Identifying opportunities in the use of information technology
Summary
This industry, due to its socio-political antecedents, is strongly rooted in practices that are susceptible to evolution through the application of information technology.
Decision support systems are a viable alternative to replace old paradigms in the selection of courses of action with solid instruments that generate timely and quality decisions.
The sugarcane agribusiness in Mexico is by itself an interesting case study whose particularities and history are worthy of extensive study.
This article seeks to show areas in which decision support tools can act for the benefit of this sector, improving its processes and endowing it with exceptional capacities for decision-making, either to react to environmental variables or allowing it to undertake proactive actions based on in complex analysis.
This industry, due to its socio-political antecedents, is strongly rooted in practices that are susceptible to evolution through the application of information technology.
Decision support systems are a viable alternative to replace old paradigms in the selection of courses of action with solid instruments that generate timely and quality decisions.
Let us remember that decision support systems with tools that serve to assist executives in decision-making tasks, and are characterized by the possibility of using models to perform sensitivity analysis, the incorporation of intelligent technologies such as artificial intelligence and the possibility of being able to solve problems through sophisticated visual aids.
Given the characteristics of these systems, three large areas of opportunity for the application of this type of technology in an agribusiness such as sugarcane are easily identifiable:
Predictive maintenance with expert systems.
An expert system is understood to be software that imitates the behavior of a human expert in solving a problem.
They can store expert knowledge for a given field and solve a problem using logical deduction of conclusions (IEI, nd).
Visual support in the planning of raw material supply. Geographic information systems allow the creation of a digital model of the terrain to be controlled, allowing a visual administration of the area to combine topographic, hydrological and infrastructure information with the thematic incursions that want to be shown through these visualizing tools.
Modeling and simulation of production processes. Production processes whose control can be carried out through industrial automation can create models on the handling of key variables. These computerized control systems allow the simulation of specific operating conditions to identify vulnerabilities in the process or the design of emergent plans in case of failures.
Predictive Maintenance
For the production areas of the industry, the continuity of their process is key to achieving their objectives since the milling capacity of the factories is limited to the sugarcane harvest season (zafra), which lasts between 5 and 8 months depending on weather conditions and production schedules. During this period, it is vital to make the most of the time available to allow the business to reach the production volumes that allow it to break even.
One way to avoid wasted time is to anticipate failures in critical equipment that can cause a production stoppage and therefore the discontinuity of the grinding, this anticipated taking of actions is carried out through the so-called predictive maintenance, which is based mainly in the analysis of vibrations as a technique for diagnosing faults and evaluating the integrity of machines and structures. In the case of rotating equipment, the advantage of vibratory analysis over other techniques such as penetrating inks, radiography, ultrasound, etc., is that the evaluation is carried out with the machine running, thus avoiding the loss of production it generates. the stoppage of a team (Nelson, nd).
These vibrations are collected using specialized instruments that download their information to computers whose software allows the classification and manipulation of the sampled data. These data are presented in the form of graphs to the analyst who through their training is able to identify anomalies by studying the patterns that the graphs follow as well as their exception values.
The software is usually accompanied by aids for the manipulation of historical information that allows the analyst to compare the behavior of the equipment with its past performance and obtain significant conclusions, also using the manufacturer's operating parameters as a reference for any possible deterioration of the components of an equipment.
All this diagnostic experience that must be acquired and cultivated by the vibration analyst could well be deployed through an expert system whose capabilities to compare the graphs obtained in the samplings with a previously fed knowledge base would allow the analyst to quickly trace the natural learning curve that this discipline requires.
On the other hand, an expert system could infer anomalies based on subtle behaviors that are difficult to detect in a conventional analysis, which could also be purchased, that is, according to the appearance of new machinery, the necessary experience could be acquired to keep the equipment in good condition in advance. optimal operating conditions, saving the analyst precious time that translates into better equipment operating conditions and, therefore, the extension of their useful life.
In addition, the explanatory components of the system would show the reason for the decision of the system in the given circumstance, which in itself constitutes an additional learning opportunity for the analyst.
