Management Science Discipline - Coursework Example

Running Head: Management Science Management Science 3rd November By ____________________________________ Business has to succeed in its activities and better its performance in order to remain in the market and compete with the industry leaders. Management shoulders the maximum responsibility of the organization so as to achieve its goals and objectives of the vision and mission. A broad view of management is that it involves procedures to forecast, plan, analyze, decide, motivate, communicate and implement. It is necessary to utilize advanced tools and techniques so as to gain competitive edge over the competitors (Armstrong, 1998, p.1). According to Bagad (2008, p.1) management enables a country to experience a substantial level of economic development by bringing together the four factors of production: men, material, money and machines. It has been declared by F.W, Taylor, father of scientific management that “management has to be scientific” and it is a social science as it deals with human beings in ways of systematic decision making, situational output, universal management process and universally accepted principles of management. Science has made its presence literally in every field of studies and it is essential to use scientific approaches which will assist the companies in decision making and other managerial activities which is the scope of present paper that aims to define the management science and explain the relationship of breakeven point & sensitivity analysis to management science, linear programming, and functional constraints. Management Science – Break-even analysis and Situation analysis The need of a modern technological military for new management techniques to deal with operational problems of an increasingly complex nature flowing from waging of the greatest war ever fought on this planet gave rise to the management approach known as management science or operational research (Montana and Charnov, 2000, p.27). Management science is concerned with developing and applying models and concepts that may prove useful in helping to illuminate management issues and solve managerial problems (http://www.lums.lancs.ac.uk). According to Daft (2009, p462) management science approach to organizational decision making is the analog to rational approach by individual managers and is an excellent device for organizational decision making when problems are analyzable and variables can be identified and measured. Further management science is built around quantitative skills wherein it deals with numbers and relationships between numbers with use of various modeling techniques that develop your intuition (Winston, Albright, Broadie, Lapin and Whisler, 2008, p.18). The techniques of management science are correctly used to solve the problems which further helps in decision making by the managers. Scientific method is an approach to problem solving and management science stresses a systems approach that views the total operating system and analyzes a problem within that system (Montana and Charnov, 2000, p.29). Management science uses a logical approach to problems solving where the problem viewed as the focal point of analysis and quantitative models are the vehicles for obtaining the solution (Stevenson, 2006, p.4). Managers while taking organizational decisions have to consider various factors like environment, economy and social and scientific approaches in management helps the managers to make correct decisions. Stevenson (2006, p.9) mentions that in management science approach, the problem is identified first and then the appropriate model will be applied to analyze so as to determine solution, test, interpret and analyze the solution and then finally implement the solution. According to Anderson, Sweeney, Williams and Martin (2007, p.16) the breakeven point is an important tool in managerial decision making as the main purpose of the management is exceed break even and make profit. The breakeven point is the point at which total revenues equal total costs and the point at which an organization can begin to earn profit (Needles, Powers and Crosson, 2007, p1052). Stevenson (2006, p11) further states that situation analysis is important part of model analysis useful in determining how sensitive a particular solution is to changes in one or more of the constraints in the model. Management science uses breakeven analysis and situation analysis to find solution to a particular problem which further helps in managerial decision making process. Whereas sensitivity analysis attempts to assess the impact of change in the input data or parameters or the proposed solution, allows flexibility and adaptation to changing conditions and to the requirements of different decision making situation. The sensitivity analysis can be used for revising models to eliminate large sensitivities, adding details about sensitive variables and scenarios, obtaining better estimates of sensitive variables, altering the real world system to reduce actual sensitivity and accepting and using the sensitive real world, leading to continuous and close monitoring of actual results (www.elearning.najah.edu). Linear Programming Linear programming is the process of minimizing a linear objective function subject to a finite number of linear equality and inequality constraints (Karloff, 1991, p.1). Little (1969) stated that manager tries to put together the various resources under his control into an activity in order to achieve the company’s objectives. Linear programming model consists of an objective function and the constraints on that function. The complex management decisions are modeled by linear functions in mathematical way so as to view the constraints and arrive at appropriate solutions to the problem. It is further stated that each decision variable is multiplied by constant coefficient with no multiplying between decision variables and no nonlinear functions such as logarithms. Linearity requires proportionality, additivity and divisibility (www.netmba.com). Linear programming is composed of decision variables, whose values are searched for; objective functions that relate decision variables to the goals and measure goal attainment