Sustainable Energy Management System Framework for Small and Medium Sized Manufacturing Facilities

The general significance of the topic stems from the fact that energy consumption by the industrial manufacturing sector in the U.S. accounted for one third of total consumption in 2016 and is expected to grow more than 25% by 2040; small and medium sized manufacturing (SMM) facilities are collectively responsible for a large portion of this consumption. Increasing the energy efficiency of SMM facilities means less energy is used, or energy is used in a more efficiency manner, decreasing the amount of natural resources consumed, reducing emissions, and lowering operating costs - potentially resulting in greater profits and a stronger economy. Based on experience with the Industrial Assessment Center program and data presented in the literature, a typical SMM facility has between 10 and 30% wasted energy.This wasted energy presents an opportunity for significant savings that could be achieved through systematic energy management. However, formal energy management systems (EnMS), such as ISO 50001, have not yet been widely adopted by SMMs. This is in large part due to numerous barriers faced by SMMs. A significant part of a successful implementation of an EnMS involves data collection and analysis tools such as submetering technology and energy information systems. This dissertation research seeks to provide SMMs with the ability to break through some of the barriers associated with implementation of EnMSs and submetering technology in order to improve their energy management. This research first makes the connection between the past quality movement and the current energy efficiency movement. Four absolutes to energy management are presented which are used to create an EnMS hierarchy, which describes the stages in an organization’s energy management system maturity. An energy management maturity grid, modeled after Crosby’s quality management maturity grid, is presented as a tool for SMMs to self-assess the state of their current EnMS. Finally, a methodology is presented to assist an SMM in implementing a formal EnMS in a way that is funded through the energy savings it identifies. This ensures a financially sustainable EnMS which can adapt to an organization’s needs over time. This methodology is validated through a conceptual example.Industrial Engineering & Managemen

Innovation efficiency of high-tech industries in China

A measurement of technical innovation efficiency reflects the competitiveness of the high-tech industry for a region or a country. The high-tech industry, which appears at the forefront of technology and scientific research, provides a country with a certain competitive advantage. Many developed countries such as the USA, UK, Germany and France, have used the high-tech industry as a means to emerge on the technological frontier. Many developing countries such as China and India have developed high-tech industries, and are home to many leading product manufacturers. However, innovation efficiency is important, since it explains the efficiency of the high-tech industry in consuming resources and providing outputs. This dissertation examines the innovation efficiency of the high-tech industry in China. The Data Envelopment Analysis (DEA) method was used to study and analyse panel data. The study focused on 28 high-tech provinces of China (DMUs, DMU: Decision Making Unit), during the years 2005-2011, along with 5 industry categories and 17 industries. Different datasets were obtained to measure the input and output indices. Variables included in the inputs index included the number of full time R&D (Research and Development) personnel, internal expenditure on R&D, expenditure on new product development, and investment in fixed assets. The output index included the number of patent applications, the output value of new products, and sales revenue for new products. The Malmquist index was calculated using static data analysis cases using Deap2 software in both cases. Several tests were employed in the analysis of the data, including the KS Test (Kolmogorov-Smirnov Test), T test (Student's t test), integral analysis, SE efficiency analysis, project analysis, total factor productivity and others. The findings indicate that the M index is unstable across the 29 provinces, and 17 industries. The Malmquist index of each DMU changes in different degrees during the 7 years. In addition, the changes have no pattern, they go from descending to rising and then declining again, or from rising to descending and then rising again. The reasons for the unstable M index were evaluated, and it becomes evident that several factors such as a total factor productivity variation, EC, TC degradation, excessive man power resources that increased the input costs. Another factor that makes the M index unstable is that many of the inputs for China were obtained from western regions, with little original research. The study also examined the STP (Science and Technology Policy) policy of the developed western countries, BRIC nations, and China, and the areas for improvement were identified. The study has made several recommendations to improve the STP policy, and for the high-tech industry to increase the innovation efficiency

Innovation efficiency of high-tech industries in China

A measurement of technical innovation efficiency reflects the competitiveness of the high-tech industry for a region or a country. The high-tech industry, which appears at the forefront of technology and scientific research, provides a country with a certain competitive advantage. Many developed countries such as the USA, UK, Germany and France, have used the high-tech industry as a means to emerge on the technological frontier. Many developing countries such as China and India have developed high-tech industries, and are home to many leading product manufacturers. However, innovation efficiency is important, since it explains the efficiency of the high-tech industry in consuming resources and providing outputs. This dissertation examines the innovation efficiency of the high-tech industry in China. The Data Envelopment Analysis (DEA) method was used to study and analyse panel data. The study focused on 28 high-tech provinces of China (DMUs, DMU: Decision Making Unit), during the years 2005-2011, along with 5 industry categories and 17 industries. Different datasets were obtained to measure the input and output indices. Variables included in the inputs index included the number of full time R&D (Research and Development) personnel, internal expenditure on R&D, expenditure on new product development, and investment in fixed assets. The output index included the number of patent applications, the output value of new products, and sales revenue for new products. The Malmquist index was calculated using static data analysis cases using Deap2 software in both cases. Several tests were employed in the analysis of the data, including the KS Test (Kolmogorov-Smirnov Test), T test (Student's t test), integral analysis, SE efficiency analysis, project analysis, total factor productivity and others. The findings indicate that the M index is unstable across the 29 provinces, and 17 industries. The Malmquist index of each DMU changes in different degrees during the 7 years. In addition, the changes have no pattern, they go from descending to rising and then declining again, or from rising to descending and then rising again. The reasons for the unstable M index were evaluated, and it becomes evident that several factors such as a total factor productivity variation, EC, TC degradation, excessive man power resources that increased the input costs. Another factor that makes the M index unstable is that many of the inputs for China were obtained from western regions, with little original research. The study also examined the STP (Science and Technology Policy) policy of the developed western countries, BRIC nations, and China, and the areas for improvement were identified. The study has made several recommendations to improve the STP policy, and for the high-tech industry to increase the innovation efficiency