Without a doubt, decision making is one of the most difficult situations that human beings face, most professionals when making a decision seek to argue quantitatively, logically under mathematical thinking.
The mathematics courses taught at school, support in various areas of knowledge, however they focus mainly on solving problems of this subject, that is why when placing them in study plans of various disciplines, their modeling can be complicated This science should have a broader and deeper purpose than just being an instrumental support for the planning and solution of problems; Another of your goals should be the development of logical thinking.
DEVELOPING
DEFINITIONS
Logical thinking is one that emerges from the relationships between objects and comes from the individual's own elaboration. It arises through the coordination of the relationships that you have previously created between objects. (DE, sf)
A mathematical model is defined as a description from the point of view of mathematics, it is one that uses some type of mathematical formality to express relationships, substantive propositions of facts, variables, parameters, entities and relationships between variables and / or entities or operations, to study behaviors of complex systems in situations difficult to observe in reality. (VALDIVIA, 2013)
LOGICAL THINKING
Logic represents the fundamental basis for the development of mathematics. We can affirm that, in turn, mathematics allows the development of logical thinking. This last statement requires distinguishing the type of logic we are talking about. If one thinks of a formal logic, as we traditionally know it, where compliance with forms and rules to validate the conclusions is unrestricted, the paths built through mathematics can become straitjackets for the development of free thinking and of the ability to learn to learn.
On the contrary, we consider that the logic that supports the purpose of mathematics as an instrument for the development of reflective learning is dialectical logic, in which the concepts that seem contradictory and contradictory, such as concrete-abstract, analysis-synthesis, induction, deduction, among others; they are not one the negation of the other but rather the dual elements that induce towards the dynamic of thought necessary to discover, interpret and generate new knowledge.
TYPES OF COMPLEX DECISIONS
First, the classification by level distinguishes three types of decisions that depend on the hierarchical position of the decision-maker. These decisions are: strategic or planning, tactical or piloting, and operational or regulatory. Its main characteristics are: (DAROS)
1- Strategic or planning decisions.
- Decision-makers are senior managers. They refer to the selection of goals, general objectives and long-term plans. The information must be timely and of quality. A mistake can be fatal.
Examples: location, financial resources, products to be manufactured, etc.
2- Tactical or piloting decisions.
- The decision-makers are the middle managers. It is the implementation of strategic decisions. They are useful to efficiently distribute limited resources.
3- Operational or regulatory decisions
Examples: plant distribution, budget, production, etc.
- Operational or regulatory decisions.
- Decision-makers are the lowest executives: supervisors and managers. They refer to functional and routine activities, on a day-to-day basis.
Examples: acceptance or rejection of credits, inventory, assignment of jobs, etc.
The classification by methods is carried out depending on the procedure used to choose the final alternative, the different decisions are:
1- Scheduled decisions
- A procedure or criterion is defined so that these decisions do not have to be dealt with again each time they arise They deal with structured, well-defined and routine problems You can define, predict and analyze the elements of the problem and their relationships. Resolution is done using habits, customs, standardized procedures, heuristics, and / or simulation.
Example: a client files a complaint for which a compensation protocol is implemented, the performance of routine tasks listed in the procedures manual, etc.
2- Unscheduled decisions
- They are new, unstructured and unusually important decisions. There are no pre-established methods to deal with these unexpected events. The decision-maker's intuition, creativity or personal judgment is used for their resolution.
Examples: a natural disaster destroys one of the company's warehouses and it must be decided to repair it or locate it elsewhere, a company wants to make the leap to the international market, etc.
Finally, a synthetic classification based on the previous two is proposed, that is, depending on the hierarchical level where the decision is made and the method used, distinguishing between structured, semi-structured and unstructured decisions.
1- Structured decisions (programmed decisions)
- The three main phases (intelligence, design, and choice) are structured. Mathematical methods and decision rules are used in all phases.
2- Semi-structured decisions
- Impossibility of using methods in the intelligence phase and even in the design and selection phase. Generally the intelligence phase is not structured, but in it, once the problem has been identified, the use of mathematical models, algorithms or rules is possible decision.
3- Unstructured decisions (not programmed)
- You cannot use mathematical methods or rules of any kind. None of the phases is structured.
DECISION-MAKING PROCESS
Decision making is the process of choosing one of several options.
- THE PRESCRIPTIVE THEORY It is a normative method that defines and tries to explain the way in which decisions should be made. It proposes the steps that must be followed to make good decisions and the key points that must be taken into account.
