다품종 온라인 패션 상품 풀필먼트센터의 VAF를 적용한 피킹 동선 개선 사례연구
Improving Picking Routes in Multi-Item Online Fashion Fulfillment Centers Using VAF
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초록
COVID-19로 인해 온라인 풀필먼트센터 전환이 가속화되었고 일일 출고 운영량을 초과하여, 출고 지연으로 주문을 취소하는 등의 문제가 생기고 있다. 이를 해결하기 위해 출고 처리 향상을 위한 풀필먼트센터 내 운영 효율화가 굉장히 중요하며, 본 연구에서는 패션 풀필먼트센터의 데이터를 기반으로 VFA 분석기법을 활용하여 실제 작업자 운영 개선 효과를 증명하고자 한다. VFA 분석기법은 주문량(Volume), 주문 빈도(Frequency), 주문 내 상품 연관성(Association Rules)의 Data를 분석하여 풀필먼트센터 출고 운영의 효율화를 꾀하는 방법이다. 온라인 주문량(Volume)과 주문 빈도(Frequency)가 높은 상품을 구분하여 입고 시 재적치하면 피킹 작업자의 동선을 줄여 피킹 시간을 단축하는 효과가 있다. 다품종 온라인 상품 주문 특성상 합포 작업이 많고 패션 제품 수가 많아서 출고 작업에 업무 부하가 심한데, 머신러닝 분석 방법의 하나인 연관성 분석(Association Rules)을 이용하여 합포 작업자 운영 속도를 개선하였다. 주문 내 연관관계를 활용하여 합포 작업시간을 줄이고 전체 출고 운영 능력을 향상시킨다. 본 연구를 바탕으로 VFA 기법을 활용한 풀필먼트센터 운영 효율화를 정량적인 데이터로 증명하였고, 온라인 풀필먼트센터에서 인력 효율화 및 생산성 향상될 것으로 기대한다.
Abstract
In light of the unprecedented challenges posed by the COVID-19 pandemic, this paper addresses several critical issues within fulfillment centers, including an increased workload due to a backlog of unprocessed orders and order cancellations resulting from delayed shipments in daily shipment operations. The significant increase in the volume of orders requiring processing emphasizes the need to optimize operational efficiency within fulfillment centers to enhance the shipping process. This paper employs Volume, Frequency, and Association Rules(VFA) analysis techniques to demonstrate the positive impact of enhancing worker operations. VFA is a data analysis technique specifically tailored to bolster operational efficiency within fulfillment centers. It operates by examining data characteristics related to volume, frequency, and association rules. A key aspect of the VFA technique involves segregating products with high online order volumes and frequencies and strategically restocking them near the warehouse location. This strategic placement yields reduced picking times by minimizing the distance required for item retrieval. Consequently, by arranging ordered items in closer proximity within the warehouse, operational efficiency is heightened, leading to a decrease in work time. Furthermore, the study advocates the use of Association Rules, a machine learning analysis method, when handling orders consisting of multiple packages.
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