Supply Chain Management System Research Articles

Sustainable Supply Chain Management of Electric Grid Power Consumption Load for Smart Cities Based on Second-order Exponential Smoothing Algorithm

Electric grid power consumption load is one of the fundamental areas that need to be faced to provide a sustainable and green ecosystem in smart cities. Consumption load as well as supply and availability of electricity to suppliers and customers is a major issue to be faced to have a balanced smart city power grid infrastructure. Balancing in this case is assumed as a well-designed supply chain management system to be applied in the Smart City (SC) of Athens, Greece. Core of such a system is the knowledge of electric power consumption load per weekly basis of a year, that is the granularity of the proposed system is one week of the system’s operation. In this paper, focus is given on the electric load forecast component of an Energy Management System (EMS) such as the Independent Power Transmission Operator (ITPO) of Greece. Concretely, stochastic data of electric energy consumption load are used to predict the demand or offering of electric power in the future. This is achieved by incorporating a machine learning second-order exponential smoothing algorithm. Such an algorithm is able to speculate near or far in the future power consumption load thus providing a promising parameter to predict smart city needs for electric power in the future. Adopted system is evaluated by the evaluation metric of Normalized Root Mean Square Error (NRMSE), which assures that the system can be used for future predictions of electric power consumption load in smart cities.

Secured supply chain management system for fisheries through IoT

Digitization is indeed a technique that can have a massive effect at the site of generation or point of capturing inside the food distribution chain. This article proposes three different conceptual models used to analyze the digitalization processes within the fishing industry. The WSN system is used for simulating the related power usage of a large number of sensors with the architectural approach's detector networks. The analytical level uses time analysis to examine information collected via wearable sensors related to marine surveillance. The proposed methodology is demonstrated via a case analysis as in the fishing industry, wherein the WSN principle is assessed depending on the relevance to ocean monitoring buoys fitted with detectors. The characteristics of the snow crab collection are therefore examined in sizable data derived by ocean buoy interpretations utilizing time-series data & scatterplots. The suggested method could be viewed as an instrument that can help with supply chain management and the implementation of more effective procedures in the fisheries industry, which also is undergoing a method of digitalization marked by the adoption of Internet of Things (IoT) answers to monitor authenticity and vendor history, among several other items.

HOW BIG DATA WILL BE AN ADDED VALUE TO SCM?

It is the era of digital information technology where almost everything is going smart. Thus, organizations move towards digitalization that cause the emergent of Big Data. Analyzing big data is the big challenge today. Being smart puts the world under a big challenge to adapt, change, and upgrade systems to analyze the Big Data using the high technology. Challenges are growing with the market and appears in different forms. Many of these challenges can be hard or difficult to handle on your own if you are a small to medium size business without the help of supply chain management system. The main objective of this paper is to contribute and examine these research questions: What are the Big Data Analytical tools used in SCM? In addition to the Impact of Analyzing Big Data on Supply Chain Management? The methodology used was a systematic review over the existing literature including Big Data, supply chain, SCM, and the impact of BD analytical tools on SCM. Data collected and unsystematically interpreted and the findings summarized in a subjective way that describes and discusses the literature from a contextual or theoretical point of view. Big data can tremendously affect the supply chain units and can add values to the overall supply chain operations by improving the processes to be more effective and efficient based on the analysis results. Big data analytics become a core differentiation factor for any organization that acquire it in the last few years.

Applying the Principles of Indigenous Logistics Systems to Supply Chain Management in Africa: Learning from Historical Culture in Nigeria

Trade in Africa has been disproportionately affected over the years by a poorly developed logistics and supply chain management system. Few explorative research has been done on indigenous logistics systems across Africa. This study examines the indigenous logistics systems in Nigeria, Africa's most populous country. Qualitative data was gathered from the six zones of the country examining the most popular logistics system, reasons for its popularity, its strengths and weaknesses, and opportunities for scaling up. For the first time ever, this paper defined indigenous logistics systems and supply chain management. The “waybill” system or sending goods through registered vehicles at motor parks is the choice of the majority of the citizens. The system is consistent with the cultural norms and habits of the people, prioritizes the use of local knowledge and resources in an integrated manner, offers competitive advantage to practitioners, and is sustainable. This paper concludes that, if standardized and improved upon, the “waybill” system has the potential of being the sole supply chain management system in Nigeria and across Africa.

