Introduction

Abstract

Striving for operational and strategic excellence, companies have continuously been trying to improve their business processes in the past years. As sophisticated planning processes are often seen as a key enabler for efficient business processes, this has led to an increased focus on developing methods to optimize processes using mathematical methods in cases where decision problems are too complex to be solved by a human decision maker. In order to transfer such methods into practice, IT-based systems such as Advanced Planning Systems (APS) that make use of these optimization methods to support and automate planning processes and execute the resulting plans are essential.

In companies handling physical goods, the planning processes for production and logistics often involve a high amount of complexity. This complexity results on the one hand from the fact that companies frequently have multiple locations in various countries and offer a large product variety associated with high manufacturing complexity. On the other hand, companies are increasingly embedded in complex global supply networks with a large number of actual or potential suppliers and customers. From the viewpoint of an individual company, its suppliers and customers and the actors further upstream or downstream are referred to by the term Supply Chain (SC). Thus, each company may be part of multiple “subjective” supply chains of other companies (Bretzke, 2006).