Pattern classification driven enhancements for human-in-the-loop decision support systems

• Published in: Decision Support Systems Vol. 50; no. 2; pp. 460 - 468
• Main Authors: Subramania, Halasya Siva, Khare, Vineet R.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 2011; Elsevier; Elsevier Sequoia S.A
• Subjects: Algorithms; Applied sciences; Artificial intelligence; Automobile industry; Automotive components; Classification; Computer science; control theory; systems; Confusion matrix; Data mining; Data processing. List processing. Character string processing; Decision making; Decision support systems; Decision theory. Utility theory; Decision trees; Diagnostic systems; Diagnostics; Durability; Errors; Exact sciences and technology; Fault diagnosis; Human expert; Information systems. Data bases; Memory organisation. Data processing; Operational research and scientific management; Operational research. Management science; Pattern classification; Proving; Software; Studies; Support vector machines; Warranties; Decision support systems; Support vector machines; Confusion matrix; Human expert; Decision trees; Pattern classification; Motor car; Costs; Production quality; Data mining; Business process; Quality assurance; Vector support machine; Data analysis; Statistical analysis; Warranty; Decision support system; Durability; Hardware in the loop simulation; Integrated management; Social decision; Multiagent system; Firm management; Reliability; Artificial intelligence; Fault diagnostic; Expertise; Dealer
• ISSN: 0167-9236; 1873-5797
• DOI: 10.1016/j.dss.2010.11.003

Data mining has been a key technology in the warranty sector for mass manufacturers to understand and improve product quality, reliability and durability. Cost savings is an important aspect of business which calls for processes that are error proof. Pattern classification methods applied to the diagnostic data could help build error proof processes by improving the diagnostic technology. In this paper we present a case study from the automotive warranty and service domain involving a human-in-the-loop decision support system (HIL-DSS). The automotive manufacturers offer warranties on products, made of parts from different suppliers, and rely on a dealer network to assess warranty claims. The dealers use diagnostic equipment manufactured by third parties and also draw on their own expertise. In addition, a subject matter expert (SME) assesses these collective decisions to distinguish between inaccurate diagnoses by the dealers or an inadequate decision algorithm in the diagnostic equipment. Altogether this makes a comprehensive HIL-DSS. The proposed methodology continuously learns from collective decision making systems, enhances the diagnostic equipment, adds to the knowledge of dealers and minimizes the SME involvement in the review process of the overall system. Improving the diagnostic equipment helps in better warranty servicing, whereas improvements in the human expert knowledge help prevent field error and avoid customer dissatisfaction due to improper fault diagnosis. ► A novel approach based on pattern-classification is presented to continuously learn from collective decision making systems. ► enhance the limited/incomplete DSS. ► add to the knowledge of human expertise and ► and minimize the human involvement in the review process of the overall system.