Time series imaging-based deep learning method for inventory control

ABSTRACT

Data-driven newsvendor methods offer promising solutions for estimating replenishment quantity within complex markets by leveraging demand-related features. However, most features utilized by these methods rely on manual selection, and some important unobservable features cannot be thoroughly utilized. These issues might result in the loss of some crucial information, thus incurring considerable cost in these decision-making results, meaning that there is still much room for improvement. To remedy this, we propose a time series imaging-based deep learning method, which automatically extracts crucial features alongside those selected manually. This method maximizes the advantages of time series imaging, convolutional neural networks, bidirectional long short-term memory networks, and multilayer perceptron networks. Experiments with four diverse product time series and real-world data show that our approach reduces the average newsvendor problem cost by 16.33% to 33.43% compared to the best benchmark, highlighting its robustness. Additionally, each component within our method significantly contributes to decision-making.

KEYWORDS:

• Data-driven method
• inventory control
• time series imaging
• deep learning

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 https://xl.16888.com/

2 In this paper, we use the same initial feature sets for macroeconomic indicators and Baidu Indices as Tian and Zhang (2023). The threshold parameters are set as follows: for the LAVIDA experiment, ${\hat{r}}_1=0.50$ and ${\hat{r}}_2=0.70$; for the COROLLA experiment, ${\hat{r}}_1=0.60$ and ${\hat{r}}_2=0.70$; for the Audi A6L experiment, ${\hat{r}}_1=0.40$ and ${\hat{r}}_2=0.85$; and for the BMW 5 Series experiment, ${\hat{r}}_1=0.50$ and ${\hat{r}}_2=0.85$. The max lag order $L_{max}=6$.

3 https://www.wind.com.cn/

4 https://index.baidu.com/