Authors : E Pavan Kumar; Kuzhalini Sivanandam; Akshay Acharya; Sandeep Kumar Giri; Bharani Kumar Depuru

Volume/Issue : Volume 9 - 2024, Issue 7 - July

Google Scholar : https://tinyurl.com/593ssznm

Scribd : https://tinyurl.com/5n7k4zpv

DOI : https://doi.org/10.38124/ijisrt/IJISRT24JUL1725

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The idea of determining the optimum bid amount in any railway tender competition has been a complex and critical task. Reasonable pricing is the main gateway in the process of winning contracts. The approach presented here is based on data-driven pricing to maximize tender wins and reduce potential profit loss. The model shall make use of historical tender data, competitor pricing data, and market indicators in order to predict the optimal bid amounts. This advanced ML model runs advanced machine learning algorithms, including regression models and ensembles, to study the intricate relationship between factors that would affect the successful execution of a bid. Deep learning models are integrated into the model to provide it with better handling of temporal dependencies and other hidden patterns in data, hence yielding accurate and robust predictions. The key objective of the work is to increase the winning rate of tender contracts by at least 10%, while competitive profitability is ensured. Because of precise competitor price predictions, business success criteria are oriented to reaching this higher win rate on tenders. The Machine learning success criteria target a price prediction accuracy of at least 90%. Another important set of economic success criteria that should be targeted is an improvement in profit margins of at least 5% through more accurate pricing strategies and a reduction in the number of rejected bids. It will offer great value to the businesses operating in the railway industry in making proper decisions on operations and strategic planning. The paper develops a fusion of traditional statistical methodologies with advanced ML and DL techniques in order to provide a robust solution for competitive advantage and increased profitability in the dynamic and competitive railway tender market.

Keywords : Bidding, Tender, Ridge Regression, Feed Forward Neural Networks.

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