Enhancing pricing strategies in the aftermarket sector with machine learning

Sarode, Mohit S.; Kumar, Anil; Prasad, Abhijit; Shetty, Abhishek Article Enhancing pricing strategies in the aftermarket sector with machine learning Modern Supply Chain Research and Applications Provided in Cooperation with: Emerald Publishing Limited Suggested Citation: Sarode, Mohit S.; Kumar, Anil; Prasad, Abhijit; Shetty, Abhishek (2024) : Enhancing pricing strategies in the aftermarket sector with machine learning, Modern Supply Chain Research and Applications, ISSN 2631-3871, Emerald, Bingley, Vol. 6, Iss. 4, pp. 411-423, https://doi.org/10.1108/MSCRA-10-2023-0042 This Version is available at: https://hdl.handle.net/10419/314934 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Sarode, Anil Kumar, Abhijit Prasad and Abhishek Shetty Daimler Truck Innovation Center India, Bangalore, India Abstract Purpose –This research explores the application of machine learning to optimize pricing strategies in the aftermarket sector, particularly focusing on parts with no assigned values and the detection of outliers. The study emphasizes the need to incorporate technical features to improve pricing accuracy and decision-making. Design/methodology/approach –The methodology involves data collection from web scraping and backend sources, followed by data preprocessing, feature engineering and model selection to capture the technical attributes of parts. A Random Forest Regressor model is chosen and trained to predict prices, achieving a 76.14% accuracy rate. Findings –The model demonstrates accurate price prediction for parts with no assigned values while remaining within an acceptable price range. Additionally, outliers representing extreme pricing scenarios are successfully identified and predicted within the acceptable range. Originality/value –This research bridges the gap between industry practice and academic research by demonstrating the effectiveness of machine learning for aftermarket pricing optimization. It offers an approach to address the challenges of pricing parts without assigned values and identifying outliers, potentially leading to increased revenue, sharper pricing tactics and a competitive advantage for aftermarket companies. Keywords Aftermarket, Machine learning, Price prediction Paper type Research paper 1. Introduction In today’s competitive business environment, pricing decisions are critical to an organization’s financial success (Kalpana et al., 2022). Businesses aiming for competitiveness and financial success must improve their pricing strategies. An emerging tool, machine learning-based predictive pricing, uses historical data to quickly forecast optimal prices, assisting in setting fair rates and adapting to changing market conditions (Banerjee and Bandyopadhyay, 2020). This approach employs machine learning techniques to decipher massive amounts of data, revealing intricate patterns and determining the most profitable price points based on customer preferences, market dynamics, and corporate goals (Gupta and Pathak, 2014;Mantrala et al., 2006). This transformative methodology enables data-driven pricing decisions, granting organizations a competitive edge in pricing strategies. Several studies have explored the application of machine learning for pricing optimization in various fields. For example, traditional methods were compared against machine learning methods such as Random Forest, Gradient Boosted Machines, and Deep Learners in the insurance industry, highlighting the effectiveness of Gradient Boosting Methods (Spedicato et al., 2018). Our research builds on this by applying a Random Forest Regressor model in the aftermarket sector. Dynamic pricing in e-commerce has been explored using Gradient Boosting Machines (GBMs), showing superior performance in capturing complex non-linear pricing patterns Modern Supply Chain Research and Applications 411 © Mohit S. Sarode, Anil Kumar, Abhijit Prasad and Abhishek Shetty. Published in Modern Supply Chain Research and Applications. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http:// creativecommons.org/licences/by/4.0/legalcode The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/2631-3871.htm Received 6 October 2023 Revised 24 January 2024 13 June 2024 10 September 2024 Accepted 13 October 2024 Modern Supply Chain Research and Applications Vol. 6 No. 4, 2024 pp. 411-423 Emerald Publishing Limited 2631-3871 DOI 10.1108/MSCRA-10-2023-0042 (Youbi et al., 2023). Similarly, machine learning algorithms like Long Short-Term Memory Networks (LSTM), Convolutional Neural Networks (CNN), and Support Vector Regression (SVR) have been used for stock price prediction, with SVR achieving the highest accuracy (Chen, 2020). Further studies have demonstrated the effectiveness of Random Forests and Artificial Neural Networks (ANN) in predicting stock closing prices (Vijh et al., 2020). The use of machine learning for property price prediction has shown that Random Forest and GBM algorithms perform well (Ho et al., 2020). Moreover, the application of machine learning for daily commodity price prediction has been highlighted, with ANNs showing effectiveness but suggesting the incorporation of domain knowledge and feature engineering for improvement (Amin, 2020). Complex price prediction tasks involve numerous factors