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| Research article summary (published 13 Jan 2003): |
Interactive decision support system to predict print quality.
Full Abstract
Customers using printers occasionally experience problems such as fuzzy images, bands, or streaks. The customer may call or otherwise contact the manufacturer, who attempts to diagnose the problem based on the customer's description of the problem. This study evaluated Bayesian inference as a tool for identifying or diagnosing 16 different types of print defects from such descriptions. The Bayesian model was trained using 1701 narrative descriptions of print defects obtained from 60 subjects with varying technical backgrounds. The Bayesian model was then implemented as an interactive decision support system, which was used by eight 'agents' to diagnose print defects reported by 16 'customers' in a simulated call centre. The 'agents' and 'customers' in the simulated call centre were all students at Purdue University. Each customer made eight telephone calls, resulting in a total of 128 telephone calls in which the customer reported defects to the agents. The results showed that the Bayesian model closely fitted the data in the training set of narratives. Overall, the model correctly predicted the actual defect category with its top prediction 70% of the time. The actual defect was in the top five predictions 94% of the time. The model in the simulated call centre performed nearly as well for the test subjects. The top prediction was correct 50% of the time, and the defect was one of the top five predictions 80% of the time. Agent accuracy in diagnosing the problem improved when using the tool. These results demonstrated that the Bayesian system learned enough from the existing narratives to accurately classify print defect categories.
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Author information
Author/s: Leman, Sugani (S); Lehto, Mark R (MR);
Affiliation: School of Industrial Engineering, Purdue University, West Lafayette, IN 47907, USA.
Journal and publication information
Publication Type: Journal Article; Research Support, Non-U.S. Gov't
Journal: Ergonomics (Ergonomics), published in England. (Language: eng)
Reference: 2003-Jan; vol 46 (issue 1-3) : pp 52-67
Dates: Created 2003/01/29; Completed 2003/04/23; Revised 2006/11/15;
PMID: 12554398, status: MEDLINE (last retrieval date: 11/6/2008)
Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.
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