Location
260-009, Owen G. Glenn Building
Start Date
12-15-2014
Description
Supply chain quality inspection (SCQI) is a widely-adopted instrument when a buyer purchases products from suppliers. However, when suppliers are deliberately cheating to manipulate the products and falsify the specific testing methods (i.e., quality deception), traditional operation management theories fail to guide the industry SCQI practices, causing tragedies like tainted milk scandals. We propose to address this problem from a perspective of information gathering and knowledge reasoning. We argue that the rationale behind quality deceptions in SCQI could be analyzed, predicted, and thus prevented, based on information collected from supply chains. In this paper, we design DSS to analyze and predict suppliers’ possible production behaviors. Based on the decision supports, buyers can make effective inspection policies to detect quality deceptions while minimizing inspection costs. We build a prototype and use a laboratory experiment to demonstrate the prototype’s superiority in supporting inspection policy making in SCQI.
Recommended Citation
Yan, Jiaqi; SUN, Sherry; Wang, Huaiqing; Shi, Yani; and Hu, Daning, "Decision Support Systems to Detect Quality Deceptions in Supply Chain Quality Inspections: Design and Experimental Evaluation" (2014). ICIS 2014 Proceedings. 2.
https://aisel.aisnet.org/icis2014/proceedings/ISDesign/2
Decision Support Systems to Detect Quality Deceptions in Supply Chain Quality Inspections: Design and Experimental Evaluation
260-009, Owen G. Glenn Building
Supply chain quality inspection (SCQI) is a widely-adopted instrument when a buyer purchases products from suppliers. However, when suppliers are deliberately cheating to manipulate the products and falsify the specific testing methods (i.e., quality deception), traditional operation management theories fail to guide the industry SCQI practices, causing tragedies like tainted milk scandals. We propose to address this problem from a perspective of information gathering and knowledge reasoning. We argue that the rationale behind quality deceptions in SCQI could be analyzed, predicted, and thus prevented, based on information collected from supply chains. In this paper, we design DSS to analyze and predict suppliers’ possible production behaviors. Based on the decision supports, buyers can make effective inspection policies to detect quality deceptions while minimizing inspection costs. We build a prototype and use a laboratory experiment to demonstrate the prototype’s superiority in supporting inspection policy making in SCQI.