Competitive advantage is often determined by the effectiveness of an organization's supply chain, and as a result, the evaluation and selection of suppliers has become an increasingly important management activity. But the evaluation process is complex. The data that must be considered are both technical and social/organizational. Much of the data are difficult to obtain and ambiguous or vague to interpret. In addition, the dynamic global environment of changing exchange rates, economic conditions, and technical infrastructure, demand that the pool of potential suppliers be re-evaluated periodically. Nonetheless, a rational process of evaluation must exist to select the most appropriate suppliers. This paper addresses one dimension of the evaluation process, the information sharing capability of potential supply chain partners. It is an especially important dimension since information technology is necessary to horizontally integrate geographically dispersed operations. Fuzzy logic, a subset of artificial intelligence, together with analytical hierarchy process is used to model this process and rank potential suppliers. It is an appropriate methodology to use for this application and has the potential to be used with other supply chain design decisions since it explicitly handles vague, ambiguous, and imprecise data.

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Project success rates have improved, and much of the credit can be given to the knowledge, practices, and standards that have contributed to the professionalization of the field. Unfortunately, too many failures still occur. Because many of them can be traced to management and decision‐making practices, it might be useful at this stage to explore a set of systematic biases to determine if understanding them can help diagnose and perhaps even prevent failures from occurring. This article begins with a framework identifying the influences on project outcomes, defines the systematic biases that may derail projects, summarizes eight project failures, uses the framework to diagnose those failures, and concludes by suggesting how organizational and project culture may contribute to these very common and natural biases.

With increasing frequency the eflective management and coordination of supply chains requires the sharing of a wide range of data. But the challenge, both technically and socially, to share information increases when customers and suppliers are spread throughout the geographic regions of the world. It is this challenge that is addressed here. First a conceptual framework is built. This framework clas.rifies the stages of information sharing within a supply chain and proposes seven variables that affect the flow of information between customers and their suppliers. These variables include industry, market and competitive environment, national culture, corporate IT culture, size, IT infrastructure, and country ITsupport. The results are generalized and summarized in a Supply Chain IT Linkage Capability Model. Case studies of four organizations are presented and analyzed to validate the role of these variables in data sharing strategies. The paper concludes with several implicationsfor global information technology supply chain management systems.