Economic support and technology; two fundamental points for the development of our agricultural producers, but to what extent they should arrive and what indicators will allow me to define; the degree of resources and the capacity of new technologies, depending on the premise. Efficient management of resources in agricultural production. This research develops the six sigma theory, as a management instrument based on parameters that allow us to have a reference for the optimal use and management of resources, as well as to project a range of improvement; The study area corresponds to the municipality of Amatlán de los Reyes, Veracruz, taking as a reference farms of 10 to 5 hectares with manual harvesting type cut and load.
The statistical parameters calculated were the mean and standard deviation of the population, the variation was described by means of a Pareto diagram, and the number of standard deviations on each side of the normal curve were calculated to analyze the displacement of the curve. According to Cpk levels, a calculation of the area under the normal curve was carried out, considering it sufficient to travel the curve an area of 1% on average, analyzing the variation factor in relation to the mean according to the Cpk formula to reach levels six sigma.
Standards were obtained on which to work on a management model to make crop management more efficient, shifting the curve 0.24σ to the left or right defines a level of improvement within the curve of 1%. has determined that to obtain Cpk values of 1.5, which means, at six sigma levels the variability must be 0.166666667 points from the mean.
INTRODUCTION
Six Sigma is a work philosophy and a business strategy, which is based on the approach of eliminating variability in processes and achieving a level of efficiency. For example, the reduction of cycle times, reduction of costs, and something very important, significant effects on the financial performance of the organization.
The change in the use of Six Sigma implies replacing the mechanistic model where activities, inputs and technology are assigned by a model of continuous improvement in search of efficiency in the management of resources, setting specific goals called sigma levels.
Sigma (σ) is a statistical parameter of dispersion that expresses the variability of a set of values with respect to its mean value, so that the lower the sigma, the lower the number of defects. Sigma quantifies the dispersion of these values with respect to the mean value and; therefore, set the upper and lower customer specification limits relative to the target central value. The lower the sigma, the lower the number of out-of-specification values, and therefore the number of defects.
One of the main objectives of this work is to measure the productive activities and inputs in the management of sugarcane cultivation in the municipality of Amatlán de los Reyes, to define the sigma levels that serve as indicators of financial efficiency and reference parameters. for the development of new technologies.
DEFINE
Identification of the key processes for the management of sugarcane cultivation; definition of production processes and monetary valuation.
a- The sample
A homogeneous sample of plots of 10 to 5 hectares of rainfed under the area of influence of the municipality of Amatlán de los Reyes with manual harvesting type cut and load has been defined in such a way that the study is taken as the simplest management of the crop currently.
b- Survey design
With a targeted survey we obtain information that allows us to know the processes, this is each of the activities carried out within the exploitation; time, equipment, materials, supplies, labor, etc.
c- Application of the surveys
A survey is carried out for each producer (one hundred in total), to be answered through a directed interview. The duration is around three weeks.
d- Selection and validation of the representative sample
The large number of regions that make up the sugarcane agro-ecosystem of the central zone of the state of Veracruz means that we have to select a representative sample of the total population, which will have to be validated according to the purposes and scope estimated for the application of the six sigma management methodology, that is, taking into account the simplest and most widely used elements in crop management (op. cit).
CHARACTERIZATION
Characterize the behavior of the process through a flow diagram, characterization of the process variables.
a- Information management with software support (Microsoft Excel ®)
All the volume of information obtained with the questionnaires must be properly analyzed and interpreted. For this, it is convenient to group the variables by fields and codes previously established in the questionnaires, in a way that allows their treatment and statistical interpretation with the support of some statistical computer software.
b- Identification of the variables of greatest importance in the process by means of the Pareto diagram
Numerous techniques can be used to analyze the causes of variability. The most common are: process flow analysis, information stratification, Pareto principle, affinity and relationship diagrams, histograms / analysis of process capabilities, and other simple statistical techniques. As a result of the application of these techniques, the key causes on which to act are identified.
c. Feedback
The achievements achieved with the application of our model will have little value in benefit of the orientation of our training, if we believe that with their assessment we have already achieved success, it still remains to theoretically verify the veracity of the contents (op. Cit.).
