BBA Practice Management Non Quantitative Techniques Decision Making

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BBA Practice Management Non-Quantitative Techniques Decision Making

BBA Practice Management Non-Quantitative Techniques Decision Making: Intuition Facts Experience Considered Opinions Quantitative Techniques for Decision Making  Operations Research Limitations of Operations research Decision Tree Construction of Decision tree Linear Programming Features of Linear Programming Uses of Decision Tree Limitations of Decision Tree Formulations Simplex Method :

BBA Practice Management Non Quantitative Techniques Decision Making
BBA Practice Management Non-Quantitative Techniques Decision Making

MCom I Semester Accounts Holding Companies Study Material Notes

NON-GUANTITATIVE TECHNIQUES FOR DECISION MAKING

Non-quantitative techniques are helpful not only for problems dealing with objectives but also for problems dealing with means to accomplish the objectives. In application  techniques were in use long ago, their importance has increased specially after World War II. With the business becoming more dynamic and requiring more information for making vital decisions, it was felt necessary to develop techniques to handle the large volume of information so as to arrive at meaningful relationships among several variables affecting a decision. Two things were developed almost simultaneously: one was the set of various models and the other was the computer to process information. The various quantitative tools were integrated into a new discipline normally known as “Operations Research’. These techniques are very helpful in making decisions under conditions of varying degrees of uncertainty. In fact, most of the time, vital decisions are made under the conditions of risk or complete uncertainty. The quantitative techniques help in making decisions under these conditions with much better precision.

OPERATIONS RESEARCH

The expression Operations Research’ (OR) means different things to different people. For some, it includes all quantitative decision-making techniques. For example, American Encyclopedia of Management has defined OR as follows:

OR by some is taken in broader sense. For example, Miller and Starr have described OR as applied decision theory where the manager seeks to achieve rationality while dealing with problems through the use of scientific, logical, or mathematical means. Thus, Churchman and others have defined OR as follows:

The definition of OR suggests that various elements of a problem on which decision is to be made are expressed in numerical forms, the relationships among these elements are established, and the decision is made on the basis of the analysis of these relationships. The essential characteristics of OR as applied to decision making can be presented below:

1 OR emphasises models-the logical physical presentation of a reality or problem. The model is a representation of objects, events, processes, or systems. The model may be simple or complex, depending on the type of variables involved.

2. It quantifies the variables in a problem to the extent possible, since only quantifiable data can be inserted into a method to yield measurable result.

3. OR emphasizes goals in a problem area and the development of measures of effectiveness in determining whether a given solution shows promise of achieving these goals. Every proposed solution of the problem will arrange the variables so that the end result can be weighed against this measure.

4.It incorporates in a model the variables in a problem that appear to be most important to its solution.

5. It puts the model and its variables, constraints, and goals in quantitative terms so that they may be clearly identified, subjected to mathematical simplification, and readily utilised for calculation by substitution of quantities for symbols.

Role of Operations Research

Operations Research has been developed basically to solve the problem of complex decision-making. The complexity of business decisions increased specially after World War II with the increase in the size of the organisations and increasing complexity of environmental variables. During the World War, the basic problem before most of the countries was how to optimise defence activities so that best results could be achieved. In a dynamic and complex society, business managers face almost the similar problem of the use of limited resources so as to achieve the organizational objectives. OR developed to optimise the defence activities can be used for business activities also. OR helps in the following ways in making decisions regarding business activities:

1 The scope for the use of OR techniques covers almost the entire business activities. Thus, OR can be used in business structure, long-range planning, production, materials handling and layout, materials control, research and development, personnel problems, distribution of products, selling and marketing, financial problems, and so on. The list covers almost the entire range of business activities.

2. OR helps in optimum allocation of resources in the organisation. The main problem before any organisation is how to allocate limited resources to various units and sub-units in the organisation so that every resource is utilised in the best way. Because of the large size of the organisation, it needs division of labour creating large number of interdependent units and sub-units, all working together to achieve objectives. However, these units have tendency to grow autonomously with each one of them pressing for higher resource allocation. This creates the problem before the head of the organisation as to how to distribute limited resources among the various competing units. OR provides techniques for overcoming this problem very effectively.

