In the SW industry, the diversification of global markets and technology trends are changing due to the emergence of new services, and IT companies are in need of strategic change different from the previous one. In this study, SWOT analysis was performed by extracting the internal environmental factors and external environmental factors through the previous studies to improve the project performance competence of small IT companies, and the SWOT Matrix was reconstructed through the pairwise comparison. Based on this, the purpose of this research was to propose the project performance competency improvement plans for small IT companies based on importance and priority, and the following four strategies were derived from the result of SWOT-AHP analysis. SO strategy was drawn in direction to invest R & D in new SW industry to enhance technology competitiveness of SMEs in order to develop SMEs, and in order to secure competitiveness of SW industry, WO strategy is drawn the cultivation of professional technical manpower through SW policy. ST Strategy presented technical management through SW system improvement policy to improve SW environment, and WT Strategy presented awareness training on the construction and necessity of project management system construction. Using the strategies presented in this study, it is expected that the managers of small IT firms will be presented as references to measure the performance competence factors of current organizations and company organizations.

This paper will present a simulation-optimization model for the scheduling of multi-projects. The objectives of this research include the minimization of value added projects execution cost, project completion time, project tardiness, and underutilization of contracted or outsourced resources. It is the three-phase research. In first phase, a mathematical and simulation models will be developed for multi-objectives. In second phase simulation model will be coupled with genetic algorithm to form a simulation-optimization model. The efficiency of genetic algorithm (GA) will be improved simultaneously with fine-tuning and hybridizing with other algorithms. The third phase will involve the presentation of a numerical example for the real time application of proposed research. Solution of numerical obtained with fine-tuned and hybridized simulation integrated GA will be compared with already available methods of simulation-optimization. This research will be useful for the scheduling of projects to achieve the befits of high profit, effective resource utilization, and customer satisfaction with on time delivery of projects.

최근 4차 산업혁명이라는 기치아래, 인공 지능, 사물 인터넷, 스마트 공장, 협업 로봇, 3D 프린트, 무인 자동차 등에 이어 최근에는 블록체인이라는 기술과 함께 비트코인이라는 광풍이 한바탕 광풍처럼 지났지만 아직도 여운이 가시지 않은 상태이다. 4차 산업혁명으로 우리 이상의 모든 것을 송두리째 바뀌는 현상이 다가온다. 그럼 우리의 화두인 Project Management는 현재 어디쯤 와 있으며, 세상의 변화 흐름에 따라 어떻게 대처하여야 하는가에 대하여 생각을 해보기로 하자. 세상은 변화를 시도하고 있고 생존을 위하여 살아있는 모든 것들은 이러한 변화에 동참해야 한다. 당면한 변화의 흐름에 따라 Project Management도 새로운 패러다임의 형태 변화가 필요한 시점이 되었다. 여기에는 Project Success에 대한 정의부터 달라져야 할지도 모른다. 새로운 변화의 시대를 맞이하여 Project와 Project Management가 어떻게 달라져야 하는지에 대하여 알아보도록 하자.

The application of the theoretical model to real assembly lines has been one of the biggest challenges for researchers and industrial engineers. There should be some realistic approach to achieve the conflicting objectives on real systems. Therefore, in this paper, a model is developed to synchronize a real system (A discrete event simulation model) with a theoretical model (An optimization model). This synchronization will enable the realistic optimization of systems. A job assignment model of the assembly line is formulated for the evaluation of proposed realistic optimization to achieve multiple conflicting objectives. The objectives, fluctuation in cycle time, throughput, labor cost, energy cost, teamwork and deviation in the skill level of operators have been modeled mathematically. To solve the formulated mathematical model, a multi-objective simulation integrated hybrid genetic algorithm (MO-SHGA) is proposed. In MO-SHGA each individual in each population acts as an input scenario of simulation. Also, it is very difficult to assign weights to the objective function in the traditional multi-objective GA because of pareto fronts. Therefore, we have proposed a probabilistic based linearization and multi-objective to single objective conversion method at population evolution phase. The performance of MO-SHGA is evaluated with the standard multi-objective genetic algorithm (MO-GA) with both deterministic and stochastic data settings. A case study of the goalkeeping gloves assembly line is also presented as a numerical example which is solved using MO-SHGA and MO-GA. The proposed research is useful for the development of synchronized human based assembly lines for real time monitoring, optimization, and control.

The application of the theoretical model to real assembly lines has been one of the biggest challenges for researchers and industrial engineers. There should be some realistic approach to achieve the conflicting objectives on real systems. Therefore, in this paper, a model is developed to synchronize a real system (A simulation model) with a theoretical model (An optimization model). This synchronization will enable the realistic optimization of systems. A job assignment model of the assembly line is formulated for the evaluation of proposed realistic optimization with multiple objectives. The objectives, fluctuation in cycle time, throughput, labor cost, energy cost, teamwork and deviation in the skill level of operators have been modeled mathematically. To solve the formulated mathematical model, a multi-objective simulation integrated hybrid genetic algorithm (MO-SHGA) is proposed. The performance of MO-SHGA is evaluated with the standard multi-objective genetic algorithm (MO-GA) with both deterministic and stochastic data settings. A case study of the goalkeeping gloves assembly line is also presented as a numerical example which is solved using MO-SHGA and MO-GA.

