DOI QR코드

DOI QR Code

중소기업 제조공장의 수요예측 기반 재고관리 모델의 효용성 평가

Effectiveness Evaluation of Demand Forecasting Based Inventory Management Model for SME Manufacturing Factory

  • 김정아 (성균관대학교 스마트팩토리융합학과) ;
  • 정종필 (성균관대학교 스마트팩토리융합학과) ;
  • 이태현 (마크에이트(주)) ;
  • 배상민 (마크에이트(주))
  • 투고 : 2018.02.04
  • 심사 : 2018.04.06
  • 발행 : 2018.04.30

초록

다품종 소량생산체제인 중소기업 제조공장은 고객의 니즈를 대응하기 위해 제품을 대량생산하여 판매하는 형태이다. 이는 기업이 재고 부족에 따른 손실을 줄이기 위해 과도한 양의 자재 수급을 의미하고 높은 재고 유지비용이 발생한다. 그리고 수요 대응에 실패한 제품은 관리 창고에 쌓여 있어 재고 보관비용이 발생하는 현실이다. 본 논문은 이러한 문제를 보완하기 위해 시계열 분석 기법인 ARIMA모형을 이용하여 계절적 요인과 같은 시간적인 변동성을 찾아 수요를 예측하고 이를 통해 경제적 주문량 모형 기반의 수요예측 모델을 개발하여 재고 부족 위험을 예방한다. 또한 시뮬레이션을 수행하여 개발 모델의 효용성 평가하고 향후 중소기업에 적용하여 개발 모델의 효과를 입증한다.

SMEs manufacturing Factory, which are small-scale production systems of various types, mass-produce and sell products in order to meet customer needs. This means that the company has an excessive amount of material supply to reduce the loss due to lack of inventory and high inventory maintenance cost. And the products that fail to respond to the demand are piled up in the management warehouse, which is the reality that the storage cost is incurred. To overcome this problem, this paper uses ARIMA model, a time series analysis technique, to predict demand in terms of seasonal factors. In this way, demand forecasting model based on economic order quantity model was developed to prevent stock shortage risk. Simulation is carried out to evaluate the effectiveness of the development model and to demonstrate the effectiveness of the development model as applied to SMEs in the future.

키워드

참고문헌

  1. J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, "Internet of Things (IoT): A vision architectural elements and future directions", Future Generation Computer Systems, Vol. 29, No. 7, pp. 1645-1660, 2013. DOI: https://doi.org/10.1016/j.future.2013.01.010
  2. Sang-bong Park, Jeong-hwa Heo, "Implementation and design of fuse controller using single wire serial communication", The Journal of the Institute of Internet, Broadcasting and Communication, Vol.15, No.6, pp.251-255, 2015. DOI: https://dx.doi.org/10.7236/JIIBC.2015.15.6.251
  3. Harris F.W, "How Many Parts to Make at Once", International Journal of Production Economics, Vol 155, pp. 8-11, 2014. DOI: https://dx.doi.org/10.1016/j.ijpe.2014.07.003
  4. Jiali Zhu, Toshiya Kaihara, Nobutada Fujil, Daisuke Kokuryo, and Swee Skuik, "Extended EOQ Model Considering Recycling, Repair and Reuse in Reverse Supply Chain with Two Types of Demand Fluctuation", International Symposium on Flexible Automation, August, 2016. DOI: https://dx.doi.org/10.1109/ISFA.2016.7790152
  5. Zhou Cheng, Leng Kaijun and Shi Wen, "An Improved EOQ Model for Fresh Agricultural Product Considering Fresh-degree Sensitive Demand and Carbon Emission", Advance Journal of Food Science and Technology, Vol. 11, No. 4, pp. 350-355, 2016. DOI: https://dx.doi.org/10.19026/ajfst.11.2422
  6. Shirajul Islam Ukil, Md. Sharif Uddin, "A Production Inventory Model of Constant Production Rate and Demand of Level Dependent Linear Trend", American Journal of Operations Research, Vol. 6, No. 1, pp. 61-70, 2016. DOI: http://dx.doi.org/10.4236/ajor.2016.61008
  7. Box, GEP, Jenkins, GM and Reinsel, "Time Series Analysis; Forecasting and Control", 4th Edition, Wiley, Oxford, 2008. DOI: http://doi.org/10.1002/9781118619193
  8. Mi-Rye Kim, In-Ho Cho, "Analysis of Operation Cost Savings Effects of Direct Delivery Logistics Strategy Considering Carbon Emission", Journal of the Korea Academia-Industrial cooperation Society, Vol. 18, No. 6 pp. 653-661, 2017. DOI: https://dx.doi.org/10.5762/KAIS.2017.18.6.653
  9. Sang-Rok Yoo, YoungSoo Park, Jung-Sik Jeong, Chu-lSeong Kim, and Jae-Yong Jeong, "A Forecast Method of Marine Traffic Volume through Time Series Analysis", Journal of the Korean Society of Marine Environment & Safety, Vol. 19, No. 6, pp. 612-620, 2013. DOI: http://dx.doi.org/10.7837/kosomes.2013.19.6.612
  10. Jin-Ho Jeon, Min-Soo Kim, "A Study on Prediction the Movement Pattern of Time Series Data using Information Criterion and Effective Data Length", The Journal of The Institute of Internet, Broadcasting and Communication, Vol. 13, No. 1, pp. 101-107, 2013. DOI: https://dx.doi.org/10.7236/JIIBC.2013.13.1.101
  11. Mohammad Sabar Jamil, Saiful Akbar, "Taxi Passenger Hotspot Predictionusing Automatic ARIMA Model", 3rd International Conference on Science in Information Technology (ICSITech), October, 2017 DOI: http://dx.doi.org/10.1109/ICSITech.2017.8257080
  12. Tara Ahmed Chawsheen, Mark Broom, "Seasonal time-series modeling and forecasting of monthly mean temperature for decision making in the Kurdistan Region of Iraq", Journal of Statistical Theory and Practice, Vol. 11, No. 4, pp. 604-633, 2017. DOI: https://doi.org/10.1080/15598608.2017.1292484
  13. Rana Sabeeh Abbood Alsudan, Jicheng Liu1, "The Use of Some of the Information Criterion in Determining the Best Model for Forecasting of Thalassemia Cases Depending on Iraqi Patient Data Using ARIMA Model", Journal of Applied Mathematics and Physics, Vol.5, No.3, pp.667-679, 2017. DOI: https://dx.doi.org/10.4236/jamp.2017.53056
  14. Makridakis, S, Wheelwrigt, S.C, and Hyndman, R.J, "Forecasting: Methods and Applications", 3rd Edition, John Wiley & Sons, New York. 1998.
  15. Woo-Kyun Gam, Dong-li Lee, "A Study of Forward Buying and Transfer Effect with Intervention ARIMA Model", Journal of Channel and Retailing, Vol. 22, No. 4, pp.1-22, 2017. DOI: https://dx.doi.org/10.17657/jcr.2017.10.31.1