Designing global manufacturing networks using Big Data

This paper seeks to extend existing methods for decision making implementation in design and operation of global manufacturing networks, creating products in value added chains distributed over several manufacturing sites. The configuration of products, processes and resources in the network are subject of design decisions and is represented by production master data. Rapidly changing global market opportunities and competition force companies to continuously adapt their manufacturing network which causes increasing dependencies and complexity. Currently, design and decisions are carried out on manufacturing site level, which makes it impossible to consider dependencies in the network. This leads to risks in outcomes of decisions and to a suboptimal design for the manufacturing network as a whole. A solution to this problem can be found, when design and decisions are based on the overall manufacturing network. Production master data for a global manufacturing network is a challenge in terms of quantity and processing performance required. In this paper, an approach for applying Big Data techniques is described, highlighting the aspects of decisions tasks, data access patterns, necessary data structures and handling of design scenarios. In case of manufacturing network design, Big Data enables overall system design, increasing validity of decisions, high performance and new analysis options for unexploited potential in the network. This approach is applicable to various decision processes within manufacturing concerning query, analysis and interaction with an overall manufacturing system. (C) 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).