## 5 Conclusions, Limitations, and Outlook  
The purpose of this paper is to enhance the accuracy of demand forecasting by investigating ensemble demand forecasting approaches and comparing selected techniques using real-life data. We assessed a tree-based ensemble model and a deep learning model on supermarket store data. Our analysis of the ETR models yielded several valuable insights. Firstly, ETR requires less data preparation, such as feature scaling. Secondly, ETR generates its own feature importance metrics, which is highly beneficial for model interpretability. Thirdly, ETR is quicker to train and tune since it has fewer hyperparameters compared to DL. Finally, defining the best network structure can be a complex task in DL, whereas ETR methods do not have such requirements. To strengthen the results, we have compared the ETR results with three additional, acknowledged tree-based ensemble models, i.e., RFR, XGB, and GBR. As expected, the results for ETR and these three additional tree-based ensemble approaches were very similar. Our paper's contribution can be summarized in the following ways. First, we extend beyond using only historical demand data by incorporating diverse features, such as price and external factors, like weather and COVID-19-related data. This enriched dataset enables a more comprehensive understanding of demand behavior. Second, our study employs a substantial dataset from a prominent supermarket, ensuring the real-world applicability of our findings. This authenticity lends credibility to our research and enhances its practical relevance. Third, we leverage the power of advanced machine learning techniques by employing two state-of-the-art models. These models are carefully chosen for their ability to handle complex datasets and capture intricate demand patterns effectively. Lastly, to comprehensively evaluate our forecasting models, we analyze their performance across three distinct perishable product categories. This approach highlights the models' effectiveness in handling diverse consumer behaviors and demand patterns. From a practitioner's and managerial point of view, our results serve as a starting basis for selecting suitable approaches for demand prediction in a retail context. ETR's ability to generate its own feature importance metrics provides businesses with a clear understanding of which factors significantly influence demand. This can guide managers in making informed decisions based on the most impactful variables. Furthermore, ETR is quicker to train and tune due to its fewer hyperparameters. This can expedite the forecasting process, enabling businesses to respond more swiftly to changing market conditions. The same applies to data preparation: tree-based models require less data preparation, such as feature scaling, compared to deep learning models. This can lead to time and resource savings for businesses, allowing them to focus on other critical areas. By incorporating diverse features such as price, weather, and COVID-19-related data, businesses can gain a holistic view of demand behavior. This can aid in crafting more effective marketing and sales strategies. Our results also show that forecasting accuracy varies across product categories. Managers can leverage these insights to tailor their demand forecasting strategies for different product lines. Notably, when predicting customer demand for fresh meat, tree-based ensembles significantly improved forecasting accuracy, achieving more than a 25% reduction in MAPE. Thus, products exhibiting similar patterns to fresh meat could also benefit from the application of tree-based ensembles in real-world scenarios. The limitations of our work are as follows. Firstly, the findings may not directly apply to other domains or datasets, given that the performance and characteristics of models can vary significantly based on the specific data to which they are applied. Secondly, the research is solely focused on comparing an ETR model and a deep learning model.