| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 1700397 | Procedia CIRP | 2014 | 6 Pages |
The proceeding globalization leads to growing and more and more complex company networks. Especially for the production industry the high cost pressure forces companies to spread their production sites over the globe. Low labour costs, distances to potential markets and productivity influences lead onto complex decision situations for managers in charge. Beside the total landed costs to produce the product portfolio, the global footprint decisions are influenced by other dimensions as well. Since it was possible to improve global footprint design decisions by an evolutionary algorithm with the web tool OptiWo, this paper specifies an approach to quantitatively implement risk management as the second dimension of these decisions. With the distribution of production sites over the globe the companies are getting more and more exposed to different potential global risks. Additionaly, the actual political situation of crisis around the world as well as geological disasters stroked the fears of losses. Finally risk-averse companies try to get an analysis on political and geographical risks for the particular footprint they are designing. The approach consists of two basic elements of a typical risk analysis: Firstly, the risk as the feasibility of a political or geological event causing a production blackout in a site is rated for every site. This risk rating is generated by professionals in this specific field. Secondly, the impact of a blackout of a specific site of a network has to be rated. Finally, these two risk dimensions are combined to get a specific footprint risk level. The approach is standardized to allow an integration into the existing tool to make the risk analysis of a global production network efficient and automatically useable. Due to this achievement managers are able to overview another dimension of global footprint design decisions efficiently. A validation of our approach is presented using a data set of a recently conducted industry project. Different network scenarios of a global manufacturer in the automation industry are compared to point out different characteristics of global footprint risks.
