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.

1. 서 론
2. Literature Review
    2.1 Demand Forecasting
    2.2 Recommendation System
3. Demand Forecasting Method for Small& Medium Distribution Companies
4. Implementation and Experiment Result
    4.1 Sales Event Model Implementation and Result
    4.2 Re-categorization Model ImplementationResult
    4.3 Demand Forecasting Model Implementationand Experimental Result
    4.4 Recommendation Model Implementationand Experimental Result
5. Conclusion
Acknowledgement
References

• Sangil Lee(Digital Convergence Research Laboratory, Air Mobility Research Division, Postal & Logistics Technology Research Center, Electronics and Telecommunications Research Institute) | 이상일 (한국전자통신연구원 디지털융합연구소 에어모빌리티연구본부 우정, 물류기술연구센터)
• Yeong-WoongYu(Digital Convergence Research Laboratory, Air Mobility Research Division, Postal & Logistics Technology Research Center, Electronics and Telecommunications Research Institute) | 유영웅 (한국전자통신연구원 디지털융합연구소 에어모빌리티연구본부 우정, 물류기술연구센터)
• Dong-Gil Na(Digital Convergence Research Laboratory, Air Mobility Research Division, Postal & Logistics Technology Research Center, Electronics and Telecommunications Research Institute) | 나동길 (한국전자통신연구원 디지털융합연구소 에어모빌리티연구본부 우정, 물류기술연구센터) Corresponding author