• Fashion & Textile Research Journal
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• v.23 no.4
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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.

• International Commerce and Information Review
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• v.16 no.3
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• pp.73-96
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• 2014

The purpose of this research is to evaluate a short-term export demand forecasting model reflecting individual passenger vehicle brands and market characteristics by using Vector Autoregressive (VAR) models that are based on multivariate time-series model. The short-term export demand forecasting model was created by discerning theoretical potential factors that affect the short-term export demand of individual passenger vehicle brands. Quarterly short-term export demand forecasting model for two Korean small vehicle brands (Accent and Avante) were created by using VAR model. Predictive value at t+1 quarter calculated with the forecasting models for each passenger vehicle brand and the actual amount of sales were compared and evaluated by altering subject period by one quarter. As a result, RMSE % of Accent and Avante was 4.3% and 20.0% respectively. They amount to 3.9 days for Accent and 18.4 days for Avante when calculated per daily sales amount. This shows that the short-term export demand forecasting model of this research is highly usable in terms of prediction and consistency.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.224-232
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• 2004

소매점에서 판매를 최대화하여 이익을 극대화하기 위해서는 투자비용을 최소화하고 재고를 최소로 유지하며, 결품을 최소화해서 매출을 보호해야 한다. 수요의 동향이 다양화 됨으로써 예측이 점점 어려워지고, 재고를 최소화하고, 빠른 납기를 충족시키고, 판매 기회의 손실을 최소화 하기란 점점 어려워지고 있다. 또한 매출의 보호와 재고의 축소는 서로 상반되는 내용을 담고 있다. 전통 Industrial Engineering(IE)에서 경제적 주문량(EOQ)을 결정하여 재고 회전율을 높이고 발주 비용과 재고 비용을 최소화하는 연구는 많이 다루어져 왔다. 본 논문에서는 TOC의 쓰루풋 증대의 관점에서 최적의 재고 보충 관리 방안을 제시하고자 한다.

• Defense and Technology
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• no.10 s.140
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• pp.64-75
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• 1990

1990년 3월 말레이지아의 쿠알라룸프르에서 열렸던 아시아지역 방산전시회 "DSA'90"은 대단한 성황을 이루었다. 주요 전시품목만 해도 2백30종 이상이었고, 28개국으로 부터 4백개 이상의 회사들이 참여하였으며, 고위급 공식 대표단들이 다녀가는등 참관자들의 수준 또한 높았다. 아세안(ASEAN) 국가지역에서의 항공우주 및 방위분야의 성장은 큰폭으로 계속되어서 향후 5년간 매년 약 1조4천억원 정도의 군수품 판매가 이곳에서 이루어질 것으로 예측하는 사람들이 많다. 그러나 이 지역에서의 직접판매는 어려울 것으로 보인다. 전시회에 출품했던 많은 회사들은 각국에서 공동생산을 같이 추진할만한 산업기반을 가진 회사들을 찾고 있다.

• Journal of KIISE
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• v.44 no.8
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• pp.803-812
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• 2017

T-commerce is technology-fusion service on which the user can purchase using data broadcasting technology based on bi-directional digital TVs. To achieve the best revenue under a limited environment in regard to the channel number and the variety of sales goods, organizing broadcast programs to maximize the expected sales considering the selling power of each product at each time slot. For this, this paper proposes a method to predict the sales of goods when it is assigned to each time slot. The proposed method predicts the sales of product at a time slot given the week-in-year and weather of the target day. Additionally, it combines a statistical predict model applying SVD (Singular Value Decomposition) to mitigate the sparsity problem caused by the bias in sales record. In experiments on the sales data of W-shopping, a T-commerce company, the proposed method showed NMAE (Normalized Mean Absolute Error) of 0.12 between the prediction and the actual sales, which confirms the effectiveness of the proposed method. The proposed method is practically applied to the T-commerce system of W-shopping and used for broadcasting organization.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.196-200
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• 2006

인터넷의 보급으로 인해 Tele Marketing과 Direct Marketing을 주로 하던 보험업의 시장 환경은 on-line판매라는 새로운 판매 채널을 크게 변하였다. 보험업의 경우 인터넷의 도입으로 인해 보험소비자는 가격 및 상품 정보를 보다 쉽게 획득할 수 있고, 비교 견적을 통하여 자신에게 보다 합리적인 의사결정을 할 수 있다. On-line 환경의 급진전에 따른 자동차 보험시장에서 후발주자에 해당하는 on-line 자동차 보험 회사의 급격한 고객 확대는 주목할 변화이다. 따라서 off-line 보험시장을 대체할 수 있는 on-line 보험의 성장 가능성 예측이 중요하다. 본 연구에서는 on-line 자동차 보험사에 대한 분석과 통계자료를 바탕으로 시스템 다이내믹스 기법을 이용하여 on-line 자동차 보험의 성장에 대해 분석 해 보았다.

• Proceedings of the CALSEC Conference
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• 2002.01a
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• pp.307-312
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• 2002

□가장 일상적으로 사용하는 체계 □상품의 모든 처리 단계에서 데이터의 색인을 구성 - 수요예측 - 주문서 작성 - 송장 -입고확인 -재고관리 -판매 □각 기업은 나름의 식별코드 □각 산업은 나름대로의 식별코드(중략)

• 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.

• Journal of Global Scholars of Marketing Science
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• v.12
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• pp.121-134
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• 2003

Weather marketing is firms' effort to incorporate changes of diverse weather factors into marketing planning and activities. The concept has already been applied in many products with mostly seasonal variation. However researches in this area have been limited only in practical areas and has not been supported by scientifIc approaches. Here, we investigated the effect of diverse weather factors like temperature, rain and wind on product sales based on empirical data and scientifIc methodology. For this, we selected the fashion clothing items in department stores. We tried to fInd the relationship between daily sales of clothing items and daily whether factors. Results showed that there is a meaningful relation between the two factors.

• Information Systems Review
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• v.10 no.2
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• pp.195-209
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• 2008

Customers' needs change every moment. Profitability of stores can't be increased anymore with an existing standardized chain store management. Accordingly, a personalized store management tool needs through prediction of customers' preference. In this study, we propose a recommending procedure using dynamic customers' preference by analyzing the transaction database. We utilize self-organizing map algorithm and association rule mining which are applied to cluster the chain stores and explore purchase sequence of customers. We demonstrate that the proposed methodology makes an effect on recommendation of products in the market which is characterized by a fast fashion and a short product life cycle.