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

• 한국경영과학회지
• /
• 제31권2호
• /
• pp.157-167
• /
• 2006

Estimating the effects of price increase on a company's sales is important task faced by managers. If consumer has prior information on price increase or expects it, there would be stockpiling and subsequent drops in sales. In addition, consumer can suppress demand in the short run. These factors make the sales dynamic and unstable. In this paper we develop a time series model to evaluate the sales patterns with stockpiling and short-term suppression of demand and also propose a forecasting procedure. For estimation, we use panel data and extend the model to Bayesian hierarchical structure. By borrowing strength across cross-sectional units, this estimation scheme gives more robust and reasonable result than one from the individual estimation. Furthermore, the proposed scheme yields improved predictive power in the forecasting of hold-out sample periods.

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

• 지능정보연구
• /
• 제23권1호
• /
• pp.1-22
• /
• 2017

사업타당성 분석이나 기업 기술가치평가 등 미래의 사업에 대한 진입이나 투자 타당성을 분석하기 위해서는 새로운 사업과 관련한 시장을 추정하고 그 안에서 확보 가능한 매출을 객관적으로 추정하는 과정이 필수 불가결하다. 이런 신규 매출이나 시장규모의 추정 방법은 다양한 방법으로 구분이 가능한데 크게 정량적인 방법과 정성적인 방법으로 구분할 수 있다. 그러나 두 가지 방법 모두 많은 자원과 시간을 필요로 한다. 그래서 우리는 신규 사업의 평가지원을 위한 데이터 기반의 지능형 매출 예측 시스템을 제안하고자 한다. 본 연구는 사업타당성 분석이나 기술가치평가를 위한 신규 사업의 매출 추정 시스템을 개발하는데, 알고리즘 기반으로 전통적인 정량 예측방법 중 하나인 유추방법에 주목했다. 동일한 국내 산업에서 최근 창업한 기업의 매출 실적을 국내 신규 사업의 매출액을 추정하는 유추 대상 변수로 활용할 수 있는지 검토한다. 여기서 유추예측 대상은 최초 매출액과 초기 성장률이며, 주요 비교 차원은 산업분류, 창업시기 등이 고려된다. 특히 본 연구는 우리나라 창업 기업이 가지는 매출 성장률의 평균회귀 현상을 활용하는 지능형 정보 지원 시스템을 제안하다. 본 연구에서는 신규 매출 추정을 위해서 역사적 자료인 창업 매출 실적을 활용하는 방법이 적절한지 판단하기 위해서 잠재성장모형 등을 활용해 산업분류에 따른 신규 사업의 초기 매출액과 연도별 성장률이 산업분류별로 차이가 있는지 분석한다. 기존 기업의 창업 후 4년간 매출 성과의 종단자료를 잠재성장모형으로 분석하는데, 특정 산업분류에서 차이를 보여주는지 분석해 산업분류가 유추 예측에서 고려해야할 유의미한 변수인지 분석하는 것이다. 본 연구의 결과는 신속하고 객관적인 신규 사업 매출 추정을 가능하게 하는 지능형 정보시스템을 개발하게 해서 사업성타당성 분석이나 기술가치평가 과정의 효율성을 개선시켜 줄 것으로 기대된다.

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

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제15권4호
• /
• pp.1232-1245
• /
• 2021

In this study, prediction of product sales as they relate to changes in temperature is proposed. This model uses long short-term memory (LSTM), which has shown excellent performance for time series predictions. For verification of the proposed sales prediction model, the sales of short pants, flip-flop sandals, and winter outerwear are predicted based on changes in temperature and time series sales data for clothing products collected from 2015 to 2019 (a total of 1,865 days). The sales predictions using the proposed model show increases in the sale of shorts and flip-flops as the temperature rises (a pattern similar to actual sales), while the sale of winter outerwear increases as the temperature decreases.

• 한국의류학회지
• /
• 제44권6호
• /
• pp.1053-1069
• /
• 2020

This study examined the sales pattern relationship with respect to product attributes to propose sales forecasting for fashion products. We analyzed 537 SKU sales data of T-shirts in the domestic sports brand using SAS program. The sales pattern of fashion products fluctuated and were influenced by exogenous factors; therefore, we removed the influence of exogenous factors found to be price discounts and holiday effects as a result of regression analysis. In addition, it was difficult to predict sales using the sales patterns of the same product since fashion products were released as new products every year. Therefore, the forecasting model was proposed using sales patterns of related product attributes when attributes were considered descriptive variables. We classified sales patterns using K-means clustering in order to explain the relationship between sales patterns and product attributes along with creating a decision tree classifier using attributes as input and sales patterns as output. As a result, the sales patterns of T-shirts were clustered into six types that featured the characteristic shape of peak and slope. It was also associated with the combination of product attributes and their values in regards to the proposed sales pattern prediction model.

• 한국빅데이터학회지
• /
• 제4권1호
• /
• pp.29-38
• /
• 2019

본 연구에서는 판매량 증대와 효율적인 재고 관리를 위해 지난 5년간 온라인 쇼핑몰 'A'에서 누적된 빅데이터를 활용하여 기온 변화에 따른 반팔 티셔츠와 아우터웨어(outer wear)의 판매량을 예측하는 판매 예측 모델을 제안한다. 제안한 모델은 2014년부터 2017년도까지 기온 변화에 따른 반팔 티셔츠와 아우터웨어의 판매량을 분석하여 2018년 기온 변화에 따른 반팔티셔츠와 아우터웨어의 판매량을 예측한다. 제안한 판매 예측 모델을 사용하여 반팔티셔츠와 아우터웨어의 판매량 예측값과 실제 2018년 판매량을 비교 분석한 결과 반팔티셔츠와 아우터웨어의 예측 오차율은 각각 ±1.5%와 ±8%를 나타내었다.

• 한국경영과학회:학술대회논문집
• /
• 한국경영과학회/대한산업공학회 2005년도 춘계공동학술대회 발표논문
• /
• pp.798-805
• /
• 2005

Estimating the effects of price increase on a company's sales is important task faced by managers. If consumer has prior information on price increase or expect it, there would be stockpiling and subsequent drops in sales. In addition, consumer can suppress demand in the short run. Above factors make the sales dynamic and unstable. We develop a time series model to evaluate the sales patterns with stockpiling and short term suppression of demand and also propose a forecasting procedure. For estimation, we use panel data and extend the model to Bayesian hierarchical structure. By borrowing strength across cross-sectional units, this estimation scheme gives more robust and reasonable result than one from the individual estimation. Furthermore, the proposed scheme yields improved predictive power in the forecasting of hold-out sample periods.

• Communications for Statistical Applications and Methods
• /
• 제19권6호
• /
• pp.849-858
• /
• 2012

Today, local environmental factors has an influence on our society. Local environmental factors, as well as weather-related natural phenomena, social phenomena are also included. In this paper, numeric factors and categorical factors were analyzed, looking for a local environmental factors affecting the company's sales.Sales model by performing a regression analysis based on this was implemented.Sales model considering the local environment had an accuracy of 88.89%.