Flow of information in supply chain is as prominent as material and financial flows and among these three aspects of a supply chain, information flow could be of great importance since it provides a basis for a steady flow of goods and finance as well. To help managers control the flow of information in an effective way, first, the parameters which affect the flow of information should be determined. Then, if the parameters can be controlled carefully, the information will be shared correctly and in a timely manner among supply chain members. This paper identifies the influencing parameters on proper flow of information in supply chain and provides a list of parameters based on the literature as well as the industrial and academic experts’ opinions. Afterwards, in order to define the degree of importance for each parameter from the experts’ perspective, fuzzy TOPSIS method is employed and the parameters are ranked based on three criteria, namely “measurability”, “being illustrative” and “parameters relevancy” to the issue of information flow. The research findings show that “Supply Chain Hardware Capabilities”, “Supply Chain Network Infrastructure”, “Information Software Capabilities”, “Information Sharing Timeliness”, “Information Recency” and “Organizational Rewards” received the highest priorities, while “Power of Internal and Inter-personal Communications”, “Users’ Trust” and “Users’ Tendency” were standing at the bottom of this ranking. The results of this research could be employed as an input for strategy development process for supply chain information management activities. Thus, the awareness of each parameter’s importance in proper flow of information, helps us make appropriate strategies to improve information management in the supply chain.

In this paper, a weighted transfer point location problem is developed in which demand points have probabilistic coordinates. The proposed model is formulated as a probabilistic unconstrained nonlinear programming and the optimum values of decision variables are obtained in the form of probability distribution functions. Because of the complexity in solving the developed model by using the traditional solution approaches, a new stochastic inference method called Probabilistic Role Base (PRB) is developed based on the derived optimum probability distribution functions of the decision variables. This method is used to infer the optimum or near optimum values of all decision variables without solving nonlinear programming model, directly. Finally, to demonstrate the efficiency of the developed algorithm, a numerical example is presented and the results are compared with the optimum solution.