Digital transformation for cost estimation system via meta-learning and an empirical study in aerospace industry

Abstract

It is increasingly challenging to estimate product cost to optimize the quotation decision for aerospace manufacturing companies owing to the diversity of designations, intermittent demands, complicated decision procedure between different functional departments, and knowledge gaps among the involved decision makers. There is a need of effective solutions to support digital transformation of manual approach in which it can expedite and enhance the accuracy of cost estimation by automating specific tasks and streamlining decision processes. Although a number of studies have been done to enhance the performance of forecasting models, little research has been done to address the interrelations between cost estimation model and the associated decisions for quotation. Focusing on realistic needs, this study aims to develop a digital cost estimation system by integrating data-driven methodologies, search engines, and rule-based decision mechanism based on domain knowledge for improving the accuracy of cost estimation and the effectiveness of quotation for revenue management. An empirical study was conducted in a global aerospace manufacturer in Taiwan for validation. The results have shown the practical viability for the proposed framework with better performance than conventional approaches. The developed solution has been implemented.