Research in Product Service Systems design increasingly focuses on how to develop ‘capabilities’ for assessing the value of solutions already in  a design project fuzzy front end. A datadriven approach shall then guide engineers in the process of identifying what to develop, and not merely to verify if a solution meets (or not) performance requirements. This process of frontloading problem identification and solution generation activities with models is of high interest to raise quality and lower risk and cost for the development work. The objective of the paper is to explore the use of a data-driven approach to enable value prediction of packaging material configurations in early design. Its main objective is to present a methodological approach and a framework to connect high-level aspects of customer value with simulations and analysis conducted on the mechanical properties of packaging material.

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.