메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색
질문

논문 기본 정보

자료유형
학술저널
저자정보
Tsuyoshi Kurihara (Certified and accredited meteorologist) Takaaki Kawanaka (The University of Tokyo) Hiroshi Yamashita (Meiji University)
저널정보
대한산업공학회 Industrial Engineering & Management Systems Industrial Engineering & Management Systems Vol.18 No.4
발행연도
2019.12
수록면
761 - 775 (15page)
DOI
10.7232/iems.2019.18.4.761

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색
질문

초록· 키워드

오류제보하기
In general, seasonally dependent products such as home air conditioners and beer are difficult to produce in a timely manner to respond to demand because of the large seasonal fluctuations in demand. However, if highly accurate demand analysis/forecast is possible, production preparation for responding to demand fluctuations will be easier. Therefore, as a basis for such a demand analysis/forecast, to analyze the whole (nationwide) demand for a seasonally dependent product, this paper proposes a new weighted additive model for the whole demand analysis of a seasonally dependent product, i.e., Japanese beer, using meteorological and regional data, considering “regional characteristics of climate” and “regional homogeneity of demand together,” with the further addition of social customs factors, policy factors, and a demand trend. Using the alternating least squares method, we attempt parameter estimation for the model with an inseparable parameter group generated by expressing “regional characteristics of climate” and “regional homogeneity of demand” together as a product of mutually independent factors (meteorological and regional factors). The results show that the proposed model is valid and provides insight into the effects of the factors influencing demand.

목차

ABSTRACT
1. INTRODUCTION
2. LITERATURE OVERVIEW
3. BEER DEMAND AND ITS FACTORS IN JAPAN
4. STUDY METHOD
5. EMPIRICAL ANALYSIS
6. CONCLUSION
REFERENCES

참고문헌 (8)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

최근 본 자료

전체보기

댓글(0)

0