Visual support in planning raw material supply
Sugar factories need to be supplied by large extensions of planted territories in order to generate the production volumes that they have defined in their production objectives, to give us an idea about the size of the surface to control, a sugar mill can have between 5,000 to 20,000 hectares of territories to be harvested (COAZUCAR, nd), this implies an impressive technical and logistical deployment so that during the sugar cane harvest both the agricultural machinery and the labor involved in the cutting and raising of the sugar cane move. raw material.
Obviously, so much area to be harvested raises the question of which cane should be harvested before another? The mills carry out the planning of their harvest, combining the criteria of the maturity of the cane, the concentration of sucrose in the plant and the geographical conditions of the land to be harvested.
These three elements can easily be placed in the same dimension through a geographic information system that allows the decision maker to detect the cane with the best harvest conditions using thematic maps that color the surfaces categorized in the criteria exposed to the tool..
The maps are created with aerial photographs digitized and treated to compensate for the curvature of the globe as a geopositioned planisphere through GPS technology, which constitutes the basis for creating a vector model of the reality that the countryside experiences.
The tool feeds the visualization criteria with information extracted from the same databases that allows the software to apply particular textures, colors and shapes to the vectorized image, giving planning managers a host of new possibilities, since they can determine with much precision to the operational commands "which" are the canes to be harvested and "how" they are depending on the color that the tool paints on the model. Allowing an exact location of the plots and an accurate weighting of the effort necessary for their collection, making the administration of resources much more intelligent at the time of the corresponding assignments of the harvesting equipment in correspondence with their capacity and hauling speed.
Modeling and simulation of production processes
In every industry, the optimization of production processes is one of the constant battles that are fought on the floor of each factory.
This optimization is often achieved only when a deep understanding of the process is achieved and when the relationship of the production variables located in time are synchronized in the indicated way.
If to this scenario we add the changing market conditions that force industries to modify their processes either to adapt to new circumstances or to take advantage of business opportunities, we can perceive the complexity that exists to achieve the "tuning" that every factory required to stabilize your processes.
Through computerized industrial control systems and simulation systems, process models can be created to simulate atypical operating conditions and infer the consequences throughout the system.
In this area there are simulation applications for statistical control of processes or dynamic systems; the latter through the identification of archetypes typical of complex systems applying a systemic approach to the perception of processes (Sterman, 2002).
The use of these tools allows those in charge of the floor to carry out sensitivity analyzes of the control variables and understand how the relationship between them affects the final result.
These models allow administrators to share the knowledge generated about the process, facilitating training and analysis prior to any decision making.
Conclusions
Decision support systems are not new in the range of alternatives that industries have to strengthen their decision-making processes, however, for agribusiness segments such as the one mentioned, it is an opportunity for viable improvement in their processes that is at hand. reach of small and medium-sized organizations in the diverse menu of information technology solutions that are currently available.
Likewise, as in any technology project, the leadership that accompanies the technological initiative is vital for its implementation. The technological avatars within companies can inspire little to the rest of their members to join a project such as the implementation of an SSD, and the sugar industry is no exception. For this reason, when venturing into these technologies, managers of IT must be able to achieve the commitment of senior management to guarantee the commitment of those who must undergo the corresponding training so that the tools deliver the expected result, without detracting from the experience and lucidity of its executives.
It is important to understand that this type of system does not replace the executives that the company may need, or the software that is used to control its operations, whether they are transactional or production administrative systems, rather, SSDs should look like that. additional help that every executive should have to become highly efficient and effective in what concerns him: making decisions.
Bibliography
Efrain Turban & Jay E. Aronson & Ting-Peng Liang. Decision Support systems and Intelligent systems. Seventh Edition. Pearson Prentice Hall
Introduction to expert systems. IngenierosenInformatica.org (IEI)
Pedro Nelson Saavedra. The measurement and analysis of vibrations as a technique for the inspection of equipment and components, applications, regulations and certification (nd). Faculty of Engineering - University of Concepción.
Committee of the Sugar Agroindustry. Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food. Federal Government of the United Mexican States.
John. D. Sterman. Business dynamics. Systems thinking and modeling for a complex World. Ed. Irwin McGraw-Hill
