Title :
Smart tracking to enable disturbance tolerant manufacturing through enhanced product intelligence
Author :
Jumyung Um;Rengarajan Srinivasan;Alan Thorne;Duncan McFarlane
Author_Institution :
Institute for Manufacturing, University of Cambridge, Cambridge, UK
fDate :
7/1/2015 12:00:00 AM
Abstract :
There is increasing need for manufacturing organisations to implement lean, just-in-time, make-to-order systems, mainly due to the cost pressures and varying customer preferences. This creates unexpected disturbances within the manufacturing systems, causing delays in delivery time. In order to quickly identify and react to disturbances, it is vital to capture real-time dynamic information related to the parts in production, resources, inventory levels and quality information. In many literatures, product intelligence achieves the fundamental requirements of managing disturbance. Current challenge, however, is that existing researches focused on developing a tracking system dealing with specific disturbance. In this paper, we present a systematic guideline for implementing such a system. The proposed guideline uses principles of product intelligence and combines them with the characteristics of disturbances and the associated information requirements. A case example is also presented to illustrate the developed concepts.
Keywords :
"Assembly","Robots","Artificial intelligence","Production","System analysis and design","Sensors"
Conference_Titel :
Industrial Informatics (INDIN), 2015 IEEE 13th International Conference on
Electronic_ISBN :
2378-363X
DOI :
10.1109/INDIN.2015.7281932