Decision Support Systems to Detect Quality Deceptions in Supply Chain Quality Inspections: Design and Experimental Evaluation

Supply chain quality inspection (SCQI), which is carried out to measure the product quality based on quality requirements, 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 the quality deception in SCQI could be analyzed, predicted, and thus prevented, based on collecting information from supply chains. In this paper, we adopt a Design Science approach to design Decision Support Systems (DSS) that analyze and predict suppliers’ possible production behaviors. Based on the decision supports, buyers can make effective inspection policies to detect quality deception while minimizing inspection costs. In order to illustrate the effectiveness of this approach, we build a prototype and use a laboratory experiment to demonstrate the prototype’s superiority in supporting inspection policy making in supply chains.