Efficient Information Flow Research Articles

Skin Cancer Classification Using Fine-Tuned Transfer Learning of DENSENET-121

Skin cancer diagnosis greatly benefits from advanced machine learning techniques, particularly fine-tuned deep learning models. In our research, we explored the impact of traditional machine learning and fine-tuned deep learning approaches on prediction accuracy. Our findings reveal significant improvements in predictability and accuracy with fine-tuning, particularly evident in deep learning models. The CNN, SVM, and Random Forest Classifier achieved high accuracy. However, fine-tuned deep learning models such as EfficientNetB0, ResNet34, VGG16, Inception _v3, and DenseNet121 demonstrated superior performance. To ensure comparability, we fine-tuned these models by incorporating additional layers, including one flatten layer and three densely interconnected layers. These layers play a crucial role in enhancing model efficiency and performance. The flatten layer preprocesses multidimensional feature maps, facilitating efficient information flow, while subsequent dense layers refine feature representations, capturing intricate patterns and relationships within the data. Leveraging LeakyReLU activation functions in the dense layers mitigates the vanishing gradient problem and promotes stable training. Finally, the output dense layer with a sigmoid activation function simplifies decision making for healthcare professionals by providing binary classification output. Our study underscores the significance of incorporating additional layers in fine-tuned neural network models for skin cancer classification, offering improved accuracy and reliability in diagnosis.

Facilitating Value Co-Creation Through Inter-Organizational Information Systems

Many company networks, especially those comprising small- and medium-sized enterprises (SMEs), face the challenge of digitally transforming their value co-creation (VCC). However, this topic, despite its high relevance, remains vastly under-researched. We thus conducted a case study in an SME network to investigate how these companies adapt their internal processes to enable an overarching VCC process in the network and use IT to support it. We first derived a framework of four propositions showing that the modularization of value creation, equality of actors, efficient information and knowledge flow, and inter-organizational information technology support facilitate VCC in these networks. The propositions framework then became our lens to analyze the SME network. Our empirical study enabled us to gain deeper insights into the relationships of the proposed facilitating factors for VCC in company networks and the role of inter-organizational information systems in this context.

Deployment and validation of a smart bed architecture for untethered patients with wireless biomonitoring stickers.

Conventional patient monitoring in healthcare has limitations such as delayed identification of deteriorating conditions, disruptions to patient routines, and discomfort due to extensive wiring for bed-bound patients. To address these, we have recently developed an innovative IoT-based healthcare system for real-time wireless patient monitoring. This system includes a flexible epidermal patch that collects vital signs using low power electronics and transmits the data to IoT nodes in hospital beds. The nodes connect to a smart gateway that aggregates the information and interfaces with the hospital information system (HIS), facilitating the exchange of electronic health records (EHR) and enhancing access to patient vital signs for healthcare professionals. Our study validates the proposed smart bed architecture in a clinical setting, assessing its ability to meet healthcare personnel needs, patient comfort, and data transmission reliability. Technical performance assessment involves analyzing key performance indicators for communication across various interfaces, including the wearable device and the smart box, and the link between the gateway and the HIS. Also, a comparative analysis is conducted on data from our architecture and traditional hospital equipment. Usability evaluation involves questionnaires completed by patients and healthcare professionals. Results demonstrate the robustness of the architecture proposed, exhibiting reliable and efficient information flow, while offering significant improvements in patient monitoring over conventional wired methods, including unrestricted mobility and improved comfort to enhance healthcare delivery.

