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Dr. Gokhan Egilmez
University of New Haven

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0 Applied Optimization
0 Industrial Engineering
0 Supply Chain Management
0 Sustainability
0 Simulation

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Sustainability
Simulation
Supply Chain Management
Industrial Engineering

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Short Biography

Dr. Gokhan Egilmez serves as associate professor in the department of Mechanical, and Industrial Engineering at University of New Haven . He previously worked as assistant professor of Industrial and Manufacturing Engineering at North Dakota State University between 2013 and 2015, and postdoctoral research associate in the Dept. of Civil, Environmental and Construction Engineering at University of Central Florida in 2013. Gokhan obtained his Ph.D. degree in Mechanical and Systems Engineering and M.S. degrees in ‘Industrial and Systems Engineering’ and ‘Civil Engineering’ at Ohio University between 2007 and 2012. He leads research and industry consulting efforts at ASOSlab (www.asoslab.com) at University of New Haven.

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Journal article
Published: 21 May 2021 in Sustainability
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Manufacturing activities of China and the U.S. account for a substantial portion of the global manufacturing output and environmental sustainability impacts. The two countries’ economies account for one third of the global economic output. Their supply chains are critically linked with and serve most of the production and service industries across the globe. Recent global trends in manufacturing necessitate a study that comparatively analyzes the two countries’ manufacturing industries from an economic and environmental perspective. In this paper, U.S. and China manufacturing industries were investigated to analyze the economic and mid and endpoint environmental impacts over a 20-year study period. The literature is abundant with single period and single country focused works, and this study contributes to the state-of-art by extending the temporal dimension to 20 years and spatial focus to the global economy (40 countries and rest of the world). In terms of the methodology, Multi-region input-output (MRIO) models were built using the World Input-Output Database (WIOD) as the primary database, global input-output tables, environmental impact and economic output multipliers, and manufacturing industries’ final demand. Twenty MRIO models, each comprised of 40 major economies and the rest of the world (ROW), were built to cover the global trade linkages, which yielded the global supply chain linked cradle-to-gate life cycle inventory (LCI) of economic outputs and environmental impacts. The environmental LCI was extended to midpoint (Global Warming Potential (GWP) and Ozone Depletion Potential (ODP)) and endpoint (human health and ecosystem) impact dimensions by ReCipe framework. Lastly, the relative impact of a unit change in Leontief inverse, final demand and Green House Gas (GHG) emission multipliers on the total economic output and environmental impacts were explored with structural decomposition analysis (SDA). Results indicated that both countries’ manufacturing industries experienced positive economic output growth, in which China was more dominant in recent years. Both countries’ manufacturing industries’ midpoint and endpoint impacts were found to be steeply rising despite the negative growth observed in emissions intensities. The amount of GHG emissions and related midpoint (global warming and ozone depletion) and endpoint (damage to ecosystems and human life) impacts seemed to be quickly worsening in China compared to the USA.

ACS Style

Mustafa Saber; Gökhan Eğilmez; Ridvan Gedik; Yong Park. A Comparative Time-Series Investigation of China and U.S. Manufacturing Industries’ Global Supply-Chain-Linked Economic, Mid and End-Point Environmental Impacts. Sustainability 2021, 13, 5819 .

AMA Style

Mustafa Saber, Gökhan Eğilmez, Ridvan Gedik, Yong Park. A Comparative Time-Series Investigation of China and U.S. Manufacturing Industries’ Global Supply-Chain-Linked Economic, Mid and End-Point Environmental Impacts. Sustainability. 2021; 13 (11):5819.

Chicago/Turabian Style

Mustafa Saber; Gökhan Eğilmez; Ridvan Gedik; Yong Park. 2021. "A Comparative Time-Series Investigation of China and U.S. Manufacturing Industries’ Global Supply-Chain-Linked Economic, Mid and End-Point Environmental Impacts." Sustainability 13, no. 11: 5819.

Journal article
Published: 13 November 2020 in Journal of Cleaner Production
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This paper proposes a framework to assess the status of sustainable development performance of OECD countries towards reaching the 2030 agenda based on the 17 Sustainable Development Goals (SDGs). Each SDG addresses a critical area of sustainable development and is represented with several social, economic, or environmental indicators. As a result of data collection efforts, 17 SDGs are represented with total of over 90 variables following the guidance of United Nation’s (UN) recent reports. Using such a high number of variables to create a benchmark score for each of the 35 OECD countries is a challenging and complex task due to the degree of correlation among the indicators and unit of measurement differences. To cope with these challenges, we proposed a Goal-Specific Principal Component Analysis (GS-PCA) approach and compared statistically with the UN reports for experimental validation purpose. It was found that SDG1 No Poverty, SDG7 Affordable and Clean Energy, SDG11 Sustainable Cities and Communities, SDG17 Partnerships to Achieve the Goal and the group mean of 17 SDGs were found to be improving. On the other hand, SDG4 Quality Education and SDG8 Decent Work and Economic Growth were in decline. The highest performance was observed in SDG 8 Decent Work and Economic Growth (78.06) and the lowest performance was observed in SDG 17 Partnerships to Achieve the Goal (29.93). In addition, substantial differences were observed in the scores and ranks of mediocre and poor performing countries compared to the benchmark reports, while both the results of this study and benchmark reports were found to be strongly positively correlated.

