Today's environment of enterprise is changing, They have to face customer' demands with the right product, the right service and supply them at the right time. And also cut down logistics and inventory cost and bring up the profit as much as they can. This means the change of putting enterprise first in importance to putting customer first importance. therefore to correspond to customer's demand, shorting lead time is becoming a essential condition. The answer to this changes of environment is supply chain management. In this paper, It consolidates the necessity on a LTV(Life Time Value) and analyzes data which is concerned of Customer Value. Under the these environments, defines the LTV(Life Time Value) rule that can improve the customer value. We solved this problems using AHP(Analytic Hierarchy Process) for consistency at relationship matrix, AHP(Analytic Hierarchy Process) is based on Saaty's consistency rate. If consistency rate is under 0.1 point, preference rate's weights are acceptable. This study develop a program for AHP weights and support Satty's consistency rate.

Today's environment of enterprise is changing. They have to face customer' demands with the right product, the right service and supply them at the right time. And also cut down logistics and inventory cost and bring up the profit as much as they can. This means the change of putting enterprise first in importance to putting customer first importance. therefore to correspond to customer's demand, shorting lead time is becoming a essential condition. The answer to this changes of environment is supply chain management. In the Supply chain, The ATP function doesn't only give customers to conformation of delivery. It can be used by the core function with ATP rule that can reconcile supplies and demands on the supply chain. Therefore We can be acquire the conformation about on the due date of supplier by using the ATP function of management about real and concurrent access on the supply chain, also decide the affect about product availability due to forecasting or customer's orders through the ATP. In this paper, It consolidates the necessity on a CTP and analyzes data which is concerned of ATP. Under the these environments, defines the ATP rule that can improve the customer value and data flow related the LTV(Life Time Value) and builds on a algorithm.

Today's environment of enterprise is changing. They have to face customer' demands with the right product, the right service and supply them at the right time. And also cut down logistics and inventory cost and bring up the profit as much as they can. This means the change of putting enterprise first in importance to putting customer first importance. therefore to correspond to customer's demand, shorting lead time is becoming a essential condition. The answer to this changes of environment is supply chain management. In this paper, It consolidates the necessity on a LTV(Life Time Value) and analyzes data which is concerned of Customer Value. Under the these environments, defines the LTV(Life Time Value) rule that can improve the customer value.

공급망상에서 ATP는 단순히 고객에게 납기를 확약해 주는 고객 서비스 기능이 아니다. 이것은 공급 망상에서 ATP Rule을 기반으로 하여 수요와 공급 일치에 도달하기 위한 핵심 기능이다 따라서 SCM Solution을 이용하여 공급 체인 전반에 걸쳐 제품 가용성에 대한 즉시 및 동시 엑세스를 관리하여 기업의 납기 일의 정확도에 대한 최고의 확신을 가져올 수 있으며, SCM Solution을 통해 주문이나 예측 수주로 인하여 새로운 수요가 제품 가용성에 미칠 영향을 결정할 수 있다. 본 논문에서는 공급체인 전체의 통합관리 솔루션을 통하여 SCM에 대한 필요성을 정리하고 ATP관련 데이터를 분석을 수행한다. 이 것을 바탕으로 고객관계 관리( Customer Relationship Management)와 연계하여 세부데이터의 흐름 및 고객 가치를 향상 할 수 있는 ATP Rule을 정의한 후, 본 논문에서는 공급망상에 ATP Rule을 적용하여 ATP관련 데이터를 유연성 있게 취합할 수 있는 방안과 분류체계를 제시한 다단계 ATP모형을 제시 한다.

Purpose - The purpose of this study was to investigate the effects of the chatbot's characteristics (ease of use, social presence, playfulness, usefulness) on service value, customer satisfaction and reuse intention when consumers purchased fashion products in the mobile shopping environments. Research design, data, and methodology – Data were collected from Korean consumers from ages 20 to 59 who have experienced using chatbot in a mobile shopping for fashion products. After a pilot survey to 53 customers, the preliminary questionnaire was revised for the final test, and the final questionnaire was administered to 1500 customers. Out of these, 300 were collected. After deleting 48 incomplete ones, 252 questionnaires were used in the statistical analysis. Frequency analysis and exploratory factor analysis using SPSS 23.0 and confirmatory factor analysis and structure equation analysis using AMOS 18.0 were employed for data analyses. Results - First, four factors were extracted for the chatbot's characteristics: ease of use, social presence, playfulness and usefulness. Second, regarding the effect of chatbot's characteristics on service value when purchasing fashion products in the mobile shopping environment, ease of use, playfulness and usefulness of chatbot significantly affected service value. Social presence did not have significant effects on service value. Third, in terms of the effect of the chatbot's characteristics on customer satisfaction when purchasing fashion products in the mobile shopping environment, social presence, playfulness and usefulness of chatbot significantly had an effect on customer satisfaction. Ease of use did not have a significant effect on customer satisfaction. Fourth, service value of chatbot when purchasing fashion products in mobile shopping environment was found to have an effect on customer satisfaction with chatbot. Fifth, service value of chatbot on reuse intention when purchasing fashion products in the mobile shopping environment was found to have an effect on reuse intention of chatbot. Sixth, customer satisfaction with chatbot had a significant impact on the reuse intention of the chatbot when purchasing fashion products in the mobile shopping environment. Conclusions - The present study provide dimensions on the chatbot’s characteristics and these may provide helpful data for further studies in this area and for marketers as well.