• International Journal of Contents
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• v.6 no.4
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• pp.69-78
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• 2010

In recent years, many leading corporations are actively adopting IT as competitive resources to improve productivity and processes efficiency with strategic alignments. In effect, IT investment also continues to increase. As a vast growth of IT investment, questions and criticism on recent IT investment results are also rapidly being raised. Especially, improper decision making and management on IT investment may cause negative impact on the company's reputation and finances, therefore companies need reasonable and wise investment decision making on new IT projects. This study applies the conceptual framework of IT governance to IT investment decision making cases to examine how IT investment governance influences the quality of IT investment decision making and how business-IT strategic alignment affects the quality of IT investment decision making. This paper contributes to identify the main factors for reasonable and effective IT investment decision making and expected to provide proper guidelines for IT investment decision making.

• The Journal of Industrial Distribution & Business
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• v.9 no.3
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• pp.7-17
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• 2018

Purpose - The purpose of this paper is to improve last mile delivery capability and ensure customers' satisfaction by approaching an analytic hierarchy process(AHP) and identifying criteria framework to determine locations of last mile delivery centre(LMDC). Research design, data, and methodology - Traffic congestion and emission policy in cities are barriers of last mile delivery in dense areas. The urban consolidation centre(UCC) cannot increase last mile delivery efficiency in dense cities because of their space and traffic limitation. In this paper, we develop a case to improve last mile delivery efficiency and to ensure customers' satisfaction by concentrating on LMDC. In addition, AHP has been applied to identify criteria framework and determine LMDC locations. The weighted priorities are derived from parcel delivery industry experts and have been calculated using Expert Choice software. Results - The framework criteria have assisted decision makers to place LMDC in a dense area to enhance customer's satisfaction with last mile delivery service. Conclusions - AHP has provided ranking framework criteria of LMDC potential for parcel delivery industry. The LMDC helps by improving last mile delivery efficiency to final destination amids conditions of CO₂ emissions, traffic congestion, and pollution problems. It especially concerns delivery service activities when delivering parcels to customers rather than UCC.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.4
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• pp.65-86
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• 2021

This study aims at testing a hypothesis that the resilience of agricultural water systems is characterized by trade-offs and synergies of effects from climate and socioeconomic change. To achieve this, an Agricultural Water System Climate Resilience Assessment (ACRA) framework is established to evaluate comprehensive resilience of an agricultural water system to the combined impacts of the climate and socioeconomic changes with a case study in South Korea. Understanding dynamic behaviors of the agricultural water systems under climate and socioeconomic drivers is not straightforward because the system structure includes complex interactions with multiple feedbacks across components in water and agriculture sectors and climate and socioeconomic factors, which has not been well addressed in the existing decision support models. No consideration of the complex interactions with feedbacks in a decision making process may lead to counterintuitive and untoward evaluation of the coupled impacts of the climate and socioeconomic changes on the system performance. In this regard, the ACRA framework employs a System Dynamics (SD) approach that has been widely used to understand dynamics of the complex systems with the feedback interactions. In the ACRA framework applied to the case study in South Korea, the SD model works along with HOMWRS simulation. The ACRA framework will help to explore resilience-based strategies with infrastructure investment and management options for agricultural water systems.

• ETRI Journal
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• v.36 no.5
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• pp.714-720
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• 2014

In this paper, we propose the use of data-driven probabilistic utterance-level decision logic to improve Weighted Finite State Transducer (WFST)-based endpoint detection. In general, endpoint detection is dealt with using two cascaded decision processes. The first process is frame-level speech/non-speech classification based on statistical hypothesis testing, and the second process is a heuristic-knowledge-based utterance-level speech boundary decision. To handle these two processes within a unified framework, we propose a WFST-based approach. However, a WFST-based approach has the same limitations as conventional approaches in that the utterance-level decision is based on heuristic knowledge and the decision parameters are tuned sequentially. Therefore, to obtain decision knowledge from a speech corpus and optimize the parameters at the same time, we propose the use of data-driven probabilistic utterance-level decision logic. The proposed method reduces the average detection failure rate by about 14% for various noisy-speech corpora collected for an endpoint detection evaluation.

