• The Journal of Bigdata
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• v.6 no.2
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• pp.17-28
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• 2021

Current development of technologies related to 4^th industrial revolution and the pandemic of COVID-19 lead the rapid expansion of e-marketplace. The level of competition among several companies gets increased by introducing different strategies. To cope with the current change in the market and satisfy the customers who request the better delivery service, the new concept, fulfillment, has been introduced. It makes the leadtime of process from order picking to delivery reduced and the efficiency improved. Still, the efficiency of operation in fulfillment centers constrains the service level of the entire delivery process. In order to solve this problem, several different approaches for demand forecasting and coordinating supplies using Bigdata, IoT and AI, which there exists the trivial limitations. Because it requires the most lead time for operation and leads the inefficiency the process from picking to packing the ordered items, the logistics service providers should try to automate this procedure. In this research, it has been proposed to develop the efficient plans to automate the process to move the ordered items from the location where it stores to stage for packing using AGV and AMR. The efficiency of automated devices depends on the number of items and total number of devices based on the demand. Therefore, the result of simulation based on several different scenarios has been analyzed. From the result of simulation, it is possible to identify the several factors which should be concerned for introducing the automated devices in the fulfillment centers. Also, it can be referred to make the optimal decisions based on the efficiency metrics.

• The Journal of Society for e-Business Studies
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• v.26 no.1
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• pp.67-78
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• 2021

The number of domestic e-commerce transactions has been breaking its own record by an annual average growth rate of over 20% based on volume for the past 5 years. Due to the rapid increase in e-commerce market, retail companies that have difficulty meeting consumers in person are in fierce competition to take the lead in the last mile service, which is the only point of contact with customers. Especially in the delivery area, where competition is most intense, the role of the fulfillment center is very important for service differentiation. It must be capable of fast product preparation ordered by consumers in accordance with the delivery service level. This study focuses on the order picking system for rapid order processing in the fulfillment center as an alternative for companies to gain competitive advantage in the e-commerce market. A mixed integer programming model was developed and implemented to optimize the stock replenishment in order picking facilities. The effectiveness was scientifically and objectively verified by simulation using the actual operation process and data.

• The Journal of Bigdata
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• v.6 no.1
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• pp.1-12
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• 2021

The rapid growth of mobile-based online shopping and the appearance of untact business initiated by COVID-19 has led to an explosive increase in demand for logistics services such as delivery services. In order to respond to the rapidly growing demand, most logistics and distribution companies are working to improve customer service levels through the establishment of a full-filament center in the city center. However, due to social factors such as high land prices and traffic congestion, it becomes more difficult to establish the logistics facilities in the city center. In this study, it has been proposed the way to choose the candidate locations for the shared distribution centers among the space nearby the tall-gate which can be idle after the smart tolling service is widely extended. In order to evaluate the candidate locations, it has been evaluated the centralities of all candidates using social network analysis (SNA). To understand the result considering the characteristics of centrality, the network structure was regenerated based on the distance and the traveling time, respectively. It is possible to refer the result of evaluation based on the cumulative relative importance to choose the best set of candidates.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1447-1452
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• 2022

Seoul Special City, one of the world's top 10 cities and Metro City, has traditional urban manufacturing industries such as printing, sewing, and mechanical metals. Small business owners in these manufacturing clusters have developed in the form of mutual assistance. Due to the nature of the agglomeration site, each process is handled by an individual company. It is difficult for relatively small business owners to prepare order processing services that provide real-time logistics movement information between processes. This paper collects and analyzes existing logistics data for smooth order and delivery of small business owners in package manufacturing and special printing fields We design an artificial intelligence Fulfillment Service Platform system with CRNN, k-NN, and ID3 Decision Tree Algorithm. Through this study, it is expected that it will greatly contribute to increasing sales and improving capabilities by allowing small business owners in integrated areas to use individual orders and delivery customized services through the Cloud network.