• 한국신뢰성학회지:신뢰성응용연구
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• 제3권1호
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• pp.13-40
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• 2003

The seasonal adjustment is an essential process in analyzing the time series of economy and business. One of the powerful adjustment methods is X11-ARIMA Model which is popularly used in Korea. This method was delivered from Canada. However, this model has been developed to be appropriate for Canadian and American environment. Therefore, we need to review whether the X11-ARIMA Model could be used properly in Korea. In this study, we have applied the method to the annual sales of refrigerator sales in A electronic company. We appreciated the adjustment by result analyzing the time series components such as seasonal component, trend-cycle component, and irregular component, with the proposed method. Additionally, in order to improve the result of seasonal adjusted time series, we suggest the demand forecasting method base on autocorrelation and seasonality with the X11-ARIMA PROC.

• 산학경영연구
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• 제4권
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• pp.281-295
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• 1991

Nothing is more incorrect than forecasting. Nevertheless, forecasting is one of the most important business activities for the effective management. There has been rapid changes of the growth rate in every respect of the Korean hospitaity industry, especially the hotel industry, before and after the 88 Olympic Games. Therefore, the hoteliers shall be in need of more-than-ever accourate demand forecasting for the more systematic management and control. Under the above circumstances, this study suggested the best forecasting technique and method for the better sales and operations of the hotel rooms. The number of rooms sold is selected as a dependent variable of this study which is regarded as the best representative factor of measuring the growth rate of the rooms division performance of the hotels. The first step was to select the most verifiable independent variable diferently from the other countries or other areas of Korea. As a result, the number of foreign visitors was chosen. Empirical research, i.e. correlation and multiple regression analysis, shows that this independent variable has a strong relationship with the dependent variable told above. The second procedure was to estimate the number of rooms will be sold in 1991 on the basis of the formula calculated through the multiple regression analysis. Time series technique was conducted using the data of the number of foreign visitors by purpose of travel from 1987 to 1990. For the more correct forecasting, however, it would be desirable to adopt the data from 1989 considering the product or the industry life cycle. In addition, deeper analysis for the monthly or seasonal forecasting method is needed as a future research.

• 한국건축시공학회지
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• 제19권5호
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• pp.439-448
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• 2019

아파트 프로젝트는 사업성패에 영향을 미치는 요인은 매우 다양하다. 그러나 분양단가가 결정되고, 분양이 시작된 이후에는 사업에 가장 큰 영향을 미치는 요인은 분양 후 1개월간의 초기분양률이다. 일반적으로 사업주체는 경제상황, 주택시장의 동향, 사업지 인근의 주택가격 등 다양한 자료에 의해 초기분양률을 예측한다. 그러나 이러한 요인들을 초기분양률과 연계하여 정량적으로 계산하는 것은 매우 어렵다. 따라서 본 연구는 실제 수행된 아파트 프로젝트의 분양결과 자료를 이용하여 초기분양률 예측 회귀모델을 제안하는 것을 목적으로 한다. 본 연구를 위해 기존 분양실적자료 수집, 영향요인들의 상관관계 분석, 초기분양률 예측 회귀모델 작성을 진행한다. 본 연구의 결과는 아파트 프로젝트의 사업성 분석 시 초기 분양률 예측을 위한 기초자료로 활용되며, 사업 리스크 모델 개발의 핵심 자료로 활용된다.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2006년도 추계학술대회
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• pp.161-164
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• 2006

The sales of consumer durables are composed of first time purchases and replacement purchases. Since the sales for most mature durable products are dominated by replacement sales, it is necessary to develop a model incorporating replacement component of sales in order to forecast total sales accurately. Several single product diffusion models incorporating replacement demand have been developed, but research addressing the multi-product diffusion models has not considered replacement sales. In this paper, we propose a model based on consumer choice behavior that simultaneously captures the diffusion and the replacement process for multi-product relationships. The proposed model enables the division of replacement sales into repurchase by previous users and transition purchase by users of different products. As a result, the model allows the partitioning of the total sales according to the customer groups (first-time buyers, repurchase buyers, and transition buyers), which allows companies to develop their production and marketing plans based on their customer mix. We apply the proposed model to the Korean automobile market, and compare the fitting and forecasting performance with other Bass-type multi-product models.

• 유통과학연구
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• 제10권11호
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• pp.31-37
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• 2012

Purpose - This study was conducted under the assumption that brand A, a store of company Z of Pangyo, with a new store at Pangyo station is targeting the Bundang-gu area of the newly developed city of Seongnam. Research design, data, methodology - As a result of demand forecasting using geometric series models, an extrapolation of past trends provided the coefficient estimates, without utilizing regression analysis on a constant increase in children's wear, for which the population size and estimated parameter were required. Results - Demand forecasting on the basis of past trends indicates the likelihood that sales of discount stores in the Bundang area, where brand A currently has a presence, would fetch a higher estimated value than that of the average discount store in the country during 2015. If past trends persist, future sales of operational stores are likely to increase. Conclusions - In evaluating location using the simple weighting model, Seohyun Lotte Mart obtained a high rating amongst new stores in Pangyo, on the basis of accessibility, demand class, and existing stores. Therefore, when opening a new counter at a relevant store, a positive effect can be predicted.

