The use of management science is widespread in many firms

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These days, managers of firms all over the world daily face problems whenever they are in the process of making decision. From thousands of surveys of businesses, it is obviously shown that many firms use management science techniques, resulting in outstanding outcomes. The use of management science is widespread in many firms over the world (Bernard and Taylor 2010). What is the management science? Management science is an approach to managerial decision making by using scientific methods with substantial use of quantitative analysis (Glen 2010). This scientific approach assists a manager in solving management problems which means it use the scientific method for attacking problems in many different types of organization including government, military, business, health care and services (Bernard and Taylor 2010). Management science involves the application of scientific methods of analysis to the troubles caused in managing system of people, materials and money (Glen 2010). Through the management science use of optimization techniques many mangers in any number of types of firms can make the best decisions with variable decision choices. Now using this technique has been essential for a manger to lead their companies in a highly competitive market.

Quantitative Analysis for solving

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To survive highly competitive business market, it becomes essential to make decisions based on rational ideas. Quantitative analysis is the most rational way for making decision (Glen 2010). Quantitative analysis is made up of four steps. The first thing that manager should do is find out the problem and all of the relevant data and information. Then the manager studies the problem and develops an accurate portray of problem. Managers should be aware of two factors that would affect the system. They include uncontrollable factors such as environmental factors, and controllable factor that the decision maker can control. When there are no uncontrollable factors, there are only controllable decisions which mean there is certainty on making the decision (Murty 1995). It is known as a deterministic decision making problem. When the variables among the uncontrollable factors are main subject, it is called probabilistic decision making problem. The second step is constructing a mathematical model of the problem. The third step is solving the model to get the solution. Finally, once the fourth step, the solution is obtained and is rechecked, the solution should be implemented (Murty 1995).

Typically a management science model is a general picture of an existing problem situation. It can be shown in the form of graph or chart. Once management science models are constructed, managers solve these models by using the management science techniques. Scientific techniques generally apply to a specific model type. In general there are three types of solving models. They are optimisation methods, heuristic methods, and simulation (Glen 2010). According to Dr John Glen (2010), the optimisation method supplies the optimal, or best, solution.

Optimisation technique and its Contributions

People originally have been looking forward interest in optimizing the performance at every task that they face to. Many decisions we make at work places nowadays affect our lives. The decisions we make have the goal of optimizing the performance of systems used in our work. An optimization technique model is the one that satisfies our desire to optimize the performance in making variable decision. An optimization model is usually expressed as a mathematical function (Murty 1995). An objective function is optimized either by maximizing or minimizing it (Murty 1995). Optimization techniques are concerned with decision making. This technique provides people the tools to make either better, or the best possible decision. In the competitive market, businesses try to survive or maintain their market position. So, they organize their operation to give better services to customers by delivering product faster without any damages in the shortest time at the least possible cost. They also try to offer better and more efficient products and service that would attract customers from competitors. These are the essentials of optimization techniques. Whoever masters optimization techniques is generally emerging as a leader of firms (Murty 1995). Many companies all over the world have shown their great achievement by using optimization techniques in their manufacturing industry which is more strenuous than any other sector.

Over the previous decade, development of optimization techniques has encouraged many firms to optimize their decision making system. For the small firms that are operated by small number of employees and capitals, optimization techniques significantly enhance small firms (Taliadoros 2007). Many firms have a problem with surviving in the long run. Optimization techniques (OP) give firms directions how to make decisions to solve the problem to survive longer especially for small firms operating day-to-day. In a highly competitive market the goal is to use OP to make decisions as efficiently as possible because OP is mathematically proven (Taliadoros 2007). As an optimization technique is recently developing, it allows many companies to perform their work faster and more accurately.

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There are good examples of successful applications of OT in real life situation. British Airway recently uses optimal technique and it led British Airway to result 21 million pounds contribution of surplus (Taliadoros 2007). The success of Athens Olympic Games in 2004 can be another successful application of OT which results financial worth of 70 million dollars on reducing the cost of managing venues (Taliadoros 2007).

Disadvantages of Using Optimisation Techniques

However, even though a number of firms have been using optimization techniques as follows trend, there are many companies that still question its ability to carry out useful contribution to critical problem emerging present-day companies (Taliadoros 2007). As it is mentioned above, small firms significantly can be enhanced through optimization technique. This fact can be argued that optimization techniques can be well used in day-to-day small operation although there is uncertainty existing. However, whenever there are critical problem in high level of large firms the story can be changed. Despite organizations having good understanding of problem that they face, there is always pre-existent preference that management scientist have (Taliadoros 2007). Whenever managers take an idea and put into the optimization technique formulation because of their consideration based on pre-existent preference they can cause the calculation expected to be different value of optimization technique. If managers have strong pre-existing preferences, it can even cause them to have biased solutions (Taliadoros 2007). Communication between client and manager can also affect the formulation (Taliadoros 2007). It is hard work for managers to have non-preferred feedback on their formulation. So, formulation made by scientists who have pre-existent preference can disrupt manager's decision making process.