OPTIMIZATION
Statistically analyze the data to establish six sigma improvement parameters, starting from the inherent fact that in every process where a tangible product is obtained, there are variations in the characteristics of the products derived from the process of obtaining them. In these media, the Origin of the variations is classified in two; the causes of variation inherent to the process itself or common causes within the system and that can only be affected if changes are made to the system, for example selection of inputs, machinery, tools, culture, traditions and on the other hand the special causes that arise as incidents at certain times and under circumstances, resulting in significant variability (op. cit.)
a- Calculation of the mean and standard deviation to create the normal curve of each of the most significant variables in the production process.
Creation of the normal curve for each of the variables according to the formula
b- Establishment of limits.
INTEGRATION
Standardize through the six sigma measure the quality parameters in the management of the production process, Six Sigma helps us to know and understand our processes, in such a way that we can modify them to the point of reducing the waste generated in them. This will be reflected in the reduction of the costs of doing things, ensuring efficiency in the management of resources without having to reduce our profits or without having to reduce the costs of doing things well, if not by eliminating costs associated with errors or waste or variation (op. cit.)
DEFINITION OF VARIABLES
Economic variables that interfere in the establishment of the crop.
- Subsoil (X1).- Independent variable that represents the action of loosening the soil layer and creating a suitable surface for sowing. Creep (X2).- Independent variable that represents the action of homogenizing and breaking up the soil layer. X3).- Independent variable that represents the action of elaborating its own ditch for the accommodation of the seed and the alignment of the plant for its proper management, an economically important activity that favors the development of activities aimed at the efficient and practical management of cultural activities Seed (X4).- Independent Variable which represents the plant material to be used as a trigger for the population development of individuals with their own characteristics and appropriate to the agro-ecosystem as well as agro-industrial characteristics favorable to the processes and workmanship (X5). - Independent variable that refers to the action required of man for the development of activities.
Economic variables that interfere with management in Soca
- First fertilization (X6).- Independent variable that represents the action of applying nutrients to the crop in order to favor the development of the plant. Second fertilization (X7).-Trunk (X8).- Independent variable that represents the action of trimming at ground level the portion of cane that remains after harvesting. Weeding (X9).- Independent variable that represents the action of creating a cut to the vine after harvest to align the planting line, tear off damaged fractions of the strain and favor root development. Because (X10).- Independent variable that represents the action of bringing soil to the strain, favoring the nutrition of the plant as well as the grip of the root system.
Economic variables that interfere in phytosanitary management
- Manual control of weeds (X11).- Independent variable that represents the action of avoiding competition from foreign plant material to the main crop by physical means Chemical control of weeds (X11).- Independent variable that represents the action of avoiding competition from plant material foreign to the main crop by chemical means Pest control (X12).- Independent variable that represents the action of eliminating or controlling living organisms that delay or inhibit the development of the crop or transmit diseases Disease control (X13).- Independent variable that represents the action of eliminating or controlling living organisms (viruses, fungi or bacteria) that delay or inhibit the development of the culture.
Economic variables that interfere with the harvest.
- Manual harvest (X14).- Independent variable that represents the action of lifting the product in the field using only human force Mechanized harvest (X14).- Independent variable that represents the action of lifting the product in the field using specialized equipment to for this purpose Semi-manual harvest (X14).- Independent variable that represents the action of lifting the product in the field using the combination of human strength and specialized equipment Freight (X15).- Independent variable that refers to the cost of transportation of the product from the field to the factory.
Economic variables that interfere with fixed costs.
- Irrigation (X16).- Independent variable that represents the action of incorporating water into the crop by non-natural means. Type of soil (X17).- Independent variable that represents the base material of the nutritional and mechanical sustenance of the crop. Price Land Rent (X18).- Independent variable that represents the cost of the land for the use of physical space and its capacities for the development of the crop and its management Distance to federal highway (X19).- Independent variable that represents the strategic location with respect to means of communication
Economic variable that expresses efficiency in crop management.
Yield (Y).- Dependent variable that represents the volume of crop production in tons per hectare.
Expression.- Y = f (X1, X2, X3, X4, X5, X6, X7, X8,, X9X10, X11, X113, X14, X15, X16, X17, X18, X19…
QUESTIONNAIRE
Figure. Survey registration form.
