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Measuring energy efficiency performance of equipments, processes and factories is the first step to effective energy management in production. Thus, enabled energy-related information allows the assessment of the progress of manufacturing companies toward their energy efficiency goals. In that respect, the study addresses this challenge where current industrial approaches lack the means and appropriate performance indicators to compare energy-use profiles of machines and processes, and for the comparison of their energy efficiency performance to that of competitors’. Focusing on this challenge, the main objective of the paper is to present a method which supports manufacturing companies in the development of energy-based performance indicators. For this purpose, we provide a 7-step method to develop production-tailored and energy-related key performance indicators (e-KPIs). These indicators allow the interpretation of cause-effect relationships and therefore support companies in their operative decision-making process. Consequently, the proposed method supports the identification of weaknesses and areas for energy efficiency improvements related to the management of production and operations. The study therefore aims to strengthen the theoretical base necessary to support energy-based decision making in manufacturing industries

The paper investigates the effects of production scheduling policies aimed towards improving productive and environmental performances in a job shop system. A green genetic algorithm allows the assessment of multi-objective problems related to sustainability. Two main considerations have emerged from the application of the algorithm. First, the algorithm is able to achieve a semi-optimal makespan similar to that obtained by the best of other methods but with a significantly lower total energy consumption. Second, the study demonstrated that the worthless energy consumption can be reduced significantly by employing complex energy-efficient machine behaviour policies

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