• Journal of the Korean Society of Systems Engineering
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• v.19 no.2
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• pp.59-73
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• 2023

Due to the steady growth of the online shopping market and contact-free consumption, the volume of parcels in South Korea continues to increase. However, there is a lack of manpower for delivery workers to handle the growing parcel volume, leading to frequent accidents related to delivery work. As a result, the government and local authorities strive to enhance last-mile logistics efficiency. As one of these measures, unmanned parcel storage lockers are installed and utilized to handle last-mile deliveries. However, the existing parcel storage involves the inconvenience of couriers having to put each parcel in each locker, and this is somewhat insufficient to relieve the workload of delivery workers. In this study, we propose parcel storage devices that use active loading technology to minimize the workload of delivery workers, extract operation risk factors to apply this system to actual sites, and establish risk reduction methods based on the ALARP concept. Through this study, we have laid the groundwork for improving the safety of the system by identifying and proposing mitigation measures for the risk factors associated with the proposed parcel storage devices utilizing active loading technology. When applied in practical settings in the future, this foundation will contribute to the development of a more efficient and secure system. By applying the ALARP concept, a systems engineering technique used in this research, to the development and maintenance of storage devices leveraging active loading technology, it is thought to make the development process more systematic and structured. Furthermore, through the risk management of the proposed system, it is anticipated that a systematic approach to quality management can be employed to minimize defects and provide a stable system. This is expected to be more useful than the existing unmanned parcel storage devices.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.2
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• pp.155-167
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• 2024

Distribution and logistics industries contribute some of the biggest GDP(gross domestic product) in South Korea and the number of related companies are quarter of the total number of industries in the country. The number of retail tech companies are quickly increased due to the acceleration of the online and untact shopping trend. Furthermore, major distribution and logistics companies try to achieve integrated data management with the fulfillment process. In contrast, small and medium distribution companies still lack of the capacity and ability to develop digital innovation and smartization. Therefore, in this paper, a deep learning-based demand forecasting & recommendation model is proposed to improve business competitiveness. The proposed model is developed based on real sales transaction data to predict future demand for each product. The proposed model consists of six deep learning models, which are MLP(multi-layers perception), CNN(convolution neural network), RNN(recurrent neural network), LSTM(long short term memory), Conv1D-BiLSTM(convolution-long short term memory) for demand forecasting and collaborative filtering for the recommendation. Each model provides the best prediction result for each product and recommendation model can recommend best sales product among companies own sales list as well as competitor's item list. The proposed demand forecasting model is expected to improve the competitiveness of the small and medium-sized distribution and logistics industry.