초록
중소기업(MSME)에서는 여전히 수기 재고 기록 및 경험적 발주 관행이 일반적이며, 이는 종종 부정확한 수요 예측, 과도한 재고 보유 비용 및 재고 부족으로 이어집니다. 본 연구는 의사결정 정확도와 비용 효율성을 향상시키기 위해 자기회귀 통합 이동평균(ARIMA) 예측 기법과 경제 주문량(EOQ) 모델을 통합한 웹 기반 재고 정보 시스템을 개발하고 평가한다. 이 시스템은 CodeIgniter 3과 MySQL을 사용하며, Python 기반의 시계열 예측 엔진을 통합하고 있다. 과거 판매 데이터는 ARIMA를 사용하여 모델링되었으며, 아카이케 정보 기준(AIC)과 베이지안 정보 기준(BIC)을 바탕으로 최적의 사양이 선정되었다. ARIMA(1,1,1) 모델은 평균 절대 백분율 오차(MAPE) 8.47%를 기록하여 운영 계획 수립에 있어 높은 예측 정확도를 보여주었습니다. 예측된 연간 수요는 EOQ(경제적 주문량) 프레임워크에 통합되어 최적 주문량, 재주문 시점(ROP), 확률적 안전 재고를 산정했습니다. 1년간의 비용 시뮬레이션 결과, EOQ 기반 정책이 기존 방식에 비해 총 재고 비용을 22.73% 절감한 것으로 나타났습니다. 블랙박스 테스트를 통한 기능 검증 결과, 지정된 요구 사항을 완벽하게 준수하는 것으로 확인되었습니다. 이러한 결과는 예측 분석을 전통적인 재고 최적화 기법과 통합하면 운영 효율성을 높이고 총 재고 비용을 절감할 수 있음을 보여줍니다. 이 시스템은 디지털 전환을 진행 중인 중소기업(MSME)을 위한 실용적이고 데이터 기반의 재고 관리 프레임워크를 제공합니다.
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