Evaluating the information sharing capabilities of supply chain partners: A fuzzy logic model

Shore, B.

Venkatachalam, A. R.



International Journal of Physical Distribution and Logistics Management

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.

(Shore & Venkatachalam, 2003)


Fuzzy logic in manufacturing: A review of literature and a specialized application

Azadegan, A.

Porobic, L.

Ghazinoory, S.

Samouei, P.

Saman Kheirkhah, A.



International Journal of Production Economics

Manufacturing decisions inherently face uncertainties and imprecision. Fuzzy logic, and tools based on fuzzy logic, allow for the inclusion of uncertainties and imperfect information in decision making models, making them well suited for manufacturing decisions. In this study, we first review the progression in the use of fuzzy tools in tackling different manufacturing issues during the past two decades. We then apply fuzzy linear programming to a less emphasized, but important issue in manufacturing, namely that of product mix prioritization. The proposed algorithm, based on linear programming with fuzzy constraints and integer variables, provides several advantages to existing algorithm as it carries increased ease in understanding, in use, and provides flexibility in its application.

(Azadegan, Porobic, Ghazinoory, Samouei, & Saman Kheirkhah, 2011)


An interactive multi-user decision support system for consensus reaching processes using fuzzy logic with linguistic quantifiers

Fedrizzi, M.

Kacprzyk, J.

Zadrozny, S. a



Decision Support Systems

We present an interactive user-friendly microcomputer-based decision support system for consensus reaching processes. The point of departure is a group of individuals (experts, decision makers …) who present their testimonies (opinions) in the form of individual fuzzy preference relations. Initially, these opinions are usually quite different, i.e., the group is far from consensus. Then, in a multistage session a moderator, who is supervising the session, tries to make the individuals change their testimonies by, e.g., rational argument, bargaining, etc. to eventually get closer to consensus. For gauging and monitoring the process a new “soft” degree (measure) of consensus is used whose essence is the determination to what degree, e.g., “most of the individuals agree as to almost all of the relevant options”. A fuzzy-logic-based calculus of linguistically quantified propositions is employed.

(Fedrizzi, Kacprzyk, & Zadrozny, 1988)