strategies of different components. After appropriate analysis of the various courses of actions, the team selects the best course of action, known as the ‘optimum strategy’.
ii) In Industry:
The system of modern industries is so complex that the optimum pointof operation in its various components cannot be intuitively judged by an individual.The business environment is always changing and any decision useful at one time maynot be so good sometime later. There is always a need to check the validity of decisionscontinuously against the situations. The industrial revolution with increased division of labor and introduction of management responsibilities has made each component anindependent unit having their own goals. For example: production departmentminimizes the cost of production but maximise output. Marketing departmentmaximizes the output, but minimizes cost of unit sales. Finance department tries tooptimize the capital investment and personnel department appoints good people atminimum cost. Thus each department plans its own objectives and all these objectivesof various department or components come to conflict with one another and may notagree to the overall objectives of the organization. The application of OR techniqueshelps in overcoming this difficulty by integrating the diversified activities of variouscomponents to serve the interest of the organization as a whole efficiently. OR methodsin industry can be applied in the fields of production, inventory controls and marketing, purchasing, transportation and competitive strategies.
In modern times, it has become necessary for every government to havecareful planning, for economic development of the country. OR techniques can befruitfully applied to maximise the per capita income, with minimum sacrifice and time.A government can thus use OR for framing future economic and social policies.
With increase in population, there is a need to increase agriculture output. But thiscannot be done arbitrarily. There are several restrictions. Hence the need to determine acourse of action serving the best under the given restrictions. You can solve this problem by applying OR techniques
v) In Hospitals
:OR methods can solve waiting problems in out-patient department of big hospitals andadministrative problems of the hospital organizations.
You can apply different OR methods to regulate the arrival of trains and processingtimes minimize the passengers waiting time and reduce congestion, formulate suitabletransportation policy, thereby reducing the costs and time of trans-shipment.
vii) Research and Development:
You can apply OR methodologies in the field of R&D for several purposes, such as tocontrol and plan product introductions.
MB0048 : Operation Research is an aid for the executive in making his decisions by providing him the needed quantitative information, based on scientific method analysis. Discuss.
Answer:- The Operations Research may be regarded as a tool which is utilized to increase the effectiveness of management decisions. In fact, OR is the objective feeling of the administrator (decision-maker). Scientific method of OR is used to understand and describe the phenomena of operating system.
OR models explain these phenomena as to what changes take place under altered conditions, and control these predictions against new observations. For example, OR may suggest the best locations for factories, warehouses as well as the most economical means of transportation. In marketing, OR may help in indicating the most profitable type, use and size of advertising campaigns subject to the final limitations.
The advantages of OR study approach in business and management decision making may be classified as follows:
1. Better Control. The management of big concerns finds it much costly to provide continuous executive supervisions over routine decisions. An OR approach directs the executives to devote their attention to more pressing matters. For example, OR approach deals with production scheduling and inventory control.
2. Better Co-ordination. Some times OR has been very useful in maintaining the law and order situation out of chaos. For example, an OR based planning model becomes a vehicle for coordinating marketing decisions with the limitation imposed on manufacturing capabilities.
3. Better System. OR study is also initiated to analyses a particular problem of decision making such as establishing a new warehouse. Later, OR approach can be further developed into a system to be employed repeatedly. Consequently, the cost of undertaking the first application may improve the profits.
4. Better Decisions. OR models frequently yield actions that do improve an intuitive decision making. Sometimes, a situation may be so complicated that the human mind can never hope to assimilate all the important factors without the help of OR and computer analysis.
QUANTITATIVE TECHNIQUES OF OR:
A brief account of some of the important OR models providing needed quantitative information base on scientific method analysis are given below:
1. Distribution (Allocation) Models: Distribution models are concerned with the allotment of available resources so as to minimize cost or maximize profit subject to prescribed restrictions. Methods for solving such type of problems are known as mathematical programming techniques. We distinguish between liner and non-liner programming problems on the basis of linearity and non-linearity of the objective function and/or constraints respectively. In linear programming problems, the objective function is linear and constraints are also linear inequalities/equations. Transportation and Assignment models can be viewed as special cases of linear programming. These can be solved by specially devised procedures called Transportation and Assignment Techniques.
In case the decision variables in a linear programming problem are restricted to either integer or zero-one value, it is known as Integer and Zero-One programming problems, respectively. The problems having multiple, conflicting and incommensurable objective functions (goals) subject to linear constraints are called linear goal programming problems. If the decision variables in a linear programming problem depend on chance, then such problems are called stochastic linear programming problems.
2. Production/Inventory Models: Inventory/Production Models are concerned with the determination of the optimal (economic) order quantity and ordering (production) intervals considering the factors such as—demand per unit time, cost of placing orders, costs associated with goods held up in the inventory and the cast due to shortage of goods, etc. Such models are also useful in dealing with quantity discounts and multiple products.
3. Waiting Line (or Queuing) Models: In queuing models an attempt is made to predict:
(i) How much average time will be spent by the customer in a queue?
(ii) What will be an average length of waiting linear or queue?
(iii) What will be the traffic intensity of a queuing system? etc.
The study of waiting line problems provides us methods to minimize the sum of cost of providing services and cost obtaining service which are primarily associated with the value of time spent by the customer in a queue.
4. Markovian Models: These models are applicable in such situation where the state of the system can be defined by some descriptive measure of numerical value and where the system moves from one state to another on probability basis. Brand-switching problems considered in marketing studies are an example of such models.
5. Competetive Strategy Models (Games Theory): These models are used to determine the behavior of decision-making under completion or conflict. Methods for solving such models have not been found suitable for industrial applications, mainly because they are referred to an idealistic world neglecting many essential features of reality.
6. Net work Models: These models are applicable in large projects involving complexities and inter-dependencies of activities. Project Evaluation and Review Techniques (PERT) And critical Method (CPM) are used for planning, scheduling and controlling complex project which can be characterized net-work.
7. Job Sequencing Models: These Models involve the selection of such a sequence of performing a series of jobs to be done on service facilities (machines) that optimize the efficiency measure of performance of the system. In other words, sequencing is conserved with such a problem in which efficiency measure depends upon the order or sequences of performing a series of jobs.
8. Replacement Models: The models deal with the determination of optimum replacement policy in situations that arise when some items or machinery need replacement by a new one. Individual and group replacement polices can be used in the case of such equipments that fail completely and instantaneously.
9. Simulation Models: Simulation is a very powerful technique for solving much complex models which cannot be solved otherwise and thus it is being extensively applied to solve to solve a variety of problems. This technique is more useful when following two types of difficulties may arise:
(i) The number of variables and constraint relationships may be so large that it is not computationally feasible to pursue such analysis.
(ii) Secondly, the model may be much away from the reality that no confidence can be placed on the computational results.
In fact, such models are solved by simulation techniques where no other method is available for its solution.
Tags: Decision making, Integer (computer science), Inventory, Linear programming, Mathematical model, Operations research, Optimization, Scientific method