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A Stochastic Frontier Analysis (SFA)-Based Method for Detecting Changes in Manufacturing Energy Efficiency by Sector and Time

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Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures (APMS 2023)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 692))

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Abstract

The manufacturing industry consumes a significant amount of energy, and therefore it is crucial to address energy efficiency issues in manufacturing. One way to study manufacturing energy efficiency is to investigate the changes in energy efficiency by sector and time. For that, the Industrial Assessment Center (IAC) database can be used since this dataset includes information needed to assess energy efficiency of the U.S. manufacturers by sector and time. Currently available efficiency analysis methods such as Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) do not fit well with the IAC database: the IAC provides only unbalanced panel data, but SFA and DEA are not mainly for analyzing unbalanced panel data. Therefore, we aim at developing a new approach based on SFA to use unbalanced panel data for energy efficiency analysis. Specifically, the whole manufacturing industry in the IAC database is classified into 20 sectors based on Standard Industrial Classification (SIC) codes. Then, we build each SFA model for each sector and evaluate energy inefficiency values for all manufacturers. From the SFA result, the average energy inefficiency values are calculated for comparative analysis of energy efficiency by sector and time. The final analysis results suggest that the annual energy efficiency was improved around 2010 in most manufacturing sectors, and manufacturing sectors can be classified into three groups (that is, increasing, maintaining, and decreasing) according to the changes of energy efficiency after 2016. We also calculate Spearman’s rank correlation coefficient for SFA models to check the consistency of the models.

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Acknowledgments

This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.RS-2022–00155911, Artificial Intelligence Convergence Innovation Human Resources Development (Kyung Hee University)).

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Authors and Affiliations

  1. Department of Industrial and Management Systems Engineering, Kyung Hee University, Yongin-Si, Gyeonggi-Do, Republic of Korea

    Ga Hyun Lee & Hyun Woo Jeon

Authors
  1. Ga Hyun Lee
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  2. Hyun Woo Jeon
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Corresponding author

Correspondence to Hyun Woo Jeon .

Editor information

Editors and Affiliations

  1. Norwegian University of Science and Technology, Trondheim, Norway

    Erlend Alfnes

  2. Norwegian University of Science and Technology, Trondheim, Norway

    Anita Romsdal

  3. Norwegian University of Science and Technology, Trondheim, Norway

    Jan Ola Strandhagen

  4. ZF Friedrichshafen AG, Friedrichshafen, Germany

    Gregor von Cieminski

  5. Tecnológico de Monterrey, Mexico City, Mexico

    David Romero

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Lee, G.H., Jeon, H.W. (2023). A Stochastic Frontier Analysis (SFA)-Based Method for Detecting Changes in Manufacturing Energy Efficiency by Sector and Time. In: Alfnes, E., Romsdal, A., Strandhagen, J.O., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures. APMS 2023. IFIP Advances in Information and Communication Technology, vol 692. Springer, Cham. https://doi.org/10.1007/978-3-031-43688-8_3

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  • DOI: https://doi.org/10.1007/978-3-031-43688-8_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43687-1

  • Online ISBN: 978-3-031-43688-8

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