ACS Style

Shyam Lamichhane; Gökhan Eğilmez; Ridvan Gedik; M. Khurrum S. Bhutta; Bulent Erenay. Benchmarking OECD countries’ sustainable development performance: A goal-specific principal component analysis approach. Journal of Cleaner Production 2020, 287, 125040 .

AMA Style

Shyam Lamichhane, Gökhan Eğilmez, Ridvan Gedik, M. Khurrum S. Bhutta, Bulent Erenay. Benchmarking OECD countries’ sustainable development performance: A goal-specific principal component analysis approach. Journal of Cleaner Production. 2020; 287 ():125040.

Chicago/Turabian Style

Shyam Lamichhane; Gökhan Eğilmez; Ridvan Gedik; M. Khurrum S. Bhutta; Bulent Erenay. 2020. "Benchmarking OECD countries’ sustainable development performance: A goal-specific principal component analysis approach." Journal of Cleaner Production 287, no. : 125040.

Conference paper
Published: 10 September 2020 in 2018 ASEE Annual Conference & Exposition Proceedings
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Industrial system simulation course is presented. Simulation has been among the courses against which students feel uncomfortable or frightened due to heavy software use, prerequisite of probability, and statistics knowledge, and its application requirements. To minimize this fear and improve student’s understanding about the subject matters and have them develop ample skills to build complex models, a project-based learning approach is proposed and used in undergraduate and graduate teaching settings. To achieve the project-based learning goals, a 15-week curriculum is designed to have a balanced lecture and lab sessions, which are specifically designed to address the needs of the term project as the semester continues. In the term project, groups of 2-3 students were asked to form a group, where each group was expected to work on a real system to 1) understand, conceptualize, and model the existing system as a mental, then software-model; 2) validate the existing system model statistically; 3) identify areas for improvement (in addition to the ones given by the supervisor); 4) complete the project with testing out system improvement scenarios and conducting cost/benefit analysis. The effectiveness of project-based learning is surveyed and studied based on the course learning outcomes. The results indicated that the proposed project-based learning approach was found to be effective in students’ learning experience and critically supportive on reaching the learning outcomes, and it was found that students’ learning and skills of simulation modeling and application are improved regardless of their grade.

ACS Style

Gokhan Egilmez; Dusan Sormaz; Ridvan Gedik. A Project-based Learning Approach in Teaching Simulation to Undergraduate and Graduate Students. 2018 ASEE Annual Conference & Exposition Proceedings 2020, 1 .

AMA Style

Gokhan Egilmez, Dusan Sormaz, Ridvan Gedik. A Project-based Learning Approach in Teaching Simulation to Undergraduate and Graduate Students. 2018 ASEE Annual Conference & Exposition Proceedings. 2020; ():1.

Chicago/Turabian Style

Gokhan Egilmez; Dusan Sormaz; Ridvan Gedik. 2020. "A Project-based Learning Approach in Teaching Simulation to Undergraduate and Graduate Students." 2018 ASEE Annual Conference & Exposition Proceedings , no. : 1.

Conference paper
Published: 10 September 2020 in 2018 ASEE Annual Conference & Exposition Proceedings
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In this educational research project, game-based in-class and after-class learning activities are developed to teach selected inventory control strategies to undergraduate and graduate students. Students from Supply Chain Management and System Simulation courses are targeted, who are taught by different instructors. The activities include teaching the inventory control policies to students in a regular class setting, then providing an overview on a game developed on MS Excel. In the game, the lead time and customer demand variables are defined uncertain, and not given to students, which make the assignment an ill-structured problem. A 12-month planning and execution period is given to students with qualitative and quantitative information about 3 products. The students are given a 1-week period to play the game. The game simulates selected inventory control strategies with reorder point and order quantity parameters for 12 months. The learning outcomes of the course related to inventory control, and students’ experience with the game are surveyed. Survey results are statistically and visually analyzed. Overall results indicated that the proposed gamification approach is found to have positive impact in learning effectiveness in the majority of evaluation categories. In addition, the contribution of the proposed gamification approach was found to be effectively supporting the learning outcomes of the course.

ACS Style

Gokhan Egilmez; Ridvan Gedik. A Gamification Approach for Experiential Education of Inventory Control. 2018 ASEE Annual Conference & Exposition Proceedings 2020, 1 .