- THE DESCRIPTIVE THEORY It deals with describing how decisions are actually made, which are often influenced by subjective factors such as the individual's personality or the pressure of the situation. (BOREA, 2002)
The way in which people who run organizations must come to a decision (prescriptive theory) and the way in which they finally do so (descriptive theory) can be very different.
When establishing the steps within the decision-making process, there is a diversity of criteria by various authors and sometimes they coincide with the stages.
A scientific methodology in decision making is presented below. (MACHADO, 2016)
1- Observation of reality:
In this first step, the problem is defined in relation to the proposed objectives and those involved in it, and the factors that influence the objectives are identified by abstraction.
2- Representation in a model:
Once the factors have been identified, those that are most relevant are selected, not taking into account those whose influence is minimal. In this phase the alternatives and other factors of the structural environment are formulated.
3- Model testing and verification:
The model explains in a useful but not entirely exact way what is involved in the problem in relation to the proposed objectives. However, a decision should not be made until the model has been tested by adequate testing. In general, verification will depend on the type of test chosen and the model developed.
4- Development of a decision rule:
Once the verification has been carried out with satisfactory results, then the model can be used in decision making. However, it must be taken into account that it is the best as long as the system for which it has been developed is not modified. Therefore, it is necessary to create a control mechanism that acts on the results and the factors considered in order to make the corresponding adjustments. Ultimately, the model becomes a decision rule that relates it more directly to the objectives.
5- Application:
Finally, the solution obtained from the model is put into practice. This step thus ends the decision process.
The way a person examines a problem and makes a decision can be described from different points of view, according to the assumptions that the person makes. Optimizing a decision-making process implies having a knowledge or approach to the elements that compose it, namely:
- The future scenario which can be affected by our decision and in turn feeds it from the understanding of the behavior of its variables The Techniques and Tools that I can use during the problem solving and decision making process And the individual, personal, socio-affective and cultural conditions that influence the person who makes the decision, which we will call: the human factor. The following model outlines this process.
Mathematical modeling and decision making. Decision-making process
DEVELOPMENT OF LOGICAL THINKING AND PROBLEM SOLVING
Various schools have tried to explain how problem-solving thinking works. For example, we find in cognitive psychology, historically the discipline that has provided useful results for this purpose, two basic approaches
The theory of associationist thought, which emphasizes how one element of a resolution chain is associated with another, and the Gestalt theory, which is based on the structural understanding of the situation to be resolved.
According to the associationist approach, the thought process is described as a trial and error application, to find the most plausible answer to any particular problem situation, considering all possible links of association to a large number of possible answers as well as trends. pre-existing response. The basic explanatory elements of this theory are: the stimulus, a particular problem-solving situation, the responses, particular problem-solving behaviors, and the associations that are established between a particular stimulus and response. A family of possible responses associated with each given problem situation is considered to be configured in the mind. Also,The answers may vary as they are ranked according to how strong the identified association is. Thus, this approach emphasizes reinforcement learning.
According to Gestalt theory, the problem solving process is a search to relate one aspect of the problem situation with another within a structural understanding of such situation, then this process develops the ability to understand how the parts of the problem they are adjusted together to meet the requirements of the solution objective. The resolution process involves reorganizing the elements of the problem in a new way that is more readable to the one who intends to solve the problem. The emphasis on adjusting the elements to form a structure of analysis (the organization), on creating solutions to new situations (productive thinking) and on reorganizing the elements of the problem (creative thinking);rests on the idea that mental structures or organizations are the units of thought. It is in this way of understanding and explaining a very high level creative mental process.
In dialectical logic, the explanation given to the dynamics of the development of logical thinking when addressing problem solving is based on the presence of conceptual dualities, such as those explained below.
