Benchmarking Environmental Efficiency of Ports Using Data Mining and RDEA: The Case of a U.S. Container Ports

This study provides step-wise benchmarking practices of each port to enhance the environmental performance using a joint application of the data-mining technique referred to as Kohonen’s self-organizing map (KSOM) and recursive data envelopment analysis (RDEA) to address the limitation of the conventional data envelopment analysis. A sample of 20 container ports in the U.S.A. were selected, and data on input variables (number of quay crane, acres, berth and depth) and output variables (number of calls, throughput and deadweight tonnage, and CO2 emissions) are used for data analysis. Among the selected samples, eight container ports are found to be environmentally inefficient. However, there appears to be a high potential to become environmentally efficient ports. In conclusion, it can be inferred that the step-wise benchmarking process using two combined methodologies substantiates that a more applicable benchmarking target set of decision-making units is be projected, which consider the similarity of the physical and operational characteristics of homogenous ports for improving environmental efficiency

An Investigation of the Impact of Social Media Platforms on Supply Chain Performance through Competitive Intelligence using AHP Model

This study investigates the use of social media platforms (SMPs) for acquiring supply chain intelligence (SCI) to improve supply chain performance. Given the growth of social media use, there is an urgency for increased understanding of the effectiveness of emerging SMPs. In today's competitive global environment, supply chain managers need to have a precise understanding about the SMPs that have become one of the premier sources of gaining SCI and in turn foster supply chain performance. Organizations need a methodology for selecting SMPs to remain proactive ahead of their competitors. The evolution of SMPs has caused a paradigm shift in how organizations obtain SCI to increase their revenues, profitability and reputation. The aim of this study is to apply a multi-criteria analysis using the analytic hierarchy process (AHP) to select SMPs. Stage 1 represents the primary goal, the decision maker wishes to gain in executing SMPs; Stage 2 consists of decision criteria; Stage 3 is composed of sub-criteria; and finally Stage 4 represents the SMP alternatives reported in the organizational hierarchy structure. The objective of this model is to rank the SMPs. The model includes key supply chain performance factors in the organization. The hierarchical models are used to breakdown the complex notion of supply chain performance into its constituent parts. The second phase of the hierarchical model consists of the performance indicators of which supply chain performance is composed. Hence, the modeled value is the supply chain performance in the organization. Our results indicate that the top three supply chain performance indicators are quality, assurance of supply and delivery. Meanwhile the top three types of supply chain intelligence are logistics intelligence, product/process intelligence and supply chain visibility intelligence. The top three SMP alternatives are, LinkedIn, Facebook and Twitter

Elementary Statistics MyOpenMath Course

This open course within MyOpenMath was created and revised under an ALG Affordable Materials Grant. Download the Word doc linked on the following page for instructions on how to access the course within MyOpenMath