DISCUSSION OF THE RESULTS
The management of the cultivation of sugar cane has basically focused on four fundamental processes; weed control, fertilization, and pest and / or disease control.
Work such as de-trunking and manual weeding, which are the activities that require the greatest labor demand, have ceased to be performed Figure 8.1.
85% of the expense is accumulated by fertilization, harvest, and freight activities; Other expenses are determined by the percentage of the cost of activities that represent commitments acquired before the mill, in addition to insurance fees and support to the peasant group.
Picture. Average cost per hectare and activity.
| Heading | Average cost / ha. | % Participation | Accumulated |
| Fert. Triple 17 | $ 8,380.00 | 36.68% | 36.68% |
| Harvest | $ 4,916.29 | 21.52% | 58.20% |
| Other expenses | $ 3,837.87 | 16.80% | 75.00% |
| Freight | $ 2,512.86 | 11.00% | 85.99% |
| Fert. Urea | $ 1,500.00 | 6.57% | 92.56% |
| Herb. Narrow Blade | $ 490.80 | 2.15% | 94.71% |
| Herbicide Application | $ 300.00 | 1.31% | 96.02% |
| Pesticide application | $ 300.00 | 1.31% | 97.33% |
| Pesticide | $ 206.55 | 0.90% | 98.24% |
| Fert. urea application | $ 150.00 | 0.66% | 98.89% |
| Fert. triple 17 app | $ 148.00 | 0.65% | 99.54% |
| Herb. Wide Blade | $ 104.51 | 0.46% | 100.00% |
| $ 22,846.88 | 100% |
Figure. Pareto diagram of the average cost per hectare and participation of each activity.
The economic and social conditions make the management of the crop very dispersed, although homogeneous conditions have been chosen, as shown in table 8.1, 85% of the variation is basically contained in 2 items; fertilization and harvest, all processes are off-center as shown by the level of efficiency Cpk that for the case of an efficiency level this must be 1.5; In the case of fertilization with triple 17, a very high positive standard deviation is observed, this indicates applications of high doses, on the contrary, the urea fertilizer table 8.3 is observed, for the case of the efficiency level Cpk less than 1 we are obtaining 0.27% of defects.
Picture. Cost standard deviation distribution
| Heading | Standard deviation | % Participation | % Accumulated |
| Fertilizer 17-17-17 | $ 4,047.15 | 45.70% | 45.70% |
| Urea fertilizer | $ 2,435.01 | 27.50% | 73.20% |
| Harvest | $ 629.69 | 7.11% | 80.31% |
| Narrow Leaf Herbicide | $ 497.41 | 5.62% | 85.93% |
| Other expenses | $ 491.57 | 5.55% | 91.48% |
| Freight | $ 321.85 | 3.63% | 95.12% |
| Pesticides | $ 155.33 | 1.75% | 96.87% |
| Broadleaf herbicide | $ 96.73 | 1.09% | 97.96% |
| Herbicide labor | $ 60.03 | 0.68% | 98.64% |
| Pesticide labor | $ 60.03 | 0.68% | 99.32% |
| Labor T17 | $ 31.40 | 0.35% | 99.67% |
| Urea workmanship | $ 29.06 | 0.33% | 100.00% |
| $ 8,855.26 |
Figure. Pareto diagram percentage distribution of standard deviation.
Picture. Effective capacity and standard deviations on each side of the curve.
| Heading | Cpk | - σ | σ | |
| Triple fertilizer 17 | 0.36 | 1.39 | 3.06 | |
| Urea fertilizer | 0.07 | 2.67 | 1.44 | |
| Triple app 17 | 0.51 | 1.66 | 7.90 | |
| Urea workmanship | 0.57 | 1.72 | 8.60 | |
| Broadleaf herbicide | 0.28 | 3.94 | 1.14 | |
| Narrow leaf herbicide | 0.44 | 1.33 | 4.21 | |
| Herbicide Application | 0.56 | 1.67 | 8.33 | |
| Pesticides | 0.16 | 2.85 | 2.17 | |
| Pesticide application | 0.56 | 1.67 | 8.33 |
ADJUSTING THE VARIATION TO REACH SIGMA EFFICIENCY LEVELS
Fertilization
Triple 17 fertilization for the cultivation of sugar cane in the study area; presents an average cost per hectare of $ 8,380.00, with a standard deviation of $ 4,047.15, said behavior is expressed in figure 8.3. The lower limit 1.39 and upper limit 3.06 denote inefficiency as indicated by the Cpk value 0.36.
