## 5 Conclusions  
We are focus on the interaction between demand forecasting and inventory planning, in the context of SMEs. The empirical results demonstrate a selection of ML models that yield superior performance in comparison to the classic statistical forecast methods. However, when ML models are solely considered, the results indicate careful consideration must be regarded, given that model evaluation can be perceived from both an academic and managerial perspective. The performance indicator from all three items signifies several ML models yield different outcomes based on the nature of the given input data and the selected hyperparameter settings. This concludes from both a practical and academic domain that no single ML model can be considered; rather, a selection of several ML models must be evaluated in accordance with the presented framework in Figure 1. Secondly, drawing from the empirical findings based on the two scenarios with non-deterioration and deterioration. Conclusively, it can be deduced that the use of RL in combination with the Q-learning algorithm obtains the most preferable economic results from all cases. Furthermore, since RL generates a unique policy based on the provided data and the environmental setting, the results show RL is a promising data-driven approach that can solve inventory problems and generate custom inventory policies for individual items, which indeed can serve to close a gap between theoretical inventory models and piratical industrial use.  
As a last remark, this paper aid in the reduction of the current industrial research gap identified by [26], which outlines the lack of RL models application on real-world problemswithin the field of production planning and control. Further, investigation is required to evaluate the performance in a multi-echelon setup. In this study, we aggregated the data on a monthly basis, as the sponsor company also operates in same manner. Definitely, for more thorough analysis, sensitivity analysis for data aggregation, rolling horizon or more practical demands, such as stock dependent demand and price dependent demand, can provide more critical insights.  
Conceptualization, H.J.W., M.H. and S.S.; methodology, H.J.W. and M.H.; software, H.J.W. and M.H.; validation, S.S., G.J.C., I.E.N. and T.S.; formal analysis, H.J.W. and M.H.; investigation, H.J.W. and M.H.; resources, G.J.C. and T.S.; data curation, M.H., G.J.C. and T.S.; writing--original draft preparation, H.J.W., M.H. and S.S.; review and editing, all authors; supervision, S.S.; project administration, S.S.; funding acquisition, I.E.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All data generated or analyzed during this study are included in this published article. The raw data are available on request from the author ([EMAIL_ADDRESS]).  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The company had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.