• The Korean Journal of Franchise Management
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• v.14 no.4
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• pp.1-16
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• 2023

Purpose: Previous studies show that perceived CSR motives have a significant impact on company evaluations. However, consumer responses to CSR motives vary depending on CSR motives. From this perspective, this study investigates the impact of CSR motives on consumers' responses in the context of food and beverage franchise companies using a scenario. Research design, data, and methodology: For achieving the purposes of the study, an example of a domestic food and beverage franchise company actively carrying out CSR activities was presented. Data was collected from 304 respondents aged 20 or older who were aware of CSR activities. The respondents answered the questionnaire after reading the scenario. The data was analyzed with SPSS 28.0 and SmartPLS 4.0 program. Result: Values-driven motive and strategic motive influence authenticity, while stakeholder-driven motive and egoistic motive did not influence authenticity. Values-driven motive influences on attitude, while stakeholder-driven motive, strategic motive and egoistic motive didn't. Lastly, authenticity influences attitude. Conclusions: Companies need to be aware that consumers may infer different motives for their CSR activities, and pay close attention to consumers' perceived motives from the planning stage of CSR activities. In particular, companies should focus on the values-driven motive and the strategic motive when planning CSR activities.

• Proceedings of the Korean Institute of Landscape Architecture Conference
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• 2021.03a
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• pp.115-117
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• 2021

• Advances in nano research
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• v.16 no.3
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• pp.231-249
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• 2024

When the amplitude of the vibrations is equivalent to that clearance, the vibrations for small amplitudes will really be significantly nonlinear. Nonlinearities will not be significant for amplitudes that are rather modest. Finally, nonlinearities will become crucial once again for big amplitudes. Therefore, the concrete panel system may experience a big amplitude in this work as a result of the high temperature. Based on the 3D modeling of the shell theory, the current work shows the influences of the von Kármán strain-displacement kinematic nonlinearity on the constitutive laws of the structure. The system's governing Equations in the nonlinear form are solved using Kronecker and Hadamard products, the discretization of Equations on the space domain, and Duffing-type Equations. Thermo-elasticity Equations. are used to represent the system's temperature. The harmonic solution technique for the displacement domain and the multiple-scale approach for the time domain are both covered in the section on solution procedures for solving nonlinear Equations. An effective data-driven solution is often utilized to predict how different systems would behave. The number of hidden layers and the learning rate are two hyperparameters for the network that are often chosen manually when required. Additionally, the data-driven method is offered for addressing the nonlinear vibration issue in order to reduce the computing cost of the current study. The conclusions of the present study may be validated by contrasting them with those of data-driven solutions and other published articles. The findings show that certain physical and geometrical characteristics have a significant effect on the existing concrete panel structure's susceptibility to temperature change and GPL weight fraction. For building construction industries, several useful recommendations for improving the thermo-mechanics' behavior of structural concrete panels are presented.

• Korean Journal of Construction Engineering and Management
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• v.20 no.2
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• pp.95-104
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• 2019

EPC plant projects are large and complex, requiring systematic working methodologies, accumulated data, and thorough planning through communications between the entities. In this study, the method of extracting the process planning information using asset data of the plant project and using it to present the initial process plan is presented through the concept of IAP(Interactive Planning). In order to carry out the effective IAP at the early stage of the project, this study extracted the schedule element information from the asset data, created the process plan for each work package, and applied it to the sample project case. Through the proposed IAP methodology, it is possible to promote the utilization of asset data, to identify schedule risks, and to develop countermeasures, which can form the basis for establishing the process management strategy to successfully complete the project.

• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.251-262
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• 2010

While outsourcing has become a basic strategy of the information system adoption, there is an emerging needs to analyze the gap between the required data and the existing data for the new system from an adopting company's perspective. In CRM adoption failure cases, the first reason is adopting company pay no attention to the data that will support investment and systems. So far, there is no attempt to consider data driven approach in information system adoption field. Hence, we propose Information System Adoption Model based on Data (ISAMD) and show how to use in real world by simulation. By using ISAMD, information system adoption decision maker can simulate the needed data and related cost with various information system alternatives in short term, and long term planning. ISAMD can prevent the possible threat of unexpected data cost in adopting new system at the adopting decision stage.

• Journal of Information Technology Applications and Management
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• v.9 no.3
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• pp.129-139
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• 2002

There exist a number of information systems planning (ISP) methodologies. Historically these methodologies have been evolving to reflect new technologies and business requirements. In fact, it is an uneasy task to select a methodology that fits a business need. Though there have been a number of studies proposing new ISP approaches, we are unable to find much research doing a comparative analysis on existing ISP methodologies. Our study, therefore, is to present a classification scheme for ISP approaches and to provide a guideline framework for selecting an approach most suitable to a particular firm's need. Our classification utilizes types of components covered in ISP deliverables and the peculiarity of these components. Such classification scheme and selection framework would help derive an IT-driven new enterprise model more effectively.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.595-598
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• 2002