Cascaded Adaptive Graph Representation Learning for Image Copy-Move Forgery Detection

In the realm of image security, there has been a burgeoning interest in harnessing deep learning techniques for the detection of digital image copy-move forgeries, resulting in promising outcomes. The generation process of such forgeries results in a distinctive topological structure among patches, and collaborative modeling based on these underlying topologies proves instrumental in enhancing the discrimination of ambiguous pixels. Despite the attention received, existing deep learning models predominantly rely on convolutional neural networks (CNNs), falling short in adequately capturing correlations among distant patches. This limitation impedes the seamless propagation of information and collaborative learning across related patches. To address this gap, our work introduces an innovative framework for image copy-move forensics rooted in graph representation learning. Initially, we introduce an adaptive graph learning approach to foster collaboration among related patches, dynamically learning the inherent topology of patches. The devised approach excels in promoting efficient information flow among related patches, encompassing both short-range and long-range correlations. Additionally, we formulate a cascaded graph learning framework, progressively refining patch representations and disseminating information to broader correlated patches based on their updated topologies. Finally, we propose a hierarchical cross-attention mechanism facilitating the exchange of information between the cascaded graph learning branch and a dedicated forgery detection branch. This equips our method with the capability to jointly grasp the homology of copy-move correspondences and identify inconsistencies between the target region and the background. Comprehensive experimental results validate the superiority of our proposed scheme, providing a robust solution to security challenges posed by digital image manipulations.

Scavenging Energy and Information through Dynamically Regulating the Electrical Double Layer

AbstractThe electrical double layer (EDL) between solids and liquids serves as the primary interface for ionic‐electronic coupling and is pivotal in nanoscale phenomena, governing electric field effects, ion transport, surface interactions, etc. Dynamically regulating the EDL through mechanical or electrostatic methods can influence charge carrier behavior, thereby impacting energy scavenging and storage processes. This regulation enabled efficient energy scavenging by governing ionic migration and optimizing charge carrier concentration at the interface, presenting a novel avenue to achieve efficient energy and information flow. Here, various scavenging energy and information devices through dynamically regulating the EDL are systematically reviewed. They are classified into three groups by regulating the distribution and movement of charge carriers throughout the entire EDL, diffuse layer, and Debye length range. The review provided a comprehensive overview of the operating principles, influencing factors, output characteristics, and typical applications, along with a discussion on future challenges. This holistic examination offers researchers valuable insights for evaluating their applicability in various scenarios.

Shaping brassinosteroid signaling through scaffold proteins.

Cellular responses to internal and external stimuli are orchestrated by intricate intracellular signaling pathways. To ensure an efficient and specific information flow, cells employ scaffold proteins as critical signaling organizers. With the ability to bind multiple signaling molecules, scaffold proteins can sequester signaling components within specific subcellular domains or modulate the efficiency of signal transduction. Scaffolds can also tune the output of signaling pathways by serving as regulatory targets. This review focuses on scaffold proteins associated with the plant GLYCOGEN SYNTHASE KINASE3-like kinase, BRASSINOSTEROID-INSENSITIVE2 (BIN2) that serve as a key negative regulator of brassinosteroid (BR) signaling. Here we summarize the current understanding of how scaffold proteins actively shape BR signaling outputs and crosstalk in plant cells via interactions with BIN2.

The resilience of systems‐thinking approaches when faced with an evolving crisis: The case of Mumbai

AbstractMotivationIndia has seen severe economic and social impacts from COVID‐19, with the most vulnerable people having suffered most. Mumbai's response has been multi‐faceted, drawing on numerous actors such as local and state government, but also businesses and citizens. Disaster management is increasingly necessary and Mumbai's policy response during wave two of COVID‐19 provides relevant lessons.PurposeWe explain Mumbai's policy response to wave two of COVID‐19, termed the Mumbai model, applying a systems‐thinking approach.Methods and approachWe adopt a systems‐thinking approach, applying a framework developed in Duvendack and Sonne (2021) to analyse a case study based on secondary data and information on the policy response to COVID‐19 in Mumbai.FindingsThe Mumbai model emerged as a pragmatic policy response. Key actors changed between waves one and two, from a broad set of actors to a narrower set focused on the unfolding healthcare crisis. This resulted in a reconfiguration of networks and partnerships. Decentralized decision‐making further changed the structure of the networks to a hub‐and‐spoke model. Communication and feedback loops shifted from primarily top‐down to two‐way information flows to support decision‐making and resource allocation driven by data. This was enabled by clear leadership and political support.Policy implicationsTo tackle a crisis of this magnitude, having diverse actors collaborating effectively through networks that enable knowledge and information flows is crucial. Governments can use the systems approach for crisis management in large cities which require swift action and efficient flow of information to assess a situation and to prioritize response efforts. Further research is required to better understand how governments can build capacity and policy structures to enable systemic approaches to crisis management in varied contexts.

