Quantitative analysis is a scientific approach to decision making. As a first step to decision making, decision model has to be evolved. The decision model depends on two factors, namely the problem and the problem environment.
Defining the Problem
The first step in decisions making is defining the problem. The problem (i.e. the threat, opportunity, etc.) must be fully understood as to its nature, dimensions, intensity and so on. Take a labour absenteeism. It becomes a problem when it is rampant affecting work schedules. (one or two absentees, here and there is no problem). What is its nature? Deliberate absenteeism, absenteeism due to unavoidable causes, absenteeism as a mark of protest to certain managements attitudes/actions, absenteeism due to healthy/family/social reasons, etc. are indicative of the nature of the problem of absenteeism. Specific nature must be understood, since each type of absenteeism needs a different approach to solving the same. Dimensions of absenteeism comes next. How does it affect production? How does it affect inter-departmental relations, work flow, co-ordination, morale, etc? What is its impact on turnover and customer satisfaction? What is the scale of idle capacity resulting from labour absenteeism? Will motivational/coercive courses work containing absenteeism? There are but a few dimensions of the problem. How much grave the problem is? Should it be solved immediately? These indicate the intensity of the problem. The wholesome knowledge of the problems is very essential so that we can solve the problem. Not all problems can be known fully. Some keep change from time to time. So, to the extent possible the decision maker must grasp the problem. This step/function is also called as the ‘intelligence’ function of decision making. All this help to recognize the problem. Problem definition goes a little for.
In the given situation what is your objective? The decision maker must set the decision objectives. What constitutes an effective solution? The answer to this question is what is dealt by decision objectives. Reduce the level of absenteeism from the present five percent of scheduled work-hours to two percent could be one of the objectives. Let us deal with deliberate absenteeism and protest absenteeism at the moment, could be another objective. That is which part of the problem must be solved now is one of the decision objectives. Let us adopt motivational approaches to solve the deliberate absenteeism could be another objective. Protest absenteeism must be resolved by hook-crook approach, otherwise the management may be weakened-this could be another objective. Objectives give directions to possible courses of actions. And that they lead to developing alternative solutions that satisfied some broad decisions parameters with objective formulation, the problem stands defined.
Take another example. There is an opportunity of diversification. If the opportunity of diversification is seized, backward and forward integration is possible; competition can be held at bay. But, how to get the necessary resources to take up the opportunity. Acquisition or “startup from scratch”, internal and external resources, debt and equity capital etc are the decision issues. In what time frame this must be achieved? What if competitors try to copy or thwart our efforts? All those make problem complex.
The problem may be a threat. The launch of new designs by competitors is eating up your market space leading to declining top line and hence bottom line. How to deal with the problem? Should the firm also launch new models? But that will take some time. Should it gear up promotion? Should it initiate a price war? How to react to competitive price war, if that results eventually?
Problem Situation or Environment
Decision situation or environment refers to situational factors that contribute to the problem, the situational factors that influence the implementation of solution to the problem and the situational factors that the choice of solution and so on.
The problem may be dynamic or static in nature. Dynamic problems keep changing from time to time and place to place. Many variables affect the problem and the decision maker has no control over these. Besides a majority of these variables are external to the organization. Competitive factors, consumer attitudes, technological factors, political and legal environment etc., are continually changing and constantly influencing business decisions. Capital expenditure decision, long-run product and marketing decisions, etc. are typical examples.
On the other hand, some decision situations are static. The variables are known and can be manipulated by the decision maker. Inventory decisions, employee compensation decisions, passenger seat-reservation-cum-cancellations etc., are certain examples where the closed decision system can be used with advantage.
Knowledge about outcome of a course of action is yet another factor constituting the decision situation. The outcome of certain courses of action can be forecast accurately, while that of others cannot be. Forecast of return from Government bonds, leave rentals, etc., can be done with 100% attitude, while that of equity investments cannot be made accurately.
While this frame work of decision making is common to all decision situations, the practice of decision making varies from case to case, depending on the decision environment. Therefore, decision models have to be so developed that they suit the different decision environment conditions.
The decision environment may be complex, volatile and less amendable to manipulation or may be simple, static and more amendable to manipulation. Decision situations with certain environment require closed decision model and with volatile environment require open decision model. The two decisions models are however, the extremes of a continuum. Mixed decision models are, therefore, more realistic in nature. However, an understanding of the open and closed decisions of the models is very essential in developing a mixed decision system.
A closed decision model has a defined environment and known components. Such a model can be modeled and its functioning programmed. The model variables are all known and no random variable enters the model. The model strives to obtain the optimum outcome. As on approach to business decision making, the closed decision model has only limited us, since a closed decision model cannot function effectively in dynamic business environment. However there are certain routine decision situations where a closed decision model can be used.
An open decision model has on environment that is generally less defined. The decision maker is not fully aware of the environment he is confronted with. The variables are too many to corporate in the model. The exact nature of the relationship among variables is not fully known. Random and unpredictable variables may enter into the system and affect the outcome. Under such conditions, the decision maker makes manageable delimitation of the problem. The model goal, variables, alternative courses of actions, etc., are defined by the maker to the best of his perception and knowledge. Such limited search is referred to as bounded rationality. The open model has a tendency to adopt to changing environment and therefore, is increasingly relevant today as a business needs to adapt on a continuous basis in this dynamic world.