Manufacturing productivity and energy efficiency: a stochastic efficiency frontier analysis

被引:26
|
作者
Shui, Huanyi [1 ]
Jin, Xiaoning [1 ]
Ni, Jun [1 ]
机构
[1] Univ Michigan, Dept Mech Engn, Ann Arbor, MI 48109 USA
基金
美国国家科学基金会;
关键词
Stochastic frontier analysis; energy efficiency; benchmarking; NEURAL-NETWORK METHOD;
D O I
10.1002/er.3368
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
This paper presents a mathematical model to estimate both production frontier and energy demand frontier, and evaluate the energy efficiency in the automotive manufacturing sector using plant-level production and utility consumption data. By using the stochastic frontier analysis, two modelsan output efficiency model (production frontier) and an input efficiency model (energy demand frontier)are developed to analyze the plant's energy efficiency performance relative to the best practice' among peers. Both the structure and parameters of two stochastic frontier functions are identified to understand the relationship between production inputs (e.g., utilities and plant-specific variables) and the overall productivity. A case study of a set of automotive engine manufacturing plants is conducted and shows that electricity has higher efficiency than other forms of utility in those production-related activities, and the regional and climate factors have significant impacts on energy efficiency. The models provide a way to measure how far from the production frontier as well as how far from the energy demand frontier the plant is. The results also provide useful information about the inefficient energy components in manufacturing facilities. The opportunities of improving plant-level efficiency in automotive manufacturing plants can be revealed by the results. Copyright (c) 2015 John Wiley & Sons, Ltd.
引用
收藏
页码:1649 / 1663
页数:15
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