT) products have received limited attention, with most studies adopting a broad perspective on ecosystems, encompassing the entire supply network around individual building projects. This study explores niche innovation ecosystems within the context of construction with CLT products. Data from 15 CLT-based building projects in mid Sweden were collected through observations and interviews and analysed using a method combining existing theory on innovation ecosystem structure with social network analysis. Innovation ecosystem structure was identified within five of the projects, encompassing two unique ecosystems. These ecosystems feature interorganisational links and actor-level expected value capture in two dimensions: value creation within the standard contractual framework of the projects and the innovation ecosystem superimposed on these projects. The novel method bridges the gap between innovation ecosystem research and CLT practices. The findings reveal that niche innovation ecosystems interact with CLT-based building projects through collaborative efforts across multiple projects and partners. These ecosystems feature alignment links that indicate both intra – and inter-project interdependencies, potentially driving innovation in construction. Role extensions within these ecosystems highlight the complexity and adaptability required in the construction industry.

Designing a resilient agriculture supply network for mitigating the disruptions

Designing a resilient agriculture supply network for mitigating the disruptions

Tracing Opioids Across the US: A High-Resolution Pharmaceutical Distribution Dataset

In the field of pharmaceutical supply chains, there is a lack of comprehensive historical data, representing a significant barrier to advancing research. To address this gap, we introduce a high-resolution dataset comprising drug packages distributed to approximately 300,000 pharmacies, hospitals, and practitioners across the US. We reconstruct 375 million distribution paths from ARCOS, a DEA-maintained database comprising half a billion shipping records between 2006 and 2014. While ARCOS tracks dyadic shipments, it does not provide information on the complete journey of single packages from manufacturers to final destinations. Our algorithm is able to reconstruct complete distribution paths from these dyadic records. The reconstructed dataset, with its high temporal and spatial resolution, offers an unprecedented view of US pharmaceutical distribution and is a valuable resource for investigating supply and distribution networks.

Provenancing copper in the middle Shang period through isotopic analysis of metallurgical remains

Archaeological investigations recently conducted at the site of Tajiasi, a Middle Shang bronze casting workshop, have led to the discovery of abundant metallurgical micro-remains from various stages of bronze production processes. Lead isotope analysis of these samples has given fresh insights into the discussion about the origin of the metal sources employed during this period. The copper melting and refining slags, characterized by 206Pb/204Pb around 18.0 and very low lead concentrations (＜4,000 ppm), provided the first reliable indicator about the geological origin of copper used in the Middle Shang period. It is suggested the Jiurui metallogenic district in the Middle Yangtze River, home to the Shang period copper smelting site of Tongling, was the copper source for the Taijiasi site. On the other hand, alloying slags and bronze objects bearing highly radiogenic lead (206Pb/204Pb > 19.0) show an elevated, but still relatively low Pb content (＜2 wt%), suggesting that the source of tin introduced Pb which was characterized by HRL. Items including alloying slags, dross, spillages and bronze objects have similar HRL characteristics to the Middle Shang bronzes from other sites. This result indicated the Taijiasi site was involved in a multi-line metal circulation during this period, and elucidated the complexity and multiplicity of supply networks for different kinds of metals in the Middle Shang period. It also highlighted the great potential of copper melting and refining slags for detecting the copper provenance.

Influence of Electronic Procurement Adoption on the Performance of Manufacturing Firms’ Supply Management in Kenya. Case of Nairobi County

The implementation of efficient management of the supply network has been determined as a potentially beneficial way for achieving a competitive edge and enhancing overall organizational performance. This study investigates the effect that electronic procurement (e-procurement) has on the operational success and effectiveness of supply management in the manufacturing sector in Kenya. The objective of this research is to evaluate the influence of e-supplier appraisal, electronic resource planning (ERP), and electronic order processing on the supply management outcomes and performance within the select firms. The goal of this investigation is to establish a conceptual model and define three key dimensions that include e-supplier appraisal, e-order processing, and electronic resource planning. The research methodology employs descriptive research, utilizing a sample size of 120 participants that were be chosen using stratified sampling methodology. The acquisition of primary data was facilitated by employing questionnaires of which 92 respondents participated. The gathered data was subjected to analysis using SPSS statistical software and subsequently presented in the form of graphical representations and tabular formats. Furthermore, a regression analysis was administered to ascertain the correlation between e-procurement dimensions and supply management results. The results showed that e-supplier appraisal, electronic resource planning, and e-order processing had significant effects on supply management performance.

Supply chain digitalization and corporate ESG performance: Evidence from supply chain innovation and application pilot policy

The increasing environmental unpredictability has amplified the strategic significance of contemporary digital supply networks. Gaining a comprehensive understanding of the mechanisms and effects of supply chains enabled by digital technology is essential for implementing reforms that improve the economy's supply side. This study utilizes the Difference-in-Differences (DID) approach to investigate the influence of supply chain digitization on company Environmental, Social, and Governance (ESG) performance, using data from 2011 to 2020. This study demonstrates that supply chain digitalization can significantly enhance corporate ESG performance. The stimulating impacts are mainly attained through enhancing corporate governance, total factor productivity, and mitigating financial limitations.

