Decision Making: A process by which individual select a course of action among several alternatives to produce a desired result
Decision Making process:
Decision making Techniques:
Marginal Analysis
Economists look at how costs and benefits change as there are small changes in actions. We call this marginal analysis, and it is perhaps the key concept in economic analysis. It is an acknowledgement that people (should) make a decision based on the incremental gains and losses that result from that decision, and that sunk costs (money, time or other things of worth already expended and unredeemable) should not matter.
Marginal analysis, quite simply, balances the additional benefits from an action against the additional cost. In any case, be it a firm deciding whether or not to expand production, a student deciding if another beer is a good idea, or a professor choosing to give an extra exam, optimal performance requires that benefits and costs be equilibrated on the margin. What this means is that if the additional benefit exceeds the additional cost, take the action. Keep taking it as long as the benefit exceeds the cost, and to ensure that all excess benefits (those that exceed costs) are accrued, do it until for the last action, the benefits just equal the costs.
The benefits from the last action (such as unit of production or consumption) are termed marginal benefits, and the costs from that action are termed marginal costs. Economic assumptions, verified by much experience, shows that for most actions the benefits per unit are falling, while the costs are increasing. Thus, one measure of economic efficiency is that marginal benefits equal marginal costs. At that point, all the units for which benefits exceed costs are used. Too little, and some excess benefits are wasted; any more, and the costs for later units exceed the benefits.
Financial analysis:
It mainly deals with financial assets and liabilities of the organization which helps manager to take decisionsaccording to Cash in flows and Cash out flows. It is purely depends on the profitability of the organization.
Another important tool is Cost Benefit Analysis or CBA is a relatively* simple and widely used technique for deciding whether to make a change. As its name suggests, you simply add up the value of the benefits of a course of action, and subtract the costs associated with it.
Costs are either one-off, or may be ongoing. Benefits are most often received over time. We build this effect of time into our analysis by calculating a payback period. This is the time it takes for the benefits of a change to repay its costs. Many companies look for payback onprojects over a specified period of time e.g. three years.
Ratio Analysis:
Ratio analysis is no longer simply the preserve of accountants and financial experts: managers at all levels use ratios for business planning and decision-making. Furthermore, ratios are often used to support systematic analysis of suppliers, customers and competitors, as well as more general market and industry trends. Ratio analysis are the statements prepared my experts in financial and accounting areas in order to determine strengths and weakness of the firm as well as historical and current status of the organization which helps in making decisions.
Break Even Analysis:
In any business the manager of a business has to make irrespective of what they produce they have to ensure that the products they produce maximize owners equity. That is the products and services they offer can make a profit and to identify loss making products and introduce new products if they have a profitable market. In addition, they must have a cost control system, which can minimize overheads and direct cost of producing goods and services.
Break-Even analysis is one of the simplest method for a business to make the above mentioned decisions, where the enterprise or business entity produces very limited number of products. As well, the cost can be analyzed in to fixed and variable cost accurately. That is, it has a costing system, which can identify variable and fixed cost. Fixed cost are costs, where the cost over a period is constant irrespective of the volume of production to a level. Variable costs are costs that varies with the level of business activity or level of production. Manly, for most businesses material costs and production labor costs are variable costs and some overheads like fuel costs are to some extent variable. However, most overhead costs for most businesses are fixed over a volume of production and therefore fixed costs. However, some costs have an element of variable and fixed cost elements called semi-fixed or semi-variable costs. These cost have to be separated using statistical regression analysis. That is the costing system has to produce for each product what is the unit variable cost, selling price of each unit, fixed cost for a period. Maximum sales possible, which is estimated for a future period. Then one can determine the production point where the profit is zero. For some products the break-even point will be at higher level and for some products the breakeven point will be at a lower level of production. As well, the margin of safety that the excess profit that can be earned after the break even point also varies. Therefore, to maximize profit earned from each product is to reduce variable cost and reduce overhead and increase sales by cost effective promotions and advertising and improving the quality of the products compared to its competitors. Therefore break-even analysis gives a tool for a manger to analyze the mix of products that maximize profit for a period and have cost control systems so that it can minimize waste and improve productivity of labor force and streaming production methods and operations.
In effect break even analysis enable business managers to make effective decisions based on sound rational basis and based on cost information and other limiting factors. As well, it gives the manager how a manger can improve profitability of the business as a whole in a dynamic and uncertain market place my monitoring cost and improving the efficiency of the organization on a continuous basis.
Simulation Analysis:
Simulation is the imitation of some real thing, state of affairs, or process. The act of simulating something generally entails representing certain key characteristics or behaviors of a selected physical or abstract system.
Simulation is used in many contexts, including themodeling of natural systems or human systems in order to gain insight into their functioning.Other contexts include simulation of technology for performance optimization,safety engineering, testing,training and education. Simulation can be used to show the eventual real effects of alternative conditions and courses of action.