3. The growing competition, both in national and international markets, has necessitated quick decision making by managers to take the advantages of market factors. The decision should be quick as well as sound. This requires the analysis of many variables simultaneously using scientific analysis. This is exactly done by OR. It combines different disciplines such as mathematics, statistics, economics, engineering, etc., so that with a combined effort of all these, the problem can be looked into greater details with more precise answer of the problem. While using OR techniques, managers have to quantify different variables which require greater precision in defining a variable involved in decision making, The various roles of OR as discussed above show that it leads to providing optimum solution to a problem. The optimum solution is required in every walk of life but more so in the case of business activities because of the competitive environment.

Non Quantitative Techniques

Limitations of Operations Research

OR contributes in various ways in making sound decisions, particularly when several variables are to be considered in a decision. However, it should not be treated as the final word in decision making but it should be treated merely as an aid in decision making. Moreover, there are certain limitations which make the role of OR somewhat limited in many cases. Though many of these limitations are related with time and cost constraints. managers should be well aware of these limitations which are of the following nature:

1 Magnitude of Calculation. OR tries to provide optimum solution of a problem taking Into account all the variables related with the problem. In modern society, these variables are numerous, and expressing them in quantity and building relationships among them cquire huge calculations. All these calculations require the use of computer which involves Ust on the part of the organisation. Therefore, managers should evaluate the cost of optimising the solution in the light of benefits accruing from it. For smaller organisations, uils cost may be prohibitive. Therefore, the use of OR techniques is limited to the organisations which can afford them.

2. Lack of Quantification of Variables. A basic limitation in OR is that it provides solution only when all the variables related to a problem can be quantified. However, a business decision is affected by numerous variables, and some of them may not be quantified. For example, tangible variables like price, output, sales, etc., can be quantified but intangible lactors like human relations, managerial perception, etc., cannot be quantified. Therefore, OR cannot make use of these factors though these may be equally or sometimes even more important variables affecting the solution of a problem. Thus, optimisation of solution cannot be arrived at if these factors are not considered adequately.

3. Gap between Manager and Operations Research Analyst. Sometimes a practical problem comes in the way of using OR techniques because of the gap between the manager (user of OR techniques) and OR analyst (analyst providing techniques). A manager may not be conversant with the OR techniques; simllarly an OR analyst may not be aware of business problems. Thus, there is a gap between those who use the solution and those who provide the solution. This is the reason why many business managers do not appreciate the role of OR in solving the business problems. However, this is basically the problem of training. Training is required for both. For managers, it is required so that they can appreciate the role of OR and the type of problems that can be solved and to OR analyst so that he can learn how to identify and quantify the variables affecting the business problems. Once the working knowledge is acquired by them, there may not be much problem.

In spite of the above limitations, OR techniques are getting popularity in solving business problems. Even the smaller organisations are going for quantitative way of decision making. With the passage of time, this is likely to happen with much faster pace. There are various techniques and tools of OR. However, for business decisions, some of the techniques commonly used are decision tree, linear programming, game theory, and queuing theory. Therefore, these will be taken for detailed discussion here. Another technique, that is network analysis, will be discussed in the chapter on Control as it is more relevant for control.

DECISION TREE

Some decisions involve a series of steps, the second step depending on the outcome of the first, the third depending on the outcome of the second, and so on. Often uncertainty surrounds each step, so the decision maker faces uncertainty piled on uncertainty. Decision trees are a model for solving such a problem. Decision tree is a graphical method for identifying alternative actions, estimating probabilities, and indicating the resulting expected payoff. This graphical form visually helps the decision maker view his alternatives and outcomes. Instead of compressing all the information regarding a complex decision into a table, decision maker can draw a schematic representation of the problem that displays the information in more easily understandable fashion. Example of problems which can be solved through decision tree may be when a new product is to be introduced, whether to tool up for it in a major way so as to assure production at the lowest possible cost or to undertake cheaper temporary tooling involving a higher manufacturing cost but lower capital losses if the product does not sell as well as estimated, etc. Here the second step of decision. that is going for major or minor tooling, depends on the outcome of the first decision that is whether to go for new product or not. Similarly, within the major tooling, there may be alternatives which can be considered in the light of decision made on tooling.

