• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.35-38
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• 1996

It's a rather difficult for new product launch strategy establishment to be settled down because we must know the correlation between the quantitative and the qualitative information. Therefore, we introduce you case-based reasoning system that use its correlation and new product launch's experience in the past. Using the real cases, this system evaluates the performance as we compare human expert's new product sales forecasting with system's.

• Korean Management Science Review
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• v.1
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• pp.35-45
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• 1984

This paper deals with the case study of the establishment of decision supporting system in shipbuilding industory. Facts or information of shipbuilding, sales, finance, production strategic planning in shipbuilding industry are considered. General transportation model for shipyard production schedule is formulated, and shipbuilding demand forecasting scheme is also introduced. This paper shows the several methods of DSS in shipbuilding industry. But production schedule strategic planning system by OR technique is emphasized. For the realization of DSS in shipbuilding industry, another efforts (data gathering and programming etc.) should be given on the basis of these methods.

• Journal of Korean Society for Quality Management
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• v.50 no.3
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• pp.349-371
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• 2022

Purpose: Compared to the rapid growth rate of the domestic automotive LED industry so far, the predictive analysis method for demand forecasting or market outlook was insufficient. Accordingly, product characteristics are analyzed through the life trend of LEDs for automotive exterior lamps and the relative strengths of p and q using the Bass model. Also, future demands are predicted. Methods: We used sales data of a leading company in domestic market of automotive LEDs. Considering the autocorrelation error term of this data, parameters m, p, and q were estimated through the modified estimation method of OLS and the NLS(Nonlinear Least Squares) method, and the optimal method was selected by comparing prediction error performance such as RMSE. Future annual demands and cumulative demands were predicted through the growth curve obtained from Bass-NLS model. In addition, various nonlinear growth curve models were applied to the data to compare the Bass-NLS model with potential market demand, and an optimal model was derived. Results: From the analysis, the parameter estimation results by Bass-NLS obtained m=1338.13, p=0.0026, q=0.3003. If the current trend continues, domestic automotive LED market is predicted to reach its maximum peak in 2021 and the maximum demand is $102.23M. Potential market demand was $1338.13M. In the nonlinear growth curve model analysis, the Gompertz model was selected as the optimal model, and the potential market size was $2864.018M. Conclusion: It is expected that the Bass-NLS method will be applied to LED sales data for automotive to find out the characteristics of the relative strength of q/p of products and to be used to predict current demand and future cumulative demand.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.2
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• pp.63-70
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• 2008

This paper resents the spatial electric load forecasting algorithm using the multiple regression analysis method which is enhanced from the algorithm of the DISPLAN(Distribution Information System PLAN). In order to improve the accuracy of the spatial electrical load forecasting, input variables are selected for GRDP(Gross Regional Domestic Product), the local population and the electrical load sales of the past year. Tests are performed to analyze the accuracy of the proposed method for Gyeong-San City, Gu-Mi City, Gim-Cheon City and Yeong-Ju City of North Gyeongsang Province. Test results show that the overall accuracy of the proposed method is improved the percentage error 11.2[%] over 12[%] of the DISPLAN. Specially, the accuracy is enhanced a lot in the case of high variability of input variables. The proposed method will be used to forecast local electric loads for the optimal investment of distribution systems.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.3
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• pp.109-115
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• 2019

Most demand forecasting studies for telecommunication services have focused on estimating market size at the introductory stage of new products or services, or on suggesting improvement methods of forecasting models. Although such studies forecast business growth and market sizes through demand forecasting for new technologies and overall demands in markets, they have not suggested more specific information like relative market share, customers' preferences on technologies or service, and potential sales power. This study focuses on the telecommunication service industry and explores ways to calculate the relative market shares between competitors, considering competitive situations at the introductory stage of a new mobile telecommunication service provider. To reflect the competitive characteristics of the telecommunication markets, suggested is an extended conjoint analysis using service coverage and service switching rates as modification variables. This study is considered to be able to provide strategic implications to businesses offering existing service and ones planning to launch new services. The result of analysis shows that the new service provider has the greatest market share at the competitive situation where the new service covers the whole country, offers about 50% of existing service price, and allows all cellphones except a few while the existing service carrier maintains its price and service and has no response to the new service introduction. This means that the market share of the new service provider soars when it is highly competitive with fast network speed and low price.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• Journal of Information Technology Services
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• v.15 no.3
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• pp.15-31
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• 2016

Sales Force Automation (SFA) is a strategic information system and one of the components of operational CRM system. SFA supports salespeople's activities such as selection of potential customers, creative value proposition, after-sales services, etc. SFA is increasingly used in many companies because it has the advantages to raise the salespeople's productivity by developing forecasting ability, value proposition ability, after sales service ability etc. Many researches have shown that implementation of SFA leads to the increase of salepeople performance, organizational performance, and quality of customer relationship. However, Some prior studies have discussed on the SFA implementation failure and pointed out that one of important causes of this failure is salespeople's resistance to SFA. Although many researches explain SFA acceptance phenomenon using Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB), these researches didn't deeply investigate the salespeople's resistance to SFA. Therefore, this study focuses on the factors influencing salespeople's resistance to SFA and the relationships among these factors. This study identified three factors (salespeople's perceived loss of power, perceived loss of autonomy, and perceived time and effort waste) influencing salespeople's resistance to SFA. The hypotheses testing results showed that salespeople's perceived loss of power and perceived time and effort waste significantly increased salespeople's resistance to SFA. And salespeople's perceived loss of power plays a mediating role between perceived loss of autonomy/perceived time and effort waste and salespeople's resistance to SFA. At the end of the paper, theoretical and managerial implications of this study and the limitations and future research directions are discussed.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.1116-1123
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• 2006

The methodological framework proposed in this paper addresses the strength of the applied Bass model by Mahajan and Muller(1996) that it reflects the substitution of next generations among products. Also this paper is to estimate and analyze the forecast of demand for products that do not exist in the marketplace. We forecast the sales of digital TV using estimated market share and data obtained by the face to face Interview. In this research, we use two methods to analyze the demand for Digital TV that are the forecasting the Demand for the Substitution and binary logit analysis. The logit analysis is to estimate the decisive factor of purchasing digital TV. The decisive factors are composed of purchasing plan, region, gender, TV price, contents, coverage, income, age, and TV program. We apply the model to South Korea's market for digital TV. The results show that (1) Income, region and TV price play a prominent part which is the decisive factor of purchasing digital TV. (2) We forecaste the demand of digital TV that will be demanded about 18 millions TVs in 2015