Improving the risk identification process for a global supply chain

Abstract

This thesis describes a proposed risk identification process that is intended to systematically identify potential risks that could materialize within a company's supply chain that would affect component supply. The process is based on a specific situation at Nokia though is intended to extend to other companies that rely extensively on outsourced component manufacturing. An analysis of the current risk identification process at Nokia revealed three areas of potential improvement: the lack of full upstream visibility, the supplier-centric nature of the process and risk reports not fully conveying desired information. Based on a review of existing literature on supply chain risk management and other risk prediction techniques, as well an analysis of the specific situation at Nokia, which has a complex and rapidly-changing supply chain, a new risk identification process was developed. This process consists of two steps: first, mapping out the network structure of the company's supply chain; second, identifying and tracking certain data that could be used as factors to identify potential supply risks. The process proposes a model based on fuzzy logic to aggregate and map the data to highlight potential risks. The thesis also contains a discussion of implementation of the proposed approach, including software requirements as well as organizational roles and responsibilities.