A Practical Approach to Enhance Data Quality Management in Government: Case Study of Indonesian Customs and Excise Office

Background: The exponential data growth emphasises the importance of efficient information flow in organisations, especially in the financial sector. Data quality significantly influences decision-making, necessitating reliable Data Quality Management (DQM) frameworks. Previous studies propose DQM to maintain data quality through regulation, technology, measurement, evaluation, and improvement. Researchers highlight high-quality data benefits in private organisations but note the lack of improvement in data utilisation in public organisations. In Indonesia, data accuracy and quality are crucial for financial policies, with frequent reports of data inaccuracies in the Directorate General of Customs and Excise (DJBC), demanding standardised DQM practices. However, However, prior studies have yet to provide comprehensive and practical solutions to improve DQM practices. This study therefore aims to measure the DQM maturity, provide recommendations based on best practices, and formulate a practical strategy for improvements along with indicators tailored to the organisation, a topic that previous research has not explored. Methods: This study falls under a mixed method approach (a quantitative study followed by a qualitative study) and employs a three-stage methodology. The authors conduct maturity assessment using Loshin model through an assisted enumeration from 5 key stakeholders followed by recommendations based on the Data Management Body of Knowledge (DMBOK) and strategy formulation from internal documents and interview. Results: The data analysis yielded a DQM maturity score of 3.10, indicating a "defined to managed" level of maturity. Among eight components, only one receives a Managed level, two components are in the Defined level and the rest belongs to a Repeatable level. This study also proposes three strategies to bolster DQM by targeting 49 weak points, which will be progressively and sequentially implemented over a three-year period, using twelve possible solutions. Conclusion: The study highlights the importance of efficient data flow, particularly in the financial sector, and suggests DQM for maintaining data quality. DJBC's import DQM level is assessed using Loshin's measurements, revealing areas for improvement through key DMBOK activities. Recommendations include data governance, strategic planning, and sequential DQM implementation. The study concludes by formulating a practical approach to be applied in a three-year span with ten indicators to measure success. Keywords: Data Quality Management, Data Quality Maturity Model, Data Quality Strategy, Loshin, DMBOK

Price Transmission of Ofada Rice in the South-West Geo-Political Zone of Nigeria

The study was conducted to examine price transmission of ofada rice in the south –west geo-political zone of Nigeria. Data on monthly prices of processed Ofada rice was collected from National Bureau of statistics from the period of January 2016- March 2021. Descriptive statistics was carried out to characterize the price pattern in the various States; this was done by the use of Eviews9 statistical software. The result showed that the price of ofada rice in Lagos and Ondo were stationary both at level I(0) and after first differencing I(I) while the other remaining States were stationary only after first differencing I(I). The maximum price of ofada rice was recorded in Ondo (₦900/kg) while the minimum price was recorded in Osun (₦250.11/kg). The result of the Jarque –Bera shows that the prices of Ofada rice were not normally distributed in Lagos, Ondo, and Osun State. Johansen test of co-integration was used to analyze the market co-integration and the test result shows that only 5 market links were co-integrated while the result of the granger-causality test shows that all the market links had Uni-directional price movement with 7 market links showing evidence of granger- causality. Therefore, it was recommended that there should be an efficient flow of information and good access to roads as these will help to improve market performance, and the activities of market intermediaries should be minimized.

Deep Optimized Broad Learning System for Applications in Tabular Data Recognition.

