Towards social life cycle assessment: a quantitative product social impact assessment

The main goal of this paper is to present the feasibility of the quantitative method presented in the Product Social Impact Assessment (PSIA) handbook throughout a case study. The case study was developed to assess the social impacts of a tire throughout its entire life cycle. We carried out this case study in the context of the Roundtable for the Product Social Metrics project in which 13 companies develop two methodologies, a qualitative and a quantitative one, for assessing the social impact of product life cycle. The quantitative methodology implemented for assessing the social impact of a Run On Flat tire mounted in a BMW 3 series consists of 26 indicators split in three groups. Each group represents a stakeholder group. Primary data of the quantitative indicators were collected along the product life cycle of the Run On Flat by involving the companies, which owned the main steps of the product life cycle. Throughout this case study, an ideal/worst-case scenario was defined for the distance-to-target approach to compare the social performances of more products when they are available. The implementation of the PSIA quantitative method to a Run On Flat illustrated the necessity to have a referencing step in order to interpret the results. This is particularly important when the results are used to support decision-making process in which no experts are involved. It frequently happens in a big company where the management level has to take often decisions on different topics. Reference values were defined using ideal or worst-case-target scenarios (Fontes et al. 2014). For those topics where it was possible, an ideal/ethical scenario was defined, e.g., 0 h of child labor per product. In other cases, we defined a worst-case scenario, e.g., 0 training hours per product. It was then possible to interpret the results using a distance-to-target approach. A matrix was developed in the case study for identifying in which step of the product life cycle data is not available; that means we need more transparency in the supply chain. Each value of the matrix can be compared to the ideal/worst scenario to compare the step to each other and to identify along the product life cycle which step and the relative supplier that needs further measures to improve the product performance. Furthermore, a quantitative value for each indicator related to the product life cycle is calculated and compared with the ideal/worst scenario. The case study on Run On Flat represents the first implementation of the quantitative method of PSIA.