• 한국의류산업학회지
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• 제19권3호
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• pp.289-295
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• 2017

This study aims to develop clothing sales forecast system using weather information. As the annual temperature variation affects changes in daily sales of seasonal clothes, sales period can be predicted growth, peak and decline period by changes of temperature. From this perspective, we analyzed the correlation between temperature and sales. Moving average method was applied in order to indicate long-term trend of temperature and sales changes. 7-day moving average temperature at the start/end points of the growth, peak, and decline period of S/S clothing sales was calculated as a reference temperature for sales forecast. According to the 2013 data analysis results, when 7-day moving average temperature value becomes $4^{\circ}C$ or higher, the growth period of S/S clothing sales starts. The peak period of S/S clothing sales starts at $17^{\circ}C$, up to the highest temperature. When temperature drops below $21^{\circ}C$ after the peak temperature, the decline period of S/S clothing sales is over. The reference temperature was applied to 2014 temperature data to forecast sales period. Through comparing the forecasted sales periods with the actual sales data, validity of the sales forecast system has been verified. Finally this study proposes 'clothing sales forecast system using weather information' as the method of clothing sales forecast.

• Industrial Engineering and Management Systems
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• 제14권2호
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• pp.196-209
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• 2015

Retail sales forecast is a special area of forecasting. Its unique characteristics call for unique data models and treatment, and unique forecasting processes. In this paper, we will address lessons learned and challenges encountered in retail sales forecast from a practical and technical perspective. In particular, starting with the data models of retail sales data, we proceed to address issues existing in estimating and processing each component in the data model. We will discuss how to estimate the multi-seasonal cycles in retail sales data, and the limitations of the existing methodologies. In addition, we will talk about the distinction between business events and forecast events, the methodologies used in event detection and event effect estimation, and the difficulties in compound event detection and effect estimation. For each of the issues and challenges, we will present our solution strategy. Some of the solution strategies can be generalized and could be helpful in solving similar forecast problems in different areas.

• 한국의류학회지
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• 제46권5호
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• pp.868-889
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• 2022

Fashion companies are using a big data approach as a key strategic analysis to predict and forecast sales. This study investigated the effectiveness of the past sales, web search volume, information amount, brand promotion, and the advertising endorser on the sales forecasting model. The study conducted the autoregressive distributed lag (ARDL) time series model using the internal and external social big data of a national fashion brand. Results indicated that the brand's past sales, search volume, promotion, and amount of advertising endorser information amount significantly affected the sales forecast, whereas the brand's advertising endorser search volume and information amount did not significantly influence the sales forecast. Moreover, the brand's promotion had the highest correlation with sales forecasting. This study adds to information-searching behavior theory by measuring consumers' brand involvement. Last, this study provides digital marketers with implications for developing profitable marketing strategies on the basis of consumers' interest in the brand and advertising endorser.

• 유통과학연구
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• 제20권12호
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• pp.117-123
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• 2022

Purpose: This study investigates the relationship between earnings forecasts estimated from a cross-sectional earnings forecast model and firm characteristics such as firm size, sales volatility, and earnings volatility. Research design, data and methodology: The association between earnings forecasts and the aforementioned firm characteristics is examined using 214 firm-year observations with analyst following and 848 firm-year observations without analyst following for the period of 2011-2019. I estimate future earnings using a cross-sectional earnings forecast model, and then compare these model-based earnings forecasts with analysts' earnings forecasts in terms of forecast bias and forecast accuracy. The earnings forecast bias and accuracy are regressed on firm size, sales volatility, and earnings volatility. Results: For a sample with analyst following, I find that the model-based earnings forecasts are more accurate as the firm size is larger, whereas the analysts' earnings forecasts are less biased and more accurate as the firm size is larger. However, for a sample without analyst following, I find that the model-based earnings forecasts are more pessimistic and less accurate as firms' past earnings are more volatile. Conclusions: Although model-based earnings forecasts are useful for evaluating firms without analyst following, their accuracy depends on the firms' earnings volatility.

• 디지털융복합연구
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• 제18권8호
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• pp.169-177
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• 2020

본 연구의 목적은 비선형적인 특성과 시계열 특성을 가지고 있는 편의점 대표상품의 매출을 예측하는데 있다. 연구결과, '아이스 컵(Ice cup)'의 경우 3월부터 매출이 증가하여 여름인 7~8월에 가장 높은 값을 나타내고, 이후에는 매출이 떨어지는 계절성 패턴을 구현하였다. 한편 담배의 경우, 여름에 높은 매출을 기록하고 겨울에 낮은 매출을 기록하는 계절성을 나타냈으며, 미래 예측치도 하락하는 양상을 보였다. 본 연구의 학문적 시사점으로는 기존 연구에는 분석되지 않았던 편의점 재무성과 향상에 영향을 미치는 상위 매출상품에 집중하여 연구를 수행하여 미래 예측치를 제시하였다.