Facility location optimization with market segmentation for additive manufacturing material supplier network using quantum annealing

The determination of the material suppliers for additive manufacturing (AM) centers is a typical facility location problem (FLP). Current methods for solving the FLP in AM network design mainly focus on improving the cost, distance, and supply chain aspects and neglect market segmentation. However, AM feedstock suppliers need to understand the market size they serve so they can better prepare their inventory plans. Therefore, in this paper, we propose a new approach for the FLP with geographic market segmentation of AM material supplier networks, in which the facility locations and market segments of AM material suppliers are optimized in a multi-objective optimization problem. In order to confirm the feasibility of the proposed method, a simplified design case for a German state has been carried out.

The impact of small scale fish farming on poverty reduction in South-East Zone of Nigeria

Over the years, successive administrations in Nigeria have embarked on numerous poverty reduction programmes aimed at reducing the poverty rate in Nigeria. These efforts have not yielded much result as poverty rate has continued to soar and pervade every geo-political zone in Nigeria. The poverty situation, coupled with the low uptake of fish farming technology in the south-east zone has prompted the need to examine the impact of small-scale fish farming on poverty reduction in the south-east zone of Nigeria. The dependent variable of the study is poverty reduction, which has household expenditure as its proxy. The independent variable is small-scale fish farming, proxy by income, employment, food security and consumption multiplier. Data collection was made through in-depth Interview (IDI) guide, while analysis was done using thematic analysis technique. A total of 40 participants, comprising of 30 fish-farm owners and 10 employees were selected purposively from the states of Anambra, Ebonyi and Imo for the interview. The study findings suggest that small-scale fish farmingin the study area impacts positively on poverty reduction, as increase in each of the independent variables tends to increase the dependent variable. The study recommended the establishment of fish farm settlements; granting of credit facilities to fish farmers; establishment of skill acquisition and training centers for farm employees; and improvement electricity supply and road network for lower energy cost and easy transportation of fish products. Keywords: Fish Farming, Small-Scale, Poverty Reduction.

Impact of policy interventions on low‐carbon technology innovation diffusion in supply networks

AbstractThe promotion of sustainability and innovation in the current dynamic landscape of global industry in the context of climate change has become imperative. This research paper explores the critical role of policy interventions in facilitating the diffusion of low‐carbon technologies through intricate supply networks. As countries endeavor to meet environmental goals and transition to a more sustainable economy, it becomes critical to understand how policies can effectively shape the diffusion of these technologies through the supply chain. This study employs game theory and evolutionary game theory to investigate the intricate interactions within supply networks, with a particular focus on the nuanced effects of policy on the diffusion of low‐carbon technology innovations. The findings suggest that dynamically adjusted policies have the potential to significantly increase the uptake of low‐carbon technologies. In particular, an effective subsidy scheme not only promotes the diffusion of innovations, but also demonstrates the ability of government subsidies to be distributed efficiently. In addition, the implementation of dynamic carbon trading schemes is considered to be an important mechanism for promoting firms' decarbonization and stabilizing strategic decision‐making processes within firms. This study highlights the significance of tailored dynamic policy frameworks in promoting the adoption of sustainable technologies in supply chains, thus making a significant contribution to the broader goal of achieving a sustainable future.

A technique for modeling conductive interferences of an AC/DC flyback converter

Formulation of the problem. Compliance with electromagnetic compatibility (EMC) standards is an important requirement for most pulsed AC/DC converters. However, during preliminary tests, a high level of conductive interference emission into the supply network is often detected. The elimination of this problem requires several iterations of additional work and subsequent product inspections in the EMC laboratory, which leads to a delay in the design time and an increase in the time of use of expensive measuring equipment. Modeling of conductive interference at the stages of circuit design makes it possible to solve the problem in a shorter time without the need for a large number of laboratory measurements. Purpose. Development of a technique for modeling conductive interference in the LTspice circuit modeling program using the example of an AC/DC flyback converter. The technique will allow for an engineering assessment of the conductive interferences level, optimize the converter filters, which reduces the design time spent on achieving the required EMC standards. Results. The proposed technique was used to build a model of a 120-watt flyback converter and simulate the conductive interference generated by it in the frequency range 9 kHz-30 MHz. The absolute error of the model data relative to the spectrograms of the converter ranged from +6.5 dBuV to -4.3 dBuV in the frequency range of 9 kHz-7 MHz. At higher frequencies, the error increased. Practical significance. The developed technique makes it possible to perform a preliminary assessment of the level of conductive interference with subsequent optimization of the converter circuit, which reduces the subsequent quantity of product modifications. The same technique can be used by developers at remote work.

Optimizing Vendor-Managed Inventory in Multi-Tier Distribution Systems

This paper addresses the concept of the vendor-managed inventory (VMI) problem, focusing on the replenishment policy and the vehicle routing problem (VRP) model. These components are integrated to tackle a three-echelon distribution issue comprising a single plant, multiple depots, and multiple retailers, with the primary objective of minimizing transportation and inventory costs within this complex distribution network. A three-phase methodology is proposed to optimize the entire supply chain, from the plant to the final retailer, and its performance is evaluated through computational experiments. This research is motivated by a real-life supply network, highlighting its practical relevance and applicability. To extend the capabilities of existing methods for solving the combined inventory and routing problem, an insertion heuristic is incorporated to enhance vehicle utilization, thereby reducing total costs. Computational results demonstrate the effectiveness of the improved algorithm, indicating that it is sufficiently robust for practical application. Significant cost savings can be achieved with the proposed approach, making it a valuable contribution to the field of supply chain optimization.