Impact of sustainable supply chain management on cost performance: empirical evidence from manufacturing companies of Bangladesh

PurposeThe aim of the study has been performed to investigate the relationship between sustainable supply chain management (SSCM) practices and the cost performance of manufacturing firms in Bangladesh. Moreover, this paper highlights the key environment-friendly approaches and their association with financial performance in Bangladesh.Design/methodology/approachThe paper empirically assesses sustainable supply chain performance using four major supply chain practices, including sustainable procurement, sustainable production, sustainable distribution and investment recovery, and compares it with the cost performance. Twenty-four variables were identified through different literature and distributed as a structured questionnaire among the managers appointed in different manufacturing firms in Bangladesh. An empirical study was conducted using the Partial Least Square-Structural Equation Modeling (PLS-SEM) technique to examine the hypothesized relationships.FindingsThe results find a positive relationship in two variables of sustainable supply chain practices, including sustainable procurement and investment recovery, while sustainable distribution negatively impacted cost performance. In addition, sustainable production found no effect on cost performance.Research limitations/implicationsThe paper emphasizes the financial perspective of a sustainable supply chain without explicit consideration of sustainability's environmental and social dimensions.Practical implicationsThis study has implications for the literature on the SSCM approaches of manufacturing firms in the least developed economies. In addition, this study could work as a guideline for some manufacturing industries that prefer a policy or standard to alter their traditional supply chain management system to a sustainable supply chain.Originality/valueThe paper provides a comprehensive framework for evaluating the coordinated effect of SSCM practices on cost performance where variables of four specific activities of SSCM and cost performance are adopted from different studies. Further studies could be initiated, including some other eco-friendly supply chain variables, and the effect could be evaluated from an environmental perspective.

Green Supply Chain Management Optimization Based on Chemical Industrial Clusters

During the period after the pandemic, the chemical sector, which is a crucial component of national progress, encounters further obstacles. The industry has been affected by the pandemic, leading to a need for faster transformation and upgrading. Additionally, there is an increased demand for green and environmental protection in the chemical sector due to global climate change and the concept of sustainable development. During the time after the pandemic, there will be a continued trend of industrial clustering and scale development in the chemical sector. It is essential to prioritize optimization via green supply chain management in order to facilitate industrial upgrading. In order to achieve this objective, this study administered a questionnaire survey to gather reliable data. The collected data was then analyzed using software tools like SPSS and AMOS. The analysis aimed to investigate the influence of various factors, including regulatory compliance, green procurement, green manufacturing, green logistics, green sales, competitors, internal environmental protection, and cost control, on the awareness and implementation of green supply chain management in chemical enterprises. Additionally, a structural equation model was constructed to further examine these relationships. The findings suggest that green procurement, manufacturing, logistics, sales, and internal environmental protection and cost control have a notable and beneficial influence on the implementation of green supply chain management. Additionally, the awareness and implementation of green supply chain management also contribute positively to both economic and environmental advantages. This paper offers a theoretical framework for enhancing the efficiency of the green supply chain management system in chemical industrial clusters, hence promoting the environmentally friendly and sustainable growth of the chemical sector.

