Optimization Plan for Excess Warehouse Storage in E-Commerce–based Plant Shops: A Case Study for Chinese Plant Industrial

Abstract

The rapid development of e-commerce in China has played a critical role in the development of the national economy and ongoing modernization. The plant industry is unique among industries that employ e-commerce sales models because its products exhibit special characteristics such as high death and damage rates. Therefore, its e-commerce and logistical requirements are stricter than in other industries and, as a result, excess warehouse storage can be extremely difficult for e-commerce-based plant shops to manage. Numerous studies have indicated the need to identify a product's most up-to-date market conditions, as well as the type, function, and size of warehouses. Therefore, based on a case study, this study proposes an optimization plan for solving excess warehouse storage in e-commerce-based plant shops. First, sales volume data of the case company, Enterprise A, were analyzed to predict future sales. Then, entropy and the technique for order preference by similarity to an ideal solution were used to construct the decision-making model. Finally, a cloud warehouse-based optimization plan was proposed to solve excess warehouse storage in e-commerce-based plant shops. This plan can serve as a reference for decision-makers or executives in e-commerce-based plant shops when handling excess warehouse storage.

Establishing a Combined Forecasting Model: A Case Study on the Logistic Demand of Nanjing’s Green Tea Industry in China

“…The following detailed solutions were proposed by the present study: (1) Choosing forecasting models with a simple theoretical basis and calculation: This study, considering the theoretical foundation and limited data available, chose the ES, RA, and GM (1, 1) models as the basis for establishing stand-alone models. (2) Constructing a combined forecasting model: The value of combined forecasting models regarding their accuracy has been verified by various studies (Lin et al, 2019;Colino et al, 2012;Jia et al, 2020;Carmona-Benítez & Nieto, 2020;Hu, 2020;Wang et al, 2018). The present study established a combined forecasting model on the basis of TOPSIS, which is an effective method involving simple calculation for multicriteria decision making.…”

“…Since Bates and Granger (1969) first proposed the idea of combining forecasts in the 1960s, studies on the methodology and application of the forecast combination approach have rapidly developed. Such as Lin et al (2019) used exponential smoothing (ES) and the technique for order preference by similarity to ideal solution (TOPSIS) to solved excess warehouse storage in the e-business based plant shops for Chinese plant industrial. Colino et al (2012) combined ARMA and VAR model to evaluate US agricultural futures markets, the results shows the proposal model can improved the accuracy of price forecasts.…”

“…Finally, the TOPSIS was applied to obtain the weight, providing the ultimate forecasting results. Relevant equations follow (Lin & Cho, 2019;Lin et al, 2020).…”

The Role of e-Commerce: A Systematic Literature Review

“…Research Development [6], [10], [13], [14], [15], [16], [18], [19] Being tested with larger sample [4], [20], [21] Being tested in different countries [4], [17] The relationship between customers and sellers [22], [23] Qualitative assessment [24], [21] Being tested with different case study [25], [19] The test on customer's perspective [26] Research opportunity is organized based on numbers of supporting literatures. There are six literatures which support research development, both for research model and technology aspect.…”

What should be lean buffer threshold for the forward-reserve warehouse?

“…Optimization is an approach which can be utilized to reduce storage wastages (Sharma and Shah, 2016b), but it has limitation of destabilizing the operations with distorted flow (Dharmapriya and Kulatunga, 2011). However, its adoption along with lean thinking has been widely adopted in designing FB capacities (Lin et al , 2019). The waiting time wastages and material flow in FRM is decided by adopted capacity design and assignment decision (Singbal et al , 2020).…”