[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120135-en":3,"doc-seo-120135-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120135,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Enhancing pricing strategies in the aftermarket sector with machine learning - Research paper","This research applies machine learning to optimize pricing strategies in the aftermarket sector, with emphasis on parts lacking assigned values and the detection of extreme outliers. The workflow collects data via web scraping and backend sources, then performs preprocessing, feature engineering, and model selection. A Random Forest Regressor predicts part prices with 76.14% accuracy, maintaining predictions within an acceptable range and identifying outliers for rare pricing scenarios. The study connects industry needs with academic modeling to support improved revenue and competitive pricing decisions.","The current issue and full text archive of this journal is available on Emerald Insight at:  \n[https://www.emerald.com/insight/2631-3871.htm](https://www.emerald.com/insight/2631-3871.htm)  \nEnhancing pricing strategies in the aftermarket sector with machine learning  \nMohit S. Sarode, Anil Kumar, Abhijit Prasad and Abhishek Shetty  \nDaimler Truck Innovation Center India, Bangalore, India  \nAbstract  \nPurpose – 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.  \nFindings–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.  \nOriginality/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.  \nKeywords Aftermarket, Machine learning, Price prediction Paper type Research paper  \n1. Introduction  \nIntoday’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) .  \nThis 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.  \nSeveral 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.  \nDynamic pricing in e-commerce has been explored using Gradient Boosting Machines (GBMs), showing superior performance in capturing complex non-linear pricing patterns  \n© 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://](http://)[ ](http://)[creativecommons.org/licences/by/4.0/legalcode](creati","cbCaiihVqNZAq1ga","https://ap.wps.com/l/cbCaiihVqNZAq1ga","pdf",5584721,1,13,"English","en",105,"# Abstract\n# Introduction\n## Motivation and background\n## Related work in pricing optimization\n# Methodology (data and modeling)\n## Data collection and preprocessing\n## Feature engineering and model selection\n# Findings\n## Price prediction for parts without assigned values\n## Outlier identification and prediction","[{\"question\":\"What problem does the research address in the aftermarket pricing domain?\",\"answer\":\"It focuses on optimizing pricing strategies for aftermarket parts that have no assigned values and on detecting outliers that represent extreme pricing scenarios.\"},{\"question\":\"What machine learning approach and model are used for price prediction?\",\"answer\":\"The study uses a Random Forest Regressor model trained on engineered technical features extracted after data collection and preprocessing.\"},{\"question\":\"How accurate is the proposed pricing prediction model?\",\"answer\":\"The model achieves a 76.14% accuracy rate while predicting prices for parts without assigned values within an acceptable price range.\"}]","Enhancing pricing strategies in the aftermarket sector with machine learning - 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