Key issues in simulation include acquisition of valid source information about the relevant selection of key characteristics and behaviors, the use of simplifying approximations and assumptions within the simulation, and fidelity and validity of the simulation outcomes.
In this technique different variables and their relationships are put into model through the computer then a set of outputs are obtained. This technique very much useful in solving complex problems.
Linear programming:
Linear programming is a considerable field of optimization for several reasons. Many practical problems in operations research can be expressed as linear programming problems. Certain special cases of linear programming, such as network flow problems andmulticommodity flow problems are considered important enough to have generated much research on specialized algorithms for their solution. A number of algorithms for other types of optimization problems work by solving LP problems as sub-problems. Historically, ideas from linear programming have inspired many of the central concepts of optimization theory, such as duality,decomposition, and the importance of convexity and its generalizations. Likewise, linear programming is heavily used inmicroeconomics and company management, such as planning, production, transportation, technology and other issues. Although the modern management issues are ever-changing, most companies would like to maximize profits or minimize costs with limited resources. Therefore, many issues can boil down to linear programming problems.
It is mainly used to solve complex problems where more than 2 variables are involved For Example: Product mix, Market mix, Scheduling product marketing, Inventory management purpose and many more.
Queueing theory
Queueing theory is the mathematical study of waiting lines (or queues). The theory enables mathematical analysis of several related processes, including arriving at the (back of the) queue, waiting in the queue (essentially a storage process), and being served by the server(s) at the front of the queue. The theory permits the derivation and calculation of several performance measures including the average waiting time in the queue or the system, the expected number waiting or receiving service and the probability of encountering the system in certain states, such as empty, full, having an available server or having to wait a certain time to be served.
In queuing theory, a queuing model is used to approximate a real queuing situation or system, so the queuing behavior can be analyzed mathematically. Queuing models allow a number of useful steady stateperformance measures to be determined, including:
§ the average number in the queue, or the system,
§ the average time spent in the queue, or the system,
§ the statistical distribution of those numbers or times,
§ the probability the queue is full, or empty, and
§ the probability of finding the system in a particular state.
These performance measures are important as issues or problems caused by queuing situations are often related to customer dissatisfaction with service or may be the root cause of economic losses in a business. Analysis of the relevant queuing models allows the cause of queuing issues to be identified and the impact of proposed changes to be assessed.
Construction and analysis
Queueing models are generally constructed to represent thesteady state of a queuing system, that is, the typical, long run or average state of the system. As a consequence, these are stochastic models that represent the probability that a queuing system will be found in a particular configuration orstate
A general procedure for constructing and analyzing such queuing models is:
1. Identify the parameters of the system, such as the arrival rate, service time, queue capacity, and perhaps draw a diagram of the system.
2. Identify the system states. (A state will generally represent the integer number of customers, people, jobs, calls, messages, etc. in the system and may or may not be limited.)
3. Draw a state transition diagram that represents the possible system states and identify the rates to enter and leave each state. This diagram is a representation of a Markov chain.
4. Because the state transition diagram represents the steady state situation between state there is a balanced flow between states so the probabilities of being in adjacent states can be related mathematically in terms of the arrival and service rates and state probabilities.
5. Express all the state probabilities in terms of the empty state probability, using the inter-state transition relationships.
6. Determine the empty state probability by using the fact that all state probabilities always sum to 1.
Whereas specific problems that have small finite state models can often be analyzed numerically, analysis of more general models, using calculus, yields useful formulae that can be applied to whole classes of problems.
Game theory
Game theory is a branch ofapplied mathematics that is used in the social sciences, most notably in economics, as well as in biology (most notably evolutionary and ecology),engineering, political science,international relations,computer science, andphilosophy. Game theory attempts to mathematically capture behavior in strategic situations, in which an individual's success in making choices depends on the choices of others. While initially developed to analyze competitions in which one individual does better at another's expense (zero sum games), it has been expanded to treat a wide class of interactions, which are classified according to severalcriteria. Today, "game theory is a sort of umbrella or 'unified field' theory for the rational side of social science, where 'social' is interpreted broadly, to include human as well as non-human players (computers, animals, plants)" (Aumann 1987).
Traditional applications of game theory attempt to find equilibrium in these games. In an equilibrium, each player of the game has adopted a strategy that they are unlikely to change. Many equilibrium concepts have been developed (most famously the Nash equilibrium) in an attempt to capture this idea. These equilibrium concepts are motivated differently depending on the field of application, although they often overlap or coincide. This methodology is not without criticism, and debates continue over the appropriateness of particular equilibrium concepts, the appropriateness of equilibria altogether, and the usefulness of mathematical models more generally.
Decision tree Techniques
A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chanceevent outcomes, resource costs, and utility. Decision trees are commonly used in operations research, specifically indecision analysis, to help identify a strategy most likely to reach a goal. Another use of decision trees is as a descriptive means for calculatingconditional probabilities. When the decisions or consequences are modeled by computational verb, then we call the decision tree a computational verb decision tree.