• 유통과학연구
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• 제14권11호
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• pp.83-90
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• 2016

Purpose - During the last two years, convenient stores (CS) are emerging as one of the most fast-growing retail trades in Korea. The goal of this work is to forecast and to analyze sales at CS using ARIMA-Intervention model (IM) and exponential smoothing method (ESM), together with sales at supermarkets in South Korea. Considering that two retail trades above are homogeneous and comparable in size and purchasing items on off-line distribution channel, individual behavior and characteristic can be detected and also relative superiority of future growth can be forecasted. In particular, the rapid growth of sales at CS is regarded as an everlasting external event, or step intervention, so that IM with season variation can be examined. At the same time, Winters ESM can be investigated as an alternative to seasonal ARIMA-IM, on the assumption that the underlying series shows exponentially decreasing weights over time. In case of sales at supermarkets, the marked intervention could not be found over the underlying periods, so that only Winters ESM is considered. Research Design, Data, and Methodology - The dataset of this research is obtained from Korean Statistical Information Service (1/2010~7/2016) and Survey of Service Trend of Korea Statistics Administration. This work is exploited time series analyses such as IM, ESM and model-fitting statistics by using TSPLOT, TSMODEL, EXSMOOTH, ARIMA and MODELFIT procedures in SPSS 23.0. Results - By applying seasonal ARIMA-Intervention model to sales at CS, the steep and persisting increase can be expected over the next one year. On the other hand, we expect the rate of sales growth of supermarkets to be lagging and tied up constantly in the next 2016 year. Conclusions - Based on 2017 one-year sales forecasts for CS and supermarkets, we can yield the useful information for the development of CS and also for all retail trades. Future study is needed to analyze sales of popular items individually such as tobacco, banana milk, soju and so on and to get segmented results. Furthermore, we can expand sales forecasts to other retail trades such as department stores, hypermarkets, non-store retailing, so that comprehensive diagnostics can be delivered in the future.

• 디지털융복합연구
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• 제19권1호
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• pp.67-75
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• 2021

국내 수입차량 판매 증가에도 불구하고 부품 물류센터의 판매 예측에 관한 연구는 매우 부족한 현실이다. 이러한 측면에서 본 연구는 부품 물류센터의 상위 판매 상품에 대한 판매 예측을 수행 하는 것을 연구의 목적으로 한다. 연구는 판매 예측에 대한 동적특성과 영향을 주는 변수의 인과관계 및 피드백 루프를 고려할 수 있는 시스템 다이내믹스 방법론을 도입하였다. 연구결과 'Oil'의 경우 시간이 지날수록 소모품 판매 수량이 증가하는 패턴을 보이고, MAPE을 실시한 결과 31.3%의 합리적 예측모델로 평가되었다. 상품 'Battery'의 경우 실제 데이터와 예측 데이터 모두 매년 10월을 기점으로 판매가 증가하여 12월에서 가장 높은 판매를 보이고 다음해 2월부터 감소하는 계절성 판매패턴을 보였다. 본 연구는 기존 연구에는 존재하지 않았던 특정 수입 자동차 부품 물류센터의 실제 데이터를 확보하고, 시스템 다이내믹스를 통하여 미래 판매 물동량 예측을 정량적으로 분석하여 제시하였다는 점에서 학문적 시사점을 갖는다.

• 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.

• 산업경영시스템학회지
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• 제1권1호
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• pp.55-65
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• 1978

• 한국의류산업학회지
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• 제23권4호
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• pp.480-490
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• 2021

Sales forecasting is crucial for many retail operations. For apparel retailers, accurate sales forecast for the next season is critical to properly manage inventory and plan their supply chains. The challenge in this increases because apparel products are always new for the next season, have numerous variations, short life cycles, long lead times, and seasonal trends. In this study, a sales forecasting model is proposed for apparel products using machine learning techniques. The sales data pertaining to outerwear items for four years were collected from a Korean sports brand and filtered with outliers. Subsequently, the data were standardized by removing the effects of exogenous variables. The sales patterns of outerwear items were clustered by applying K-means clustering, and outerwear attributes associated with the specific sales-pattern type were determined by using a decision tree classifier. Six types of sales pattern clusters were derived and classified using a hybrid model of clustering and decision tree algorithm, and finally, the relationship between outerwear attributes and sales patterns was revealed. Each sales pattern can be used to predict stock-keeping-unit-level sales based on item attributes.