Abstract
Reducing energy use is essential for sustainable manufacturing. Monitoring existing rates of energy consumption in various plants is important and analysis and benchmarking them are gaining more attention as prerequisites for any energy management activities. This paper proposes a novel methodology for energy use analysis and benchmarking of manufacturing lines. A method for energy use data processing and analysis of machines in a production line is presented. It relates equipment energy data and corresponding machine states which allows extensive analysis for every measured piece of equipment. A local benchmarking concept has been developed. It is composed of six main steps including detailed analysis of the constituents of typical machining cycles and calculation of average energy consumption for different equipment states. The concept of local energy benchmarking is utilized to meet the challenges of collecting and comparing data from different plants with different operational variants. The developed methodology was applied to a real case study of engine blocks machining line at a major automotive Original Equipment Manufacturer (OEM) plant. Main categories of equipment consuming energy in the entire line were identified and compared for the first time, and total energy consumption per engine block was obtained as well as performing detailed analysis of the constituent CNC machines. Results reflect energy use, percentages of energy waste and values/efficiencies of benchmarking as well as lost production opportunities. It was also shown that average energy consumptions for different equipment states (productive and non-productive) are close, which highlights the importance of optimized process planning and scheduling as well as better utilization of equipment to improve energy use efficiency. The proposed methodology provides a practical decision support tool that can be readily applied by energy management practitioners in industry to assess energy use at the current state of the system. It also guides improvement efforts and identifies potential energy savings. This helps setting realistic targets in individual plants, at the manufacturing line level, for low-cost energy saving projects.