The broad learning system (BLS) is a versatile and effective tool for analyzing tabular data. However, the rapid expansion of big data has resulted in an overwhelming amount of tabular data, necessitating the development of specialized tools for effective management and analysis. This article introduces an optimized BLS (OBLS) specifically tailored for big data analysis. In addition, a deep-optimized BLS (DOBLS) network is developed further to enhance the performance and efficiency of the OBLS. The main contributions of this article are: 1) by retracing the network's error from the output space to the latent space, the OBLS adjusts parameters in the feature and enhancement node layers. This process aims to achieve more resilient representations, resulting in improved performance; 2) the DOBLS is a multilayered structure consisting of multiple OBLSs, wherein each OBLS connects to the input and output layers, enabling direct data propagation. This design helps reduce information loss between layers, ensuring an efficient flow of information throughout the network; and 3) the proposed methods demonstrate robustness across various applications, including multiview feature embedding, one-class classification (OCC), camera model identification, electroencephalogram (EEG) signal processing, and radar signal analysis. Experimental results validate the effectiveness of the proposed models. To ensure reproducibility, the source code is available at https://github.com/1027051515/OBLS_DOBLS.

Investigating the interplay between segregation and integration in developing cortical assemblies.

The human brain is an intricate structure composed of interconnected modular networks, whose organization is known to balance the principles of segregation and integration, enabling rapid information exchange and the generation of coherent brain states. Segregation involves the specialization of brain regions for specific tasks, while integration facilitates communication among these regions, allowing for efficient information flow. Several factors influence this balance, including maturation, aging, and the insurgence of neurological disorders like epilepsy, stroke, or cancer. To gain insights into information processing and connectivity recovery, we devised a controllable in vitro model to mimic and investigate the effects of different segregation and integration ratios over time. We designed a cross-shaped polymeric mask to initially establish four independent sub-populations of cortical neurons and analyzed how the timing of its removal affected network development. We evaluated the morphological and functional features of the networks from 11 to 18 days in vitro (DIVs) with immunofluorescence techniques and micro-electrode arrays (MEAs). The removal of the mask at different developmental stages of the network lead to strong variations in the degree of intercommunication among the four assemblies (altering the segregation/integration balance), impacting firing and bursting parameters. Early removal (after 5 DIVs) resulted in networks with a level of integration similar to homogeneous controls (without physical constraints). In contrast, late removal (after 15 DIVs) hindered the formation of strong inter-compartment connectivity, leading to more clustered and segregated assemblies. A critical balance between segregation and integration was observed when the mask was removed at DIV 10, allowing for the formation of a strong connectivity among the still-separated compartments, thus demonstrating the existence of a time window in network development in which it is possible to achieve a balance between segregation and integration.

Which Convolutional Neural Network Architecture is the Best at Detecting Alzheimer’s Disease: A head‐to‐head Comparison Using Relevance Maps

AbstractBackgroundAlzheimer’s disease (AD) is a neurodegenerative disorder which causes gradual and irreversible damage to the brain. These patterns of brain damage can be detected using T1‐weighted MRI scans by modern neuroimaging methods. Recent developments in convolutional neural networks (CNN) have achieved promising results in various computer‐vision tasks. Feature attribution methods such as layer‐wise relevance propagation (LRP) allow tracing back the information flow in CNNs to derive relevance heatmaps, which approximate the contribution of the input image regions on the model decision. We addressed the open question, which of the most common CNN architectures is best suited for medical image detection, i.e. AD classification based on MRI data.MethodFour CNN architectures are widely used in the literature: AlexNet, VGG, ResNet, and DenseNet. We adapted these CNN architectures to be used with 3D brain MRI data and trained the models on a heterogeneous dataset with N>2200 from four large studies. We applied tenfold cross‐validation and additionally evaluated the results on an independent test dataset.ResultThe more complex CNN architectures DenseNet and ResNet with ‘skip connections’, provided the best results for both group separation tasks of ‐ AD vs Cognitively Normal (CN), and Mild Cognitive Impairment (MCI) vs CN, although the overall differences in accuracy did not reach statistical significance. From the mean relevance maps obtained using the LRP relevance propagation method (see Fig. 1.), we found that DenseNet focused primarily on the medial temporal lobe and posterior cingulate cortex atrophy. ResNet’s mean relevance maps were more noisy and heterogeneous.ConclusionDenseNet provided the most focused relevance maps, best matching apriori expectations of brain regions contributing to the detection of AD. Its ‘dense connections’ between the layers enabled a highly efficient information flow at various scales. Our evaluation setup also demonstrated the value of a holistic evaluation of models, where relevance maps are used in combination with classical performance metrics.