Product-Service Systems for Circular Supply Chain Management: A Functional Approach

Growing environmental concerns, as well as market competitiveness, are inciting manufacturers to optimize the performance of their products throughout their entire lifecycle. To address these objectives, manufacturing, field service engineering and customer support should be aligned and should strive towards the same end-goal. While several studies have revolved around this context, the product-service system (PSS) approach has emerged as a promising way of meeting manufacturer, customer and environmental requirements alike. Nevertheless, most of these studies revolve around the design and development of a PSS, whilst a minimal amount of research has targeted its impact from the supply chain perspective, leaving aside inventory management. Consequently, the following study utilizes functional hierarchy modeling (FHM) and the PSS concept matrix to propose solutions that make it possible to meet customer requirements and improve the environmental performance and associated costs at the same time. An application at a medical equipment manufacturer brings forward the positive effects suggested by the research and reduces the aforementioned gaps. In detail, the results show a smaller variability in the supply of spare parts combined with optimized maintenance planning, which translates into lesser costs to the manufacturer, a prolonged product life cycle and a reduced environmental impact.

An integrated methodology for selecting the building material suppliers with interval-valued intuitionistic fuzzy information

At present, with the continuous changes in the market situation and the continuous improvement of the supply chain network structure, the competition in all walks of life has become more and more intense, which has risen from simple enterprise competition to competition in the entire supply chain. In the construction industry, the structure of the construction supply chain is more complex and diverse, and it is more necessary to select high-quality suppliers for sincere cooperation. This requires construction companies to establish a complete supply chain management system, select high-quality suppliers to achieve win-win cooperation and improve their competitiveness. Therefore, construction enterprises need to comprehensively consider various factors, build a reasonable and feasible evaluation index system according to their own demand for materials, and use appropriate evaluation methods to select material suppliers with specific advantages, so as to ensure the entire construction supply chain of the project. of smooth operation. In this paper, we introduced some calculating laws on interval-valued intuitionistic fuzzy sets (IVIFSs), Hamacher sum and Hamacher product and further propose the induced interval-valued intuitionistic fuzzy Hamacher ordered weighted average (I-IVIFHOWA) operator. Meanwhile, we also study some ideal properties of built operator. Then, we apply the I-IVIFHOWA operator to deal with the multiple attribute decision making (MADM) problems under IVIFSs. Finally, an example for selecting the building material suppliers is used to test this new approach.

An IIoT based secure and sustainable smart supply chain system using sensor networks

AbstractIndustrial Internet of Things (IIoT) is a very effective and efficient way to track products during their production, packaging, storage, and shipments. It can also help in monitoring their real‐time conditions such as location, speed of moments, and temperature. Presently most of the supply chain management systems are not effective due to their inefficient tracking of real‐time shipments, long delays in shipments and lost identification on a real‐time basis, and uneconomical storage of goods. To solve these problems, an IoT‐based secure and sustainable smart supply chain system using sensor networks is proposed that provides real‐time shipment tracking, storage, delay, and lost identification. Threshold‐based weighted probability formula is devised by considering the average distance and node spacing of sensor nodes, total network energy, residual, and maximum energy parameters to alleviate the hot spot problem. Additionally, the communication signals are transmitted in compression form with end‐to‐end encryption that reduces the use of bandwidth by decreasing the signal size. The percentage increment in the stability and the network lifespan of the proposed method is 35.19% and 7.23% & 119.33% and 71.72% as compared to the T. Senthil et al method and S. Singh's method, respectively.

Modeling an optimal supply chain management system in transport systems

Objective. The purpose of the work is to develop universal logistics approaches to modeling the optimal supply chain management system in the conditions of a multimodal service center.Method. The methodological basis of the study is theoretical and experimental research in the field of creating an effective model of supply chain management in transport systems.Result. A process approach to the strategic management of supply chains for the logistics of transport systems has been formalized, taking into account the influence of a group of factors; developed a methodology for managing relationships with suppliers, taking into account their integration in supply chain management; rational parameters for evaluating the effectiveness of the logistics management system for logistics under given working conditions were determined; a methodology has been developed for managing supply chains of logistics in the conditions of operation of a multimodal service center based on the application of the theory of experiment planning.Conclusion. Optimization of supply chain management (Supply Chain Management, SCM) allows you to effectively plan, form, control and regulate material, financial, information flows throughout the entire life cycle of goods. The resulting regression equations are recommended to be used as a mechanism for analyzing the work with the selected supplier when organizing the optimal management of supply and procurement processes in the transport system in the conditions of operation of the multimodal service center of GRAND MOTORS LLC.

