Modeling resale value of road compaction equipment: a data mining approach

Abstract

Resale value is an important aspect to be addressed when companies aim to shift from one-sale models to Product Service Systems (PSS). In the initial stages of the PSS design process, it is beneficial to predict how the mechanical features of the PSS hardware will impact resale value, so to orient business strategy decisions accordingly. The objective of this work is to propose a methodology to model the resale value of road compaction equipment using data mining techniques. By scrapping and merging data sets from the machine manufacturer and from dealers of second-hand machines, the work discusses how the derived correlations may be used to populate value models for early stage decision making.

Data-Driven Design in Concept Development: Systematic Review and Missed Opportunities

“…Keivanpour and Ait Kadi ( 2018) and Li and Wang (2010) used historical design data to established target specifications thought respectively neural network and rough set theory and decision tree. A different approach was taken by Chowdhery and Bertoni (2018) mining the second-hand online purchase data to set targets for the new machine to be designed. The descriptions of previous product concepts is a frequently used source of data for the generation and selection of new concepts, either through applying mathematical modeling (Zhang et al, 2017), step-forward and path-track (Chen et al, 2019), or text mining .…”

Data-Driven Design Automation for Product-Service Systems Design: Framework and Lessons Learned From Empirical Studies

“…With such a purpose, the paper builds on previous research on the data-driven design in the construction industry (e.g. Bertoni et al, 2017;Chowdhery and Bertoni, 2018) and describes the findings collected through a participatory action research approach (Whyte, 1989) in two well-established Swedish construction machinery companies. Based on the empirical research the paper has a twofold objective: • Firstly, it presents a number of lessons learned on using operational data as decision support for early-stage design decisions.…”