The present research proposes a structural equation model to integrate four latent variables: managerial commitment, preventive maintenance, total productive maintenance, and productivity benefits. In addition, these variables are related through six research hypotheses that are validated using collected data from 368 surveys administered in the Mexican manufacturing industry. Consequently, the model is evaluated using partial least squares. The results show that managerial commitment is critical to achieve productivity benefits, while preventive maintenance is indispensable to total preventive maintenance. These results may encourage company managers to focus on managerial commitment and implement preventive maintenance programs to guarantee the success of total productive maintenance.

The tribological behavior of a new composite material containing graphene nanosheets (GNS) is presented. The composite material was obtained by Friction Stir Processing, using as metallic matrix the AA1050-H24 alloy. Different tool rotation and advancing speeds were tested in friction stir processing (FSP). The worn surfaces of obtained materials were analyzed by Scanning Electron Microscopy (SEM). Raman spectroscopy demonstrated that graphene reinforcements are successfully mixed into the aluminum matrix. The results proved the feasibility of using GNSs to obtain nanocomposites by FSP. The coefficient of friction of the aluminum alloy was 0.57, decreasing to 0.38 for the nanocomposite GNSs/AA1050. These values decrease for samples obtained at lower tool rotation speeds. The weight losses of the composites are less than that of unreinforced AA1050-H24 alloy for conditions with lower advancing speeds (40 and 60 mm/min) and 1120 rpm.

Usually, agricultural tractor investments are assessed using traditional economic techniques that only involve financial attributes, resulting in reductionist evaluations. However, tractors have qualitative and quantitative attributes that must be simultaneously integrated into the evaluation process. This article reports a hybrid and multi-attribute approach to assessing a set of agricultural tractors based on AHP-TOPSIS. To identify the attributes in the model, a survey including eighteen attributes was given to agricultural machinery salesmen and farmers for determining their importance. The list of attributes was presented to a decision group for a case of study, and their importance was estimated using AHP and integrated into the TOPSIS technique. In this case, one tractor was selected from a set of six alternatives, integrating six attributes in the model: initial cost, annual maintenance cost, liters of diesel per hour, safety of the operator, maintainability and after-sale customer service offered by the supplier. Based on the results obtained, the model can be considered easy to apply and to have good acceptance among farmers and salesmen, as there are no special software requirements for the application.

Supplier evaluation and selection are fundamental tasks since they are part of the production process and even initiate the supply chain (SC). Despite their importance in the production system, supplier evaluation and selection may be challenging activities to be performed if companies look at the wide range of available evaluation techniques and methodologies, which now seek to integrate both traditional and green attributes. In addition, companies may refuse to take into account green attributes during the supplier selection process, because they do not know their impact on commercial benefits. To overcome this limitation, this study examines the Mexican manufacturing sector and measures the impact of supplier traditional attributes and green attributes on business performance, namely production process benefits and commercial benefits. As data collection instrument, we administered a survey to 253 supplier evaluators and selectors; then, using the gathered data, we constructed a structural equation model. The model includes four variables to determine the impact of traditional and green attributes on business performance: green attributes, traditional attributes, production process benefits, and commercial benefits. The results indicate that all the latent variables have positive direct effects on one another. For instance, process benefits show the largest effects on commercial benefits, but the most significant effect is caused by traditional attributes on commercial benefits through green attributes and production process benefits.