Introduction of Linear Programming

Many major decisions that a manager of a business faces focus on the best way to solve the problem and develop the companies. One of the most common objectives of companies is to maximize the profit and minimize the cost under the restriction placed such as time, labour, material, and money (Murty 1995). However whenever they try to make decision, they always have decision variables. For all variable decisions, the solution state mathematical values. When a manager of a business attempts to solve a problem with the restrictions placed on the manager, the linear programming method which is highly developed is usually used (Murty 1995). The reason why linear programming is frequently used in many businesses area is because it is simplest construct to understand and study the problem among optimization models. It is applicable to many areas of businesses. Figure 1 illustrates an example linear programming. Linear programming requires making choice under alternative decisions (Murty 1995). Business firms have to decide how many different types of products they have to produce. Recognizing the problem and outlining the decision variables are generally first step in formulating linear programming (Murty 1995). There are always restrictions existed such as limited resources. These restrictions also should be defined by mathematical analysis as illustrated in the linear function in figure 1.

Advantages and Disadvantages of Linear Programming

Linear programming can be expressed only as straight equation lines which means LP is applicable to problem where the only constrains and objective function are linear. Uncontrollable factors with uncertainty are not counted for solution. Even though the problem generally includes multiple goals, in real situation only one objective is dealt with. In assumption of LP, limitation is on constant level; however it is not constant in real life situation.

The linear programming helps managers to make best decision with restricted resources like money, labor, and capitals. Because the decisions are made objectively not subjectively, managers are able to make best quality of decision by linear programming. Through the LP, there is definitely saving in resources such as times and money.

Examples of Linear Programming

As it has been mentioned above, linear programming is used in many markets. For example, in beginning of 1996, Quebec's Ministry of Natural Resources began using a detailed mathematical programming model to support various negotiations in the wood-fiber markets (Gautier, Lamond, Pare and Rouleau 2000). The linear programming was used to solve an economic equilibrium program and encouraged representative of ministry to come out with accurate and exact analyses for the wood-fiber market. The tool that they developed used much of the data available to government agencies to predict the general economic trends that paper and lumber suppliers had to face to. Through their development by operation research, they have reached unprecedented level of understanding of wood-fiber market. It is a good successful example for using linear programming (Gautier, Lamond, Pare and Rouleau 2000).

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Other example is Nu-cote's spreadsheet linear programming model for optimizing transportation. Nu-cote international produces fax supplies such as inkjet, laser, and toner cartridges (LeBlanc, Hill, Greenwell, and Czesnat 2004). They had developed linear programming model to minimize the cost of shipments between manufacturing plants, warehouses, and customers. Their linear programming has somewhere between 5000 and 9500 variable decisions with 2000 restrictions (LeBlanc, Hill, Greenwell, and Czesnat 2004). They have used linear programming in nonlinear problem to find out better way of shipment that would reduce the costs of shipment by approximately one million dollars and even save 2 days of transportation (LeBlanc, Hill, Greenwell, and Czesnat 2004). Its LP model helped Nu-cote managers to decide better option to improve customer services with significant savings.

Another example of linear programming is the Kellogg Planning System (KPS). KPS has shown their successful case of using large-scale linear programming model. Under the KPS, they could estimate the units of product, the amount of inventory held in warehouse, and the shipment between plants and warehouses (Brown, Keegan, Vigus and Wood 2001). Through the KPS, the company Kellogg could find out not only variable decisions but also model constraints such as processing time and cost of producing products. The KPS has finished with saving 4.5 million dollars by reducing production and inventory cost in 1995 (Brown, Keegan, Vigus and Wood 2001). Since the mid 1990's, it is estimated that KPS helps Kellogg save several million dollars annually.

Example of other programming

Besides linear programming, there are other optimisation techniques that are widely used in the business world. One is called constant programming which is special type of model which value cannot be changed while practicing as illustrated in figure 2 (Bernard and Taylor 2010). The other is known as quadratic programming (QP) which is a form of mathematical optimization technique, shown in figure 2. It is a very similar concept to linear programming. Mangers use quadratic programming to maximize profit or minimize cost by making best decision within several variable alternatives. Minnesota's nutrition coordinating centre's uses mathematical optimization of LP and quadratic programming to estimate food nutrient values is a good example of using QP. These two models were used to compare nutrient values of 31 products (Westrich, Altmann and Potthoff 1998). Their optimization models guide the managers to estimate the nutrient values and found out that their new model produced results four times faster than the original methods used before (Westrich, Altmann and Potthoff 1998). Minnesota's nutrition coordinating centre uses their new version of model to maintain a food composition database (Westrich, Altmann and Potthoff 1998).

Conclusion

In conclusion, optimization techniques have contributed to many areas of business by assisting managers and leading them to make the best decision for their firms. Whenever uncertainty exists in management decision making process, optimization techniques such as linear programming shows variable alternatives that manager can choose to maximize the profit or minimize the cost of products. As illustrated above, when managers face the problem and attempt to solve the problem by analyzing the variables, optimization techniques like linear programming is frequently used. Optimization techniques not only have advantages but also disadvantages. LP (Optimization techniques) can be turned into anti-catalyst to delay or disrupt managers to reach solutions. Because of their pre-existent preference, biased formulation can be derived out based on their strong preference. LP (Optimization techniques) has encouraged mangers in a number of businesses as the catalyst for a new or other way of reaching solution. Whichever optimization models a manager uses, the final aim is to maximize the profit and minimize the cost of product. So, managers of different types of business should be able to decide which model is best to derive best suited to generate the largest benefit for their company.