Study protocol for developing a novel approach for improving supply chain management for SARS-CoV-2 point of care diagnostic services in resource-limited settings: a case study of Mopani District in Limpopo province, South Africa

IntroductionRecent evidence shows that point-of-care (POC) testing is a more feasible alternative for diagnosis of COVID-19 in settings that have poor access to laboratory diagnostic services. Equitable access to POC...

Assessment of frontline health workers in providing services for malaria elimination in the tribal district of Mandla, Madhya Pradesh.

The Accredited Social Health Activists (ASHAs) and Auxiliary Nurse Midwives (ANMs) are the frontline health staff that provide essential health care services, including diagnosis and treatment of malaria. To support India's malaria-free goal by 2030, a project known as the Malaria Elimination Demonstration Project (MEDP) in the tribal district of Mandla was initiated. This study assessed the capabilities of ASHAs and ANMs of Mandla district to diagnose and treat malaria. A cross-sectional study was conducted in the 71 sub-centers and their villages where at least one positive malaria case was diagnosed in 2019. Pre-designed and validated tools were used to assess the knowledge, attitude, and practices of ASHAs and ANMs. Analysis was performed using descriptive statistics and multivariate logistic regressions. Malaria is the fifth priority of the ASHAs and ANMs of Mandla district. Good level of knowledge regarding malaria etiology, diagnosis, and prevention was found, but the ability to treat a malaria case as per the national drug policy was below expectations. Frequent and prolonged stockouts of drugs and diagnostics were found. Logistic regressions revealed better capacity of ANMs to dispense correct treatment as compared to the ASHAs. Improvement was seen in the ASHAs ability to interpret rapid diagnostic test (RDT) results following trainings by MEDP Mandla. There is a need to increase the capabilities of the frontline health staff of Mandla for malaria diagnosis and treatment. Continuous trainings and a robust supply chain management system is required to equip the ASHAs and ANMs to effectively deliver malaria diagnosis and treatment services.

Blockchain-Based Formal Model for Food Supply Chain Management System Using VDM-SL

In modern society, the food supply chain management system has become an important research area realized nationally and internationally, which has established a collaborative relationship between producers, manufacturers, processors and retailers. Now, consumers are more interested in food quality, safety and the authentication of the products. Food safety has become an important issue in public health in the food market because people of all types and races around the world are affected due its poor quality. The traditional supply chains are centralized and face different issues such as lack of transparency, accountability and audit ability. The general issues in supply chain management include lack of communication, trust among the stakeholders, and interference of entities and wastage of food. A lot of work has been completed by researchers to address the above issues, but still, there is a need for effective mechanisms for the modeling of supply chain management systems. In this paper, a trusted, self-organized, traceable formal system based on blockchain and Internet of Things (IoT) is developed by using wireless sensors networks and finite automata. In the proposed model, smart contracts are designed to assure the automated payment procedures. The proposed model reduced the need for centralized authority. Unified Modeling Language (UML) is used to realize the requirements, and automata is used to capture behavior of the system. A blockchain-based model is used to provides the privacy and security mechanism for the transitions record. Wireless sensors are used to sense the information, and actors are used for decision making in case of any violation in the contact. A lot of work has been completed by researchers on smart contracts. Different smart contracts are designed to achieve the better traceability among producers, transporter/logistics and consumers. Our system provides the smart contract algorithm to show the interaction of entities in the food supply chain management system. Vienna Development Method-Specification Language (VDM-SL) is used to describe the formal system and the VDM-SL toolbox is used for the validation and analysis of the system.