Concrete - abstract: The concrete and the abstract cannot be separated; they are two aspects of solidarity, two inseparable characters of knowledge that ceaselessly pass from one to the other. “The true concrete is not found in the sensible, in the immediate. The sensible is, in a sense, the first abstraction. Sensation and perception separate one of the aspects of the object; his relationship with us, the aspect that matters to us and affects us at that moment ”. To penetrate the real is to reach, through intelligence and reason, mediate knowledge that are thoughts, ideas. To penetrate the real is to overcome the immediate to reach "an increasingly vast set of relationships, details, elements, of particularities apprehended in a whole". This set, that whole, cannot, on the other hand, coincide with the totality of reality, with the world.The act of thinking isolates from the totality - by means of a real or "ideal" layering - that which is precisely called an "object of thought." Such an "abstract" product of thought is no more mysterious than a product of practical action. Thus, although knowledge starts from the concrete, global and "confusingly apprehended in the sensitive perception", it walks through the understanding of the different aspects and elements of the situation through abstract and unilateral points of view. Through the deepening of the content and rational investigation it is directed towards the understanding of the whole.Such an "abstract" product of thought is no more mysterious than a product of practical action. Thus, although knowledge starts from the concrete, global and "confusingly apprehended in sensitive perception", it walks through the understanding of the different aspects and elements of the situation through abstract and unilateral points of view. Through the deepening of the content and rational investigation it is directed towards the understanding of the whole.Such an "abstract" product of thought is no more mysterious than a product of practical action. Thus, although knowledge starts from the concrete, global and "confusingly apprehended in the sensitive perception", it walks through the understanding of the different aspects and elements of the situation through abstract and unilateral points of view. Through the deepening of the content and rational investigation it is directed towards the understanding of the whole.Through the deepening of the content and rational investigation it is directed towards the understanding of the whole.Through the deepening of the content and rational investigation it is directed towards the understanding of the whole.
An á li s i s - synthesis: Analysis strives to penetrate the object from the outside, through thought. The beings, the concrete, appear relatively closed before us, since each being is a whole. But those beings are not absolutely inaccessible. Analysis penetrates them and separates them, breaks them, be it really or ideally. The analysis can never be exhaustive because it is infinite; because the concrete is much deeper and more concrete than previously thought (consider the example of the analysis of the human body where from the organs, we can pass to the cells and from there to the chemistry of the atoms). At all times the analysis must bear in mind, and apprehend, that complex, very often contradictory relationship of the elements with each other and with the whole. On the other hand, the synthesis appears as complementary to the analysis. The synthesis is defined,in general, as an operation is experimental (real) or rational (ideal) by means of which the path traveled by the analysis is remade in reverse. Synthesis rebuilds the whole, making sure nothing is omitted. However, the synthesis is not limited to working on a synoptic table created by the analysis, but rather makes it maintain contact with the whole at all times, for that very reason it guides the analysis, prevents it from getting lost.for that reason it guides the analysis, prevents it from getting lost.for that reason it guides the analysis, prevents it from getting lost.
Indu c tion - deduction:The induction goes from the facts to the law - from a set of particular facts to a general conclusion -, either in a rigorous way, when "the law summarizes in a formula all the particular cases studied", be it amplifying, when it goes from a number finite number of studied facts, which are necessarily past facts, to an infinite number of possible facts. To return to the application of this law to new facts, the deduction is necessary. Truth-error Scientific truths are not eternal or immovable, if so they would be infertile because they would deny the effort of thought to go from ignorance to knowledge, “from minor truths to deeper truths through partial or momentary errors”. Every error can be in itself a partial truth or the aspect of a truth, it allows expanding the limits of a truth,initially denying it. That is, the truth becomes an error before being expanded
Ab solute-relative Each truth reached is relative because “it is destined to be surpassed, to appear under new aspects, to be surpassed by more precise laws and theories”, but in a certain sense it is absolute, since the scientific knowledge acquired later implies, verifies and it complements the previous one, places it in its truth. "Absolute" truths are reached through relative discoveries and individual thoughts, whose scope is limited. "" The relationship of the absolute with the relative is none other than that of human thought with the thought of individuals "It is this very thing that explains the dualities: general-particular, generic models-specific situations.
T e ory-practiceTo understand the familiar it is necessary to overcome individual understanding, the particular point of view, immediate practice; It is necessary to move to another scale, to an order of broader, apparently abstract and theoretical reflections, without forgetting or omitting the fact that it is one. Even the mathematical truths are submitted to discussion by philosophers and mathematicians and are increasingly perceived under new aspects in order to deepen them. the real, the concrete, the human, what one tries to know and that it will be necessary to return to it to understand it. Although scientists recognize that any result achieved with these reflections is already knowledge, they look again for the unknown to deepen its truth. However, being part of this world that we want to understand, to know objects it will be necessary to act on them,Only in this way can it be guaranteed in some way that the knowledge acquired, although acquired, is relative to the place we occupy in the universe, to the precision of our measuring instruments, to the effectiveness of our actions; it will be in a certain way real both with respect to the idealization that we have made of its nature and with respect to its structure that we have built subjectively in our thinking.
