Big Data Analytics and Its Applications in Supply Chain Management

In today’s competitive marketplace, development of information technology, rising customer expectations, economic globalization, and the other modern competitive priorities have forced organizations to change. Therefore, competition among enterprises is replaced by competition among enterprises and their supply chains. In current competitive environment, supply chain professionals are struggling in handling the huge data in order to reach integrated, efficient, effective, and agile supply chain. Hence, explosive growth in volume and different types of data throughout the supply chain has created the need to develop technologies that can intelligently and rapidly analyze large volume of data. Big data analytics capability (BDA) is one of the best techniques, which can help organizations to overcome their problem. BDA provides a tool for extracting valuable patterns and information in large volume of data. So, the main purpose of this book chapter is to explore the application of BDA in supply chain management (SCM)

Risk Identification and Prioritization in Public-Private Partnership Water Transfer Projects (Case Study: Water Transfer Project to Safadasht Lands)

In the face of growing water demand and insufficient public budgets in developing countries, public-private partnerships play an important role in infrastructure development, such as water supply and transmission. However, carrying out such projects always carries with it many and varied risks that threaten the success of such projects. Due to the research gap on risk identification and assessment in the context of public-private partnership water transfer projects, the purpose of the present research is to identify and prioritize the risks in Safadasht public-private partnership water transfer project. The first phase of the present research identifies the risks of public-private partnership projects. At this stage of the research, through the brainstorming method, an attempt was made to identify the risks related to the project under study. In the second stage, three research criteria including "probability of occurrence", "cost effect" and "time effect" were weighed using the best-worst method. The third stage, which was the final stage of the research, was dedicated to prioritizing the risks identified in the first stage using the simple additive weighting technique. Based on the analyzes performed, respectively, changes in legal conditions and standards by the government, sudden changes in bank interest rates, severe climate change, damage to the investor's manpower and improper performance of the investor project factors were identified as the most important risks. Finally, while pointing out the limitations of the research, some suggestions for future studies were presented