AMA Style

Gokhan Egilmez, Ridvan Gedik. A Gamification Approach for Experiential Education of Inventory Control. 2018 ASEE Annual Conference & Exposition Proceedings. 2020; ():1.

Chicago/Turabian Style

Gokhan Egilmez; Ridvan Gedik. 2020. "A Gamification Approach for Experiential Education of Inventory Control." 2018 ASEE Annual Conference & Exposition Proceedings , no. : 1.

Conference paper
Published: 10 September 2020 in 2019 ASEE Annual Conference & Exposition Proceedings
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Today, engineers play a crucial role in the direction of technology, research, social wellbeing, and economic growth, thus the lives of people. An engineer’s professional responsibility for complying with ethical standards and conduct is essential to the needs and requirements of individuals, organizations, and the society. Educating the future engineering workforce and establishing effective and timely policies that ensure engineering professional’s compliance with requirements are two important pillars of sustaining the ethical knowledge and practice in engineering profession. In this study, the researchers focused on investigating the learning effectiveness of an online ethics module developed for and implemented in a senior year Engineering Ethics Seminar course. The module consisted of three pillars: code of ethics, case studies, and methods for applying ethical reasoning. Each pillar requires the student to take a quiz consisting of 4 to 7 questions, and a final 10 question quiz at completion of the module. In-class activities and assignments complement the module. The research team conducted a two-semester assessment on learning effectiveness of the online ethics module with a sample of 41 engineering students from well-represented diverse majors, self-identification and racial/ethnic backgrounds compared to the enrollment population. Results indicate that the proposed online module positively impacted the students’ proficiency in knowledge of ethics and ethical reasoning in terms of students’ perception of improved confidence and the instructor’s assessment. The same interpretation was reached by the instructor’s assessment as well. The team did not identify any correlation between the students’ answers to the survey questions and their final grades, which indicates that the students’ positive response on their learning experience was found to be independent of their letter grade.

ACS Style

Gokhan Egilmez; Phillip A. Viscomi; Maria-Isabel Carnasciali. Assessing an Online Engineering Ethics Module from Experiential Learning Perspective. 2019 ASEE Annual Conference & Exposition Proceedings 2020, 1 .

AMA Style

Gokhan Egilmez, Phillip A. Viscomi, Maria-Isabel Carnasciali. Assessing an Online Engineering Ethics Module from Experiential Learning Perspective. 2019 ASEE Annual Conference & Exposition Proceedings. 2020; ():1.

Chicago/Turabian Style

Gokhan Egilmez; Phillip A. Viscomi; Maria-Isabel Carnasciali. 2020. "Assessing an Online Engineering Ethics Module from Experiential Learning Perspective." 2019 ASEE Annual Conference & Exposition Proceedings , no. : 1.

Journal article
Published: 13 December 2019 in Journal of Cleaner Production
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This study investigated the global supply chain-linked renewable and nonrenewable energy use impacts and economic output of U.S. manufacturing industries over a twenty-year period. Considering energy use impacts and economic outputs together within the scope of a global-trade-linked, cradle-to-gate life cycle provides a comprehensive understanding of the environmental and economic impacts of industrial activities. In the first phase of the methodology, twenty multi-region input-output models were built to assess the energy and economic output nexus using a time series approach. Sixteen energy carriers were considered and aggregated in terms of renewable and nonrenewable energy use impacts. The second phase of the methodology focused on benchmarking U.S. manufacturing industries’ eco-efficiency, considering renewable to nonrenewable energy use and economic output. To accomplish this, data envelopment analysis (DEA) models were developed, and two benchmarking (eco-efficiency) measures were proposed, namely: renewability ratio (RR) and economic-output-induced renewability ratio (E-RR). The results indicated that the economic output of the manufacturing industries exhibited a steady, sustainable growth. Similar growth was observed in nonrenewable energy use. In contrast, the trend in renewable energy use was seen to be stagnant. No statistically significant improvement was observed in either the RR or E-RR measures, which were found to parallel the multi-region input-output analysis (MRIO) results. Although an increase in both the mean RR (0.3–0.4) and the mean E-RR (0.38–0.52) scores was recorded from 1995 to 2014, this was concluded to be unsatisfactory, since the majority of the industries’ eco-efficiency results were still below 0.5. Such an unsatisfactory result could be attributed to an imbalanced growth in nonrenewable energy use and economic output relative to renewable energy use. The findings of this study suggest that substantial policy changes are required immediately to shift the negative trend in renewable energy use to comply with UN Sustainability Development Goals 7 and 13.

ACS Style

Bahadir Ezici; Gökhan Eğilmez; Ridvan Gedik. Assessing the eco-efficiency of U.S. manufacturing industries with a focus on renewable vs. non-renewable energy use: An integrated time series MRIO and DEA approach. Journal of Cleaner Production 2019, 253, 119630 .

