## 3 Results  
The manager of the stock keeping unit (SKU) of our sponsored company needs to manage thousands of products. Therefore, the product categorization is key, although it is not directly associated with our study. But we briefly introduce one of the practices used for product categorization before jumping into the main analysis. The product categorization is performed by the determination of coefficient of variation (CV) = \(\frac{SidDev}{Mean}\) for forecast-ability. Although we only consider only three items, we still follow the sponsor company's current practice of item classification as shown in Figure 2.  
Therefore, we found that the requirements are not unique. One of the objectives of the study is also to test which forecasting method or replenishment decision is suitable for slow-, medium- and fast-moving raw materials. Another type of classification is variance analysis, which is commonly used in industry, and which might support the categorization of SKU items [54]. Therefore, we found that, (i) Item-1: Slow-moving, High Cost, and Low Variance, (ii) Item-2: Slow-moving, High Cost, and High Variance, and (iii) Item-3: Fast-Moving, Low Cost, and Medium Variance. The data were aggregated on a monthly basis and converted to a time series data set consisting of 46 data points, from April 2019 to January 2023, and the three critical items are used with no missing value. These products are mostly, sourced from Denmark and Asia and used in almost every finished product. As we mentioned earlier, with all forecasting methods we divide the data set into 80:20 ratio, to train and test the outcome, when we rely on machine learning methods. As we mentioned earlier, whether we keep the focus on forecasting accuracy based on classical measures or KPIs, we can find that based on the choice; there is no unique method that ensures higher performance. The item with the lowest cV = 36.94, ARIMA(2,1,0) is the best performing, in terms of MAE and RMSE, while SVR has the best MAPE score. From a managerial perspective, LSTM and W-LSTM have the highest fill rate, ARIMA has the lowest AIL and THC, while LSTM and W-LSTM have the least stockouts. The item with the highest cV = 64.95, is best performing with RF and SES when looking at MAE, W-ANN has the best MAPE score of, while LSTM has the best RMSE. Looking at FR, W-LSTM is the best performing, while W-ANN performs the best looking at AIL and THC. Both SVR and W-LSTM have the least stockouts. Figure 2: Product categorization. Noticeably, if the cV is increased, for instance, itemID with the highest cV, wavelet-incorporated machine learning methods lead to higher performance, from a managerial perspective. Definitely, some of the advantages of wavelet transformation incorporated with machine learning are: (i) the capture of both smooth and abrupt changes in data, and (ii) highlighting both high and low-frequency components. In most studies, when forecasting measures are considered, they ignore the effect of industrial KPIs. Therefore, our results clearly demonstrate that the selection of forecasting techniques based on classical measures (e.g., MAPE, RMSE) might fail to ensure managerial transition. It also reflects that data-driven methods should not be blindly implemented as a forecasting tool in the industrial context. Managers in SMEs have several operational limitations, such as limited technology infrastructure, given SMEs industrial data management processes and data quality often lacks volume and accuracy [55]. If there is a frequent stockout scenario (negative AIL), it does not bring any value in terms of delivery performance, and it might hurt the reputation of SMEs. Noticeably, the 100% fill rate is an idealistic scenario, and some of the forecasting methods (W-LSTM), in our study context, ensure a 100% fill rate, but companies would have to keep an extensively high inventory. And their capacity may be over-utilized, still we found that classical approaches, such as ARIMA, might be a viable option, and in terms of a data-driven approach, LSTM is a possible solution.