Which Convolutional Neural Network Architecture is the Best at Detecting Alzheimer’s Disease: A head‐to‐head Comparison Using Relevance Maps

AbstractBackgroundAlzheimer’s disease (AD) is a neurodegenerative disorder which causes gradual and irreversible damage to the brain. These patterns of brain damage can be detected using T1‐weighted MRI scans by modern neuroimaging methods. Recent developments in convolutional neural networks (CNN) have achieved promising results in various computer‐vision tasks. Feature attribution methods such as layer‐wise relevance propagation (LRP) allow tracing back the information flow in CNNs to derive relevance heatmaps, which approximate the contribution of the input image regions on the model decision. We addressed the open question, which of the most common CNN architectures is best suited for medical image detection, i.e. AD classification based on MRI data.MethodFour CNN architectures are widely used in the literature: AlexNet, VGG, ResNet, and DenseNet. We adapted these CNN architectures to be used with 3D brain MRI data and trained the models on a heterogeneous dataset with N>2200 from four large studies. We applied tenfold cross‐validation and additionally evaluated the results on an independent test dataset.ResultThe more complex CNN architectures DenseNet and ResNet with ‘skip connections’, provided the best results for both group separation tasks of ‐ AD vs Cognitively Normal (CN), and Mild Cognitive Impairment (MCI) vs CN, although the overall differences in accuracy did not reach statistical significance. From the mean relevance maps obtained using the LRP relevance propagation method (see Fig. 1.), we found that DenseNet focused primarily on the medial temporal lobe and posterior cingulate cortex atrophy. ResNet’s mean relevance maps were more noisy and heterogeneous.ConclusionDenseNet provided the most focused relevance maps, best matching apriori expectations of brain regions contributing to the detection of AD. Its ‘dense connections’ between the layers enabled a highly efficient information flow at various scales. Our evaluation setup also demonstrated the value of a holistic evaluation of models, where relevance maps are used in combination with classical performance metrics.

WEB BASED MEDICAL CONSULTING INFORMATION FLOW FOR HOSPITAL OUT-PATIENTS USING MACHINE LEARNING TECHNIQUES

Medical consulting today is characterized by numerous demanding tasks that are prone to illness and need to be efficiently managed by medical practitioners using emerging techniques and solutions. Although there are concerted efforts by both the doctors and other medical stakeholders aimed at improving the condition, human errors abound. Sequel to this, the level of management bottlenecks recorded in the hospitals on a daily basis abound, such as the unordered information flow in the outpatients department (OPD); thus, effective health service delivery is being stifled. In this regard, the application of natural language-based medical consulting information flow cannot be undermined. This work seeks to develop a natural language-based medical consulting information flow for hospital outpatients using machine learning techniques. The architecture of the system was modeled using universal modeling language (UML) tools. The medical information flow is processed using machine learning techniques (MLT), which are named Word Rank. The word rank positions and sorts all sentences accordingly in an input corpus of a patient case report and is presented in a summarized version of the word. So, the word-rank algorithm generates a summary of the patient’s record based on the input corpus. The implemented system can be deployed in the Nigerian Electronic Health Records (NEHR) used in hospitals to ensure efficient information flow for patients in the outpatient department of every hospital. The system would therefore reduce the effort expended by the medical practitioners in comprehending a case report and offering various medical services since it would be very easy to view the salient parts promptly, resulting in a reasonably smooth information flow, an easier consultation process, and a reduction in patient waiting time. Cosine similarity Identify the applicable funding agency here. If none, delete this. is used as the metric to evaluate the accuracy of the output from the algorithm when weighted against the traditional information flow in the outpatient department.