There can be included a variety of uncertainties in all measurement results whether we can perceive or not on the causes. These uncertainties may end up in lowering the reliability of measurement results and also deteriorate the level of quality. For the purpose, we tried to combine the strengths of measurement uncertainty and measurement system analysis together to present a practical flowchart so as to verify those potential variation factors in general measurement processes. As a case study, we did an experiment and gathered data on the length between two holes of an engine cylinder head which is a core part for vehicles with a coordinate measuring machine and estimated nine uncertainty factors of it. Consequently, it was identified that the four primary factors among the nine which were related to the measurement standard, random errors or spread of the repeat measurements, differences between the coefficients of thermal expansion and the environment especially had been the influence around the laboratory. Since it is impossible to analyze the equipment and appraisal variations respectively through the only measurement uncertainty, we have used the measurement system analysis following the flowchart. Showing the result of being just about 0.5 % lower for the appraisal variation, and the equipment variation occupied about 7% for the total Gage R&R. Through this research, we have come to a conclusion that much more detail analysis on variation factors can be possible to be identified in measurement processes by using the developed flowchart which is composed of measurement uncertainty and measurement system analysis. Therefore, we expect engineers who are involved in quality and measurements to utilize this developed method.

Many organizations have transformed their business in order to survive and compete in the future. They generate projects by creating a vision, using strategies and objectives with funds aligning strategies and make efforts to complete them successfully because project success leads to business success. All projects have triple constraints such as scope, time, and cost to be completed. Project cost performance is a key factor to achieve project goals and which is mostly related with risks among various cost drivers. Projects require a cost estimation method to complete them within their budget and on time. An accurate budget cannot be estimated due to the uncertainties and risks. Thus some additional money should be funded in addition to the base budget as a contingency reserve for identified risks and a management reserve for unidentified risks. While research on contingency reserve for identified risks included in project budget baseline have been presented, research on management reserve for unidentified risks included in total project budget is still scarce. The lack of research on estimation method and role of the management reserve have made project managers little confidence to estimate project budget accurately with reasonable basis. This study proposes a practical model to estimate budgets including contingency and management reserves for not only project cost management but also to keep the balance of organization’s total funds to maximize return on investments for project portfolio management. The advantages of the proposed model are demonstrated by its application to construction projects in Korea and the processes to apply this model for verification are also provided.

There can be included a variety of uncertainties in all measurement results whether we can perceive or not on the causes. These uncertainties may end up in lowering the reliability of measurement results and also deteriorate the level of quality. For the purpose of finding and improving these causes , we tried to combine the strengths of measurement uncertainty and measurement system analysis together to present a practical flowchart so as to verify those potential variation factors in general measurement processes. Through the case study we have come to a conclusion that we can expect engineers who are involved in quality and measurements to utilize this developed method.

Optimum lot size calculation for real world manufacturing environment has been focused since last few decades. Several extensions have been made to the basic economic order and production order quantity models to realize the possible practical situations in industry. However, focus on work-in-process inventory has been ignored relatively. This paper provides a comprehensive review of the models developed for group technology based manufacturing environment focusing on work-in-process inventory. Models have been extended from a perfect manufacturing conditions to an imperfect manufacturing situation considering rework, rejection and inspection. Optimum lot size has been evaluated using a simple algebraic optimization approach. Significant parameters are highlighted using sensitivity analysis for the developed models. Numerical example is used to illustrate the utilization of such models in day-to-day production setups and the impact of significant factors’ variation on total cost and optimum lot size.

The purpose of this research is to initiate discussion to simplify existing tools and methods for managing projects which make project management a harder job to perform and a tougher task to handle. Additionally, summarizing the definitions of “project” that have been found from reviewing the relevant literature, it deliberates on revising the duties and responsibilities of project manager and proposes performance expectancy triangle explaining a relationship among accountability, responsibility and authority – the Tri-butes. It sheds light on several factors critical to the success of projects. Finally, we propose a function indicating that customer demands or desires greatly affect project complexity.

Smart phones have brought rapid changes in this competitive world. Smart phone application developers are trying their best to consider the customer requirements in the most efficient way while considering all its attributes. However smart phone service quality has been given less consideration comparatively during the last few years. This paper proposes a measurement method for improving service quality of smart phone application. This method combines the service quality performance model (SQPM) and process capability index (PCI). The service quality performance model is used to identify service items that require improvement. Process capability index is used as a measure for prioritization of those improvements. Case study was carried out to search out important communication application service attributes. customer satisfaction level data was collected for users who used the application service. A total of twenty four service attributes were found during this survey. Using the joint approach of SQPM and PCI, five significant service attributes were prioritized for service quality improvement.

Smart phones have drastically changed the life of today’s competitive world. Smart phone application (App) developers are trying their best to meet the customer requirements in the most efficient way while considering all its attributes. However smart phone service quality has been given less consideration comparatively during the last few years. This paper proposes a measurement method for improving service quality of smart phone application. This method combines the service quality performance model and process capability index. The service quality performance model is used to identify service items that require improvement. Process capability index is used to prioritize these items for improvement. We collected data on satisfaction level of customers who have used application service. By the proposed method, we found five significant service items from three categories - execution, security and resilience and prioritized those items for improvement.

Companies strive for quality improvement and use process data obtained through measurement process to monitor and control the process. Measurement data contain variation due to error of operator and instrument. The total variation is sum of product variat

In some manufacturing processes, the variance can change, depending on the process mean. For example, if the output value reflects process yield, an increased mean might naturally lead to an increase in variance. When the variance is a function of the mean, the coefficient of variation (CV) is an appropriate measure for process variability. Since the CV control chart was first introduced by Kang et al (2007), there were some trials to improve the performance of CV control charts depending on the shift size. In this research, we present some CV related control charts and compare the performance of those control charts for better use in the field. The CV control chart shows good sensitivity to large shift in CV. The CV-EWMA control chart(2008), the CV-DEWMA control chart(2011) and the CV-GWMA control chart(2011) were developed to control processes sensitively responding to small shifts of CV. The FIR CV-GWMA control chart is more effective control chart to detecting off-target processes in the stage of set-up or start-up of process.