The area included in the negative limit, (left tail) towards where we want to shift the curve, figure 8.1.1.2, to optimize the process with the development of technology and / or the implementation of an improvement program, represents a displacement of 0.23σ, which in the curve means an area of 1.00%, according to the formula formulated 3σ 2 + σ- $ 8,380.00, for the process to be centered according to six sigma levels it must have a variability of 0.166666667 points from the mean, thus we have that to reach Cpk levels the process should have a variability of $ 1,396.67.
The application of triple fertilizer 17 presents an average cost per hectare of $ 148.00, with a standard deviation of $ 31.40 figure.
Figure. Fertilizer application behavior.
In this case, the curve must shift by 0.24σ, representing an area on the curve of 0.92%, that is, in technological and / or process innovation, it will have a statistical difference that means less than 1%, according to the expression 3σ 2 + σ- $ 148.00, to obtain six sigma levels the process must have a variability of 0.166666667 points from the mean.
Picture. Calculation of the area under the normal curve, triple fertilization 17.
Fertilization with urea has an average cost per hectare of $ 1,500.00, with a standard deviation of $ 2,435.01, for the case we observe a higher concentration towards the left tail of the curve.
Picture. Calculation of the area under the normal curve of the urea fertilizer.
Shifting 0.23σ to the right means 1.00% of the area on the curve but in this case; It should be in the sense of promoting the use of the recommended doses, or, where appropriate, developing a more economical source that promotes its intensive use. According to the expression 3σ 2 + σ- $ 1,500.00, the variability to obtain six sigma levels must be 0.166666667 points from the mean.
The labor in the application of urea fertilizer in sugar cane; presents an average cost per hectare of $ 150.00, with a standard deviation of $ 29.06 figure.
Picture. Calculation of the area under the normal curve for the application of urea fertilizer.
The area included in the negative limit (left tail), towards where we want to move the curve, represents 0.23σ, which in the curve means an area of 0.96%, that is, in the efficient management of resources, or in the development of a technology that replaces labor with urea, it should be cheaper by 0.23σ and obtain a significant statistical difference in terms of the cost of the product; According to the expression 3σ 2 + σ- $ 150.00, the six sigma level is achieved by having a variability of 0.166666667 points from the mean.
Weeds
The average cost per hectare of narrowleaf weed control in the study area; is $ 490.80, with a standard deviation of $ 497.41, said behavior is expressed in the figure.
The Cpk levels denote a shift towards the left tail of the curve; shifting the curve 0.23σ in the curve represents the area of 1.00%, that is, in the efficient management of the resource, or in the development of a technology that replaces the control of narrow-leaf weeds, it must be cheaper by 0.23σ; which would mean having a highly significant statistical difference in the cost of the product or activity. According to the equation 3σ 2 + σ- $ 490.80; to obtain six sigma levels, the variation must be 0.166666667 points from the mean.
The average cost per hectare of broadleaf weed control in the study area is $ 104.51; With a standard deviation of $ 96.73, this behavior is expressed in the figure.
Shift the curve 0.23σ; represents shifting the curve by an area of 1.00%, that is, in the efficient management of the resource, or in the development of a technology that replaces the control of broadleaf weeds, it should be cheaper by 0.23σ to obtain a significant statistical difference in terms of at the cost of the product. The six sigma levels according to the equation 3σ 2 + σ $ 104.51, must be 0.166666667 points from the mean.
The average cost per hectare for herbicide application in the study area is $ 300.00, with a standard deviation of $ 60.03, figure 8.9.
The area within the negative limit; Where we will shift the curve 0.23σ, it means an area of 0.94%, this is in the efficient management of resources, or in the development of a technology that replaces labor in weed control, it must be cheaper by 0.23 σ, to obtain a statistically significant difference regarding the cost of labor. According to the expression 3σ 2 + σ- $ 300.00, to obtain six sigma levels it must be 0.166666667 points from the mean
8.1.3 Pests
The average cost per hectare of pesticide use in the study area; is $ 206.55, with a standard deviation of $ 155.33, said behavior is expressed in the figure.