Triboiontronics for efficient energy and information flow

Triboiontronics for efficient energy and information flow

The Morality of Investment

This article details how the influence of financial investment on insurance has produced a morality discourse that provides new insights on a social theory of stigma. Specifically, it examines the Insurance Development Forum (IDF) and the role morality plays in its commitments to climate governance. Initially conceived by political leaders advising on disaster risk reduction at a 2013 UN General Assembly meeting, the IDF recognizes financial institutions, practices, and devices as integral to addressing climate change. Through its activities and investments in an open-access database, insurance has come to shape material claims about the reputational status of people, things, natures, and places. At the heart of the article's analysis is the observation that the organizational structure of the IDF depends on the efficient flow of information to manage the stigma often associated with finance capitalism.

Agroforestry Biomass Recovery Supply Chain Management: A More Efficient Information Flow Model Based on a Web Platform

Background: With the increasing societal focus on sustainability and the critical need for innovative energy solutions, this research emphasizes the undervalued biomass originating from waste products of agroforestry activities. The traditional practice of disposing of these waste products through open-air burning has led to environmental challenges and a tragic loss of their inherent energy potential. Methods: This study adopts a multifaceted approach, integrating literature reviews, expert interviews from both the academic and professional sectors, and surveys. The central focus is on understanding supply chain inefficiencies and communication gaps that contribute to waste and addressing them through the Lean philosophy, renowned for its waste reduction benefits. Results: Our research culminated in the development of a unique information management model based on a web application. Additionally, the study provides a theoretical groundwork for an application that backs the proposed model. Conclusions: The presented strategy and web-based model offer promising avenues for managing waste products from agroforestry activities more sustainably and efficiently. This approach not only addresses the environmental issues arising from waste disposal but also taps into the significant energy potential these waste products hold.

Information-Driven Integrated Healthcare: An Analysis of the Cooperation Strategy of County Medical Community Based on Multi-Subject Simulation.

The fragmentation and uneven quality of primary medical resources in China call for a deepening of integrated healthcare reform. China is promoting its county medical community (CMC) reform on a large scale in county-level administrative regions to promote the integration of local primary healthcare systems through information technology, which is consistent with the current trend of the digital governance era. Considering that the construction of a county medical community involves collaborative relationships between multi-level subjects, the evolutionary game theory was adopted in this study to construct a game model between the lead hospital of a CMC and primary healthcare institutions, and then the incentives of government department support were introduced to analyze the behavioral evolution of these three subjects. Taking into account the uncertainty of the real-life environment and information transformation, white Gaussian noise was introduced as a random disturbance term, and a numerical simulation was performed. In the two-subject model we focus on four parameters: information and management authority ceded by the primary healthcare institutions, integration coefficient of CMC information construction, intensity factor of information integration in the CMC, and medical resources delivered by the lead hospital. In the three-subject model we focus on three parameters: information and portion of authority ceded by the primary healthcare institutions and government departments, policy effect coefficient of CMC construction, and intensity of government departments' support for CMC construction. The simulation results show that there is a positive incentive for the concession of management power and information from the primary healthcare institutions to the lead hospital, but further determination of empowerment boundaries is needed. The lead hospital can improve the balance of medical resources in the county through the downward transfer of medical resources, but long-term resource delivery may inhibit the enthusiasm of the lead hospital. An improvement in the information integration intensity of the CMC can promote the efficient flow of information and knowledge and enhance the organizational closeness of the county medical community. At the same time, the integration of CMC information construction reduces the cost of collaboration among medical community members and streamlines and consolidates business modules, which can promote more efficient use of medical resources. The government departments' policies and funds provide obvious incentives to the lead hospital and primary healthcare institutions, but there is a need to explore appropriate financial payment ratios to balance the government's financial pressure.