Ma cro-micro: An individual is only truly understood if his singularities are discovered on the one hand and his more general features on the other, since he becomes aware of them only through these. On the other hand, every being immersed in a set of social relationships is a set of qualities. Then, to understand an individual, it is necessary to observe him in an alternative way from the social point of view (of his general features) and from the private point of view. "Dialectical reason apprehends the individual (singular) but in the totality and by the totality" Considering the dual concepts that have been presented, we affirm that the study of mathematics emphasizes the development of capacities and abilities intrinsically related to dialectical logical thinking.Let's see below how these concepts are reflected in models, particularly mathematical models, of reality. (ROSALES, 2005)
MODELS FOR DECISION-MAKING IN ORGANIZATIONS
Next, a series of models is presented by which decision-making can be simplified, it is not sought to delve into each of the disciplines, but to have a global vision of them, simplifying their explanation with the possible adaptation to the practice of our organizations. These models are: Maximin or Wald, Maximax, Hurwicz, Laplace and Savage.
You always have to have that probabilistic uncertainty threshold, so the decision will vary depending on external factors that are not controllable but, as far as possible, if avoidable and reduce its impact as much as possible for / with the decision made reaching the objective that is had proposed.
A common example is presented which will be developed in all models, you are facing a situation of change in the organization's strategy, there are doubts about how to redirect it and focus on a new market niche. (RODRIGO, 2012)
An assessment is made based on the variables obtained.
| Scenarios | |||
| one | two | 3 | |
| TO | 7 | 8 | one |
| B | 10 | two | 5 |
| Solutions C | 5 | 4 | 9 |
1. Maximin or Wald model
What the Maximin or Wald model proposes is to set the lowest valuations within all the solutions, that is, the lowest valuations are 1 for solution A, 2 for B and 4 for C, then within this range C is chosen, as it is the highest among the worst, the philosophy is the best of the worst, this supposes a loss of information because the rest of the fields are not taken into account and the option chosen could not be the most optimal.
2. Maximax model
Unlike the previous one, the Maximax model proposes to work with the data that have obtained the highest score, for example, in the table the ones with the highest score are 8 for A, 10 for B and 9 for C, applying the logic of this model B is taken as a final decision because its score is higher than the rest, the best of the best, so it is the one that would give the most benefits.
3. Hurwicz model
This model takes an intermediate logic between the previous ones, and for the worst value it gives a value of 1-α, while for the highest value it gives a value of α, where α is the optimism value it uses, this value ranges from 0 to 1, without reaching the extremes so as not to coincide with the previous theories, a reasonable value is 1/2, in this case we work with α = 1/4. So the result is the following:
A: 1 * 3/4 + 8 * 1/4 = 2.75
B: 2 * 3/4 + 10 * 1/4 = 4
C: 4 * 3/4 + 9 * 1/4 = 5.25
The option to choose in this case is C, as it has the highest score.
4. Insufficient reason or Laplace model
Laplace proposes the use of all the values that have been obtained previously. The logic that applies is to assign to each value the same probability (1 / n) in such a way that all are on equal terms. N shows the possible states of nature, that is, an example for this organization would be: increased profits, losses or stagnation.
With these criteria, the option is still C because a priori it seems the most complete and balanced, this method does not risk in decision-making.
A: 7 * 1/3 + 8 * 1/3 + 1 * 1/3 = 5.3
B: 10 * 1/3 + 2 * 1/3 + 5 * 1/3 = 5.6
C: 5 * 1/3 + 4 * 1/3 + 9 * 1/3 = 6
5. Savage model
The maximum profit is sought through the minimum loss. Then for each of the solutions there are different results, the scenarios (columns) are taken as a reference and within these the highest value is chosen to subtract it for each value within that same column for each solution.
For this example, the maximum value of the first column is 10, so 7,10 and 5 are subtracted respectively, this is done in the following columns. So solution C is presented as the best of all.
| Scenarios | uma | |||
| one | two | 3 | -– | |
| TO | 3 | 0 | 8 | eleven |
| B | 0 | 6 | 4 | 10 |
| Solutions C | 5 | 4 | 0 | 9 |
MATH MODELING TOOLS
It is usually stated that mathematics rests on a limited number of elementary propositions, called axioms, from which all the others are derived solely by processes of logical inference and deduction; However, mathematics requires observation, experimentation, induction, causality; for they arise from the activity of the human mind in a continuous exercise of introspection of the inner world of thoughts in relation to the outer world of reality; such a relationship is one of correspondence “more or less like a shadow with the object that projects it, or like the hollow palm of one hand with the closed fist that embraces the other.
