Authors: Felsberger Andreas, Oberegger Bernhard, Reiner Gerald

Content Provider: Zenodo
In the field of manufacturing systems automated data acquisition and development of technological innovations like manufacturing execution systems (MES), Enterprise Resource Planning (ERP), Advanced Planning Systems (APS) and new trends in Big Data and Business Intelligence (BI) have given rise to new applications and methods of existing decisionsupport technologies.
Today manufacturers need an adaptive system that helps to react and adapt to the constantly changing business environment. The internal data processing system of a company can only offer minimum support because it is related to transactions. In this case, decision support systems (DSS) combine human skills with the capabilities of computers to provide efficient management of data, reporting, analytics, modeling and planning issues. DSS provide a distinction between structured, semi structured and unstructured data. In particular, a DSS reduces the quantity of data to a high quality structured amount; due to this, decisions are made to support the manufacturing process.
In addition, the goal of these systems is to avoid problems within the production process even before they emerge. This paper gives an overview of the state-ofthe-art literature on DSS and describes current techniques of relevant DSS applications within manufacturing environments.
This article is distributed under license  Creative Commons Attribution 4.0

Illustration Photo: Solar Modules manufacture (credits: solar giga / Flickr Attribution-NonCommercial-NoDerivs 2.0 Generic (CC BY-NC-ND 2.0))


No comments to display.