Optimal inventory policy for fast-moving consumer goods under e-commerce environment

Jianbin Li; Mengcheng Guan; Zhiyuan Chen

doi:10.3934/jimo.2019028

Article Contents

2020, Volume 16, Issue 4: 1769-1781. Doi: 10.3934/jimo.2019028

This issue Previous Article Optimal credit periods under two-level trade credit Next Article Supervised distance preserving projection using alternating direction method of multipliers

Optimal inventory policy for fast-moving consumer goods under e-commerce environment

1.
School of Management, Huazhong University of Science and Technology, Wuhan, Hubei, China
2.
School of Economics and Management, Wuhan University, Wuhan, Hubei, China

^* Corresponding author: Zhiyuan Chen
^* Corresponding author: Zhiyuan Chen

Received: January 2018

Revised: August 2018

Early access: May 2019

Published: May 2020

This work is partially supported by the Key Program of National Natural Science Foundation of China (NSFC) under grant No.71831007 and the General Programs of NSFC under grant Nos. 71571079, 71871166, and by the Ministry of Education Innovation Century Talents Support Fund (NCET-13-0228) and the Fundamental Research Funds for the Central Universities

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Abstract

Coming up with effective inventory-ordering strategies for fast-moving consumer goods (FMCGs) through online channels has a major characteristic that the goods are promoted frequently. In this paper, a multi-period inventory model is employed wherein each period represents the promotion period, and the inventory level can be adjusted by replenishing or salvaging the inventory at the beginning of each promotion period. A two-threshold ordering policy is proven to be optimal for each promotion period. The benefits of salvaging can be significantly high for decision makers. This study contributes to the literature of inventory management that products are frequently promoted under an e-commerce environment.

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Mathematics Subject Classification: Primary: 90B05, 90B60.

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Figure 1. Policy structure

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Figure 2. The relationship of the benefit of salvaging $ \Delta $ and model parameters

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Table 1. SKU of FMCGs from YihaoDian

Categories	27th Week	28th Week	28th Week	30th Week
Food and beverage	12,949	12,926	12,799	12,811
Maternal and infant products	5,936	5,301	5,296	5,291
Kitchen and cleaning products	5,217	5,231	5,155	5,188

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Table 2. $[![]!]

k	1	2	5	10
x₀ = 60	4.5483	2.0535	0.1946	0.0085
x₀ = 80	32.6130	16.7936	2.4027	0.0760
x₀ = 100	84.8075	49.5771	9.9460	0.3748

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Table 3. Average gap between two thresholds varies with covariance

$ \tau $	$ T=4 $	$ T=10 $	$ T=20 $	$ T=100 $
$ \tau= \; 0.99 $	$ 7.50 $	$ 7.60 $	$ 7.05 $	$ 7.34 $
$ \tau= \; 0.50 $	$ 7.50 $	$ 7.20 $	$ 7.35 $	$ 7.17 $
$ \tau= \; 0.00 $	$ 7.25 $	$ 7.10 $	$ 6.80 $	$ 7.00 $
$ \tau=-0.50 $	$ 7.00 $	$ 7.60 $	$ 7.10 $	$ 6.99 $
$ \tau=-0.99 $	$ 7.50 $	$ 7.70 $	$ 6.80 $	$ 6.97 $

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