influencing price changes. Traditional methods often struggle to account for these complexities, while machine learning has shown promise in optimizing prices (Indira et al., 2023). Additionally, a model-based pricing framework for machine learning models has been proposed to address gaps in data market pricing, demonstrating high revenue potential and low runtime costs (Chen et al., 2019). Existing literature also investigates micro-marketing pricing strategies based on supermarket scanner data (Montgomery, 1997), forecasts stock prices using various models and data representation techniques (Patel et al., 2015), investigates pricing strategies in B2B aftermarkets based on firm size, industry, and location (Gunaydan, 2023), and optimizes prices in dynamic markets with limited information (Dodin et al., 2021). 1.1 Gap in literature Although machine learning is widely used in pricing, it is still necessary to apply it in certain industries, such as the aftermarket, which handles replacement parts and components for goods that have already been made. The field of machine learning in pricing has been the subject of extensive research in the literature. Spedicato et al. (2018) conducted a comparison between machine learning models and traditional pricing techniques. These studies do not take into account the unique features of the aftermarket sector, where technical attributes of parts have a significant impact on pricing, nor do they concentrate on specific methodological procedures. This drawback may be seen in research by Youbi et al. (2023), although Youbi’s technique works well in dynamic contexts, it is not directly applicable to the aftermarket sector since it ignores technical factors that have a big influence on pricing decisions. Finding irregular observations that point to mistakes, poor data quality, or unusual pricing trends is the first step in detecting anomalies in pricing data. Similarly, outlier detection identifies data points that differ significantly from the majority, frequently representing extreme price points or unique market circumstances. Furthermore, research by Indira et al. (2023) emphasizes the importance of incorporating industry-specific data for accurate price prediction. It highlights the need for more specialized approaches in sectors with unique characteristics, such as the aftermarket. Indira’s work points out that existing models often fail to account for technical specifications, which are critical in determining prices for aftermarket parts. Our study directly addresses these gaps by focusing on the unique needs of the aftermarket industry, where the pricing of components like fuel tanks is determined not only by market dynamics but also by technical attributes such as material quality, size, and durability. By offering a thorough, domain-specific technique for price prediction and anomaly identification, our research expands on the fundamental stages presented by Spedicato et al. (2018). Our study uses a Random Forest Regression model trained on a dataset supplemented with technical specifications of aftermarket parts, in contrast to the generic techniques in the literature currently in publication. This focus on technical elements distinguishes our research from earlier studies and offers a more accurate and relevant approach for the aftermarket sector. MSCRA 6,4 412 1.2 Research objectives Ensuring customer satisfaction is critical for significant players in the trucking industry. By ensuring parts availability at the right time, location, and price without sacrificing quality, downtime can be reduced, and meaningful business opportunities can be retained. The aftermarket industry, where pricing decisions directly impact revenue and profitability, can benefit significantly from accurately forecasting optimal parts prices, striking a balance between attracting customers and maximizing profit margins. The primary objective of this research is to address the challenges of predicting prices for aftermarket parts that lack assigned values and to detect anomalies and outliers by leveraging machine learning algorithms. In line with the findings of Indira et al. (2023), our approach emphasizes incorporating industry-specific data—specifically the technical features of parts—to improve the accuracy of price predictions. This study aims to develop a robust methodology that accurately forecasts optimal prices and identifies irregular pricing patterns that could indicate errors, poor data quality, or extreme market scenarios. To achieve this, we compiled a comprehensive dataset focusing on fuel tanks due to their critical role in the trucking industry and their revenue potential. Given that existing methodologies, as highlighted by Spedicato et al. (2018) and Youbi et al. (2023), did not produce satisfactory results for our specific dataset, we adapted these techniques using our domain knowledge to develop a self-contained methodology that is readily applicable to realworld and industry-specific scenarios. 