• 산업공학
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• 제16권4호
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• pp.400-410
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• 2003

Most of the demand forecasting which have been studied is about long-term time series over 15 years demand forecasting. In this paper, we set up the most optimal ARIMA model for the short-term time series demand forecasting and suggest demand forecasting system for short-term time series by appraising suitability and predictability. We are going to use the univariate ARIMA model in parallel with the bivariate transfer function model to improve the accuracy of forecasting. We also analyze the effect of advertisement cost, scale of branch stores, and number of clerk on the establishment of marketing policy by applying statistical methods. After then we are going to show you customer's needs, which are number of buying products. We have applied this method to forecast the annual sales of refrigerator in four branch stores of A company.

• 통상정보연구
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• 제16권3호
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• pp.73-96
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• 2014

본 연구는 우리나라 수출 상위 5개 품목 중 하나인 자동차 수출을 대상으로, 승용차 브랜드별 단기 수출수요에 영향을 미치는 이론적 잠재요인을 발굴 및 설계하여 이론적 수출수요예측모델을 개발하고, 다변량시계열분석 기반의 VAR(Vector Auto Regressive)모형을 이용한 실증분석을 통해 개별상품과 시장특성이 반영된 단기수출수요예측모델을 검정하고자 하였다. 따라서 미국에 수출되고 있는 우리나라 소형 승용차 2개 브랜드(엑센트, 아반떼)에 대해 VAR모형을 이용한 분기단위 단기수요예측모델을 개발하고, 브랜드별 예측모델을 통해 산출된 t+1분기 시점의 예측값과 실제 판매된 판매대수를 대상기간을 1분기씩 달리하여 비교평가 하였다. 그 결과 엑센트와 아반떼의 RMSE %는 각각 4.3%와 20.0%로 났으며, 일평균 판매량을 기준으로 보았을 때 엑센트는 3.9일에 해당하고 아반떼는 18.4일에 해당하는 물량임을 알 수 있었다. 따라서 본 연구의 단기수출수요예측모델은 예측력과 검정시점별 일관성 측면에서 활용성이 높은 것으로 평가할 수 있었다.

• 한국산학기술학회논문지
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• 제17권6호
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• pp.157-165
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• 2016

하이테크 제품 중에서 이동통신 단말기는 빠른 속도로 혁신이 이루어지고 있으며 이에 따라 제품수명주기도 짧아지고 있다. 이렇게 짧아진 제품수명주기를 정확히 예측하기 위해서는 정확한 수요예측방법론의 선택이 중요하며 이는 전략적 경영계획 수립에 가장 기본적인 요소라고 할 수 있다. 본 연구의 목적은 이동통신 단말기의 전체 확산 수명에 적용될 수 있는 최적의 모형을 제시하는 것이다. 우리는 2013년 3월부터 2014년 8월까지 국내 특정 이동통신 서비스 사업자의 이동통신 단말기 판매 데이터를 활용하여 이동통신 단말기의 판매추이 및 수요예측을 위한 최적의 모형을 제시하고자 한다. 본 연구에서는 네 가지 모형의 성능을 비교분석하였는데 두 가지 S자형 확산모형인 Gompertz와 logistic 모형, 두 가지 비선형 회귀모형인 Michaelis-Menten과 logarithmic 모형을 비교한다. 모형 적합도에 따르면 logistic 모형이 모형일치성에 있어서 다른 세 개의 모형보다 성능이 우수한 것으로 발견되었으며 수요예측모델로는 확산이 정체하기 전까지는 logistic 모형이 우수하며 포화단계에 근접할수록 Gompertz 모형이 적합한 것으로 나타났다. 이러한 분석결과는 이동통신 단말기 시장 규모를 추정하거나 이동통신 단말기의 재고 및 주문관리를 하는데 있어서 유용한 자료로 활용될 수 있을 것이다.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 1993년도 춘계공동학술대회 발표논문 및 초록집; 계명대학교, 대구; 30 Apr.-1 May 1993
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• pp.437-437
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• 1993

• 산업경영시스템학회지
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• 제33권3호
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• pp.10-21
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• 2010

Promotion system can be used as strategical management weapon to enhance the sales power. Planned order system has some similarities with promotion system to create purchasing power and to supply the service parts with low price on purpose. The only difference is whether it is prearranged event or not. The effectiveness of forecasting has increased with normal state of ordering process. However, the accuracy of forecasting has diminished with irregular state of ordering, such as demand occurrences by unexpected climate change or intended planned order by the company. A planned order system is examined through the process of computing the effectiveness on the basis of forecasting in this paper. And it is suggested that how to increase the accuracy of forecasting capability under the planned order system.