Customer relationship management (CRM) systems track and measure marketing campaigns over different networks. There are literally many studies associated with the implementation of CRM in different industries. This paper presents an empirical investigation to determine effective factors influencing on the success of CRM implementation. The study designs a questionnaire in Likert scale on measuring various factors and distributes it among selected employees who work for the biggest software maker firm in city of Tehran, Iran. Using principal component analysis, the study has detected five factors influencing on the succes of CRM including customer relationship technologies, customer oriented, enterprise development strategies, customer services and business plan.

Procurement risks analysis is a crucial study in supply chain risk management. According to the phenomenon of snow ball effect, if a risk occurs at the primitive node between buyer and supplier, it will be strengthened and extended throughout the entire network. Hence, it can have more disruptive influences comparing with risks that happen in further nodes. In this paper, through a case study, the most important risk factors and its multi-dimensional consequences in procurement risk analysis is derived from the literature review and implementing a Delphi method that takes into account the nine risks factors and 39 preliminary variables as risk consequences. The nine risk factors are limits of global communication, packaging and transportation, procedures and standards, sanction, supply of equipment, price of equipment, Communication, management of technology and delivery mistakes.

Complexity is one of the most important issues influencing success of any construction project and there are literally different studies devoted to detect important factors increasing complexity of projects. During the past few years, there have been growing interests in developing mass construction projects in Iran. The proposed study of this paper uses Delphi technique to find out about important factors as barriers of construction projects in Iran. The results show that among 47 project complexity factors, 19 factors are more important than others are. The study groups different factors into seven categories including environmental, organizational, objectives, tasks, stakeholders, technological, information systems and determines the relative importance of each. In each group, many other sub group activities are determined and they are carefully investigated. The study provides some detailed suggestions on each category to reduce the complexity of construction project.

An organization has to make the right decisions in time depending on demand information to enhance the commercial competitive advantage in a constantly fluctuating business environment. Therefore, estimating the demand quantity for the next period most likely appears to be crucial. Manufacturing companies consider forecasting a crucial process for effectively guiding several activities, and research has devoted particular attention to this issue. The objective of the paper is to propose a new forecasting mechanism which is modeled by integrating Fuzzy Delhi Method (FDM) with Artificial Neural Network (ANN) techniques to manage the demand with incomplete information. Artificial neural networks has been applied as it is capable to model complex, nonlinear processes without having to assume the form of the relationship between input and output variables. The effectiveness of the proposed approach to the demand forecasting issue is demonstrated for a 20/25 MVA Distribution Transformer from Energypac Engineering Limited, a leading power engineering company of Bangladesh.