1.3 Research contributions and application This study contributes to the existing body of literature by expanding on the methodologies presented by Spedicato et al. (2018),Indira et al. (2023), and Youbi et al. (2023), addressing their limitations by incorporating technical features that are critical for accurate pricing in the aftermarket sector. By tackling the research question of how machine learning algorithms can be utilized to predict the prices of aftermarket parts with no assigned values and detect pricing anomalies, this study provides a robust, data-driven framework that can be readily applied to real-world scenarios in the aftermarket industry. By addressing the gaps and limitations identified in previous studies, our research offers practical insights that enhance pricing strategies, profitability, and competitiveness in the aftermarket sector. 2. Methodology The first step was to gather data. The dataset was then preprocessed, and key attributes were identified. Selected attributes were scaled, and different models were compared to determine which model was the best. The Random Forest Regressor model was chosen, and it was then trained and tested to predict the prices of the parts. Figure 1 depicts a block diagram of the entire Figure 1. Block diagram of methodology Modern Supply Chain Research and Applications 413 methodology. Web scraping techniques, backend data, and part drawings were used to create the dataset. It had hundreds of thousands of data points. However,a few thousand-part numbers were chosen from the large dataset to focus on one specific truck assembly, the fuel tank. To ensure data quality, attributes with insufficient data points or lacking relevant information were removed. Figure 2 shows a bar chart that was used to calculate the percentage of data covered in each of the remaining attributes. Attributes with less than 80% coverage in the data were removed. Furthermore, all attribute values were cleaned of noise and standardized to follow the same format. Numerical attributes with missing values were filled with the median value, whereas categorical attributes were filled with the most frequently occurring categories. The number of attributes was reduced by less than 60% as a result of this. Figure 3 depicts the missing value matrix for these attributes, where the white spaces between the matrix indicate the amount of missing data. The frequency of the price points was observed using a histogram in the initial analysis. Based on this histogram, an average range of prices was determined, with prices outside of this range considered outliers. Figure 4 depicts the acceptable cost range and the outliers. To investigate the impact on prices, feature engineering was carried out by calculating new attributes such as area, mass, and volume based on existing attributes such as diameter and length. To improve the model’s performance, new attributes such as shape and vent were loaded and preprocessed into the dataset. Figure 5 shows a heat map created with the Seaborn library in Python to determine the significant attributes for price prediction. Using the Random Forest Regressor model, various feature selection techniques were employed to evaluate the impact of attributes on price prediction. Variance Threshold, SelectKBest, and Recursive Feature Elimination with Cross-Validation (RFECV) were the 3 techniques used to evaluate the attributes. The Variance Threshold method, represented by Equation (i), is effective at eliminating features with low variance, assuming they have a minimal contribution to the predictive model. During the selection process, the method automatically identifies and removes zero variance features. selector ¼VarianceThresholdðÞ (i) Equation (ii) depicts the SelectKBest method, which uses a score function called f_regression and a parameter kof 13. The parameter k is set to 13, indicating a preference to keep the top 13 features deemed most influential in predicting aftermarket part prices. This method assesses the statistical relationship between each feature and the target variable, ranking them according to their significance. The chosen parameters strike a balance between feature richness and model efficiency, taking into account the linear relationship between features and the target variable. selector ¼SelectKBest�score func ¼f regression;k¼13�(ii) The RFECV method, as shown in Equations (iii) and (iv), employs the Support Vector Regressor (SVR) as an estimator with a linear kernel. This method systematically evaluates feature relevance by recursively removing the least informative features. The use of a linear kernel corresponds to the assumed linear relationship between features and prices in our dataset. Step of 1 and cv of 10 were chosen as parameters to ensure thorough feature evaluation while maintaining computational efficiency. estimator ¼SVRðkernel ¼linearÞ(iii) selector ¼RFECVðestimator;step ¼1;cv ¼10Þ(iv) Based on our dataset, RFECV elimination produced the most favorable results, and Table 1 shows the rankings of all RFECV-calculated attributes. This information was used to conduct trial and error tests on attributes to determine which contributed to the highest accuracy. MSCRA 6,4 414 Figure 2. Percentage data covered in each attribute Modern Supply Chain Research and Applications 415 Recursive Feature Elimination with Cross-Validation (RFECV) technique outperforms SelectKBest and Variance Threshold because it can capture intricate relationships and dependencies between features. By removing less relevant features during cross-validation, RFECV excels at evaluating the collective impact of features in our