Data Analysis of Shipment for Textiles and Apparel from Logistics Warehouse to Store Considering Disposal Risk

Rina Tanaka, Aya Ishigaki, Tomomichi Suzuki, Masato Hamada and Wataru Kawai
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Rina Tanaka: Industrial Administration, Tokyo University of Science, Noda, Chiba 278-8510, Japan
Aya Ishigaki: Industrial Administration, Tokyo University of Science, Noda, Chiba 278-8510, Japan
Tomomichi Suzuki: Industrial Administration, Tokyo University of Science, Noda, Chiba 278-8510, Japan
Masato Hamada: Data-Chef Co., Ltd., Koto-ku, Tokyo 135-0004, Japan
Wataru Kawai: Data-Chef Co., Ltd., Koto-ku, Tokyo 135-0004, Japan

Sustainability, 2019, vol. 11, issue 1, 1-14

Abstract: Given the rapid diversification of products in the textile and apparel industry, manufacturers face significant new challenges in production. The life cycle of apparel products has contracted and is now, generally, a several-week season, during which time a majority of products are supposed to be sold. Products that do not sell well may be sold at a price lower than the fixed price, and products that do not sell at all within the sales period may eventually become forced disposal. This creates long-term management and environmental problems. In practice, shipping personnel determine when to ship products to stores after reviewing product sales information. However, they may not schedule or structure these shipments properly because they cannot effectively monitor sales for a large number of products. In this paper, shipment is considered to reduce the risk of product disposal on the premise of selling at a fixed price. Although shipment quantities are determined by various factors, we only consider the change in inventory at the logistics warehouse, since it is difficult to incorporate all factors into the analysis. From cluster analysis, it is found that shipping personnel should recognize a policy to sell products gradually over time. Furthermore, to reduce the risk of disposal, we forecast the inventory from conditional probability and are able to extract products out of a standard grouping using past data.

Keywords: apparel products; supply chain management; quick response; clustering; forecasting; sale at fixed price (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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