There are some mathematical techniques used to support decision making.
OPERATIONS RESEARCH
The operations research branch (IO) comes from scientific management which added mathematical methods such as computer technology and a broader orientation.
The IO adopts the scientific method as a structure for solving problems with a strong emphasis on objective judgment.
Definitions of OR vary from specific mathematical techniques to the scientific method itself. In general, these definitions include three basic aspects common to the IO approach to managerial decision making.
- Systematic view of the problems to be solved Use of the scientific method in problem solving Use of specific techniques of statistics, probability and mathematical models to help the decision maker to solve problems
The IO approaches the analysis of operations of a system and not only as a particular problem, the IO uses:
- The probability in the IO approach for decisions under conditions of risk and uncertainty Statistics in data systematization and analysis to obtain a solution Mathematics in the formulation of quantitative models
The operations research methodology uses six phases:
- Formulate the problem.- With the analysis of the system and its objectives and action alternatives.Construct a mathematical model to represent the system- The model expresses the system as a set of variables, of which one by one at least, is subject to control Deduce a model solution.- The optimal solution of a model through the analytical process or the numerical process Test the model and the model solution.- Build the model that represents reality and that must be capable of accurately anticipate the effect of system changes and overall system efficiency Establish control over the solution.- the solution of a model will be adequate as long as the uncontrolled variables retain their values and the relationships between the variables remain constant. Put the solution into operation (implementation). The solution needs to be tested and transformed into a series of operational processes
The main techniques of operations investigation are:
- Game theory Queuing theory Graph theory Linear programming Dynamic programming Statistical analysis and probability calculus.
1- Game theory
Game theory proposed by mathematicians Johann Von Neumann proposes a mathematical formulation for strategy and conflict analysis.
The conflict situation occurs when one player wins and another loses, as the targets in the crosshairs are invisible, antagonistic and incompatible with each other.
The number of available Strategies is finite and therefore innumerable. Each strategy describes what will be done in any situation.
Game theory applies when:
- The number of participants is finite Each participant has a finite number of possible courses of action Each participant knows the courses of action Each participant knows the courses of action available to the adversary, even if they do not know which will be the course of action chosen by him The two parties intervene each time and the game is “zero sum”, that is to say, purely competitive, the benefits of one player are the losses of the other, and vice versa
2.- Theory of queues
Queue theory is the theory that takes care of choke points and waiting times, that is, of the delays observed in some service point.
In queuing theory the points of interest are: customer waiting time; the number of customers in the queue; and the ratio between the waiting time and the service provision time.
In a queue situation, there are the following components:
- Customers or operations A passage or point of service through which customers or operation must pass An entry process (imputa) A discipline on the queue a service organization
3.- Theory of graphs
Graph Theory is based on networks and arrow diagrams for various purposes. It offers planning and programming techniques through networks used in construction and industrial assembly activities. Both PERT (Rebién Technique Evaluation Program) and APM (Critical Path Method) are arrow diagrams that identify the critical path by establishing a direct relationship between time and cost factors, indicating the “economic optimum” of a project.
The Neopert is a simplified variation of the Pert, making it possible to save time in its preparation.
The networks or arrow diagrams are applied in projects that involve several operations and stages, several resources, different organs involved, deadlines and minimal costs.
The networks or arrow diagrams have the following advantages:
- Execution of the project in the shortest time and at the lowest cost They allow the interrelation of the stages and operations of the project Optimal distribution of available resources and facilitate their redistribution in case of modification Provide alternatives for the execution of the project and facilitate decision-making. "Critical" tasks or operations that do not offer slack in time for their execution, and thus fully concentrate on them. "Critical" tasks or operations affect the term for the completion of the overall project. They define responsibility to become or people involved in the draft
4.- Linear programming
Linear programming (PL) is a mathematical technique that allows you to analyze production resources to maximize profits and minimize cost. It is a problem-solving technique that requires the definition of the values of the variables involved in the decision to optimize an objective to be achieved within a set of limitations or restrictions, which constitute the rules of the game. Such problems involve resource allocation, linear relationships between the decision variables, objective to be achieved, and constraints.
