• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.81-88
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• 2021

The presented scientific research substantiates the principles of the mechanism of the investment resources involvement in order to introduce innovations at enterprises in the context of digitalization using a resource-functional approach. The importance of attracting investment resources, which contributes to the modernization of production systems, the creation of a stable economic field of development of economic entities, is justified. The expediency of application of the resource-functional approach on research of the mechanism of attraction of investment resources for introduction of innovations at the enterprises in the conditions of digitalization is proved. The investment process is presented in the form of a chain of interdependent processes which include: attraction of investment resources, investments, increase of investment value, profit. It is proved that the mechanism of attracting investment resources for the introduction of innovations in enterprises in the context of digitalization cannot be considered in isolation from the process, due to the fact that the mechanism is aimed at performing specific functions. The functions of the mechanism include management, complex, coordination, monitoring, performance and control functions. Functions of the mechanism of attraction of investment resources for introduction of innovations at the enterprises in the conditions of digitalization are caused by the purposes of attraction of investment resources for innovative development; the presence of an objective nature; relative independence and homogeneity; implementation of functions in the process of investing in innovative activities of the enterprise.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• Journal of the Korean Geotechnical Society
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• v.22 no.8
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• pp.129-136
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• 2006

Engineered mixtures, which consist of rigid sand particles and soft fine-grained rubber particles, are tested to characterize their small and large-strain responses. Engineered soils are prepared with different volumetric sand fraction, sf, to identify the transition from a rigid to a soft granular skeleton using wave propagation, $K_{o}-loading$, and triaxial testing. Deformation moduli at small, middle and large-strain do not change linearly with the volume fraction of rigid particles; instead, deformation moduli increase dramatically when the sand fraction exceeds a threshold value between sf=0.6 to 0.8 that marks the formation of a percolating network of stiff particles. The friction angle increases with the volume fraction of rigid particles. Conversely, the axial strain at peak strength increases with the content of soft particles, and no apparent peak strength is observed in specimens when sand fraction is less than 60%. The presence of soft particles alters the formation of force chains. While soft particles are not part of high-load carrying chains, they play the important role of preventing the buckling of stiff particle chains.

• International Area Studies Review
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• v.18 no.3
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• pp.205-229
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• 2014

Global production networks (GPNs) emerged as multinational companies strategically relocated different stages of their value chain over many regions. Since GPNs require moving materials, parts, components and finished products across national borders multiple times, as well as coordinating it efficiently, they are intensified further within an integrated region. Within the region, developed countries which enjoy a comparative advantage in higher value-added tasks specialize in the production of ICT parts and components and exhibit high export RCA indices while developing countries show high import RCA indices. But, as developing countries upgrade technological capabilities and achieve industrial upgrading through participation in GPNs, their level of sophistication improves. East Asian countries have participated in GPNs to a greater degree when compared to countries in other regions because of a variety of factors. They have benefited much as shown by a significant increase in the level of ICT sophistication and export shares, which in turn led to uneven regional developments of GPNs in the ICT parts and components industry.

• Information Systems Review
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• v.4 no.2
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• pp.323-341
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• 2002

Due to the development of information technology, IT industry has become the core factor of a country competence. So we recognized the importance of information network technology as a basis of IT industry. The infrastructure and service in domestic super-highway information networks show the rapid growth both in quantity and quality because of the government programs. However, foreign information network equipment companies have most of the domestic market-share and have controlled core part of the industry, thus national companies are having a difficulty in penetrating the industry market. In this paper, we will analyze domestic super-highway information network equipment industry and make its activating plan using competitive strategy model.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.4
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• pp.593-601
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• 2017

In this paper, we constructed the model of production/investment, distribution, and screening by using variables that can be considered at each stage according to the value chain stage of the movie industry. To increase the predictive power of the model, a regression analysis was used to derive meaningful variables. Based on the given variables, we compared the difference in predictive power between the artificial neural network, which is a machine learning analysis method, and the decision tree analysis method. As a result, the accuracy of artificial neural network was higher than that of decision trees when all variables were added in production/ investment model and distribution model. However, decision trees were more accurate when selected variables were applied according to regression analysis results. In the screening model, the accuracy of the artificial neural network was higher than the accuracy of the decision tree regardless of whether the regression analysis result was reflected or not. This paper has an implication which we tried to improve the performance of movie prediction model by using machine learning analysis. In addition, we tried to overcome a limitation of linear approach by reflecting the results of regression analysis to ANN and decision tree model.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.2
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• pp.155-161
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• 2019

Currently, numerous companies and institutes provide services using the Internet, and establish and operate Information Systems to manage them efficiently and reliably. The Information System implies the possibility of losing the ability to provide normal services due to a disaster or disability. It is preparing for this by utilizing a disaster recovery system. However, existing disaster recovery systems cannot perform normal recovery if files for system recovery are corrupted. In this paper, we proposed a system that can verify the integrity of the system recovery file and proceed with recovery by utilizing hyper-ledger fabric blockchain. The PBFT consensus algorithm is used to generate the blocks and is performed by the leader node of the blockchain network. In the event of failure, verify the integrity of the recovery file by comparing the hash value of the recovery file with the hash value in the blockchain and proceed with recovery. For the evaluation of proposed techniques, a comparative analysis was conducted based on four items: existing system recovery techniques and data consistency, able to data retention, recovery file integrity, and using the proposed technique, the amount of traffic generated was analyzed to determine whether it was actually applicable.