AMA Style

Bahadir Ezici, Gökhan Eğilmez, Ridvan Gedik. Assessing the eco-efficiency of U.S. manufacturing industries with a focus on renewable vs. non-renewable energy use: An integrated time series MRIO and DEA approach. Journal of Cleaner Production. 2019; 253 ():119630.

Chicago/Turabian Style

Bahadir Ezici; Gökhan Eğilmez; Ridvan Gedik. 2019. "Assessing the eco-efficiency of U.S. manufacturing industries with a focus on renewable vs. non-renewable energy use: An integrated time series MRIO and DEA approach." Journal of Cleaner Production 253, no. : 119630.

Journal article
Published: 01 January 2019 in International Journal of Services and Operations Management
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ACS Style

Gürsel A. Süer; Gokhan Egilmez; Bulent Erenay. Hybrid cellular manufacturing system design with cellularisation ratio: an integrated mixed integer nonlinear programming and discrete event simulation approach. International Journal of Services and Operations Management 2019, 32, 1 .

AMA Style

Gürsel A. Süer, Gokhan Egilmez, Bulent Erenay. Hybrid cellular manufacturing system design with cellularisation ratio: an integrated mixed integer nonlinear programming and discrete event simulation approach. International Journal of Services and Operations Management. 2019; 32 (1):1.

Chicago/Turabian Style

Gürsel A. Süer; Gokhan Egilmez; Bulent Erenay. 2019. "Hybrid cellular manufacturing system design with cellularisation ratio: an integrated mixed integer nonlinear programming and discrete event simulation approach." International Journal of Services and Operations Management 32, no. 1: 1.

Journal article
Published: 01 January 2019 in International Journal of Services and Operations Management
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This study investigates hybrid cellular manufacturing system design where products with highly similar process routes are formed as manufacturing cells, and products with dissimilar routes are produced in a process layout. A cell similarity threshold, cellularisation ratio (C-ratio), is proposed for product assignment to cells and process layout, and integrated into a mathematical modelling-based cell formation approach. The cell formation phase is carried out with the proposed mathematical model. Several case problems are tested, and selected cases are also studied with a newly developed simulation model where manufacturing system performance is also evaluated along with the proposed hybrid cellular manufacturing design alternatives. Results indicated that considering a C-ratio during cell formation can provide higher cell similarity thus better cell formation. The increase in the coverage levels resulted in higher number of cells formed. The proposed approach can be significantly beneficial for manufacturing design decisions where product mix and process complexity are high, and for a hybrid layout that consists of cellular plus process layouts could be implemented.

ACS Style

Gokhan Egilmez; Bulent Erenay; Gürsel A. Süer. Hybrid cellular manufacturing system design with cellularisation ratio: an integrated mixed integer nonlinear programming and discrete event simulation approach. International Journal of Services and Operations Management 2019, 32, 1 .

AMA Style

Gokhan Egilmez, Bulent Erenay, Gürsel A. Süer. Hybrid cellular manufacturing system design with cellularisation ratio: an integrated mixed integer nonlinear programming and discrete event simulation approach. International Journal of Services and Operations Management. 2019; 32 (1):1.

Chicago/Turabian Style

Gokhan Egilmez; Bulent Erenay; Gürsel A. Süer. 2019. "Hybrid cellular manufacturing system design with cellularisation ratio: an integrated mixed integer nonlinear programming and discrete event simulation approach." International Journal of Services and Operations Management 32, no. 1: 1.

Original articles
Published: 06 August 2018 in International Journal of Logistics Research and Applications
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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.

ACS Style

Yong Shin Park; N. Muhammad Aslaam Mohamed Abdul Ghani; Fesseha Gebremikael; Gokhan Egilmez. Benchmarking environmental efficiency of ports using data mining and RDEA: the case of a U.S. container ports. International Journal of Logistics Research and Applications 2018, 22, 172 -187.

AMA Style

Yong Shin Park, N. Muhammad Aslaam Mohamed Abdul Ghani, Fesseha Gebremikael, Gokhan Egilmez. Benchmarking environmental efficiency of ports using data mining and RDEA: the case of a U.S. container ports. International Journal of Logistics Research and Applications. 2018; 22 (2):172-187.

Chicago/Turabian Style

Yong Shin Park; N. Muhammad Aslaam Mohamed Abdul Ghani; Fesseha Gebremikael; Gokhan Egilmez. 2018. "Benchmarking environmental efficiency of ports using data mining and RDEA: the case of a U.S. container ports." International Journal of Logistics Research and Applications 22, no. 2: 172-187.

