• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.66
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• pp.37-45
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• 2001

Because the structure of the economy is being changed from product-oriented and company-centered economy to service-oriented and customer-centered economy, and the market competition is varying with the competition of non-price factors, the importance for customer service of logistics system is being increased. Thus, the level of customer service should be represented as an element of the logistics decision and the facility location decision. The level of customer service provided by logistics system has an effect on customers\` purchase decisions, hence on the market demand. That is, the market demand is elastic for customer service as it is influenced by product price. Considering the effect of customer service on demand, this study develops the market area which each facility will serve. That area is circular, and distance norm is considered Euclidean and Rectilinear (or Manhattan) distance norm. The market demand for product at a particular area is affected by the level of customer service that facility provides, and the relationship between the market demand and the level of customer service is represented with a mathematical function.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.2
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• pp.138-146
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• 2012

This paper studies the effect of information quality level and customer demand on performance measures in a supply chain. The information quality level compares 2 types, the information levels of a customer demand and a lead time. The customer demand process follows a general auto-correlated AR(1) process without seasonality. In the AR(1) process, ${\sigma}$ indicates the degree of demand fluctuation and ${\rho}$ means the trend of customer demand. ANOVA tests using a 5% significance level are performed in SPSS to examine significant performance changes among various cases.

• Journal of Information Processing Systems
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• v.19 no.2
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• pp.258-266
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• 2023

Aiming at the problems of poor customer satisfaction and poor accuracy of customer classification, this paper proposes a customer classification model based on speech recognition. First, this paper analyzes the temporal data characteristics of customer demand data, identifies the influencing factors of customer demand behavior, and determines the process of feature extraction of customer voice signals. Then, the emotional association rules of customer demands are designed, and the classification model of customer demands is constructed through cluster analysis. Next, the Euclidean distance method is used to preprocess customer behavior data. The fuzzy clustering characteristics of customer demands are obtained by the fuzzy clustering method. Finally, on the basis of naive Bayesian algorithm, a customer demand classification model based on speech recognition is completed. Experimental results show that the proposed method improves the accuracy of the customer demand classification to more than 80%, and improves customer satisfaction to more than 90%. It solves the problems of poor customer satisfaction and low customer classification accuracy of the existing classification methods, which have practical application value.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1379-1383
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• 2005

Introducing the market into the electricity industry lets the multiple participants get into new competition. These multiple participants of the market need new business strategies for providing value added services to customer. Therefore they need the accurate customer information about the electricity demand. Demand characteristic is the most important one for analyzing customer information. In this study load profile data, which can be collected through the Automatic Meter Reading System, are analyzed for getting demand patterns of customer. The load profile data include electricity demand in 15 minutes interval. An algorithm for clustering similar demand patterns is developed using the load profile data. As results of classification, customers are separated into several groups. And the representative curves for the groups are generated. The number of groups is automatically generated. And it depends on the threshold value for distance to separate groups. The demand characteristics of the groups are discussed. Also, the compositions of demand contracts and standard industrial classification in each group are presented. It is expected that the classified curves will be used for tariff design, load forecasting, load management and so on. Also it will be a good infrastructure for making a value added service related to electricity.

• Korean Journal of Human Ecology
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• v.14 no.1
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• pp.193-208
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• 2005

The purpose of this study is to find out not only customer demands and satisfaction with service quality in restaurants, but the difference between customer satisfaction and employee practice. This study also identifies the factors affecting customer satisfaction. For the purpose, 116 copies of a questionnaire for employees and 213 for customers were analyzed with frequency, percentile, mean, multiple regression analysis, T-test, one-way ANOVA, and Duncan's Multiple range test, using SPSS/WIN 10.0 program. The major findings obtained in this study are as follows: First, customer demands were affected mostly by educational level among socio-demographic variables. Second, customer demand for service quality was not fully being met, considering customer satisfaction level. Third, employee practice was generally above customer satisfaction. Fourth, One of the variables that chiefly affect customer satisfaction was demand for information and facilities.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.51
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• pp.29-40
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• 1999

The distribution center location and routing problem involves interdependent decisions among facility, transportation, and inventory decisions. The design of distribution system affects the customers' purchase decision by sets the level of customer service to be offered. Thus the lower product availability may cause a loss of demand as falls off the customers' purchase intention, and this is related to the firm's profit reduction. This study considers the product availability of the distribution centers as the measure of the demand level change of the demand points, and represents relation between customer service and demand level with linear demand function. And this study represents the distribution center location and routing to demand point in order to maximize the total profit that considers the products' sales revenue by customer service, the production cost and the distribution system related costs.

• Journal of the Korea Safety Management & Science
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• v.6 no.3
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• pp.131-140
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• 2004

The company was focusing on production which was partial mission rather than acquiring the information of customer in intensive process industry. The company accepted loss which is from over-production, losing of opportunity. After changing to web environment, supply chain is more complicated and need of customer is more various. As a result the company hard works on controlling production rates, production quantities in production area and gathering exact information which is about available resource and available quantities. Cooperated demand planning have to get decreasing of inventory, improving of customer service in supply chain management. Specially demand planning that considers allocation of capacity is executed in Iron-Industry. Demand planning must be classified by customer, region and supply position level.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.8
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• pp.1342-1348
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• 2008

One important objective of the electricity market is to decrease the price by ensuring stability in the market operation. Interconnected to this is another objective; namely, to realize sustainable consumption of electricity by equitably distributing the effects and benefits of participating in the market among all participants of the industry. One method that can help achieve these objectives is the ^{(R)}$demand-response program, - which allows for active adjustment of the loadage from the demand side in response to the price. The demand-response program requires a customer baseline load (CBL), a criterion of calculating the success of decreases in demand. This study was conducted in order to calculate undistorted CBL by analyzing the correlations between such external or seasonal factors as temperature, humidity, and discomfort indices and the amounts of electricity consumed. The method and findings of this study are accordingly explicated.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.1
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• pp.21-28
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• 2013

The paper proposes an optimal sizing method of a customer's battery energy storage system (BESS) which aims at managing the electricity demand of the customer to minimize electricity cost under the time of use(TOU) pricing. Peak load limit of the customer and charging and discharging schedules of the BESS are optimized on annual basis to minimize annual electricity cost, which consists of peak load related basic cost and actual usage cost. The optimal scheduling is used to assess the maximum cost savings for all sets of candidate capacities of BESS. An optimal size of BESS is determined from the cost saving curves via capacity of BESS. Case study uses real data from an apartment-type factory customer and shows how the proposed method can be employed to optimally design the size of BESS for customer demand management.

• Journal of the Korea Safety Management & Science
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• v.16 no.4
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• pp.323-330
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• 2014

Various inventory control theories have tried to modelling and analyzing supply chains by using quantitative methods and characterization of optimal control policies. However, despite of various efforts in this research filed, the existing models cannot afford to be applied to the realistic problems. The most unrealistic assumption for these models is customer demand. Most of previous researches assume that the customer demand is stationary with a known distribution, whereas, in reality, the customer demand is not known a priori and changes over time. In this paper, we propose a reinforcement learning based adaptive echelon base-stock inventory control policy for a multi-stage, serial supply chain with non-stationary customer demand under the service level constraint. Using various simulation experiments, we prove that the proposed inventory control policy can meet the target service level quite well under various experimental environments.