to be optimized; objective function coefficient; constraints, constant variables, and input output coefficient technology which indicate resource utilization for decision variables (www.elearning.najah.edu). Winston et al (2008, p.3) mentioned that the key to virtually every management science application is a mathematical model which is a quantitative representation or idealization of real problem. Whereas linear programming is concerned with a decision variable which is part of mathematical progr amming that deals with models comprised of an objective function and set of constraints. It is further stated that integer, non linear, dynamic, goal and stochastic programming are types of mathematical programming apart from linear programming. Linear programming models are special class of mathematical problems for which the objective functions and all constraints are in linear format which can be applied to variety of business problems such as marketing mix determination, financial decision making, production scheduling, workforce assignment and resource blending (http://www.referenceforbusiness.com). Further numerous formalisms and dedicated algorithms have designed to model and solve decision making problems. Modeling techniques are used to find a solution to a particular problem depending upon the situation and that provided data and analysis for appropriate decision making for business. Types of functional constraints Typical types of constraints in management science are classified into functional constraints and non negativity constraints. Functional constraints take on a functional form e.g., linear, quadratic, logarithmic, etc and be either binding whereas non negativity constraints restrict a decision variable to either being positive or equal to zero (agbus, n.d.) . Hillier and Hillier (2010, p323) mentioned three types of functional constraints viz. resource constraint, benefit constraint and fixed requirement constraint. It is further elaborated by Hillier and Hillier (2009) that functional constraints with < sign are called resource constraints as they require that the amount used some resource must be less than or equal to the amount available of that resource. Constraints with > sign are called benefit constraints since their form is that the level achieved for some benefit must be greater than or equal to the minimum acceptable level. Benefit constraints express goals prescribed by management. The third and final functional constraint is fixed requirement constraint which is denoted by = sign because they express the fixed requirement that, for some quantity, the amount provided must be equal to the required amount. Transportation problems is one of the fixed requirement constraints which typically involves funding transportation costs whereas assignment problem, another problem under the category, which involves assigning people to tasks so as to minimize total cost of performing these tasks. Conclusion Management science is a discipline that attempts to aid managerial decision making by applying a scientific approach to managerial problems that involve quantitative factors. It is a whole body of knowledge and techniques that are based on scientific foundation. Management science study only provides an analysis and recommendations based on quantitative factors involved in the problem, as input to the manager assisting them in decision making process. As it is called management science, it uses scientific approaches that include mathematics and computer science as well. Most of the businesses use mathematical modeling approach as a tool in management science to identify their problems, gather data, and apply the model to represent the model, develop a computer based procedure for deriving solutions to the problem, analyze the results and develop recommendations for the management (Hillier and Hillier, 2009). Linear programming, one of the mathematical model tools is mostly used to deal with objective functions and its constraints. However management science is essential to deal with most of the operational constraints in time of globalization, recession, competition, and other socioeconomic and political problems. Managers need to think out of their capacity in order to gain competitive edge over their rivals and scientific approaches are the best way to deal the situation and find solutions that may assist the managers in the process of decision making. References 1. Agbus (n.d.) Introduction to Management Science, Chapter 1 Hillier and Hillier, SlideFinder, http://www.slidefinder.net/ 2. Anderson, D.R., Sweeney, D.J., Williams, T.A. and Martin K.R., (2007) An introduction to management science: quantitative approaches to decision making, Ed.12th, Cengage Learning, US 3. Armstrong M (1998) Managing people: a practical guide for line managers, Kogan Page Publishers, US 4. Bagad V.S (2008) Management Science, Technical Publications, India 5. Daft, R.L. (2009) Organization Theory and Design, Ed 10th Cengage Learning, US 6. Hillier & Hillier, (2009) Introduction to Management Science: A Modeling and Case Studies Approach with Spreadsheets, (3rd Ed), New York, NY, 10020 McGraw-Hill/Irwin Publishing Company, India 7. Hillier & Hillier, (2010) Introduction to Management Science: A Modeling and Case Studies Approach with Spreadsheets, (4rd Ed), New York, NY, 10020 McGraw-Hill/Irwin Publishing Company   8. Karloff, H (1991) Linear Programming, Birkhäuser, Chicago 9. Linear Programming Components, www.elearning.najah.edu 10. Little John (1969) Models and Managers: The concept of a Decision Calculus. Massachusetts, US 11. Montana P.J and Charnov B.H (2000) Management, Ed.3rd, Barrons Educational Seris, US 12. Needles, B.E., Powers, M. and Crosson, S.V. (2007) Principles of Accounting, Ed 10th, Cengage Learning, US 13. Net MBA (2010) Linear Programming, http://www.netmba.com/operations/lp/ 14. Reference for Business, (2010) Encyclopedia of Business 2nd Ed, Management Science, http://www.referenceforbusiness.com/ 15. Stevenson (2006) Introduction To Management Science, Tata McGraw-Hill, US 16. Wayne L. Winston, S. Christian Albright, Mark Nathan Broadie, Lawrence L. Lapinn and William D. Whisler (2008) Practical management science, Ed.3rd, Cengage Learning, US Read More