## 4 Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No** & **Input** & **Value** & **Description** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{Mode of supply} & \multirow{2}{*}{0–1} & 1: In-house production \\  & & & 0: External sourcing \\ \hline
2 & Annual demand & 1–100 & The demand quantity (\(\times\)1000 units) \\ \hline \multirow{2}{*}{3} & \multirow{2}{*}{\begin{tabular}{c} Finished product \\ reference number \\ \end{tabular} } & \multirow{2}{*}{1–45} & \multirow{2}{*}{\begin{tabular}{c} The number of finished products(s) \\ that the material belongs to \\ \end{tabular} } \\  & & & \\ \hline \multirow{2}{*}{4} & \multirow{2}{*}{\
\begin{tabular}{c} Use case in \\ pre-assembly \\ \end{tabular} } & \multirow{2}{*}{0–1} & 1: Used only on the relevant \\  & & & assembly line \\  & & & 0: Used in pre-assembly line \\ \hline \multirow{2}{*}{5} & \multirow{2}{*}{
\begin{tabular}{c} Compatibility of the \\ material size with the \\ kitting vehicle \\ \end{tabular} } & \multirow{2}{*}{0–1} & 1: Compatible with the \\  & & & kitting vehicle \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of input parameters. Figure 2: The framework of the proposed study. The dataset contains the material details obtained from the company's enterprise resource planning (ERP) system, production logs, and warehouse inventory records. The data preprocessing applied in the study includes removing duplicate records for the materials listed under more than one product category, correcting missing values for the demand rates by interpolation based on historical production data, and correcting inconsistencies in the material feeding strategy classification labels. Categorical inputs are encoded using binary and one-hot encoding methods to facilitate model interpretation. It was established that annual demand values significantly deviating from production trends were to be identified as outliers and then validated against production schedules to confirm the validity of the data and correct data entry errors. It is imperative to acknowledge the potential for bias inherent in the observed data, which may have originated from historical preferences concerning feeding methods or long-standing practices in the company. The MRMR algorithm is used to observe the material feeding method for different values of input parameters. The MRMR algorithm, first proposed by [PERSON] et al. [75], measures the redundancies among input parameters and the relevance between input parameters and output class labels for a given set of inputs. The primary function of the MRMR algorithm is to minimize the redundancy of an input set and maximize the relevance of an input set to the output variable [76; 77]. The inputs that were ranked highest by MRMR are considered to exhibit an optimal balance between relevance to the output and minimal redundancy. The MRMR method is characterized by its high level of computational efficiency and its capacity for generalization concerning different ML models [78]. In the MRMR algorithm, each input is assigned a weight that signifies its importance or relevance to the output variable, with higher weights deemed more relevant. The MRMR weights for the input parameters have been calculated and are presented in Table 2. The MRMR algorithm results indicated that all selected inputs were relevant to the material feeding method. The training dataset, which consists of 2221 rows, is split into two sets (training and testing) before running the ML algorithms. Cross-validation (CV) is a popular strategy to split the data (one or more times), in which the split part of the data is used to train the algorithm, and the remaining part is used to estimate the risk of the algorithm [59]. CV also helps to prevent overfitting to biased subsets of data and increase generalizability [79].