## 1 Introduction
### Literature Review
\begin{table}
\begin{tabular}{c c c c c} \hline
**Paper** & **Supply Chain** & **Approach** & **Findings** & **Practical Implications** \\  & **Environment Settings** & & & \\ \hline  & & The authors consider a vanilla policy gradient algorithm to solve the supply chain problem through profit maximization. The production target and quantities target are either discrete or continuous variables. Afterward, the results are compared with the outcome of a (r,Q) policy. & The results show that the two methods, (1) Action clipping and (2) Output activation function, both yield better results than the (r,Q) policy. The free DRL agent & The results show that the two methods, (1) Action clipping and (2) Output activation function, both yield better results than the (r,Q) policy. The free DRL models do not consider transition probability and are thereby able to make decisions without knowing the demand probability. & This paper highlights that heuristic models such as (s,S) or (R,Q) models are not feasible. The paper seeks to explore algorithms that can be used for paralleled decision making if model is correct, and the RL models can further aid in minimizing the waste of perishable product. & This paper highlights that heuristic models such as (s,S) or (R,Q) models are not feasible. The paper seeks to explore algorithms that can be used for paralleled decision making if model is reward systems. The findings, which are based on actual data, demonstrate the potential strengths of an RL system that can handle complex environment settings with multiple objectives. & The numerical results show reward shaping is a relevant approach, since the method reduces computational requirements. Additionally, reward-shaped DQN yields a higher learning rate and better policy in comparison to an unshaped DQN. & The numerical results show reward shaping is a relevant approach, since the method reduces computational requirements. Additionally, reward-shaped DQN yields a higher learning rate and better policy in comparison to an unshaped DQN. & Reward shaping by using known inventory policies as teachers can serve as an enabling argument for the integration of DRL model in companies. \\ \hline  & & The paper’s findings showcase the DRL models’ ability to adapt to inventory problems with different complex cost structures, which rules-based heuristic has issues coping with. & Incorporation of two or more historical inventory records improves the DDLS model’s performance. \\ \hline  & & The paper’s findings showcase the DRL models’ ability to adapt to inventory problems with different complex cost structures, which rules-based heuristic has issues coping with. & Information sharing of parameter setting within supply chains can enhance performance of the DRL model, decreasing volatility during the training phase. \\ \hline \end{tabular}
\end{table} 
Table 1: Cont. Table 1 reflects that various algorithms are implemented for inventory planning. We refer to recent review work by [26] for a more detailed overview in this stream of research. Regarding the application of forecasting methods, we also refer to [27; 28] for recent development. Note that researchers mostly focus on inventory planning and forecasting as two separate directions. Evaluations are performed while either disregarding the importance of forecasting or assuming the idealistic forecast is already available. However, this is not the case, at least for our sponsor company. And for SMEs, the improvement of forecast accuracy and inventory performance might not be aligned and create issues with various magnitudes. Therefore, our study supplements the literature and highlights the possible consequences for SMEs.