Journal article
Published: 01 July 2018 in Computers & Industrial Engineering
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This paper addresses the non-preemptive unrelated parallel machine scheduling problem (PMSP) with job sequence and machine dependent setup times. This is a widely seen NP-hard (non-deterministic polynomial-time) problem with the objective to minimize the makespan. This study provides a noval constraint programming (CP) model with two customized branching strategies that utilize CP’s global constraints, interval decision variables, and domain filtering algorithms. The performance of the CP model is evaluated against the state-of-art algorithms. In addition, we compare the performance of the default branching method in the CP solver against the two customized variants. In terms of average solution quality, the computational results indicate that the CP model slightly outperforms all of the state-of-art algorithms in solving small problem instances, is able to prove the optimality of 283 currently best-known solutions. It is also effective in finding good quality feasible solutions for the larger problem instances.

ACS Style

Ridvan Gedik; Darshan Kalathia; Gokhan Egilmez; Emre Kirac. A constraint programming approach for solving unrelated parallel machine scheduling problem. Computers & Industrial Engineering 2018, 121, 139 -149.

AMA Style

Ridvan Gedik, Darshan Kalathia, Gokhan Egilmez, Emre Kirac. A constraint programming approach for solving unrelated parallel machine scheduling problem. Computers & Industrial Engineering. 2018; 121 ():139-149.

Chicago/Turabian Style

Ridvan Gedik; Darshan Kalathia; Gokhan Egilmez; Emre Kirac. 2018. "A constraint programming approach for solving unrelated parallel machine scheduling problem." Computers & Industrial Engineering 121, no. : 139-149.

Journal article
Published: 01 January 2018 in International Journal of Services and Operations Management
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In this paper, single machine stochastic scheduling problem is addressed where jobs have probabilistic processing times and deterministic due dates. In classical scheduling literature, a job is called 'tardy' if it is completed after its due date; otherwise it's called 'early'. However, in probabilistic concept, a job can have a non-zero probability of tardiness. To capture this situation, a stochastic nonlinear mathematical model is developed. The objective is to minimise the expected number of tardy jobs and the total probability of tardiness. Experimentation is performed with datasets having varying number of jobs from 10 to 100. Results are compared with deterministic model. The proposed approach resulted in a significant decrease in the number of risky jobs and the total probability of tardiness. In addition, as the variance of processing times increased, the proposed stochastic approach provided safer schedules in terms of the total probability of tardiness.

ACS Style

Gokhan Egilmez; Aslican Arinsoy; Gürsel A. Süer. A stochastic scheduling approach to minimise the number of risky jobs and total probability of tardiness. International Journal of Services and Operations Management 2018, 30, 186 .

AMA Style

Gokhan Egilmez, Aslican Arinsoy, Gürsel A. Süer. A stochastic scheduling approach to minimise the number of risky jobs and total probability of tardiness. International Journal of Services and Operations Management. 2018; 30 (2):186.

Chicago/Turabian Style

Gokhan Egilmez; Aslican Arinsoy; Gürsel A. Süer. 2018. "A stochastic scheduling approach to minimise the number of risky jobs and total probability of tardiness." International Journal of Services and Operations Management 30, no. 2: 186.

Journal article
Published: 04 December 2017 in Industrial Management & Data Systems
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PurposeCarbon footprint assessment requires a holistic approach, where all possible lifecycle stages of products from raw material extraction to the end of life are considered. The purpose of this paper is to develop an analytical sustainability assessment framework to assess the carbon footprint of US economic supply chains from two perspectives: supply chain layers (tiers) and carbon footprint sources.Design/methodology/approachThe methodology consists of two phases. In the first phase, the data were collected from EORA input output and environmental impact assessment database. In the second phase, 48 input-output-based lifecycle assessment models were developed (seven CO2 sources and total CO2 impact, and six supply chain tiers). In the third phase, the results are analyzed by using data visualization, data analytics, and statistical approaches in order to identify the heavy carbon emitter industries and their percentage shares in the supply chains by each layer and the CO2 source.FindingsVast majority of carbon footprint was found to be attributed to the power generation, petroleum refineries, used and secondhand goods, natural gas distribution, scrap, and truck transportation. These industries dominated the entire supply chain structure and found to be the top drivers in all six layers.Practical implicationsThis study decomposes the sources of the total carbon footprint of US economic supply chains into six layers and assesses the percentage contribution of each sector in each layer. Thus, it paves the way for quantifying the carbon footprint of each layer in today’s complex supply chain structure and highlights the importance of handling CO2 source in each layer separately while maintaining a holistic focus on the overall carbon footprint impacts in the big picture. In practice, one size fits all type of policy making may not be as effective as it could be expected.Originality/valueThis paper provides a two-dimensional viewpoint for tracing/analyzing carbon footprint across a national economy. In the first dimension, the national economic system is divided into six layers. In the second dimension, carbon footprint analysis is performed considering specific CO2 sources, including energy production, solvent, cement and minerals, agricultural burning, natural decay, and waste. Thus, this paper contributes to the state-of-art sustainability assessment by providing a comprehensive overview of CO2 sources in the US economic supply chains.