The area included in the negative limit of the curve towards which we wish to shift 0.23σ; which in the curve means an area of 0.99%, that is, in the efficient management of resources, or in the development of a new technology, it must be 0.23σ below the mean in order to obtain a significant statistical difference. To obtain six sigma levels according to the expression 3σ 2 + σ- $ 206.55., It must be 0.166666667 points from the mean, The average cost per hectare in the application of pesticides in the study area is $ 300.00; With a standard deviation of $ 60.03, this behavior is expressed in the figure.
The area included in the negative limit, towards which we will shift the curve by 0.23σ, which in the curve represents an area of 0.98%, this is in the efficient management of resources, or in the development of a technology that replaces the hand work on weed control; it must be cheaper by 0.23σ, to obtain a statistically significant difference in labor cost. To obtain six sigma levels according to the expression 3σ 2 + σ- $ 300.00, they must be 0.166666667 points from the mean
Total cost
The average cost is $ 22,723.51 per hectare, with a standard deviation of $ 6,110.02, said behavior is expressed in figure 8.12.
Shifting the mean 0.23σ to the left side, represents an area of 1.00% on the curve, this is in the efficient management of resources, or in the development of a technology that impacts the total cost of sugarcane production.
To obtain sigma levels according to the expression 3σ 2 + σ- $ 22,723.5, the variation must be 0.166666667 points from the mean.
IX CONCLUSIONS
The six sigma methodology has based its application in the industrial area where the processes can be calibrated, where the variation is smaller, in the case that we are dealing with the production process is subject to greater variation since the production in an agro-ecosystem interacts a number of factors that are not very controllable, so the development of the calculations of the area under the normal curve was not used the standard normal curve, but for each case the area of each curve was calculated.
It has been defined as a sigma level of efficiency or performance in resource management of 0.24σ; In other words, in a production process, for the performance to be significant in terms of cost, it must be less than the expense exerted by 0.24 standard deviations or greater.
On the other hand, it was observed that to establish sigma levels; the average variation that must exist in relation to the mean, must be 0.166666667 points from it.
In the case of income from production per hectare; This presents the same level of efficiency in 0.24σ, therefore any improvement or technology included in the production process, it must improve income by 0.24 times its standard deviation.
The methodology sets guidelines for the development of new technologies; For the case presented, it is not enough to incorporate a new technology into the production process, but it must have sigma efficiency levels; in terms of saving resources or generating them.
XI BIBLIOGRAPHY
- Álvarez, DA 1997. The hoe makes the sugar. Canaveral magazine, vol. 2 No. 1 January - March. Havana. pp. 16 - 21.Álvarez, DA 1999. Undesirable plants: the second great damage to agricultural yields. cañaveral, vol. 5 No. 3. Havana, Cuba. pp. 2 - 8. National Academy of Sciences 1993. Harmful plants and how to combat them, 2nd. Revised and expanded edition. Vol. Editora Limusa. Mexico. pp. 93 - 106.Carño GR 2002. Six Sigma and the capacity of the process in projects, Technological trends. IIE Mexico Bulletin Castro, GM et. to the. 2001. Plant Production, Sugarcane, Tecnológico de Monterrey, Querétaro campus. http://www.qro.itesm.mx/agronomia2/extensivos/ CCanaIndicedecultivo.htm # Ca% F1adeazucarChinea, MA Rodríguez, LE Diseases of the sugar cane 100 p. Cock, MJ 1995. Biological control of weeds. FAO Rome. pp. 164-172.Crespo, LR 1998. Comprehensive management of sugarcane agrosystems. User information. Notes from the master's degree in sugar cane. FCBA - UV. Cordoba, Veracruz. 