Is the Use Of Waiting-Line Models More Important in Service Industries Than in Product Manufacturing - Essay Example

Waiting line models or queuing theory has a lot to do with the mathematical study of queues or waiting for lines as noted by Sundarapandian (2009, p. 24). The models are invaluable for managers (and other people) who have to make decisions concerning how many resources are needed to provide a service. While waiting line models are commonly applied in situations that involve the provision of services, they are no less important or applicable in manufacturing setups (wps.prenhall.com, n.d.).

Queuing theory may be applied in service industries including banking, supermarkets, machine maintenance, harbors, and highway toll booths among many others. Waiting-line models can equally be applied in the manufacturing of soda, canned foods, automotive components, and plastic products, the list being practically inexhaustible. By analyzing queues in terms of average waiting time, and the length of the waiting line, among other factors, managers in the manufacturing setting can make vital decisions regarding how many machines they should engage in production when to schedule maintenance for particular machines, and establish the probability of having a given quantity of materials or inventory running in the system (wps.prenhall.com, n.d.).

Queuing theory may help managers establish the probability of a system being idle which is important in deciding when and how often to schedule maintenance services for machines and equipment. Simply stated, queuing theory is no less important in product manufacturing than it is in service industries. Linear programming is a widely used mathematical technique designed to help operations managers plan and make decisions. Linear programming according to purple math (n.d.) is the process of analyzing the different linear inequalities that apply to a given situation to find the optimum value that can be obtained under those conditions. In this respect, linear programming is important in obtaining the “best” (optimum) value of a variable for a given set of conditions (purple math, n.d.). For decision-makers, linear programming is important in finding optimum (maximum and minimum) values which are important such as in maximizing profits and minimizing expenses among other desirable results.

A linear programming problem has four basic components; decision variables, data/parameters, constraints, and objective functions (amaiu.pbworks.com, n.d.). While the data quantifies the relationships that exist between the constraints and the objective function, the decision variables represent the quantities that are under underdetermination (amaiu.pbworks.com, n.d., p. 2). The objective function on its part represents the way decision variables impact the value that is to be optimized. In this case, optimization involves either maximization or minimization.

On their part, constraints represent how resources are used by decision variables (amaiu.pbworks.com, n.d., p. 2). The resources in this sense are always limited. Linear programming shows that while resources can be used to different levels to achieve a goal, not all sets of input combinations provide optimum values for output. In other words, there is only a certain combination of inputs that leads to optimum values for the output. Optimum values, as previously noted, are minimum or maximum values. For example, a manager can determine what amounts of ingredients X and ingredients Y can be used to produce a given product under certain conditions and constraints to make a product that will bring the business maximum profit when sold.