Affiliations: [a]
School of Computer Engineering & Science, Shanghai University, Baoshan District, Shanghai, China
| [b]
Sydney Institute of Language and Commerce, Shanghai University, Jiading District, Shanghai, China
Correspondence:
[*]
Corresponding author. L. Yue, School of Computer Engineering & Science, Shanghai University, 99 Shangda Road,
Baoshan District, Shanghai 200444, China. Tel.: +86 138 1773 7563; E-mail: [email protected].
Abstract: Demand forecasting is one of the most essential components of supply chain management, which directly influences a company’s overall performance and competitiveness. However, it is difficult to accurately forecast the demand of fashion products with short life cycle and high volatility characteristics such as footwear and apparel products. An integrated demand forecasting method named Improved ABC-PF is proposed in this paper based on Product Life Cycle (PLC) theory considering the characteristics of fashion products. First, a PLC model based on cubic polynomial which is divided into two stages by the best-selling point, is established instead of traditional PLC modeling methods. Second, an improved Artificial Bee Colony (ABC) algorithm is utilized to optimize the parameters of the two-stage PLC function, which is conducted by initial population selection, optimization function design and convergence rate improvement. After that, an inventory control strategy based on PLC analysis is studied and applied in the “Precise Order” mode. Finally, the proposed method is validated by real-world data from a Chinese footwear and apparel retailer. After being compared with the other demand forecasting methods such as Moving Average (MA), Support Vector Machine (SVM) and Radial Basis Function Neural Network (RBFNN), it is indicated that the proposed improved ABC-PF method can achieve higher prediction accuracy and lower safety inventory level, which improve the overall profitability of the company, therefore generate product demand management insights for footwear and apparel enterprises.
Keywords: Demand forecasting, product life cycle, artificial bee colony algorithm