ACS Style

Gokhan Egilmez; N. Muhammad Aslaam Mohamed Abdul Ghani; Ridvan Gedik. Layer analysis of CO2 sources in the US economic supply chains: an input output LCA study. Industrial Management & Data Systems 2017, 117, 2171 -2193.

AMA Style

Gokhan Egilmez, N. Muhammad Aslaam Mohamed Abdul Ghani, Ridvan Gedik. Layer analysis of CO2 sources in the US economic supply chains: an input output LCA study. Industrial Management & Data Systems. 2017; 117 (10):2171-2193.

Chicago/Turabian Style

Gokhan Egilmez; N. Muhammad Aslaam Mohamed Abdul Ghani; Ridvan Gedik. 2017. "Layer analysis of CO2 sources in the US economic supply chains: an input output LCA study." Industrial Management & Data Systems 117, no. 10: 2171-2193.

Journal article
Published: 12 June 2017 in Management of Environmental Quality: An International Journal
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Purpose The purpose of this paper is to focus on tracing GHG emissions across the supply chain industries associated with the US residential, commercial and industrial building stock and provides optimized GHG reduction policy plans for sustainable development. Design/methodology/approach A two-step hierarchical approach is developed. First, Economic Input-Output-based Life Cycle Assessment (EIO-LCA) is utilized to quantify the GHG emissions associated with the US residential, commercial and industrial building stock. Second, a mixed integer linear programming (MILP) based optimization framework is developed to identify the optimal GHG emissions’ reduction (percent) for each industry across the supply chain network of the US economy. Findings The results indicated that “ready-mix concrete manufacturing”, “electric power generation, transmission and distribution” and “lighting fixture manufacturing” sectors were found to be the main culprits in the GHG emissions’ stock. Additionally, the majorly responsible industries in the supply chains of each building construction categories were also highlighted as the hot-spots in the supply chains with respect to the GHG emission reduction (percent) requirements. Practical implications The decision making in terms of construction-related expenses and energy use options have considerable impacts across the supply chains. Therefore, regulations and actions should be re-organized around the systematic understanding considering the principles of “circular economy” within the context of sustainable development. Originality/value Although the literature is abundant with works that address quantifying environmental impacts of building structures, environmental life cycle impact-based optimization methods are scarce. This paper successfully fills this gap by integrating EIO-LCA and MILP frameworks to identify the most pollutant industries in the supply chains of building structures.

ACS Style

N. Muhammad Aslaam Mohamed Abdul Ghani; Gokhan Egilmez; Murat Kucukvar; M. Khurrum S. Bhutta. From green buildings to green supply chains. Management of Environmental Quality: An International Journal 2017, 28, 532 -548.

AMA Style

N. Muhammad Aslaam Mohamed Abdul Ghani, Gokhan Egilmez, Murat Kucukvar, M. Khurrum S. Bhutta. From green buildings to green supply chains. Management of Environmental Quality: An International Journal. 2017; 28 (4):532-548.

Chicago/Turabian Style

N. Muhammad Aslaam Mohamed Abdul Ghani; Gokhan Egilmez; Murat Kucukvar; M. Khurrum S. Bhutta. 2017. "From green buildings to green supply chains." Management of Environmental Quality: An International Journal 28, no. 4: 532-548.

Journal article
Published: 12 June 2017 in Industrial Management & Data Systems
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Purpose The purpose of this paper is to provide an input-output life cycle assessment model to estimate the carbon footprint of US manufacturing sectors. To achieve this, the paper sets out the following objectives: develop a time series carbon footprint estimation model for US manufacturing sectors; analyze the annual and cumulative carbon footprint; analyze and identify the most carbon emitting and carbon intensive manufacturing industries in the last four decades; and analyze the supply chains of US manufacturing industries to help identify the most critical carbon emitting industries. Design/methodology/approach Initially, the economic input-output tables of US economy and carbon footprint multipliers were collected from EORA database (Lenzen et al., 2012). Then, economic input-output life cycle assessment models were developed to quantify the carbon footprint extents of the US manufacturing sectors between 1970 and 2011. The carbon footprint is assessed in metric tons of CO2-equivalent, whereas the economic outputs were measured in million dollar economic activity. Findings The salient finding of this paper is that the carbon footprint stock has been increasing substantially over the last four decades. The steep growth in economic output unfortunately over-shadowed the potential benefits that were obtained from lower CO2 intensities. Analysis of specific industry results indicate that the top five manufacturing sectors based on total carbon footprint share are “petroleum refineries,” “Animal (except poultry) slaughtering, rendering, and processing,” “Other basic organic chemical manufacturing,” “Motor vehicle parts manufacturing,” and “Iron and steel mills and ferroalloy manufacturing.” Originality/value This paper proposes a state-of-art time series input-output-based carbon footprint assessment for the US manufacturing industries considering direct (onsite) and indirect (supply chain) impacts. In addition, the paper provides carbon intensity and carbon stock variables that are assessed over time for each of the US manufacturing industries from a supply chain footprint perspective.

