| In the second part of operational research management, talks about mathematical models that help analysts examine systems and work better |
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With the relevant information in hand, the analyst is ready to select the most appropriate analytical technique. Analysts can use any of several techniques, including simulation, linear and nonlinear programming, dynamic programming, queuing and other stochastic-process models, Markov decision processes, econometric methods, data envelopment analysis, neural networks, expert systems, decision analysis, and the analytic hierarchy process. Nearly all of these techniques, however, involve the construction of a mathematical model that attempts to describe the system being studied. The use of models enables the analyst to assign values to the different components and clarify the relationships among them. The values can be altered to examine what may happen to the system under different circumstances.
In most cases, the computer program developed to solve the model must be modified and run repeatedly to obtain different solutions. A model for truck transport scheduling, for example, might include variables for the cities to be connected, the amount of fuel required to ply the routes, projected levels of customer demand, varying fuel prices, toll prices, driver costs per day, rotation schedules of drivers and maintenance costs. By locating the right combination of values for the variable, the analyst is able to produce the best transport schedule consistent with particular assumptions. Upon concluding the analysis, the operations research analyst presents management with recommendations based on the results. Additional computer runs to consider different assumptions may be needed before the analyst presents the final recommendation. Once management reaches a decision, the analyst usually works with others in the organization to ensure the plans successful implementation.
Industrial engineering
Industrial engineering is concerned with the design, improvement, and installation of integrated systems of personnel, material, and equipment. Together, operations research and industrial engineering provide a rational approach to engineering and managerial problem solving through the deliberate application of scientific methods with the goal of predicting system behavior
and improving or optimizing industrial performance. In practice, operations research and industrial engineering addresses both the performance objectives and the resource constraints of an organization: it works toward establishing the policies that are most beneficial to the organization as a whole. Problems may be as specific as improving the efficiency of a production line or as broad as developing a long-range corporate strategy to address a combination of financial, marketing, and technological concerns.
Financial engineering
Financial engineering (FE) is the process of employing mathematical finance and computer modeling skills to make pricing, hedging, trading and portfolio management decisions. Utilizing various derivative securities and other methods, financial engineering aims to precisely control the financial risk that an entity takes. Methods can be employed to take on unlimited risks under certain events, or completely eliminate other risks by utilizing combinations of derivative and other securities. FE is a process involving the creation and combination of a variety of financial instruments in order achieve a defined financial objective within certain cost, tax and legal constraints. It may include creating new financial instruments by combining other derivatives, or more generally, by using derivatives pricing techniques. The course develops an ability to analyze the time/risk ratio for each activity. Graduates of this course would work as finance analysts, sector specialists and progress to positions-as portfolio managers and strategic finance planners.
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