Abstract
It is inherent to food supply chain networks that performance deviations occur occasionally due to variations in product quality and quantity. To reduce losses, one wants to be informed about such deviations as soon as possible, preferably even before they occur. Then it is possible to take actions to prevent or reduce negative consequences. In practice, extensive operational data is recorded, forming a valuable source for early warning and proactive control systems, i.e. decision support systems for prediction and prevention of such performance problems. Data mining methods are ideal for analyzing such data sources and extracting useable information from them. In this paper, we present a novel framework for early warning and proactive control systems that combine expert knowledge and data mining methods to exploit recorded data. We discuss the implementation of a prototype system and the experiences from a case study regarding the applicability of the framework.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.