ACS Style

Gokhan Egilmez; Khurrum Bhutta; Bulent Erenay; Yong Shin Park; Ridvan Gedik. Carbon footprint stock analysis of US manufacturing: a time series input-output LCA. Industrial Management & Data Systems 2017, 117, 853 -872.

AMA Style

Gokhan Egilmez, Khurrum Bhutta, Bulent Erenay, Yong Shin Park, Ridvan Gedik. Carbon footprint stock analysis of US manufacturing: a time series input-output LCA. Industrial Management & Data Systems. 2017; 117 (5):853-872.

Chicago/Turabian Style

Gokhan Egilmez; Khurrum Bhutta; Bulent Erenay; Yong Shin Park; Ridvan Gedik. 2017. "Carbon footprint stock analysis of US manufacturing: a time series input-output LCA." Industrial Management & Data Systems 117, no. 5: 853-872.

Journal article
Published: 01 January 2017 in International Journal of Services and Operations Management
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ACS Style

Gokhan Egilmez; Samrat Singh; Orhan Ozguner. Cell formation in a cellular manufacturing system under uncertain demand and processing times: a stochastic genetic algorithm approach. International Journal of Services and Operations Management 2017, 26, 162 .

AMA Style

Gokhan Egilmez, Samrat Singh, Orhan Ozguner. Cell formation in a cellular manufacturing system under uncertain demand and processing times: a stochastic genetic algorithm approach. International Journal of Services and Operations Management. 2017; 26 (2):162.

Chicago/Turabian Style

Gokhan Egilmez; Samrat Singh; Orhan Ozguner. 2017. "Cell formation in a cellular manufacturing system under uncertain demand and processing times: a stochastic genetic algorithm approach." International Journal of Services and Operations Management 26, no. 2: 162.

Journal article
Published: 01 January 2017 in International Journal of Metaheuristics
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Road crashes are among the top five leading causes of deaths in the US although the national trend in fatal crashes has reached to the lowest level since 1949. Therefore, this paper introduces a non-parametric prediction models, artificial neural network (ANN), to assist policy-makers in minimising fatal crashes across the United States. Seven input variables from four safety performance input domains while fatal crash was utilised as the single output variable for the scope of the research. ANN was utilised and the best neural network model was developed out of 1,000 networks. The proposed neural network model predicted data with 84% coefficient of determination. In addition, developed ANN model was benchmarked with a multiple linear regression model and outperformed in all performance metrics including r, R-square and the standard error of estimate.

ACS Style

Gokhan Egilmez; Deborah McAvoy. Predicting nationwide road fatalities in the US: a neural network approach. International Journal of Metaheuristics 2017, 6, 257 .

AMA Style

Gokhan Egilmez, Deborah McAvoy. Predicting nationwide road fatalities in the US: a neural network approach. International Journal of Metaheuristics. 2017; 6 (4):257.

Chicago/Turabian Style

Gokhan Egilmez; Deborah McAvoy. 2017. "Predicting nationwide road fatalities in the US: a neural network approach." International Journal of Metaheuristics 6, no. 4: 257.

Journal article
Published: 01 January 2017 in International Journal of Services and Operations Management
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This research was undertaken to study the implementation and adoption of Lean management practices across several industrial sectors in Pakistan. A total of 100 companies were surveyed across five industries. The objective was to determine if there was disparity in the implementation of Lean practices and to provide a yardstick to measure that disparity. Furthermore, this study identified Lean management best practices across several industries in Pakistan, thus providing benchmarks for other industrial sectors. The data collected was analysed using various descriptive statistical methods. The results indicated robust adoption and implementation of Lean practices in Pakistani industry, though there were few areas which still require greater acceptance, and hence implementation rates in these areas are modest.

ACS Style

M. Khurrum Bhutta; Gokhan Egilmez; Kamran A. Chatha; Faizul Huq. Survey of Lean management practices in Pakistani industrial sectors. International Journal of Services and Operations Management 2017, 28, 309 .

AMA Style

M. Khurrum Bhutta, Gokhan Egilmez, Kamran A. Chatha, Faizul Huq. Survey of Lean management practices in Pakistani industrial sectors. International Journal of Services and Operations Management. 2017; 28 (3):309.

Chicago/Turabian Style

M. Khurrum Bhutta; Gokhan Egilmez; Kamran A. Chatha; Faizul Huq. 2017. "Survey of Lean management practices in Pakistani industrial sectors." International Journal of Services and Operations Management 28, no. 3: 309.

Journal article
Published: 01 January 2017 in International Journal of Services and Operations Management
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ACS Style

Faizul Huq; Gokhan Egilmez; Kamran A. Chatha; M. Khurrum S. Bhutta. Survey of Lean management practices in Pakistani industrial sectors. International Journal of Services and Operations Management 2017, 28, 309 .