Laser scanners are getting used more and more in reverse engineering and inspection. For CNC-driven laser scanners, it is important to automate the scanning operations to improve the accuracy of capture point data and to reduce scanning time in industry. However, there are few research works on laser scan planning system. In addition, it is difficult to directly analyze multi-patched freeform models. In this paper, we propose an STL (Stereolithography) mesh based laser scan planning system for complex freeform surfaces. The scan planning system consists of three steps and it is assumed that the CAD model of the part exists. Firstly, the surface model is approximated into STL meshes. From the mesh model, normal vector of each node point is estimated. Second, scan directions and regions are determined through the region growing method. Also, scan paths are generated by calculating the minimum-bounding rectangle of points that can be scanned in each scan direction. Finally, the generated scan directions and paths are validated by checking optical constraints and the collision between the laser probe and the part to be scanned.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.284-291
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• 2022

Historical data from comparable projects can serve as benchmarking data for an ongoing project's planning during the project scoping phase. As project owners typically store substantial amounts of data generated throughout project life cycles in digitized databases, they can capture appropriate data to support various project planning activities by accessing digital databases. One of the most important work tasks in this process is identifying one or more past projects comparable to a new project. The uniqueness and complexity of construction projects along with unorganized data, impede the reliable identification of comparable past projects. A project scope document provides the preliminary overview of a project in terms of the extent of the project and project requirements. However, narratives and free-formatted descriptions of project scopes are a significant and time-consuming barrier if a human needs to review them and determine similar projects. This study proposes an Artificial Intelligence-driven model for analyzing project scope descriptions and evaluating project similarity using natural language processing (NLP) techniques. The proposed algorithm can intelligently a) extract major work activities from unstructured descriptions held in a database and b) quantify similarities by considering the semantic features of texts representing work activities. The proposed model enhances historical comparable project identification by systematically analyzing project scopes.

• The Journal of Information Systems
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• v.30 no.1
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• pp.151-177
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• 2021

Purpose This paper aims to improve management performance by effectively responding to production needs and reducing inventory through synchronizing production planning and procurement in the aviation industry. In this study, the differences in production planning and execution were first analyzed in terms of demand, supply, inventory, and process using the big data collected from a domestic aircraft manufacturers. This paper analyzed the problems in procurement and inventory management using legacy big data from ERP system in the company. Based on the analysis, we performed a simulation to derive an efficient procurement and inventory management plan. Through analysis and simulation of operational data, we were able to discover procurement and inventory policies to effectively respond to production needs. Design/methodology/approach This is an empirical study to analyze the cause of decrease in inventory turnover and increase in inventory cost due to dis-synchronize between production requirements and procurement. The actual operation data, a total of 21,306,611 transaction data which are 18 months data from January 2019 to June 2020, were extracted from the ERP system. All them are such as basic information on materials, material consumption and movement history, inventory/receipt/shipment status, and production orders. To perform data analysis, it went through three steps. At first, we identified the current states and problems of production process to grasp the situation of what happened, and secondly, analyzed the data to identify expected problems through cross-link analysis between transactions, and finally, defined what to do. Many analysis techniques such as correlation analysis, moving average analysis, and linear regression analysis were applied to predict the status of inventory. A simulation was performed to analyze the appropriate inventory level according to the control of fluctuations in the production planing. In the simulation, we tested four alternatives how to coordinate the synchronization between the procurement plan and the production plan. All the alternatives give us more plausible results than actual operation in the past. Findings Based on the big data extracted from the ERP system, the relationship between the level of delivery and the distribution of fluctuations was analyzed in terms of demand, supply, inventory, and process. As a result of analyzing the inventory turnover rate, the root cause of the inventory increase were identified. In addition, based on the data on delivery and receipt performance, it was possible to accurately analyze how much gap occurs between supply and demand, and to figure out how much this affects the inventory level. Moreover, we were able to obtain the more predictable and insightful results through simulation that organizational performance such as inventory cost and lead time can be improved by synchronizing the production planning and purchase procurement with supply and demand information. The results of big data analysis and simulation gave us more insights in production planning, procurement, and inventory management for smart manufacturing and performance improvement.

• International Journal of Advanced Culture Technology
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• v.12 no.3
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• pp.140-147
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• 2024

Oil and gas industry is a unique sector with complex activities, long supply chains and strict rules for the business. It is important to use enterprise resource planning (ERP) systems to address these challenges as it helps in simplifying operations, improving efficiency and facilitating evidence-based decision making. Nonetheless, successful integration of ERP systems in this industry involves careful planning, customization and alignment with specific business processes including regulatory requirements. Several critical factors, such as strong change management, support of top managers and training that works have been identified in the study. Amongst the hurdles are employee resistance towards the changes, data migration complications and integration with existing systems. Nonetheless, NOCL's ERP implementation resulted in significant improvements in operating efficiency, better data visibility and compliance management. It also led to a decrease in financial reporting timeframes, more accurate inventory tracking and improved decision-making capabilities. The study provides useful insights on how to optimize oil and gas sector ERP implementations; key among them is practical advice including strengthening change management strategies, prioritizing data security and collaborating with ERP vendors. The research highlights the importance of tailoring ERP solutions to specific industry needs as well as emphasizes the strategic role of ongoing monitoring/feedback for future benefits sustainability.