The allocation problem involves situations such as scheduling production to maximize profits, mixing ingredients of a product to minimize costs, selecting an excellent portfolio of investments, assigning sales personnel in a territory, or defining an intermodal transport network with the lowest cost and faster.
Linear programming presents characteristics such as:
- Find the optimal position in relation to a target. The purpose is to minimize costs and maximize benefits according to the pre-established objective. It involves the choice between alternatives or a combination of those alternatives. It considers limits or restrictions that surround the decision. The variables must be quantifiable and have linear relationships with each other.
5.- Dynamic programming
Dynamic programming is applied in problems that have several interrelated stages, where a suitable decision for each of the stages must be adopted, without losing sight of the final objective. Only when the effect of each decision is evaluated is the final choice made.
6.- Probability and statistical analysis
Statistical analysis is the mathematical method used to obtain the same information with the least amount of data. One of its best known applications is statistical quality control (CEQ) in the production area. Statistical methods allow the maximum amount of information to be produced from the available data.
The application of statistics to quality problems began with Malter A. Shewhart in the course of World War II.
a.- Statistical quality control
The initial idea was to apply statistical methodology in the quality inspection and reaching the assured quality in order to obtain conformity with the specifications and provide a high degree of reliability, durability and performance in the products.
Statistical quality control is based on techniques for determining the moment when tolerated errors in production begin to exceed tolerance limits, this is when corrective action becomes necessary.
Statistical quality control aims to locate deviations, errors, defects or failures in the production process, comparing performance with the established standard. This comparison can be made in three ways:
- 100% quality control. Corresponds to total quality inspection. The total quality control (QC) is part of the production process and all products are inspected. Quality control by sampling This is done by batches of samples collected for inspection. Sample control replaces total control as it does not interfere with the production process. If the sample is approved, the entire lot is approved. The sample is rejected, the whole lot must be inspected. Random Quality Control It is the probabilistic QC and consists of inspecting only a certain percentage of products or work in a random way.
b.- Total quality
JM Juran (born in 1904). He extended the quality concepts to the entire company with his full quality control.
While statistical quality control is applied only at the operational level, and preferably in the production and manufacturing area, total quality extends the concept of quality to the entire organization, from the operational level to the institutional level, covering the entire staff from the office and factory base as a whole.
The advantages are:
- Reduction of waste Reduction of time cycles and results times Improvement of the quality of results (products or services).
Both constitute incremental approaches for excellence in the quality of products and processes, in addition to providing a formidable cost reduction.
SIX-SIGMA
Sigma is a measure of statistical variation. When applied to an organizational process, it refers to the frequency with which a certain operation or transaction uses more than the minimum resources to satisfy the customer.
The 6-sigma program uses several techniques in a step-by-step process method to achieve well-defined goals. The main difference is that with the 6-sigma since quality is not sought for quality, but it is intended to improve all the processes of an organization. In practice, 6-sigma differs from total quality in four basic areas:
- Greater breadth of application. It is applied within the area of product and manufacturing and not in the project, finance, etc. The 6-sigma is for the whole organization Simpler implementation structure Black belts are fully dedicated to change and stay out of the day. Management rewards or punishes for improving business. Deeper Tools 6-sigma digs deep to describe the current situation and foresee the future. There is a strong dose of applied statistics and a better understanding of how the processes behave, an auxiliary software and a map for the application of the tools. Application tools allows you to clarify problems and improve.Strong link to the (financial) health of businesses The 6-sigma addresses the objectives of the company and certifies that all key areas for the future health of the company contain measurable measures with better measures and detailed plans and application.
The 6-sigma seeks organizational effectiveness in three dimensions that must work together:
- Waste reduction. Through the concept of exact entrepreneurship, without surpluses, only the essentials, or future time effort, or reduction of the time cycle or even elimination of what has no value for the customer, printing speed to the company. defect It is the 6-sigma itself, Involvement of the people Through the so-called "human architecture".
BALANCE SCORE CARD (BSC)
Measures and indicators significantly affect the behavior of people in organizations. (SOURCES, 2008)
What an organization defines as an indicator is what will be obtained as results. The central point of the systems and measures traditionally used in organizations focuses purely on financial or quantitative aspects, and tries to control behaviors.
