Frugal Innovation as Intersection between Complexity of Early Cost Estimation, Machine Learning and Expert-Based Decision System

Abstract

This chapter discusses the empirical data analysis that will form the basis of the early pricing framework. It focusses on the complexity of furniture production and describes the historical production data collected from companies, along with the potential applications of machine learning for knowledge management purposes. The chapter then presents the results of machine learning for early cost estimation as part of a lean innovation that is affordable and accessible for small and medium-sized enterprises (SMEs). Finally, the chapter describes an experiment on the structured expert evaluation methodology, which shows that a well-formed panel of experts can increase the predictive power of machine learning solutions, particularly at extreme points.

Citation

Moskvina, J., Hanea, A., Vedlūga, T. and Mockevičienė, B. (2024), "Frugal Innovation as Intersection between Complexity of Early Cost Estimation, Machine Learning and Expert-Based Decision System", Mockevičienė, B. (Ed.) Participation Based Intelligent Manufacturing: Customisation, Costs, and Engagement, Emerald Publishing Limited, Leeds, pp. 151-238. https://doi.org/10.1108/978-1-83797-362-020241006