“Is Energy That Different from Labor?” Similarity in Determinants of Intensity for Auto Assembly Plants

Amir Abolhassani (), Gale Boyd, Majid Jaridi, Bhaskaran Gopalakrishnan and James Harner
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Amir Abolhassani: Social Science Research Institute & Department of Economics, Duke University, Durham, NC 27708, USA
Gale Boyd: Social Science Research Institute & Department of Economics, Duke University, Durham, NC 27708, USA
Majid Jaridi: Industrial and Management Systems Engineering Department, West Virginia University, Morgantown, WV 26506, USA
Bhaskaran Gopalakrishnan: Industrial and Management Systems Engineering Department, West Virginia University, Morgantown, WV 26506, USA
James Harner: Department of Statistics, West Virginia University, Morgantown, WV 26506, USA

Energies, 2023, vol. 16, issue 4, 1-35

Abstract: This paper addresses the question “Is energy that different from labor?” from the perspective of efficiency. It presents a novel statistical analysis for the auto assembly industry in North America to examine the determinants of relative energy intensity, and contrasts this with a similar analysis of the determinants of another important factor of production, labor intensity. The data used combine two non-public sources of data previously used to separately study key performance indicators (KPIs) for energy and labor intensity. The study found these two KPIs are statistically correlated (the correlation coefficient is 0.67) and the relationship is one-to-one. The paper identifies 11 factors that may influence both energy and labor intensity KPIs. The study then contrasts which of the empirical factors the two KPIs’ share and how they differ. Two novel statistical methods, Huber estimators and Multiple M-estimators, combined with regularized algorithms, are identified as the preferred methods for robust statistical models to estimate energy intensity. Based on our analysis, the underlying determinants of energy efficiency and labor productivity are quite similar. This implies that strategies to improve energy may have spillover benefits to labor, and vice versa. The study shows vehicle variety, car model types, and launch of a new vehicle penalize both energy and labor intensity, while flexible manufacturing, production volume, and year of production improve both energy and labor intensity. In addition, the study found that the plants that produce small cars are more energy-efficient and productive compared to plants that produce large vehicles. Moreover, in a given functional unit, i.e., on a per-unit basis, Japanese plants are more energy-efficient and productive compared to American plants. Plant managers can use the proposed data-driven approach to make the right decisions about the energy efficiency targets and improve plants’ energy efficiency up to 38% using hybrid regression methods, mathematical modeling, plants’ resources, and constraints.

Keywords: energy-efficient manufacturing; productivity; unit energy intensity; automotive industry (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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