Non Quantitative Techniques

Construction of Decision Tree

The construction of a decision tree requires definition of alternatives. estimation probabilities for various events, and calculation of expected pavoff resulting from an a This process can be undertaken in the following way:

1 The first basic step in construction of a decision tree is the definition of various alternatives available at the first stage. For example, if an organisation wants to introduce a new product in the market, it has two alternatives available to it: el it can go for creating permanent tooling or it can go for temporary tooling. Each alternative will require different investment. However, since the chances for success or failure of the new product cannot be predicted with certainty, the decision cannot be made purely on the basis of investment criterion.

2. The second step in decision tree requires the estimation of probabilities for various events. The estimation of probabilities will be based on manager’s own experience, consultation with others, and information available in this respect. There may be three alternative events so far as the success or failure of the product is concerned: it may have high success; it may have moderate success; or it may fail. The probabilities of each event will be different.

3. The third step is the calculation of payoff from each alternative in various events. Had there been certainty of events, the payoff table can be prepared like one given in Table 10.2. However, with given probabilities, the payoff table can be prepared as given in Table 10.3. Thus, payoff is calculated by multiplying the probability of the event with the expected payoff of that alternative. Now the decision tree can be constructed based on these figures. However, if the product succeeds or has moderate success, there is likely to be competition in future. Therefore, this process has to be carried a stage further where the probabilities of competition and resultant payoffs have to be estimated. Thus, the decision tree can be presented as in Figure 10.3.

The payoff for an ultimate alternative has been calculated by taking into account the probabilities of the ultimate alternative as well as for the previous alternative and multiplied by the expected payoff of the first alternative without its probability. For example, the expected payoff of the permanent tooling if product succeeds and competition exists is 01.00 0.5) (0.8) or 0.5 x 0.8 or Rs. 40 lakhs. In the same way, payoffs of other alternatives will be calculated. The total payoff of each alternative will be the basis for decision. In the same case, the total payoff for permanent tooling is Rs. 45 lakhs per annum while that of temporary tooling is Rs. 29 lakhs per annum. Therefore, in terms of total payoff, it is better to select permanent tooling. However, if return on investment is taken into consideration, temporary tooling may be selected. Therefore, besides payoff as given in decision tree, other factors like availability of funds, risk taking capacity and preference will also decide the selection of an alternative.

When decisions are based on decision tree, it becomes more complicated as chance events increase. The compounding of probabilities makes the solution much more difficult. Therefore, computer is needed to process the information. Also there may be decision points in a single decision over a period of time. For example, in the above case, the manager may go for temporary tooling at the initial stage but may think of permanent tooling at a later stage, or may incorporate new features in the product resulting in change in probability of its success, or may substitute the product, and so on. Such decisions can be chalked out on the basis of decision tree.

Non Quantitative Techniques

Uses of Decision Tree

A decision tree is basically used to make decisions in complex situations particularly when outcome of a later situation is dependent on outcome of the former. What is significant about the decision tree approach is that it does several things for decision makers. In the first place, it makes possible for them to see at least the major alternatives open to them and that the subsequent decisions may depend on events of the future.

In the second place, by incorporating probabilities of various events in the decision tree, it is possible to comprehend the true probability of a decision leading to results desired. Moreover, it forces replacement of broad areas of judgement with focus on the critical elements in a decision, thrusts into the open premises often hidden in judgement, and discloses the steps in reasoning by which decisions under uncertainty are made. A basic value of decision tree lies in expressing all outcomes or events in quantitative terms which provide precision in decision making. With use of computer in processing large volume of information with complex relationships, the calculation aspect has become easier and has resulted in increased use of decision tree.

Non Quantitative Techniques

Limitations of Decision Tree

Decision tree, however, is not free from certain limitations. The first basic limitation is because of its complexity. A decision tree, while simple in essence, is complex in application. One of the main problems in decision tree is that it becomes so complex that relationships among various events and alternatives cannot be established. At the ultimatele can be too many alternatives. Even with simple two or three branch forks. simple two or three branch forks, the tree can be quite complex. It will be just like a bush with many branch forks. Another problam Another problem in the way of effective use of decision tree is the making of assumptions and provaunues to various events at various levels in a decision tree. There is often inconsis in assigning probabilities to various events. Moreover, since many managers are m in this process, often it becomes time consuming. Therefore, managers have to weigh the values of a decision tree as against its contributions. Thus, it should be applied only case of major decisions. In spite of these limitations, a decision tree oller’s mucho solution for making decision under uncertainty. Many of the problems can be overcome with the help of computer and managers’ commitment to go for precise decisions.

