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Methodology In general, methodology played a major role to find the validity of the data.
In this work, the data was collected through survey questionnaires circulated to about ten various levels of people amounting to sixty three numbers and working in different automotive manufacturing industries in Tamilnadu.
The first stream of research uses survey data to explore the impact of lean production practices on industrial performance between lean activities that occur at different intervals based on turnover rate, employee training and customer satisfaction percentage, lean activity occurrences interval and delivery defect rate.
The hypothetical relations between various issues are tested with data collected from different automobile manufacturing industries located in Tamilnadu.
Reduction of the defect rate leads to the productivity enhancement in the industries.

They are compared with those of virgin specimens, showing significant increase of hardness, tensile and yield strength and remarkable reduction of elongation and ductility for shocked specimens.
Typical measurement results of Vickers microhardness distributions in the cross-section of a recovered specimen plate of A2017/TP10MT10 5[mm] EEEExxxxppppllll oooossssiiii vvvveeee(((( PPPPEEEETTTTNNNN)))) SSSSiiii ddddeeee Center 0 5 10 15 20 25 120 130 140 150 160 170 180 190 200 Location from center [mm] Hv 185.1 164.3 186.3 166.7 185.7 165.7 186.9 163.8 191.7 176.6 199.3 178.9 Average data Experiments Average data of virgin material A2017-T4 TP10MT10 Figure 4.
In the tensile tests, tensile strength Bσ , elongation λ and reduction of area ϕ (RA) were obtained measuring breaking load, elongated length between two Vickers indentations with distances of gauge length L and section area, which showed very small reduction as shown in Fig. 6(a).
Figure 8 represents all the data of fracture ductility fε and proof stress 2.0σ obtained from the tensile and compressional tests for shock-loaded specimens of A2017, A2017 (AT attached) and 304SS.
It is noticeable that this tendency almost corresponds with the data of Fig. 8, where the ratio of the products of fεσ ⋅2.0 in Eq. 4 of precompressed specimens to those for virgin materials are 63.9% (A2017), 84.6% (A2017/AT-attached) and 122.4% (304SS), although cylinder fragmentation phenomenon must be affected by some other factors.

On the basis of the flow stress data, dynamic materials model (DMM) and Prasad's instability criterion, the processing maps for as-cast 42CrMo steel were constructed at the strains of 0.4 and 0.6.
On the basis of the dynamic materials model and the flow stress data, the processing maps for as-cast 42CrMo steel were constructed and analyzed.
Fig. 2 Optical microstructures of as-cast 42CrMo steel deformed at strain rate 0.05s-1 and height reduction of 60% for various deformation temperatures of (a)850℃ and (b)1050℃.
Fig. 3 Optical microstructures of as-cast 42CrMo steel deformed at deformation temperature 1150℃ and height reduction of 60% for various strain rates of (a)0.05s-1, and (b)5 s-1.
Fig. 4 Cracks observe at temperature of 1050℃, strain rate of 5s-1 and height reduction of 60%.

Each blend is evaluated for five tests to eliminate the inconclusive data.
The CO reduction is possible due to presence of oxygen in biodiesel fuel that enhances a complete combustion and as such carbon monoxide is converted in to carbon dioxide.
From the data collected it can be observed a reduction of HC emissions due to the fact that has no aromatic compounds like diesel fuel (Fig.4).
The data collected in the experiments show that engine performance and emissions when is fueled with palm biodiesel and its blends were comparable with diesel fuel.
Impacts of biodiesel combustion on NOx emissions and their reduction approaches, in Renew Sust Energy Rev 23 (2013) pp. 473–90

Data processing examples show that the model's practicality and reliability.
The main characteristic of grey system theory is the research about small data and uncertainty, and the basic tool is grey generation.
Behavioral data of the system may be chaotic and complex, but there is always some kind of law among them.
Grey generation is to find the law from these behavioral data, and establish grey model according to the law, further predict the system by solving the model [1].
Example There are the fatigue experimental data (Mpa) in [9]: = [ 560,540,l523,500,475] Corresponds temperature (oC): t = [100, 150,200,250,300] The number corresponding to the temperature: k = [1,2,3,4,5] The model was obtained by using this method proposed in this paper: The fitting value of the data: =[560.6246,540.2185,520.5551,501.6074,,475] The relative error: e(%)=[-0.11154,-0.040461,0.46748,-0.32149,0] The mean of the relative error is 0.18819%.

Steel plates with martensitic structure were cold-rolled up to 40% reduction in thickness.
These iron plates with ferritic structure were cold-rolled up to 90% reduction in thickness.
Dislocation density increases in proportion to the thickness reduction by cold rolling.
The data of cold-rolled martensite were on the line extended from the data of cold-rolled ferrite and the following equation was obtained to the density of stored dislocation ρ for the cold-worked ultra low carbon iron.
(3) � However, the data of as-quenched material locates far away under the line.

(8) (9) (10) (11) , (12) Step 3: Data collection and attributes reduction.
The collected data is different from each other in unit and the criteria, so the difference must be eliminated before the date is used.
In this paper, the VPRS model is used for evaluation attribute reduction as previous introduced.
The condition attributes representing a pattern's elements were scaled approximately onto [0, 1] by the formula (13): (13) Where and are minimal and maximal values of the attribute within the training set data.
According to process of attribute reduction, the generalized rules are listed in table 3.

Erik et al. [10] generated different classifiers by using different representations of the data.
By combining the results with voting techniques, they managed to improve the best reported performances on standard data sets for baseNPs and arbitrary Noun Phrases.
The SVMs can summarize the multi-dimension input data.
We refer to such process as dimensionality reduction.
Experiments We select 10 863-Evaluation-Corpus Documents as our experiment data set.

The percent volume reduction was selected at the beginning as being 20 %.
Results of the topology optimization for the percent volume reduction of: a) 20 %; b) 30 %; c) 45 % Femap NX Nastran offers the possibility to analyze the numerical data on a series of graphics as the ones presented in Figure 6.
The lowest value of stiffness resulted in the case when a percent volume reduction of 20 % has been considered (see Figure 6a).
Weight of the component in accordance with the loading force and the percent volume reduction coefficient By analyzing the results presented in Table 1 and Figure 7, it is possible to observe that by increasing the value of the loading force and percent volume reduction coefficient, in both cases of material type that were considered, the weight decrease is significant.
Taking into account the fact that more than 200 components like this are mounted in an airplane, the fuel consumption reduction is also significant in this case.

Production planning and control decisions are based mainly on the results of the analysis of big data as a basis for effective data analysis can provide the most appropriate decisions.
Timelines and projects TW-DRG National Health Insurance Bureau released data is also used to simulate and analyze.
Raw data integration and segmentation is used to build a decision tree process encountered many combinations of relationships.
This study is based in part on data from the National Health Insurance Research Database.
[7] OECD health data 2010, http://www.oecd.org/health/healthdata, 2010