dataset, which includes technical attributes of automobile parts. The method retains the most relevant features, improving accuracy and interpretability. SelectKBest, which is efficient at selecting top features based on individual metrics, does not consider feature interactions. Variance Threshold, which focuses on variance within individual features, may miss important associations required for accurate pricing predictions. Figure 4. Price distribution including acceptable range and outliers Figure 3. Attributes included after preprocessing MSCRA 6,4 416 As a result, RFECV’s consideration of feature interactions and dependencies makes it more suitable for selecting impactful features and improving predictive model accuracy. Feature scaling was used to ensure that the range of values was uniform. Numerical values were normalized, and categorical data was encoded using labels. Normalization was used to adjust numerical values such as length, diameter, area, mass, and so on to a standard scale, eliminating potential biases caused by different measurement units or scales. Categorical data, on the other hand, such as material, finish, shape, and so on, was encoded using labels, Table 1. Ranking based on RFECV Features Ranking Features Ranking Length 2 Anti-Siphon 16 Thickness 6 GROSS_WT 3 Outer diameter 1 TOTAL_SALES_DEMAND 1 Inner diameter 1 Area 1 Material 9 Volume 11 Finish 13 Mass 8 Mounting location 12 SHAPE 10 Fuel tank capacity 4 INSTA_HEAT 14 Filler neck to end length 1 VENT 5 Baffles included 7 Integral Fuel Tank 15 Source(s): Authors’ own work Figure 5. Heat map correlating all the attributes Modern Supply Chain Research and Applications 417 allowing for the representation of qualitative information in a numerical format suitable for computational analysis. Given the large volume of available data, the dataset was divided into a 70% training set and a 30% test set. The validation dataset was assigned a nominal amount (10%) of the training dataset. To estimate the performance of the models, the error metric Root Mean Squared Error (RMSE) was chosen. Because of its comprehensibility, ability to capture prediction accuracy, and emphasis on penalizing larger errors, the Root Mean Square Error (RMSE) metric stands out as a superior choice in various modeling scenarios. The average magnitude of the errors between predicted and actual values is measured by RMSE, providing a straightforward understanding of how far off the model’s predictions are from the true values. Furthermore, because of its squared nature, RMSE gives significant weight to larger errors, making the metric more sensitive to outliers or extreme deviations. Furthermore, RMSE is well-suited for regression-type problems in which the goal is to minimize prediction errors. A critical aspect of our methodology was the evaluation of various regression models, such as Random Forest Regression, AdaBoost Regression, Bagging Regression, Support Vector Regression (SVR), and K-nearest Neighbor Regression. The accuracy on the validation set, mean RMSE score, and standard deviation were all considered when selecting a model. Notably, the Random Forest Regressor emerged as the superior choice and this section investigates the limitations of alternative models while explaining why the Random Forest Regressor was chosen. 2.1 Limitations of alternative regression models Other algorithms considered for this task had limitations, but Random Forest emerged as the best option. AdaBoost’s sensitivity to noisy data and outliers could pose a problem for our dataset, which may have quality variations. Bagging, while effective in reducing variance, may impair interpretability due to its ensemble nature. Furthermore, Support Vector Regression (SVR) necessitates meticulous hyperparameter tuning, which increases the time required for implementation. For high-dimensional datasets, K-Nearest Neighbors (KNN) can suffer from the “curse of dimensionality,” which can have an impact on accuracy. When dealing with large and high-dimensional datasets, Random Forest excels at predictive analysis, especially when there are complex interactions between features or nonlinear relationships with the target variable. It performs well even without extensive hyperparameter tuning and effectively handles noisy data. Random Forest appeared as the best option among the mentioned algorithms because the goal is to obtain robust predictions while dealing with diverse types of data and maintaining good interpretability, as shown in Table 2. It is critical to accurately assess model performance during the development process. Validation techniques help with this by assessing how well the Random Forest Regressor (rfr) model generalizes to new data. In this study, the model was trained using the expected price range, and then its validation score was calculated. This step involved determining how well the model predicted prices on data that had not been previously trained on. However, more Table 2. Comparison of different models Models Accuracy Mean RMSE score Standard deviation RandomForestRegressor 0.717507 61.800927 14.306699 AdaBoostRegressor 0.451401 70.588170 11.112890 BaggingRegressor 0.673993 64.175979 14.116304 SVR 0.464940 113.950125 16.046542 KNeighborsRegressor 0.622910 100.102034 16.436865 Source(s): Authors’ own work MSCRA 6,4 418