Design and Deployment of IoT enabled Blockchain based resilient Supply-chain Management System using Ethereum

Design and Deployment of IoT enabled Blockchain based resilient Supply-chain Management System using Ethereum

Adaptation of risk management in the supply chains of e-commerce enterprises under the conditions of political instability

The research considers the construction of a supply chain management system for e-commerce enterprises in the face of political threats and the manifestation of the risks of military conflicts. In the system of political risks to the functioning of supply chains (SC) of e-commerce enterprises, the threats of military conflicts and civil confrontations are highlighted. It has been established that despite the indirect and relatively low sensitivity of e-commerce to political risks, there is a high probability of negative impact on the mechanism of functioning of supply chains of e-commerce enterprises of military conflicts. The characteristic directions of influence of the risks of military conflicts on the activities of e-commerce enterprises are revealed. Five groups of risks for the supply chain of an e-commerce enterprise caused by military conflicts have been identified: infrastructure, contractual, economic, financial, social, reputational. The consequences and directions of influence of factors of military-political instability on the functional areas of logistics of the supply chain of e-commerce enterprises are determined. The ranking was carried out using the expert method of risks of military conflicts for supply chains of e-commerce enterprises. It was established that in 1st place with an importance coefficient of 0.18 there is a risk of deterioration of information relations. It was estimated that the average period for the restoration of supply chains since the beginning of hostilities is 2–3 months. The study schematically depicts the cyclical causality of the functioning of the supply chain of an e-commerce enterprise in the face of aggravation of military-political conflicts. An algorithm for the construction of a risk management system for e-commerce enterprises has been developed

The Effect of Green Supply Chain Management, Green Intellectual Capital, and Green Information System on Environmental Performance and Financial Performance

Environmental degradation is increasing and resource depletion has become a problem. Based on the results of the preliminary survey, the majority of respondents are more concerned with financial performance than environmental performance. This study aims to examine the effect of green supply chain management (GSCM), green intellectual capital (GIC), and green information system (GIS) on environmental performance (EnvP) and financial performance (FinP). The sample in this study was 219 respondents who came from manufacturing companies in Riau Province. Data analysis used structural equation analysis with SMART PLS application. The results show that only green supply chain management has a positive effect on financial performance. Then green intellectual capital and environmental performance each have a positive effect on financial performance. This research develops a new measurement for green supply chain management and green information system variables. Companies can develop systems and information technology that can reduce waste so that their environmental and financial performance can be better.

A Machine Learning Algorithm for Supplier Credit Risk Assessment Based on Supply Chain Management

In today’s complex and ever-changing world, a distribution network in lending impact analysis is an evaluation of a client’s procedures, rules, and financial well-being to evaluate as considerable risk and it provides to the contracting company. A creditor’s capability to pay the current lender’s obligations is considered while doing a lender’s threat assessment. Traditionally, it refers to the concern that the borrower may not be able to collect the sequence and interest. The challenges in lenders’ threat assessment are a lack of adequate data storage and retrieval, problematic delays caused by a lack of access to the relevant data at the right time, extended lead times that lead their shipments at risk, and demand for speedier deliveries. This paper introduces a machine learning-based linear regression algorithm (ML-LRA) for supplier credit risk (SCR) assessment based on supply chain management (SCM) in credit risk frameworks that depend significantly on modeling ML. Regression models are logistical constraints that can be used to simulate the impacts of multiple variables on a customer’s creditworthiness. The chain of distribution forecasting tool assesses specific decisions based on assumptions in variability. As a result of the findings in this study, it can be assumed that ML-LR approaches have a significant role in a variety of business processes such as supplier selection, risk prediction along with the supply chain, and demand and sales estimation. Finally, the study’s consequences for the most critical constraints and obstacles are examined to enhance the supply chain management system and ensure overall system sustainability.

