Computer technology continues to play an integral role in management science. Practitioners and researchers are able to use ever-increasing computing power in conjunction with management science methods to solve larger and more complex problems.
In addition, management scientists are constantly developing new algorithms and improving existing algorithms; these efforts also enable management scientists to solve larger and more complex problems.
Management science techniques are used on a wide variety of problems from a vast array of applications. For example, integer programming has been used by baseball fans to allocate season tickets in a fair manner. When seven baseball fans purchased a pair of season tickets for the Seattle Mariners, the Mariners turned to management science and a computer program to assign games to each group member based on member priorities.
In marketing, optimal television scheduling has been determined using integer programming. Variables such as time slot, day of the week, show attributes, and competitive effects can be used to optimize the scheduling of programs. Optimal product designs based on consumer preferences have also been determined using integer programming.
Similarly, linear programming can be used in marketing research to help determine the timing of interviews. Such a model can determine the interviewing schedule that maximizes the overall response rate while providing appropriate representation across various demographics and household characteristics. In the area of finance, management science can be employed to help determine optimal portfolio allocations, borrowing strategies, capital budgeting, asset allocations, and make-or-buy decisions.
In portfolio allocations, for instance, linear programming can be used to help a financial manager select specific industries and investment vehicles e. With regard to production scheduling, management science techniques can be applied to scheduling, inventory, and capacity problems.
Production managers can deal with multi-period scheduling problems to develop low-cost production schedules. Production costs, inventory holding costs, and changes in production levels are among the types of variables that can be considered in such analyses.
Workforce assignment problems can also be solved with management science techniques. For example, when some personnel have been cross-trained and can work in more than one department, linear programming may be used to determine optimal staffing assignments.
Airports are frequently designed using queuing theory to model the arrivals and departures of air-craft and simulation to simultaneously model the traffic on multiple runways. Such an analysis can yield information to be used in deciding how many runways to build and how many departing and arriving flights to allow by assessing the potential queues that can develop under various airport designs.
Mathematical programming deals with models comprised of an objective function and a set of constraints. Linear, integer, nonlinear, dynamic, goal, and stochastic programming are all types of mathematical programming. An objective function is a mathematical expression of the quantity to be maximized or minimized. Manufacturers may wish to maximize production or minimize costs, advertisers may wish to maximize a product's exposure, and financial analysts may wish to maximize rate of return.
Constraints are mathematical expressions of restrictions that are placed on potential values of the objective function. Production may be constrained by the total amount of labor at hand and machine production capacity, an advertiser may be constrained by an advertising budget, and an investment portfolio may be restricted by the allowable risk. Linear programming problems are a special class of mathematical programming problems for which the objective function and all constraints are linear.
A classic example of the application of linear programming is the maximization of profits given various production or cost constraints. Linear programming can be applied to a variety of business problems, such as marketing mix determination, financial decision making, production scheduling, workforce assignment, and resource blending. Such problems are generally solved using the "simplex method.
The local Chamber of Commerce periodically sponsors public service seminars and programs. Promotional plans are under way for this year's program. Advertising alternatives include television, radio, and newspaper. Audience estimates, costs, and maximum media usage limitations are shown in Exhibit 1. Linear programming can find the answer.
The simplex method is a specific algebraic procedure for solving linear programming problems. The simplex method begins with simultaneous linear Exhibit 1. This method first finds an initial basic feasible solution and then tries to find a better solution. A series of iterations results in an optimal solution. Georgia Television buys components that are used to manufacture two television models.
One model is called High Quality and the other is called Medium Quality. A weekly production schedule needs to be developed given the following production considerations. Only hours of production time are available for the next time period.
High Quality models require a total production time of six hours and Medium Quality models require eight hours. In addition, there are only forty-five Medium Quality components on hand. To complicate matters, only square feet of warehouse space can be used for new production. The High Quality model requires 9 square feet of space while the Medium Quality model requires 7 square feet. Given the above situation, the simplex method can provide a solution for the production allocation of High Quality models and Medium Quality models.
Dynamic programming is a process of segmenting a large problem into a several smaller problems. The approach is to solve the all the smaller, easier problems individually in order to reach a solution to the original problem.
This technique is useful for making decisions that consist of several steps, each of which also requires a decision. In addition, it is assumed that the smaller problems are not independent of one another given they contribute to the larger question. Dynamic programming can be utilized in the areas of capital budgeting, inventory control, resource allocation, production scheduling, and equipment replacement.
