• The Research Journal of the Costume Culture
• /
• v.25 no.2
• /
• pp.117-129
• /
• 2017

The purpose of this study was to investigate meteorological factors' effects on clothing sales based on empirical data from a leading apparel company. The daily sales data were aggregated from "A" company's store records for the Fall/Winter season from 2012 to 2015. Daily weather data corresponding to sales volume data were collected from the Korea Meteorological Administration. The weekend effect and meteorological factors including temperature, wind, humidity, rainfall, fine dust, sea level pressure, and sunshine hours were selected as independent variables to calculate their effects on A company's apparel sales volume. The analysis used a SAS program including correlation analysis, t-test, and multiple-regression analysis. The study results were: First, the weekend effect was the most influential factor affecting sales volume, followed by fine dust and temperature. Second, there were significant differences in the independent variables'effects on sales volume according to the garments' classification. Third, temperature significantly affected outer garments'sales volume, while top garments' sales volume was not influenced significantly. Fourth, humidity, sea level pressure and sunshine affected sales volume partly according to the garments' item. This study can provide proof of significant relationships between meteorological factors and the sales volume of garments, which will serve well to establish better inventory strategies.

• Fashion & Textile Research Journal
• /
• v.19 no.4
• /
• pp.434-447
• /
• 2017

This study investigated the effects of influencing factors on the sales volume of apparel products. Based on previous studies, weekend effect, discount rate, and meteorological factors including daily average temperature, rainfall, sea level pressure, and fine dust were selected as independent variables to calculate their effects on sales quantity of apparel products. The daily sales data during 2015 - 2016 were collected from casual brands and outdoor brands which "A" apparel manufacturing company had operated. The actual data of "A" company were analyzed using SAS(R) 9.4 and SAS(R) Enterprise Miner 14.1. The results of this study were as follows: First, the influencing factors on total sales volume of apparel products were proved to be the weekend effect, discount rate, and fine dust. Second, the analysis of influencing factors on sales volume of apparel products according to season showed: 1) In casual brands, the average temperature had a significant influence on the sales volume of spring/summer products, and the sea level pressure affected the sales volume of summer/fall/winter products significantly. 2) In outdoor brands, the average temperature and the fine dust had a significant influence on the sales volume of all season's products. The sea level pressure affected the sales volume of summer/fall/ winter products significantly. The weekend effect and the discount effect affected the sales volume of apparel products partly. Third, the effect of rainfall was not proven significant, which was different from the results of past studies.

• The Journal of Bigdata
• /
• v.4 no.1
• /
• pp.29-38
• /
• 2019

In this paper, we propose a sales forecasting model that forecasts the sales volume of short sleeves and outerwear according to the temperature change by utilizing accumulated big data from the online shopping mall 'A' over the past five years to increase sales volume and efficient inventory management. The proposed model predicts sales of short sleeves and outerwear according to temperature changes in 2018 by analyzing sales volume of short sleeves and outerwear from 2014 to 2017. Using the proposed sales forecasting model, we compared the sales forecasts of 2018 with the actual sales volume and found that the error rates are ±1.5% and ±8% for short sleeve and outerwear respectively.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.46 no.5
• /
• 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.

• Journal of Global Scholars of Marketing Science
• /
• v.16 no.1
• /
• pp.117-141
• /
• 2006

Daily sales volume in retailers seems to be changed because of five-days-work in a week resulting in growth of leisure time in Korea recently. The day of the week effect of sales volume that can not be theoretically explained, which sales pattern varies depending on days systematically and consistently, is so important. Especially the day of the week effect of sales volume exists in which retail branch and the extents of the day of the week effect from the perspective of marketing in retailing is very important. Thus, the purpose of this study was to investigate whether the day of the week effect of sales volume exists in men's clothing retailers and if so, there is difference in daily sales volume. There was insufficient researches in the field of anomaly such as the day of the week effect of sales volume in marketing. For this reason, this study has drawn upon research findings of finance, general demand theory, and previous studies of the day of the week effect in stock markets. In doing so, these works are referenced in theoretical background and applicability in retailing market of this study. This study empirically investigated the day of the week effect of sales volume through the revenues of a men's clothing retailers (P company) in past five years. As the result of this study, the day of the week effect of sales volume existed in men's clothing retailers and the day of the week effect showed positive from Monday to Sunday, which means Sunday, the biggest. Also, the day of the week effect by season was different. The result of this study is expected to provide some helpful evidence that offers effective operational strategies to retailers.