Non Quantitative Techniques

LINEAR PROGRAMMING

Linear programming (LP) is the technique used in optimum allocation of resources in the organisation. An important problem before a typical business organisation is how to achieve the best allocation of its limited resources, viz., money, materials, machines, space, time, etc. Each resource is available in its own specific quantity: and at any point of time, this availability is the maximum setting a limit or restriction. The problem confronting the management is to decide the manner in which these limited resources are to be earmarked for different uses so as to maximise the achievement of organisational objectives. In this case, the problem can be solved by linear programming.

Linear programming is a mathematical technique for the purpose of allocation of limited resources in an optimum manner. The ‘linear’ means that the relationships handled are those which are represented by straight lines. The word ‘programming’ means making decisions systematically. In other words, it is a decision-making technique under given constraints based on the assumption that the relationships among the variables representing different phenomena show linearity. Therefore, LP is the maximization (or minimization) of a linear function of variables subject to a constraint of linear inequalities.

Non Quantitative Techniques

Features of Linear Programming

LP is based on certain basic concepts which are as follows:

1 Objective Function. The objective sought to be achieved by the application of LP in solving a problem is known as objective function. In the problem, there must be clearly defined objectives and should be expressed quantitatively. In business problems, this can be expressed as maximisation of profit or minimisation of costs, etc.

2. Constraints. Constraints are the restrictions imposed on decision variables. They may be in terms of availability of raw materials, machine time, labour, etc. The constraints must be capable of arithmetical expression.

3. Non-negative Conditions. The LP model essentially seeks that the values for each variable can be either positive or zero. In no case it can be negative, otherwise whole technique will be rendered useless. For example, if an organisation is considering allocation of resources for three different products, each product should be either positive or zero, that is not produced at all. Negative condition means that a product is dismantled which is not logical

4. Linear Relationship. The various relationships to be expressed either in the form of muations or inequalities must be linear. The adjective linear’ is used to describe a relationship between two or among more variables. This means that additional. inputs will produce the output in the same proportion in which the inputs are increased. Thus, the output of two machines and two workers should be double of one machine and one worker, and so on.

Non Quantitative Techniques

Formulation of LP Model: Graphical Method

An LP problem can be solved either by the graphical method or by simplex method. The graphical method involves the use of familiar graphical methods of economics bearing a close resemblance to the indifferent curve analysis. This method is very simple but it can be used conveniently where two decision variables are involved. To explain the graphical method of LP, we can take the following example:

The organisation has set its objective as revenue maximisation out of the use of two machines in optimum way. In order to arrive at this decision through graphic method of LP. we can proceed in the following way:

1 Formulation of LP Problem. The first step is to formulate the LP problem by restating the above information in mathematical form. First of all, we state the objective function. Here the term would refer to an equation showing the relationship between output and revenue. Thus, Total revenue = 20X + 25Y (Total sale proceeds of two items)

Non Quantitative Techniques

2. Constraints. The basic constraint in this case is that maximum time available on two machines is 24 hours for A and 16 hours for B which is to be distributed for producing two products. The time requirement of each product also differs. Thus, various combinations of two products can be produced within the constraints of time availability on these two machines. These constraints can be expressed in the following equations:

3. Plotting the Constraints on Graph. The next step is to plot the constraints on graph showing product Xon X-axis and Yon Y-axis. The constraint 4X+6Y 24 can be plotted on the graph by first locating two terminal points and then joining these points by a straight line. The two terminal points for the constraint can be found in the following manner: (1) If we assume that all the time available on machine Ais utilised for producing X 6 unit of X can be produced. Thus, on X-axis, the first point would be 6. 10 Similarly, in order to locate the second point, we can assume that the total time available on machine Ais utilised to produce Yand 4 units are produced. Thus, the second point on Y-axis is 4. After locating these two points, these can be connected by drawing a straight line (CD) as shown in Fig. 10.4. Similar exercise can be taken for machine B. Thus, it will give 4 units on X-axis and 8 unit on Y-axis. A straight line (EF) can be drawn joining these two points. The total graph will be like given in

4. Identification of Feasibility Region. The next step is to identify the feasibility region. The feasibility area is formed by the intersection of two lines CD and EF. The possible combination of production of two products X and Y within the given constraints of machine time available on A and B machines would be within the feasibility region OCGF (shaded area) in Figure 10.4. The optimum combination of two machines for two products would lie on any corner of OCGF. In order to find out the most acceptable solution, each corner point must be tested in the light of objectives set. Numerically, it can be presented as follows:

Since the basic objective in the problem is to maximise revenue, combination at corner point G which gives output of 3 units of Xand 2 units of Y will be selected.

