• 한국경영과학회지
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• 제31권2호
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• pp.157-167
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• 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.

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

• 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.

• 지능정보연구
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• 제23권1호
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• pp.1-22
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• 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.

• Asia pacific journal of information systems
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• 제15권3호
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• pp.209-221
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• 2005

One of the most fundamental problems in business is to evaluate the value of each product. The difficulty is that the profit of one product not only comes from its own sales, but also its influence on the sales of other products, i.e., the "cross-selling effect". This study integrates a measure for cross selling and an algorithm for profit estimation. Sales transaction data and post sales survey data from on-line and off-line shopping mall is used to show the effectiveness of the method against other heuristic for profit estimation based on product-specific profitability. We show that with the use of the new method we are able to identify the cross-selling potential of each product and use the information for better product selection.

• 유통과학연구
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• 제15권6호
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• pp.77-96
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• 2017

Purpose - This study aims to develop correspondence strategies to the environment change in domestic retail store types. Recently, new types of retails have emerged in retail industries. Therefore, trade area platform has developed focusing on the speed of data, no longer trade area from district border. Besides, 'trade area smart' brings about change in retail types with the development of giga internet. Thus, context shopping is changing the way of consumers' purchase pattern through data capture, technology capability, and algorithm development. For these reasons, the sales estimation model has been shown to be flawed using the notion of former scale and time, and it is necessary to construct a new model. Research design, data, and methodology - This study focuses on measuring retail change in large multi-shopping mall for the outlook for retail industry and competition for trade area with the theoretical background understanding of retail store types and overall domestic retail conditions. The competition among retail store types are strong, whereas the borders among them are fading. There is a greater need to analyze on a new model because sales expectation can be hard to get with business area competition. For comprehensive research, therefore, the research method based on the statistical analysis was excluded, and field survey and literature investigation method were used to identify problems and propose an alternative. In research material, research fidelity has improved with complementing research data related with retail specialists' as well as department stores. Results - This study analyzed trade area survival and its pattern through sales estimation and empirical studies on trade areas. The sales estimation, based on Huff model system, counts the number of households shopping absorption expectation from trade areas. Based on the results, this paper estimated sales scale, and then deducted modified probability model. Conclusions - In times of retail store chain destruction and off-line store reorganization, modified Huff model has problems in estimating sales. Transformation probability model, supplemented by the existing problems, was analyzed to be more effective in competitiveness business condition. This study offers a viable alternative to figure out related trade areas' sale estimation by reconstructing new-modified probability model. As a result, the future task is to enlarge the borders from IT infrastructure with data and evidence based business into DT infrastructure.

• Communications for Statistical Applications and Methods
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• 제8권2호
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• pp.375-382
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• 2001

The agricultural machine estimated sales survey is a survey to estimate annual sales quantities of eight major agricultural machines such as tracter, combine, etc. The purpose of this study is to design a multipurpose sample for the agricultural machine estimated sales survey. Main achievements of this study are to present an efficient stratification criterion and to suggest a reasonable estimation method by using the concept of post-stratification.

• 유통과학연구
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• 제20권5호
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• pp.119-127
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• 2022

Purpose: The purpose of this study is to analyze the Cost-Effectiveness Analysis (CEA) of wholesale and retail brokerage businesses in Korea. And give managerial implications and contribute to academics. Research design, data and methodology: This research empirically analyzes the relationship between expenses and business performance. As for business performance, this research considered two financial performances; sales and profit. As for antecedent variables, this research measured three cost investment expenses; human resource management (HRM), marketing (MKT) and corporate social responsibility (CSR). This research used frequency analysis, correlation analysis, stepwise regression analysis and curve estimation analysis. Results: The result shows that HRM and CSR positive significant influence on sales yet marketing negatively significant influence on sales. And for profit, HRM and CSR give a positive significant influence. However, marketing's influence was not significant. According to curve estimation analysis, the relation between individual cost and performance, best functional relation was all quadratic functions. Some results show ∩ shape and others show shape. Conclusions: Based on this study result, implications for practical management to Wholesale and Retail Brokerage companies in Korea. And the contribution to academics is expected. Also, based on the limitation of this study, future research is suggested.

• 산업경영시스템학회지
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• 제46권2호
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• pp.176-182
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• 2023

With about 80% of the global economy expected to shift to the global market by 2030, exports of reverse direct purchase products, in which foreign consumers purchase products from online shopping malls in Korea, are growing 55% annually. As of 2021, sales of reverse direct purchases in South Korea increased 50.6% from the previous year, surpassing 40 million. In order for domestic SMEs(Small and medium sized enterprises) to enter overseas markets, it is important to come up with export strategies based on various market analysis information, but for domestic small and medium-sized sellers, entry barriers are high, such as lack of information on overseas markets and difficulty in selecting local preferred products and determining competitive sales prices. This study develops an AI-based product recommendation and sales price estimation model to collect and analyze global shopping malls and product trends to provide marketing information that presents promising and appropriate product sales prices to small and medium-sized sellers who have difficulty collecting global market information. The product recommendation model is based on the LTR (Learning To Rank) methodology. As a result of comparing performance with nDCG, the Pair-wise-based XGBoost-LambdaMART Model was measured to be excellent. The sales price estimation model uses a regression algorithm. According to the R-Squared value, the Light Gradient Boosting Machine performs best in this model.

• 한국산학기술학회논문지
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• 제15권5호
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• pp.3146-3152
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• 2014

본고는 경기지역 IT산업의 하도급거래에 대한 설문조사를 이용해 위탁기업(납품대상업체)과 수탁기업(납품업체)을 중심으로 순위로짓 및 회귀분석 기법을 적용하여 하도급거래 지배력 및 집중도 결정요인을 다각도로 분석하였다. 실증분석 결과, 첫째, 제조업 관련 기업일수록, 수출비중이 높을수록, 기업규모가 클수록 하도급 지배력이 큰 것으로 나타났으며, 본고의 주요한 관심변수인 기술집약도의 경우도 하도급 지배력의 크기에 비례하는 것으로 나타났다. 둘째, 수탁업체를 대상으로 한 하도급거래 집중도 분석 결과, 하도급거래 집중도가 높은 기업일수록 기술집약도가 낮은 것으로 나타났다. 셋째, 동일한 분석을 위탁업체에 적용한 결과, 하도급거래 집중도가 높은 기업일수록 기술집약도 또한 높아지는 것으로 나타나 수탁업체의 추정결과와 대조를 이루었다. 전반적인 실증분석 결과는 하도급거래 집중도가 높을수록 수탁기업의 매출액대비 연구개발 투자 비중이 축소되는 등 거래비용이론의 예측을 지지하지 않는 것으로 나타났다.