Tracing the lean thinking in supply chain management: a comprehensive main path analysis

PurposeThe purpose of this paper is to contribute to the existing knowledge system of lean supply chain management (LSCM), i.e. by using main path analysis, and the authors extract the main development track of LSCM. It is advantageous for relevant scholars to deepen their understanding of this academic field from a bibliometrics view to grasp the future directions better.Design/methodology/approachStructuring a citation network with the processed data set containing 866 papers and relevant information collected from Web of Science (WoS). Conducting review analyses aiming at the main paths that are extracted from the above citation network.FindingsThere are two different evolution paths in LSCM field, i.e. improving corporate sustainability performance through combining lean and green practices, and seeking the balance between lean and agility to structure leagile supply chain for specific industries. LSCM research studies mainly focus on five aspects: (1) establishment and development of LSCM theory; (2) structuration of lean supply chain; (3) research studies of the relationship between LSCM and corporate performance; (4) supply chain evaluation system; and (5) review and vista of LSCM field. The intersection of two knowledge evolute routes would be Industry 4.0, which is an integrated theory system combining lean, agility, green and other supply chain thinking.Research limitations/implicationsThe data set collected from WoS cannot contain all the research studies about LSCM is the main research limitation. Sustainability, as represented by environmental performance, will continue to be a major pursuit of this field. Integrating LSCM in Industry 4.0 will be the next hotspot in LSCM field.Practical implicationsProviding the main research contents and common methods of LSCM field. It is conducive to deepening the understanding of relevant practitioners and scholars to LSCM field from a dynamic perspective.Originality/valueTo the best of the authors’ knowledge, it is the first time to reveal the knowledge diffuse trajectories of LSCM under different view with main path analysis. This study is unique that it provides a new view to understand the field of LSCM.

Construction of a Smart Supply Chain for Sand Factory Using the Edge-Computing-Based Deep Learning Algorithm

The diminishing natural sand has facilitated the booming of the sand manufacturing industry, and intelligent management of sand factories, in a time- and cost-efficient way, has become a growing tendency for the future. A role has been played in achieving intelligent management by constructing a smart supply chain. However, the smart sand factories are hardly involved in previously reported studies, which is inconsistent with related studies on smart factories and the Industrial Internet of Things (IIoT). In this paper, a smart supply chain management system (SSCMS) is constructed to realize the intelligence and automatization of the management of sand factories, using edge-computing and deep learning techniques. Along the supply chain, the deep learning model is used to realize the automatic identification of sand, avoiding the disadvantages of human identification, while improving the quality of sand factory operations. In order to relieve the pressure of network bandwidth, reduce system delay, and improve system operation efficiency, we use edge-computing technology to process data at the edge. To verify the performance of the constructed system, a sand factory simulation platform is established. Experiments show that the most critical indicator in the system, the accuracy rate of sand type identification, is above 98%, and the sand type identification time is only 0.022 s. In general, compared with traditional supply chain management, the constructed smart supply chain improves the quality and efficiency of sand factory operations, and all indicators of the designed system have achieved satisfactory results.

The FBProphet forecasting model to evaluate the spread of COVID-19 pandemic: A machine learning approach

The pandemic due to the COVID-19 virus causes financial disruption almost in all the countries in the world. It breaks the supply chain management system, especially in the food, energy and industrial sectors. This COVID-19 pandemic hits the world over different periods of time in a year and continues from the year 2020. It is important to predict the severity of the infection rate and death rate to prepare and take necessary actions for the future. But it is still difficult to forecast the outbreaks for the long term with higher accuracy. Here, a machine learning (ML) algorithm, FBProphet, is employed to forecast COVID-19 outbreaks in Bangladesh as a time series forecasting. This model predicts the daily and cumulative infection and death rates with a high and low error rate. Due to the seasonality feature of the FBProphet model, it can predict the different waves of outbreaks with higher accuracy. Furthermore, cross-validation of the predicted results has been performed to ensure the accuracy of the results. The effort and the outcomes of this computational study will help the decision makers of this country to take necessary actions for the future which can save lives and prevent economic disruption.