It is a management method focused on organizational balance and is based on four basic perspectives, which are the following:
- Finance. Analyze the business from a financial point of view. This point involves financial and accounting indicators and measures that allow evaluating the organization's behavior against points such as profit, return on investments, value added to equity and other indicators that the organization adopts as relevant to its business. Client Analyzes the business from the point of view of customers. It includes indicators and measures such as satisfaction, market share, trends, customer retention and acquisition of potential customers, as well as value added to products / services, market position, level of services added to the community by which customers contribute indirectly, etc. Internal processes Analyze the business from the internal point of view of the organization.It includes indicators that guarantee the intrinsic quality of products and processes, innovation, creativity, reproduction capacity and optimization with demands, logistics and optimization of flows, as well as the quality of information, internal communication and interfaces. Learning / organizational growth Analyze the business from the point of view of what is essential to achieve the future successfully.Learning / organizational growth Analyze the business from the point of view of what is essential to achieve the future successfully.Learning / organizational growth Analyze the business from the point of view of what is essential to achieve the future successfully.
MONTECARLO TECHNIQUE
It is a simplified simulation method, but it also includes probability factors. The simulation is guided by a random sampling to take into account the probability of the event happening.
Random sampling is used to simulate natural events in order to determine the probability of the events under study.
A table of random numbers is used to obtain the random sample. Monte Carlo is a means of testing to see what would happen when certain events, normal and abnormal, occur.
This approach is productive and tells what is likely to happen in actual events without looking at existing testable events. The possible applications are very numerous.
They can be used to solve problems with these typical questions:
- What is the probability of an event, or combination of events, occurring in a given process? What decision should be made based on the possible alternatives?
WAITING LINES (ROWS)
Administrative problems arise due to:
- Employees, machines or materials are made to wait due to insufficient facilities to handle them immediately Facilities utilization occurs at less than maximum due to the sequence of the arrival of resources used by the facilities
There are wasted time, unused labor, and excessive costs caused by waiting lines or queues. Minimizing these losses is the goal of this technique.
The rows are related to flow; Example: material waiting to be processed by a machine, airplanes circling over an airport awaiting instructions, include the flow of the combination and the materials. (BARRERA, 2006)
CONCLUSION
In daily life we make endless decisions, sometimes we evade the long way and we lean towards quantitative decisions based on feelings and emotions, perhaps due to ignorance of mathematical modeling methods, however, this should not be an impediment, we should not close ourselves to the possibility of its use, although this represents an arduous investigation of the subject and training.
There are some “simple” decisions that are influenced by multiple emotional factors and indicators, in these cases the modeling would be complex, but it must always be taken into account that the analysis and mathematical logic serve to reduce risks by obtaining quantifiable results.
THANKS
Thankful to God for all his blessings, also for the opportunity to work in the process of improving myself.
To my “alma mater” the Orizaba Technological Institute for their dedication in training quality professionals, to my MAE Professor Fernando Aguirre y Hernández for their dedication, dedication and commitment in sharing their knowledge.
To God for life and for science!
THESIS PROPOSAL
IMPLEMENTATION OF SIX SIGMA IN THE INSPECTION AREA AT BOTTLING TROPICAL ORIZABA, VER.
Objective: Using six sigma, reduce the number of defects per million in the inspection area of the Pepsi Orizaba plant.
BIBLIOGRAPHY
- BARRERA, ME (2006). Obtained from GESTIOPOLIS: http://www.gestiopolis.com/tecnicas-para-la-toma-de-decisiones/BOREA, F. (2002). CIENCIARED. Obtained from http://www.cienciared.com.ar/ra/usr/4/26/m0.pdf DAROS, LC (sf). UNIVERSITY OF VALENCIA. Obtained from: https://riunet.upv.es/bitstream/handle/10251/16502/TomaDecisiones.pdfD (sf). DEFINITION OF. Obtained from http://definicion.de/pensamiento-logico/FUENTES, ST (2008). GESTIOPOLIS. Obtained from: https://www.gestiopolis.com/teoria-matematica-administracion-investigacion-operaciones/MACHADO, DU (2016). Obtained from GESTIOPOLIS: https://www.gestiopolis.com/modelos-economico-matematicos-la-decision-empresarial/RODRIGO, G. (2012). PDCAHOME. Obtained from: https://www.pdcahome.com/4655/modelos-para-la-toma-de-decisiones/VALDIVIA, FA (2013). SCRIBD. Obtained from: https: //es.scribd.com / doc / 225097600 / Concept-of-Mathematical-Modeling