Non Quantitative Techniques

Formulation of LP: Simplex Method

The simplex method offers a means of solving the more complicated programming problems involving large number of variables. Two aspects of simplex method are important. Fyrst the basic theory of the simplex method requires comparatively advanced mathematics involving extensive use of algebraic equations and their manipulation. Second, the tional procedure is so cumbersome that without computer, it is hardly possible to

me of calculations involved in solving business problems. In the simplex method, the equations are formed on the basis formed on the basis of objective function and constraints. Since there may be many constraints if more variables exist in decision situation, there will be numerous equations. These equations are manipulated in different combinations to find out the optimum combination. All these can be done with the help of a computer.

Non Quantitative Techniques

Uses of Linear Programming

Linear programming is widely used in a variety of resource allocation problems. Simple allocation decisions can be made by observation and experience, but in large operations, the problems are complex involving thousands of possible choices. LP is a sophisticated, short-cut technique that makes it possible to achieve solutions in a few hours that would otherwise require too much time to obtain. Computers are programmed with standard solutions to many LP problems, thus, cutting computational time. LP also makes possible the modification of problems to take into account changes in any element of the problem. Once the basic model is set up. a change in a variable such as cost of labour can be substituted in the equations without redoing the entire analysis. It provides information on the most desirable mix of resources as well as problems of product mix, and the movement of materials from origin to destination. LP model can be used in a variety of decisions related to agriculture, industry, contract awards, etc.

Non Quantitative Techniques

GAME THEORY

Game theory is quite helpful in making decisions under competitive situations. Fundamentally the game theory attempts to provide an answer to the question: What may be considered a rational course of action for an individual confronted with a situation whose outcome depends not only on his own actions but also on the actions of others, who, in turn, are faced with a similar problem of choosing a rational course of action? In fact, the game theory is simply the logic of rational decisions. The game theory was developed basically for use in wars so that actions of the army can be decided in the light of actions taken by opposite army. Neumann and Morgenstern have developed more precision in game theory.

The term ‘game’ represents a conflict between two or more parties. Therefore, game theory is basically a science of conflict. A game is described by its set of rules. These rules specify clearly what each person, called player, is required to do under all possible set of circumstances. The rules also define the amount of information, if any, each person receives. A game is finite when each player has a finite number of moves and finite number of choices at each move. Thus, game theory is not concerned with finding an optimum or winning strategy for a particular conflict situation but it provides general rules concerning the logic that underlies strategic behaviour in competitive situation. A competitive situation is called game if it has the following features:

1 There is a conflict of interests between two or more opponents usually referred to as players. Because of conflict of interests, the interest of one player will be served at the cost of the other.

2. The strategies adopted by each player affect the respective outcome of the game.

3. Each player chooses his courses of action. The choices are supposed to be made simultaneously so that no player knows his opponent’s choice until he has decided his own course of action.

4. All players act rationally, intelligently, and are well informed about the decision situation except the opponent’s actions in a particular period.

5. For each player, the outcome may represent a gain, a loss, or a draw. Each outcome of the game may be represented by a single payoff number such as gain or loss in rupees.

several types of games depending on the competitive situations. However, the basic objective of all these games is to provide a basis for making decisions in the actions taken by the competitors. Thus, game theory can be used in competitive situations like elections, advertising and marketing strategies, military battles, etc.

Non Quantitative Techniques

QUEUING THEORY OR WAITING LINE THEORY

Queuing theory involves the mathematical study of queues’ or ‘waiting lines’. A group of items waiting to receive service, including those receiving the service, is known as queues. The formation of waiting lines is a common phenomenon. It may happen at the service, station, booking window, retail store, etc. At some point of time, there may be a long queue while at other time, there may not be a single person waiting to get service. In waiting line situations, problems arise because of either () too much demand on the facilities, in which case we may say that there is an excess of waiting time or that there are not enough service facilities, or (ii) too little demand, in which case there is either too much idle facility time or too many facilities. One would like to obtain balance between the costs associated with waiting time and idle time. Queuing theory helps in achieving this balance.

