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This paper presents a new metric for describing the sustainability improvements achieved, relative to the company's initial situation, after implementing a lean and green manufacturing system. The final value of this metric is identified as the Overall Environmental Equipment Effectiveness (OEEE), which is used to analyze the evolution between two identified states of the Overall Equipment Effectiveness (OEE) and the sustainability together, and references, globally and individually, the production steps. The OEE is a known measure of equipment utilization, which includes the availability, quality and performance of each production step, In addition to these factors, the OEEE incorporates the concept of sustainability based on the calculated environmental impact of the complete product life cycle. Action research based on the different manufacturing processes of a tube fabrication company is conducted to assess the potential impact of this new indicator. The case study demonstrates the compatibility between green and lean manufacturing, using a common metric. The OEEE allows sustainability to be integrated into business decisions, and compares the environmental impact of two states, by identifying the improvements undertaken within the company's processes.

The influence of technical parameters for volumetric error compensation in large-volume machine tools (MTs) is presented in this paper. The techniques presented are based on characterization models using nonlinear optimization procedures. The parameters presented allow for the characterization of different errors in the MT studied and depend on the kinematics and geometry of the system, regardless of the optimization methodology. The kinematics is affected by the MT errors on the number and type of axes and movements. To relate the coordinates of the tool to the coordinates of a laser tracker, a kinematic model of the MT that includes the measurement system must be defined. Kinematic models can be realized by using homogeneous transformation matrices or independent rotation and translation arrays according to the type of machine. Chebyshev, simple or Legendre polynomial regression functions can be used to characterize the geometric errors of the MT and are presented and compared. The distribution of measurement points, mesh or cloud, and optimization constraints of polynomial regressions are factors that also affect volumetric error compensation. Therefore, these parameters were studied and presented as well. In addition to the parameters discussed above, another parameter that affects the accuracy of data capture is the measurement noise. To improve the measurement accuracy, multilateration techniques need to be applied. Each of the aforementioned parameters has been studied by using a synthetic test generated by a parametric synthetic data generator. The selected parameters constitute a package of optimization improvement regardless of the optimization methodology, which have improved the nonlinear optimization from 60–70% to 98%.