• Journal of the Korea Fashion and Costume Design Association
• /
• v.16 no.1
• /
• pp.41-52
• /
• 2014

The purposes of this study were to explore differences in utilization of information sources depending on the length of buyers' career and annual sales volume of stores where buyers work for. The questionnaire was prepared by the researcher and was answered by 200 buyers who purchase their items from Dondaemun market. The researcher analyzed the data using both ANOVA and Tukey's test as a post-hoc test. The conclusion of this study is summarized below. First, there were significant differences in utilization of information sources among buyer groups depending on the length of buyers' career. The buyers with more than 10 years career showed more effective utilization of information source such as resident buying offices, manufacturers, trade publications, trade associations, fashion reports, celebrities, window shopping, professional magazines, and advice from others. Second, there were significant differences in utilization of information sources among buyer groups depending on annual sales volume of the stores where the buyers work for. The buyer who work for the store with its annual sales volume in excess of 2 billion won showed more effective utilization of information source such as trade association, professional magazines, sales record, want slips, advertising results, sales trends, customer surveys, sales meetings, customer advisory panel, in-store merchandising bureau and advice from other experienced buyers. However, buyers of the store with its annual sales volume lower than 100 million won showed different pattern utilization of information sources such as vendors, trade publication, celebrities and advice from others.

• The Journal of Bigdata
• /
• v.4 no.2
• /
• pp.85-91
• /
• 2019

Since online shopping has become common, people can easily buy fashion goods anytime, anywhere. Therefore, consumers quickly respond to various environmental variables such as weather and sales prices. Thus, utilizing big data for efficient inventory management has become very important in the fashion industry. In this paper, the changes in sales volume of fashion goods due to changes in temperature is analyzed via the proposed big data analysis algorithm by utilizing actual big data from Korean fashion company 'B'. According to the analytic results, the proposed big data analysis algorithm found both expected and unexpected changes in sales volume depending on the characteristics of the fashion goods.

• The Journal of Fisheries Business Administration
• /
• v.55 no.2
• /
• pp.17-37
• /
• 2024

In this study, the effect of Distribution efficiency through the fishery production base distribution center (FPC) on the production site board facility was studied. FPC is a new distribution system for Korean fishery products that has been promoted in earnest since 2012, and in this study, the effect before and after the introduction of FPC was analyzed using the DID (Difference in Difference) model for the effect of FPC in the fishery industry. The results of analysis shows that in the case of Wando Geumil FPC, the volume and unit price of consignment sales decreased during the analysis period, which was statistically significant. In the case of Sokcho FPC, the volume of consignment sales decreased during the analysis period, which was statistically insignificant. But the unit price of consignment sales rose during the analysis period, which was statistically significant. In the case of Gyeongju FPC, the volume of consignment sales increased during the analysis period, which was statistically significant at the 90% confidence level. But the unit price of consignment sales fell during the analysis period, which was statistically significant.

• Journal of the Korea Society of Computer and Information
• /
• v.15 no.7
• /
• pp.109-116
• /
• 2010

In this paper, we analyse empirically the relation between the IT competency of the Group enterprises, and the management performance of the subsidiary IT service firms in Korea. The 24 IT service firms and relative Group enterprises are included for analysis. The various hypotheses established are validated by t-test method statistically. The results shows that IT competency level of the Group enterprises was affected by not only their sales volumes, new investment volume of IT service, but system management(SM) and inner captive market's sales volume of subsidiary IT service firms also. Additionally the sales volume of IT service firms was affected positively by their inner-captive market's sales, system management sales volume, and the sales volume of their Group enterprise. The service competency of the subsidiary IT service firms is shown as more high in the lower intensity of outer-captive and system integration(SI) business, as well as in the upper intensity of inner-captive and SI business.

• Journal of Korean Institute of Industrial Engineers
• /
• v.26 no.1
• /
• pp.66-72
• /
• 2000

Inventory management of a product not to be produced any more has a great impact on the financial status of a company. Clearance pricing can make bigger sales volume together with great savings of inventory holding cost specially for a durable goods with relatively large inventory carrying cost and accordingly cash inflow can be improved. This paper deals with the inventory management by non-linear clearance pricing with the sales rate which depends on the accumulated sales volume and selling price.