The balance between the costs associated with waiting time and idle time is necessary because non-provision of enough facilities would cause the waiting line to become excessively long at times. Excessive waiting is costly because it may result in loss of customers leading to the achievement of objectives adversely. Similarly if too much facilities have been provided. some of the facilities will remain underutilised resulting in non-achievement of objectives. Queuing theory helps in arriving at a decision about the provision of optimum facilities. However, it should be noted that this theory does not directly solve the problem of minimising the total waiting and service costs but it provides the management with information necessary to take relevant decisions for the purpose. It does the job by estimating different characteristics of the waiting line such as () average arrival rate. (11) average service rate, (iii) average length of the queue, (iv) average waiting time, and (u) average time spent in the system.

A large portion of waiting line problems that arise in practice involve making one or a combination of the following decisions: (1) number of servers at a service facility. (to efficiency of the servers, and (ili) number of service facilities. Such problems can be solved by simple observations if the number involved in each case is limited. However, when number is large enough, waiting line models have to be built. There are several such models which solve the problems of waiting lines.

Non Quantitative Techniques

KEY CONCEPTS FOR REVIEW

Administrative man model Brainstorming Certainty Consensus mapping Creativity Decision making Decision-making conditions Decision-making process Decision tree Delphi technique Innovation Linear programming Nominal group technique Non-programmed decision Operational decision Operations research Problem identification Problem solving Programmed decision Queuing theory

Non Quantitative Techniques

Case: Paramount Enterprises Limited

Paramount Enterprises Limited was formed in 1985 as a result of amalgamation of Paramount Sales Limited and Sigma Industries Limited. Paramount Sales was promoted by S. Srinivas in 1978 to take up agency business in electrical fans, sewing machines, refrigerators. airconditioners, etc. Srinivas was an MBA with marketing major from a reputed university.

Before forming Paramount Sales, he served as a sales manager in a large company manufacturing and marketing refrigerators and other consumer durables. The agency business of Paramount Sales progressed at a very fast rate and the firm earned substantial profit. In 1995, Srinivas decided to enter manufacturing. However, it was felt that the firm was primarily a marketing organisation and, therefore, it was thought that undertaking manufacturing project through grass-root level was not advisable. Instead, it was decided to takeover controlling interest in Sigma Industries Limited which was engaged in manufacturing electrical fans of different types. While assessing the acquisition bid, criteria used were addition of managerial talent in manufacturing so as to have synergy with the existing marketing functions. Paramount Sales and Sigma Industries Limited were merged and the new entity was named as Paramount Enterprises Limited.

Paramount Enterprises expanded its business in other areas also such as entertainment electronics. This business expansion was through takeover route. In 2003. the company

Principles and Practice of Management ucu vo takeover a company of a substantial size to make its presence felt in business field. Srinivas as the chief executive of the company constituted a team of managers to identify suitable targets for takeover. The only criterion that was laid down was the synergy that would result to Paramount through takeover. Finance was not a limiting factor as the company was having large cash surplus.

The team evaluated several targets for takeover and arrived at a conclusion that Pama Electronics Limited manufacturing television sets and Whitegoods Limited manufacturing washing machines would be good targets. Both the companies were having good technology but did not do well because of poor marketing infrastructure. The team submitted the report to Srinivas. He thanked the team members for doing a fine job and said that he would like to go into the details of the proposals and make a final decision shortly. He took the report to his home. After reading the report, he discussed the matter with his wife Meenakshi. Meenakshi had done MBBS and was very fond of doing medical practice but Srinivas persuaded her not to do so. On the takeover matter, she suggested to take over a pharmaceutical company as this business was evergreen. Srinivas was slightly perplexed but ultimately agreed for that. The next morning, in the meeting of top management, he put forward his proposal in these words. “Gentlemen, I am proud of your good work of suggesting takeover targets. However, I feel that this proposal should be kept in abeyance. I propose that we should go for taking over a pharmaceutical company. Over the years, we have developed very strong marketing capability which is essential for the success of a pharmaceutical business.” On this proposal, the managers looked at each other but did not offer any comment. The meeting was adjourned.

Non Quantitative Techniques

QUESTIONS

1 Was Srinivas rational in making the decision to takeover the pharmaceutical business? Explain

2. In what way, will or will not the marketing capability of Paramount be relevant to the pharmaceutical business?

 

Non Quantitative Techniques

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