• The Journal of Bigdata
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• v.4 no.2
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• pp.159-173
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• 2019

Last-Mile delivery optimization plays a key role in the urban supply chain operation, which is the most expensive and time-consuming and most complicated part of the whole delivery process. The urban consolidation center (UCC) is regarded as a significant asset for supporting customer demand in the last-mile delivery service. It is the key benefit of UCC to improve the load balance of vehicles and to reduce the total traveling distance by finding the better route with the well-organized multi-leg vehicle journey in the urban area. This paper presents the model using multiple scenario analysis integrated with mathematical optimization techniques using Geographic Information System (GIS). The model aims to find the best solution for the distribution network consisted of DC and UCC, which is applied to the case of Ulaanbaatar Mongolia. The proposed methodology integrates two sub-models, location-allocation model and vehicle routing problem. The multiple scenarios devised by selecting locations of UCC are compared considering the general performance and delivery patterns together. It has been adopted to make better decisions the quantitative metrics such as the economic value of capital cost, operating cost, and balance of using available resources. The result of this research may help the manager or public authorities who should design the distribution network for the last mile delivery service optimization using UCC within the urban area.

• Journal of Distribution Science
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• v.9 no.3
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• pp.83-94
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• 2011

Most domestic companies, with the exclusion of major firms, are reluctant to implement a supply chain management (SCM) network into their operations. Most small- and medium-sized enterprises are not even aware of SCM. Due to the inherent total-systems efficiency of SCM, it coordinates domestic manufacturers, subcontractors, distributors, and physical distributors and cuts down on cost of inventory control, as well as demand management. Furthermore, a lack of SCM causes a decrease in competitiveness for domestic companies. The reason lies in the fundamentality of SCM, which is the characteristic of information sharing, process innovation throughout SCM, and the vast range of problems the SCM management tool is able to address. This study suggests the contemplation and reformation of the current SCM situation by analyzing the SCM strategic plan, discourses and logical discussions on the topic, and a successful case for adapting SCM; hence, the study plans to productively "process" SCM. First, it is necessary to contemplate the theoretical background of SCM before discussing how to successfully process SCM. I will describe the concept and background of SCM in Chapter 2, with a definition of SCM, types of SCM promotional activities, fields of SCM, necessity of applying SCM, and the effects of SCM. All of the defects in currently processing SCM will be introduced in Chapter 3. Discussion items include the following: the Bullwhip Effect; the breakdown in supply chain and sales networks due to e-business; the issue that even though the key to a successful SCM is cooperation between the production and distribution company, during the process of SCM, the companies, many times, put their profits first, resulting in a possible defect in demands estimation. Furthermore, the problems of processing SCM in a domestic distribution-production company concern Information Technology; for example, the new system introduced to the company is not compatible with the pre-existing document architecture. Second, for effective management, distribution and production companies should cooperate and enhance their partnership in the aspect of the corporation; however, in reality, this seldom occurs. Third, in the aspect of the work process, introducing SCM could provoke corporations during the integration of the distribution-production process. Fourth, to increase the achievement of the SCM strategy process, they need to set up a cross-functional team; however, many times, business partners lack the cooperation and business-information sharing tools necessary to effect the transition to SCM. Chapter 4 will address an SCM strategic plan and a case study of LG Electronics. The purpose of the strategic plan, strategic plans for types of business, adopting SCM in a distribution company, and the global supply chain process of LG Electronics will be introduced. The conclusion of the study is located in Chapter 5, which addresses the issue of the fierce competition that companies currently face in the global market environment and their increased investment in SCM, in order to better cope with short product life cycle and high customer expectations. The SCM management system has evolved through the adaptation of improved information, communication, and transportation technologies; now, it demands the utilization of various strategic resources. The introduction of SCM provides benefits to the management of a network of interconnected businesses by securing customer loyalty with cost and time savings, derived through the consolidation of many distribution systems; additionally, SCM helps enterprises form a wide range of marketing strategies. Thus, we could conclude that not only the distributors but all types of businesses should adopt the systems approach to supply chain strategies. SCM deals with the basic stream of distribution and increases the value of a company by replacing physical distribution with information. By the company obtaining and sharing ready information, it is able to create customer satisfaction at the end point of delivery to the consumer.