A residual connection enabled deep neural network model for optic disk and optic cup segmentation for glaucoma diagnosis.

Glaucoma diagnosis at an early stage is vital for the timely initiation of its treatment for and preventing possible vision loss. For glaucoma diagnosis, an accurate estimation of the cup-to-disk ratio (CDR) is required. The current automatic CDR computation techniques attribute lower accuracy and higher complexity, which are important considerations for diagnostics system design to be used for such critical diagnoses. The current methods involve a deeper deep learning model, comprising a large number of parameters, which results in higher system complexity and training/testing time. To address these challenges, this paper proposes a Residual Connection (non-identity)-based Deep Neural Network (RC-DNN), which is based on non-identity residual connectivity for joint optic disk (OD) and optic cup (OC) detection. The proposed model is emboldened by efficient residual connectivity, which is beneficial in several ways. First, the model is efficient and can perform simultaneous segmentation of the OC and OD. Second, the efficient residual information flow permeates the vanishing gradient problem which results in faster converges of the model. Third, feature inspiration empowers the network to perform the segmentation with only a few network layers. We performed a comprehensive performance evaluation of the developed model based on its training in RIM-ONE and DRISHTIGS databases. For OC segmentation, for the images (test set) from {DRISHTI-GS and RIM-ONE} datasets, our proposed model achieves the dice coefficient, Jaccard coefficient, sensitivity, specificity, and balanced accuracy of {92.62, 86.52}, {86.87, 77.54}, {94.21, 95.36}, {99.83, 99.639}, and {94.2, 98.9}, respectively. These experimental results indicate that the developed model provides significant performance enhancement for joint OC and OD segmentation. Additionally, the reduced computational complexity based on reduced model parameters and higher segmentation accuracy provides the additional features of efficacy, robustness, and reliability of the developed model. These attributes of the developed model advocate for its deployment of population-scale glaucoma screening programs.

AN ASSESSMENT OF CHALLENGES AND FACTOR INFLUENCING THE FREIGHT FORWARDING BUSINESS IN THE LOGISTICS INDUSTRY

This research was undertaken with the sole aim of understanding some of the challenges and Factor Influencing the Freight Forwarding Business in the organizations involved the Forwarding and distribution of shipper Cargo encounter in their logistics operations. A freight forwarder is a person or company that organizes shipments for individuals or corporations to get large orders from the manufacturer or producer to market or final point of distribution. Forwarders will contract with a carrier to facilitate the movement of goods. A forwarder is not typically a carrier but is an expert in supply chain management. In other words, a freight forwarder is a "travel agent," for the cargo industry, or a third-party (non-asset-based) logistics provider. A forwarder will contract with asset- based carriers to move cargo ranging from raw agricultural products to manufactured goods. The term Logistics Management or supply chain management is that part of Supply Chain Management that plans, implements, and controls the efficient, effective, forward, and reverse flow and storage of goods, services, and related information between the point of origin and the point of consumption in order to meet customer's requirements. The research targeted a selected sample of Employees and representatives of transporters of goods and key management personnel of Companies Central Distribution Center based in the industrial area Chennai. The primary objective of this project is to conduct ‘A study on Challenges and factor influencing the freight forwarding business in the Freightkare Multimodal Logistics Pvt. ltd. It is important to analyze the problems faced by freight forwarders from the carriers as well as companies and individual customers. By studying and comparing the various problems faced by the freight forwarders. It is easy identifying the solutions to avoid the problems and make sure the smooth flow of business. This will help Freightkare Multimodal Logistics Pvt. ltd to establish a reasonable share in the marketplace.