SummaryThis 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 models—an 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 © 2015 John Wiley & Sons, Ltd.

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Progressive stamping processes have been applied to fabricate an extended range of products from centimeter-scale parts to meter-scale parts. The quality of stamped products may vary and be out of specification due to various anomalies during manufacturing process. Therefore, an effective online health monitoring and fault diagnosis technique is of great practical significance. This paper develops a two-stage systematic approach to enhance the fault detection and fault identification capability for the progressive stamping process with aggregated system-level tonnage signals. The first stage uses a combined Haar transform and power spectrum analysis to map features extracted from aggregated signals to individual operations. The second stage develops a two-step control chart strategy for anomaly detection and identification. The proposed method can improve the monitoring effectiveness and the quality assessment of individual operations based on an aggregated tonnage signal especially when single working range of different operations in the multi-station system are highly overlapped. The results show the method efficacy of quick and accurate anomaly detection and identification in real time.