151 P. Cuéllar, AIA, R. Villegas, ME De León and H. Pérez., 2001. Manual of fertilization of sugar cane in Cuba. Ed. PUBLINICA, Havana. 127 p.Cuéllar, IA, León, OME, Gómez, RA, Piñón, GO, Villegas, DR and Santana, I., 2003. Sugar cane, sustainability paradigm. Ed. PUBLINICA. Havana. 175 P. Chowdhury, Up. 2003. Six Sigma is evolving as the US automotive industry's mantra to win the customer's heart on quality. Action LINE The Magazine of the Automotive Industry Action Group Jan / Feb 2003De Jesús M., A. 1997. Advances in the Selection of Varieties in Ingenios del CAZE. Summary of the presentation presented at the XX National Convention of the ATAM. Veracruz, Ver.ECC Year 1 No.13 November, 2001. The Power of Six Sigma. Executives in Consulting and Training® Ferreiro O. 2003 Methodologies for the Control and Improvement of Processes. Alberto Hurtado University, Santiago, Chile. http://www.dictuc.cl/ucyc/AulaVirtual/DGC09/Modulo11OF/M%C3%A9todos%20para%20el%20Co ntrol% 20y% 20Mejoramiento% 20de% 20Procesos.docFlores, CS 1995 Agricultura Cañera. CNIAA. Job offer. Cytcaña Mexico. 31 p. Garcia, ch. LR 1997. The Mexican sugar agro-industry in front of the opening of CIESTAAM. Chapingo Autonomous University, Mexico. pp. 7-27. Garza, K.; Acosta E. 2004 deployment of six sigma to strengthen the implementation of the international standard iso http://6sigma.mty.itesm.mx/Publicaciones/ISO6S.pdf.1996. Topographic chart 1: 50000. Córdoba E 14857. Aguascaliente, Ags. Mexico.National Institute for Sugar Cane Research (INICA). City of Havana, Cuba, 1994. (1982). Technical report on sugar cane. Balsas Region, Mexico. pp. 27 - 42.León, M. 2004. Six sigma - towards a new paradigm in management. http://www.gestiopolis.com/recursos/documentos/fulldocs/ger1/pa rassiglefco.htm.Lopez G. 2002. Methodology 6 sigma industrial quality. Autonomous University of Baja California, México.Márquez, G. October 25, 2001. The change: Improved Productivity. Executives in Consulting and Training ®.Martín, OJR; G. Galvez; R. De Armas; E. Espinoza; R. Vigoa and A. León. 1987. Sugarcane in Cuba. Scientific-Technical Ed, Havana. 612 p.Mercado, RE and RM Marín 2000. Sugarcane cultivation in the states of Veracruz and Tabasco, current situation and perspectives.SIGOLFO-PRODUCE Forum Formulation of Comprehensive Projects for the Agricultural, Forestry and Aquaculture Sector Milanese, RN, Gómez, JI, Castillo, MA Y Aguilar, RN, 2003. Estimates, quality and use of sugar cane derivatives. FCBA-UV. Sugar cane mastery. Leaflet 58 p.Milanés, RN, Gómez, JI, Castillo, MA Y Aguilar, RN, 2003. Estimates, quality and use of sugarcane derivatives. FCBA-UV. Sugar cane mastery. Booklet 58 p. Morales, MD (1983). Weeds and their combat. IMPA, Mexico. pp. 9 - 25. Office of the Government Program. 2002. Government of the State of Veracruz - Municipality of Amatlán de los http://www.amatlandelosreyes.gob.mx/.Ordoñez, BP 2000. Study of weeds in different environments of sugar cane (saccharam spp) in Ingenio San Miguelito, Veracruz Mexico.Thesis that to obtain the degree of master of science. FCBA - UV. Cordoba, Veracruz. 123 P. Reyes, AP 2002. Lean Manufacturing and Six Sigma in Mexican Companies: Experiences and Reflections. Accounting and Administration Magazine N °. 205, April - June 2002. http://www.ejournal.unam.mx/rca/205/RCA20505.pdf. Shenk, MD 1979. Weed. Control program progress report. Oregon state University / CATIE Turrialba Costa Rica. 127 p.Steding, PE Quality by design. Action line the magazine of the automotive industry. Action Group May 1998, Xavier Tort-Martorell Llabrés. 2003. Six sigma: A model that bases decisions on data. 27th National Congress of Statistics in Operations Research. Lleida April 8 - 11, 2003. Spain.Zaragoza, c. 1999. Herbicide resistance prevention committee. Plant protection service. Mayor Rovira.Madrid Spain.
