• Journal of the Korean Society of Safety
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• v.36 no.2
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• pp.39-48
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• 2021

This study was conducted to suggest a direction in which safety managers can concentrate on industrial accident prevention and safety management for the organization. The job resources of safety managers were divided into organizational and individual levels, and the magnitude of the impact on organizational commitment was compared. Furthermore, job demands were classified into environmental risk factors and personal psychological factors to confirm their effect on organizational commitment. The moderating effect of job resources and sub-factors of the variable in the relationship between job demands and organizational commitment was verified. In this study, a questionnaire survey was conducted on 193 safety managers in the domestic electric construction business, data were collected, and a questionnaire of 180 people was used for the final analysis. Based on the results, organization-level resources among the sub-factors of job resources and individual psychological factors among the sub-factors of job demand had a more significant influence on organizational commitment. In the relationship between job resources and organizational commitment, the moderating effect of job demand was verified, confirming that job demand had a negative moderating effect. Individual psychological factors had a modulating effect, whereas environmental factors did not. The significance, implications, and limitations of this study are discussed based on the research results.

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.1
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• pp.74-82
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• 2011

The Bass model is a cornerstone in diffusion theory which is used for forecasting demand of durables or new services. Three well-known estimation methods for parameters of the Bass model are Ordinary Least Square (OLS), Maximum Likelihood Estimator (MLE), Nonlinear Least Square (NLS). In this paper, a hybrid method incorporating OLS and NLS is presented and it's performance is analyzed and compared with OLS and NLS by using simulation data and empirical data. The results show that NLS has the best performance in terms of accuracy and our hybrid method has the best performance in terms of stability. Specifically, hybrid method has better performance with less data. This result means much in practical aspect because the avaliable data is little when a diffusion model is used for forecasting demand of a new product.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.1
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• pp.61-72
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• 2013

This research aims to identify promising IT items from the perspectives of Korean SMEs and further to development a policy for SMEs in the IT industry. For this purpose, we adopted a bottom-up approach by discovering IT items on high demand by SMEs as their now growth engines and thus used a survey method. We also analyzed the ecosystem characteristics for the items to help policy-makers establish customized strategy to support their growth. We believe that this research is timely when the concept of ecosystem has emerged and the role of SMEs is emphasized in the IT industry. And the research results are expected to produce valuable information to make a policy for promoting IT items for SMEs and ultimately leading to balanced growth of large firms and SMEs.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.106-111
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• 1992

In order to achieve stable and efficient use of energy at iron and steel works, a model for the prediction of supply and demand of electric power system is developed on the basis of the information on operation and particular patterns of electric power consumption. The optimal amount of electric power to be purchased and the optimal fuel allocation for the in-house electric power plants are also obtained by a mixed-integer linear programming(MILP) and a nonlinear programming (NLP) solutions, respectively. The validity and the effectiveness of the proposed model are investigated by several illustrative examples. The simulation results show the satisfactory energy saving by the optimal solution obtained through this research.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.1
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• pp.117-129
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• 2009

The bullwhip effect is known as the significant factor which causes unnecessary inventory, lost sales or cost increase in supply chains. Therefore, the causes of the bullwhip effect must be examined and removed. In this paper, we develop two analytical tools for the bullwhip effect control in supply chains. First, we develop the quantitative models for computing the bullwhip effect in a three-stage supply chain consisted of a single retailer, a single distributor and a single manufacturer when the fixed-interval replenishment policy is applied at each stage. The quantitative models are developed under the different conditions for the demand forecasting and share of customer demand information. They are validated through the computational experiments. Second, we develop a simulation-based decision support system for the bullwhip effect control in a more diverse dynamic supply chain environment. The system includes a what-if analysis function to examine the effects of varying input parameters such as operating policies and costs on the bullwhip effect.

• Industrial Engineering and Management Systems
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• v.12 no.3
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• pp.207-223
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• 2013

Revenue management (RM) has been widely used to model products characterized as perishable. Classical RM model assumed that price is the sole factor in the model. Thus price adjustment becomes a crucial and costly factor in business. In this paper, an optimal pricing model is developed based on minimization of soft customer cost, one kind of price adjustment cost and is solved by Lagrange multiplier method. It is formed by expected discounted revenue/bid price integrating quantity-based RM and pricing-based RM. Quantity-based RM consists of two capacity models, namely, booking limit and overbooking. Booking limit, built by assuming uncertain customer arrival, decides the optimal capacity allocation for two market segments. Overbooking determines the level of accepted order exceeding capacity to anticipate probability of cancellation. Furthermore, pricing-based RM models occupancy/demand rate influenced by internal and competitor price changes. In this paper, a mathematical model based on game theoretic approach is developed for two conditions of deterministic and stochastic demand. Based on the equilibrium point, the best strategy for both hotels can be determined.

• IE interfaces
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• v.16 no.1
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• pp.103-110
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• 2003

Growth curves are widely used in forecasting the market demand. When there are only a few data points available, the estimated model parameters have a low confidence. In this case, if some expert opinions are available, it would be better for predicting future demand to adjust the model parameters using these information. This paper proposes the methodology for re-estimation of model parameters in growth curves when adjusting market potential and/or time of maximum sales. We also provide the detailed procedures for five growth curves including Bass, Logistic, Gompertz, Weibull and Cumulative Lognormal models. Applications to real data are also included.

• Journal of the Korea Safety Management & Science
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• v.6 no.1
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• pp.135-146
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• 2004

This paper first addresses a distribution planning method on centrally controlled supply chain. The distribution channels are assumed to be network of arborescence form. For such distribution networks, this study proposes a distribution planning scheme when the demands for retail sites are provided for a given planning horizon. As the planning horizon rolls forward, for a new horizon, forecasted demand distributions of periods in the horizon are updated. An idea of controlling customer service level by the selection of demand to be used in the planning (Demand Used in Planning, DUP) from the forecasted values is also discussed.

• The Journal of Society for e-Business Studies
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• v.6 no.3
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• pp.197-207
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• 2001

The industrial structure comes to be complicated and for the production of the enterprise the rational and scientific forecast is necessary. The demand forecast has been widely used to linear regression, and up to now the linear regression was sharp the relationskp between then dependent variable and the independent variables. But, The real society demands accurate demand forecast from uncertain environment and subjective concept. This paper proposes the demand quantity forecast method to using of the fuzzy linear regression in uncertain and vague environment. Also, the optimum decision making of the demand quantity forecast uses integral calculus of the Sugeno to reflecting with the expert's (inventory manager) opinion.

• Proceedings of the KIPE Conference
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• 2001.10a
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• pp.552-555
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• 2001

In this paper, an intelligent demand control system was introduced. This system are composed of demand controller, RTU, power line modem and HMI program. The proposed demand controller was capable of synchronizing with watt-hour meter recommended by KEPCO(Korea Electrical Power Corporation). To control remote loads, network function using powerline communication is implemented in RTU with HMI program and novices are able to operate system easily. Additionally using the power line, the cost and time of installation can b saved. The system performance was proved with a several experiments.