AMA Style

Faizul Huq, Gokhan Egilmez, Kamran A. Chatha, M. Khurrum S. Bhutta. Survey of Lean management practices in Pakistani industrial sectors. International Journal of Services and Operations Management. 2017; 28 (3):309.

Chicago/Turabian Style

Faizul Huq; Gokhan Egilmez; Kamran A. Chatha; M. Khurrum S. Bhutta. 2017. "Survey of Lean management practices in Pakistani industrial sectors." International Journal of Services and Operations Management 28, no. 3: 309.

Journal article
Published: 01 December 2016 in Applied Energy
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The main objectives of this research are to improve our understanding of energy-climate-manufacturing nexus within the context of regional and global manufacturing supply chains as well as show the significance of full coverage of entire supply chain tiers in order to prevent significant underestimations, which might lead to invalid policy conclusions. With this motivation, a multi region input–output (MRIO) sustainability assessment model is developed by using the World Input–Output Database, which is a dynamic MRIO framework on the world’s 40 largest economies covering 1440 economic sectors. The method presented in this study is the first environmentally-extended MRIO model that harmonizes energy and carbon footprint accounts for Turkish manufacturing sectors and a global trade-linked carbon and energy footprint analysis of Turkish manufacturing sectors is performed as a case study. The results are presented by distinguishing the contributions of five common supply chain phases such as upstream suppliers, onsite manufacturing, transportation, wholesale, and retail trade. The findings showed that onsite and upstream supply chains are found to have over 90% of total energy use and carbon footprint for all industrial sectors. Electricity, Gas and Water Supply sector is usually found to be as the main contributor to global climate change, and Coke, Refined Petroleum, and Nuclear Fuel sector is the main driver of energy use in upstream supply chains. Overall, the largest portion of total carbon emissions of Turkish manufacturing industries is found in Turkey’s regional boundary that ranged between 40% and 60% of total carbon emissions. In 2009, China, United States, and Rest-of-the-World’s contribution is found to be more than 50% of total energy use of Turkish manufacturing. The authors envision that a global MRIO framework can provide a vital guidance for policy makers to analyze the role of global manufacturing supply chains and prevent significant underestimations due to inclusion of limited number of tiers for sustainable supply chain management research

ACS Style

Murat Kucukvar; Bunyamin Cansev; Gokhan Egilmez; Nuri C. Onat; Hamidreza Samadi. Energy-climate-manufacturing nexus: New insights from the regional and global supply chains of manufacturing industries. Applied Energy 2016, 184, 889 -904.

AMA Style

Murat Kucukvar, Bunyamin Cansev, Gokhan Egilmez, Nuri C. Onat, Hamidreza Samadi. Energy-climate-manufacturing nexus: New insights from the regional and global supply chains of manufacturing industries. Applied Energy. 2016; 184 ():889-904.

Chicago/Turabian Style

Murat Kucukvar; Bunyamin Cansev; Gokhan Egilmez; Nuri C. Onat; Hamidreza Samadi. 2016. "Energy-climate-manufacturing nexus: New insights from the regional and global supply chains of manufacturing industries." Applied Energy 184, no. : 889-904.

Journal article
Published: 21 September 2016 in Transportation Research Part D: Transport and Environment
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Sustainable transportation in the U.S. is essential for long-term economic growth and mobility, and environmental preservation. Using a non-radial slack-based measurement data envelopment analysis (SBM-DEA) model and state-level data, this study assesses the environmental efficiency of the transportation sector in the U.S. from years 2004 to 2012. In addition to environmental efficiency, carbon efficiency and potential carbon reduction were estimated for the 50 U.S. states. The findings of this study reveal that U.S. transportation sector was environmentally inefficient; U.S. states had an average transportation environmental efficiency score below 0.64. Therefore the states could substantially reduce carbon emissions to improve the environmental efficiency of their transportation sectors.

ACS Style

Yong Shin Park; Siew Hoon Lim; Gokhan Egilmez; Joseph Szmerekovsky. Environmental efficiency assessment of U.S. transport sector: A slack-based data envelopment analysis approach. Transportation Research Part D: Transport and Environment 2016, 61, 152 -164.

AMA Style

Yong Shin Park, Siew Hoon Lim, Gokhan Egilmez, Joseph Szmerekovsky. Environmental efficiency assessment of U.S. transport sector: A slack-based data envelopment analysis approach. Transportation Research Part D: Transport and Environment. 2016; 61 ():152-164.

Chicago/Turabian Style

Yong Shin Park; Siew Hoon Lim; Gokhan Egilmez; Joseph Szmerekovsky. 2016. "Environmental efficiency assessment of U.S. transport sector: A slack-based data envelopment analysis approach." Transportation Research Part D: Transport and Environment 61, no. : 152-164.