Analytics Models for Corporate Social Responsibility in Global Supply Chains

Abstract

There have been several infamous incidences where world-renowned corporations have been caught by surprise when a low-tier downstream supplier has been publicly found to be non-compliant with basic corporate social responsibilities (CSR) codes. In such instances, the company reputation, and consequently financial health, suffer greatly. Motivated by the advances in predictive modeling, we present a predictive analytics model for detecting possible supplier deviations before they become a corporate liability. The model will be built based on publicly available data such as news and online content. We apply text mining and machine learning tools to design a corporate social responsibility "early warning system" on the upstream side of the supply chain. In our literature review we found that there is a lack of studies that focus on the social aspect of sustainability. Our research will help fill this gap by providing performance measures that can be used to build prescriptive analytics models to help in the selection of suppliers. To this end, we use the output of the predictive model to create a supplier selection optimization model that takes into account CSR compliance in global supply chain context. We propose a heuristic to solve the problem and computationally study its effectiveness as well as the impact of introducing CSR on procurement costs as well as ordering and supplier selection patterns. Our models provide analytics tools to companies to detect supplier deviance behaviour and act upon it so as to contain its impact and possible disruptions that can shake the whole supply chain.