• Information Systems Review
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• v.14 no.1
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• pp.1-19
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• 2012

A large-scale IS development project takes a long time, thus its project manager needs to be more careful on risk management. In particular, appropriate cutover decision making is critical in large-scale IS development projects because the opening of the large-scale IS significantly impacts the organization. Regardless of its importance, cutover decision making in conventional IS development projects has been done in a quite simple way. Conventional cutover decisions have been made by considering just whether the new IS operates or not from the system, application, and data implementation perspectives. However, this approach may lead to unsatisfactory performance or system failure in complex large-scale IS development. Under this background, we propose a new framework for cutover decision making on large-scale IS projects. To validate the applicability, we applied the framework to a core banking system development case. The case study shows that our framework is effective in proper cutover decision making.

• Journal of the Korea Safety Management & Science
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• v.9 no.2
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• pp.195-214
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• 2007

The purpose of this study is to explore the applicability of AHP(Analytic Hierarchy Process) to select more productive projects among various proposed projects in a particular company. To achieve this research objective, the characteristics of project evaluation and selection are first reviewed with respect to when, where, and how the decision is made. Then the theoretical basis of the AHP is briefly reviewed along with its mathematical underpinnings to construct the framework of project evaluation and selection. To be more specific, the evaluation and selection criteria were reorganized in the AHP-based framework to make the process of project evaluation and selection more productive. Project evaluation and selection is one of the most important activities for the most companies to be more advantageous in the market. Despite the importance of decision making process of project selection, not many of how to choose the best project were suggested as the reliable project selection methods in the industries. It may be because it involves various activities related to conflict resolution among different evaluation criteria, high uncertainties of market, and the unclear tradeoff among various project objectives. Furthermore, the decision, once made at this point, tends to be irrevocable until the whole process turns out to be a complete success or failure. As the result, the AHP method showed better financial performance rather than the traditional method in a case study.

• Journal of Information Technology Services
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• v.11 no.1
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• pp.173-196
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• 2012

Cloud computing is provided on demand service via the internet, allowing users to pay for the service they actually use. Since cloud computing is emerging stage in industry, many companies and government consider adopting the cloud computing. Actually a variety of factors may influence on the adopting decision making of cloud computing. The objective of this study is to explore the significant factors affecting the adoption decision of enterprise cloud computing. A research model has been suggested based on TOE framework and outsourcing decision framework. Based on 302 data collected from managers in various industries, the major findings are following. First, the benefit factors of cloud computing service such as agility and cost reduction have direct and positive effects on adoption of the service. Second, lock-in as a risk factor of cloud computing service has a negative effect while security has not. Third, both internal and external environment factors have positive effects on adoption of the service.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.47-51
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• 2013

Existing buildings now represent the greatest opportunity to improve building energy efficiency. Building performance analysis is becoming increasingly important because decision makers can have a better visualization of their building's performance and quickly make the solution for improving building energy efficiency and reducing environmental impacts. Nowadays, building information models (BIMs) have been widely created during the design phase of new buildings, and it can be easily imported to third party software to conduct various analyses. However, a BIM is not always available for all existing buildings. Even if a BIM is available during the design and construction phases, it is very challenging to keep updating it while a building is aged. A manual process to create or update a BIM is very time consuming and labor intensive. A laser scanning technology has been a popular tool to create as-is BIM. However it still needs labor-intensive manual processes to create a BIM out of point clouds. This paper introduces automatic as-is simplified BIM creation from point clouds for energy simulations. A framework of decision support system that can assist decision makers on retrofits for existing buildings is introduced as well. A case study on a residential house was tested in this study to validate the proposed framework, and the technical feasibility of the developed system was positively demonstrated.

• Smart Structures and Systems
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• v.29 no.1
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• pp.251-266
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• 2022

Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easy-to-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.

• Asia pacific journal of information systems
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• v.7 no.2
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• pp.35-49
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• 1997

The usability of the Internet including WWW (World Wide Web) is dramatically growing in current business environment. These allow decision makers to enhance the productivity of decision making by referring valuable information in the remote sites, This paper presents the possibilities how WWW can be applied to build distributed and collaborative DSS, especially model management subsystem. A framework of Internet-based DSS is delineated, and then an idea of representing and managing models in the Internet-based DSS is suggested.