These applications generally begin with a longer time horizon, such as a year, and then break down the problem into smaller time units such as months or weeks. For example, it may be necessary to determine an optimal production schedule for a twelve-month period. Dynamic programming would first find a solution for smaller time periods, for example, monthly production schedules. By answering such questions, dynamic programming can identify solutions to a problem that are most efficient or that best serve other business needs given various constraints.
Goal programming is a technique for solving multi-criteria rather than single-criteria decision problems, usually within the framework of linear programming. For example, in a location decision a bank would use not just one criterion, but several. The bank would consider cost of construction, land cost, and customer attractiveness, among other factors. Goal programming establishes primary and secondary goals.
The primary goal is generally referred to as a priority level 1 goal. Secondary goals are often labeled level 2, priority level 3, and so on. It should be noted that trade-offs are not allowed between higher and lower level goals.
Assume a bank is searching for a site to locate a new branch. The primary goal is to be located in a five-mile proximity to a population of 40, consumers. A secondary goal might be to be located at least two miles from a competitor. Given the no trade-off rule, we would first search for a target solution of locating close to 40, consumers. The XYZ Company mixes three raw materials to produce two products: a fuel additive and a solvent.
Production is constrained by a limited availability of the three raw materials. For the current production period XYZ has the following quantities of each raw material: 20 tons of material A, 5 tons of material B, and 21 tons of material C.
Management would like to achieve the following priority level goals:. Integer programming is useful when values of one or more decision variables are limited to integer values. This is particularly useful when modeling production processes for which fractional amounts of products cannot be produced. Integer variables are often limited to two values—zero or one. For the model to work and make effective predictions, things should be quantifiable and easily measured.
If they are, then mathematical calculations will work accurately and the outcomes can be analyzed with care. For example, while resources and equipment can be standardized, human behavior is much harder to generalize, as certain people can perform well in specific conditions in which someone else might fail.
Therefore, by creating artificial generalization and standardization, the management will reduce the effectiveness of the predictions. If the set of processes analyzed is not correctly quantified, the outcomes might not be the most accurate. In effect, this means the resulting decisions might not yield the optimum results. In addition to the above, management science has a problem with scaling.
Since the framework requires plenty of data and the data has to be as accurate as possible, the implementation process can be much easier for smaller organizations. Creating a process for data collection, analysis and prediction is easier when you have only a limited number of data available with a small organization.
On the other hand, the cost of establishing an efficient management science system can be high and the expensive element of the framework can make it unattractive for smaller firms. Watch this interesting case study on how management science could be applied to understanding mobile users warning: only for super nerds like me.
Management science has a number of benefits, which has meant that different fields have started using it to enhance operational and managerial efficiency. Since its early start as part of a core approach to the military, the application has found its way to industries as varied as medical, political, public administration and business.
Management science has provided solutions and identified deeper insights into the industries in a number of ways. The following examples are among the best examples of how management science can be applied in a meaningful manner:.
As the examples show, there are different ways to utilize management science. The application of the framework helps organizations create enhanced efficiency in areas such as cost, production and the level of service by solving the different managerial problems. In terms of applying management science in business, there is a six-step formula for making the most of it.
The steps will help streamline business operations and create a process-based environment for the organization. Source and Copyrights: World Health Organisation website. The organization must first identify the different management processes it currently has in place. By implementing the analytical approach, you will notice which processes need scaling, implementation or adjustment.
This step is the key part of management science; it is about diagnosis and the identification of the solution. In some instances, it can even help with creating systems that prevent future problems. With the analysis done, the focus should move to identifying the right process for achieving the right results. Management science tends to present a number of solutions and predictions, which means the organization has to identify the most effective processes for its needs.
Redesigning of the processes might require additional resources, either in terms of money or labor. Ensure the appropriate amount and type of resources is identifying to guarantee the newly established processes work as planned. The fifth step is about implementation of the above processes. As mentioned in the section about the disadvantages of management science, the system can easily cause fragmentation if the organization is not careful.
Finally, the implemented processes require constant analysis and review. Therefore, you must make sure you continue to collect data and analyze the effectiveness of the processes in place. Only this will guarantee they are working as intended and will provide the organization better chances of tweaking the approach as you go. Management science is a logical and analytical approach to management and how it impacts an organization.
The approach has been used in a variety of industries since its inception during World War II. By using the approach, an organization is able to identify different management processes and whether they are working as efficiently as they could.
It can provide new ways to approach management issues and it helps streamline the decision-making process by creating models the organization can use. While management science can provide plenty of benefits in terms of improvements in productivity and cutting costs, the implementation can also have drawbacks on the workplace. Employee satisfaction can suffer and the organization has to deal with the fragmentation of processes. The approach is not a quick remedy for solving issues, but when it is applied correctly, the results can lead to success.
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